FN Thomson Reuters Web of Scienceā„¢ VR 1.0 PT J AU Lv, YH Zhai, CX AF Lv, Yuanhua Zhai, ChengXiang TI Negative query generation: bridging the gap between query likelihood retrieval models and relevance SO INFORMATION RETRIEVAL LA English DT Article DE Negative query generation; Query likelihood; Language model; Relevance; Probability ranking principle; Principle of maximum entropy ID PROBABILISTIC MODELS; INFORMATION-RETRIEVAL; IR AB The language modeling approach to information retrieval has recently attracted much attention. In the language modeling retrieval models, we can score and rank documents based on the query likelihood method. From the theoretical perspective, however, the justification of the existing (standard) query likelihood method based on the probability ranking principle requires an unrealistic assumption about the generation of a "negative query" from a document, which states that the probability that a user who dislikes a document would use a query does not depend on the particular document. This assumption enables ignoring the negative query generation so as to justify using the basic query likelihood method as a retrieval function. In reality, however, this assumption does not hold because a user who dislikes a document would more likely avoid using words in the document when posing a query. This suggests that the standard query likelihood function is a potentially non-optimal retrieval function. In this paper, we attempt to improve the standard language modeling retrieval models by bringing back the component of negative query generation. Specifically, we propose a general and efficient approach to estimate document-dependent probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. In addition, we further develop a more general probabilistic distance retrieval method to naturally incorporate query language models, which covers the proposed query likelihood with negative query generation as its special case. The proposed approaches not only bridge the theoretic gap between the standard language modeling retrieval models and the notion of relevance, but also improves the retrieval effectiveness with (almost) no additional computational cost. C1 [Lv, Yuanhua] Microsoft Res, Redmond, WA 98052 USA. [Zhai, ChengXiang] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Lv, YH (reprint author), Microsoft Res, Redmond, WA 98052 USA. EM yuanhual@microsoft.com; czhai@illinois.edu CR Aly R, 2014, INFORM RETRIEVAL, V17, P177, DOI 10.1007/s10791-013-9226-3 Balog K., 2006, P SIGIR, V35, P43 Bendersky Michael, 2008, P 31 ANN INT ACM SIG, P491, DOI DOI 10.1145/1390334.1390419 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Collins-Thompson K., 2007, P SIGIR 2007, P303, DOI 10.1145/1277741.1277795 Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200 Fang H, 2007, LECT NOTES COMPUT SC, V4425, P418 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Hiemstra D., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009025 Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hiemstra D., 2001, THESIS U TWENTE JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620 Kurland O., 2005, P 28 ANN INT ACM SIG, P19, DOI 10.1145/1076034.1076041 Lafferty J., 2002, LANGUAGE MODELING IN, P1 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lafferty J. D., 2003, LANGUAGE MODELING IN, V13 Lavrenko V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Liu X., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Luk RWP, 2008, INFORM RETRIEVAL, V11, P539, DOI 10.1007/s10791-008-9062-z Lv Y., 2012, P CIKM, P1799 Lv Y., 2011, P 20 ACM INT C INF K, P7 LV YH, 2009, P 32 ANN INT ACM, P299 Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579 Mei Q., 2007, P 30 ANN INT ACM SIG, P319, DOI 10.1145/1277741.1277797 Metzler D., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009110 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Ogilvie P., 2003, P 26 ANN INT ACM SIG, P143, DOI DOI 10.1145/860435.860463 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 Robertson S., 2001, P WORKSH LANG MOD IN, P21 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Si L., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Sparck-Jones K., 2001, P WORKSH LANG MOD IN, P12 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P779, DOI 10.1016/S0306-4573(00)00015-7 Sparck-Jones K., 2003, KLUWER INT SERIES IN, V13 Tan B, 2006, P 12 ACM SIGKDD INT, P718, DOI DOI 10.1145/1150402.1150493 Tao T, 2006, P MAIN C HUM LANG TE, P407, DOI 10.3115/1220835.1220887 Tsagkias Manos, 2011, P 34 INT ACM SIGIR C, P485 Wang X., 2008, P 31 ANN INT ACM SIG, P219, DOI 10.1145/1390334.1390374 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 Xu J., 2001, P 24 ANN INT ACM SIG, P105, DOI 10.1145/383952.383968 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2002, P 25 ANN INT ACM SIG, P49 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhang Y., 2002, P 25 ANN INT ACM SIG, P81 Zhao Le, 2010, P 19 ACM INT C INF K, P259, DOI 10.1145/1871437.1871474 NR 57 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD AUG PY 2015 VL 18 IS 4 BP 359 EP 378 DI 10.1007/s10791-015-9257-z PG 20 WC Computer Science, Information Systems SC Computer Science GA CL2LM UT WOS:000356775500003 ER PT J AU Kazemian, HB Ahmed, S AF Kazemian, H. B. Ahmed, S. TI Comparisons of machine learning techniques for detecting malicious webpages SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE K-Nearest Neighbor; Support Vector Machine; Naive Bayes; Affinity Propagation; K-Means; Supervised and unsupervised learning ID VISUAL SIMILARITY ASSESSMENT; TEXT CATEGORIZATION; RETRIEVAL AB This paper compares machine learning techniques for detecting malicious webpages. The conventional method of detecting malicious webpages is going through the black list and checking whether the webpages are listed. Black list is a list of webpages which are classified as malicious from a user's point of view. These black lists are created by trusted organizations and volunteers. They are then used by modern web browsers such as Chrome, Firefox, Internet Explorer, etc. However, black list is ineffective because of the frequent-changing nature of webpages, growing numbers of webpages that pose scalability issues and the crawlers' inability to visit intranet webpages that require computer operators to log in as authenticated users. In this paper therefore alternative and novel approaches are used by applying machine learning algorithms to detect malicious webpages. In this paper three supervised machine learning techniques such as K-Nearest Neighbor, Support Vector Machine and Naive Bayes Classifier, and two unsupervised machine learning techniques such as K-Means and Affinity Propagation are employed. Please note that K-Means and Affinity Propagation have not been applied to detection of malicious webpages by other researchers. All these machine learning techniques have been used to build predictive models to analyze large number of malicious and safe webpages. These webpages were downloaded by a concurrent crawler taking advantage of gevent. The webpages were parsed and various features such as content, URL and screenshot of webpages were extracted to feed into the machine learning models. Computer simulation results have produced an accuracy of up to 98% for the supervised techniques and silhouette coefficient of close to 0.96 for the unsupervised techniques. These predictive models have been applied in a practical context whereby Google Chrome can harness the predictive capabilities of the classifiers that have the advantages of both the lightweight and the heavyweight classifiers. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Kazemian, H. B.; Ahmed, S.] London Metropolitan Univ, Intelligent Syst Res Ctr, Sch Comp, London, England. RP Kazemian, HB (reprint author), London Metropolitan Univ, Intelligent Syst Res Ctr, Sch Comp, London, England. FU Technology Strategy Board KTP, UK [ktp006367] FX The authors would like to thank Technology Strategy Board KTP, UK for their generous support for part of the research [Grant No. ktp006367]. CR Abbasi A, 2012, ACM T INFORM SYST, V30, DOI 10.1145/2382438.2382441 Abbasi A, 2010, MIS QUART, V34, P435 ALTMAN NS, 1992, AM STAT, V46, P175, DOI 10.2307/2685209 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Bannur S. N., 2011, P 4 ACM WORKSH SEC A, P1 Bayes M., 1763, PHILOS T, V53, P370, DOI DOI 10.1098/RSTL.1763.0053 Braganza R., 2006, Network Security, V2006, DOI 10.1016/S1353-4858(06)70425-1 Chang C. C., 2012, LIBSVM LIB SUPPORT V Cohen W., 1996, P 13 NAT C ART INT A, P709 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Dueck D, 2009, AFFINITY PROPAGATION Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Facebook, 2010, NETWORK SECURITY, V2010, P20 Frakes W. B., 1992, INFORM RETRIEVAL DAT Fu AY, 2006, IEEE T DEPEND SECURE, V3, P301, DOI 10.1109/TDSC.2006.50 Garera S, 2007, WORM'07: PROCEEDINGS OF THE 2007 ACM WORKSHOP ON RECURRING MALCODE, P1 Guan D., 2009, P JOINT WORKSH INF S Hansen R., 2008, CLICKJACKING Harley D., 2008, 3 INT C MAL UNW SOFT, P98 Jordan A., 2002, ADV NEURAL INFORM PR, V14, P841 Kan M.Y., 2005, P 14 ACM INT C INF K, P325, DOI 10.1145/1099554.1099649 Le A., 2010, CORR Liu WY, 2006, IEEE INTERNET COMPUT, V10, P58 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lucca G. A. D., 2004, P 6 IEEE INT WORKSH, P71 Ma J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1245 Ma J., 2009, P 26 ANN INT C MACH, P681, DOI DOI 10.1145/1553374.1553462 MacQueen J., 1967, P 5 BERK S MATH STAT, P281, DOI DOI 10.1234/12345678 Malware, 2011, COMPUTER FRAUD SECUR, V2011, P20 Mcgrath D. K., 2008, P USENIX WORKSH LARG Okanovic V., 2011, MIPRO 2011 P 34 INT, P1315 Porter M. F., 1997, READINGS INFORM RETR, P313 Provos N., 2008, P USENIX SEC S, P1 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahami Mehran, 1998, LEARN TEXT CAT 1998, V62 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sood Aditya K, 2011, Network Security, V2011, DOI [10.1016/S1353-4858(11)70050-2, 10.1016/S1353-4858(11)70105-2] Spertus E., 1997, P INN APPL ART INT I, P1058 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 43 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD FEB 15 PY 2015 VL 42 IS 3 BP 1166 EP 1177 DI 10.1016/j.eswa.2014.08.046 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AU6TN UT WOS:000345734700017 ER PT J AU Holliday, JD Sani, N Willett, P AF Holliday, John D. Sani, Nor Willett, Peter TI Calculation of Substructural Analysis Weights Using a Genetic Algorithm SO JOURNAL OF CHEMICAL INFORMATION AND MODELING LA English DT Article ID STATISTICAL-HEURISTIC METHOD; ACTIVITY SPECTRA; DRUG DESIGN; SELECTION; PASS; SUBSTANCES; CLASSIFIER; PREDICTION; ENRICHMENT; RETRIEVAL AB This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier. C1 [Holliday, John D.; Sani, Nor; Willett, Peter] Univ Sheffield, Informat Sch, Sheffield S1 4DP, S Yorkshire, England. [Sani, Nor] Natl Univ Malaysia, Fac Informat Sci & Technol, Ukm Bangi 43600, Malaysia. RP Willett, P (reprint author), Univ Sheffield, Informat Sch, 211 Portobello St, Sheffield S1 4DP, S Yorkshire, England. EM p.willett@sheffield.ac.uk CR Adamson G.W., 1972, J CHEM SOC PERKIN TR, V1, P2428 Anzali S, 2001, J MED CHEM, V44, P2432, DOI 10.1021/jm0010670 Bemis GW, 1996, J MED CHEM, V39, P2887, DOI 10.1021/jm9602928 Bender A, 2004, J CHEM INF COMP SCI, V44, P170, DOI 10.1021/ci034207y Capelli AM, 2006, J CHEM INF MODEL, V46, P659, DOI 10.1021/ci050353n Chen NG, 2013, MOL INFORM, V32, P355, DOI 10.1002/minf.201300006 Clark D. E., 2000, EVOLUTIONARY ALGORIT Clark RD, 2008, J COMPUT AID MOL DES, V22, P141, DOI 10.1007/s10822-008-9181-z Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 Gardiner E.J., 2009, STAT ANAL DATA MININ, V2, P103 Gillet VJ, 1999, J CHEM INF COMP SCI, V39, P169, DOI 10.1021/ci980332b Glick M, 2006, J CHEM INF MODEL, V46, P193, DOI 10.1021/ci050374h Goldman BB, 2006, ANN REP COMP CHEM, V2, P127, DOI 10.1016/S1574-1400(06)02008-1 Hassan M, 2006, MOL DIVERS, V10, P283, DOI 10.1007/s11030-006-9041-5 Hert J, 2006, J CHEM INF MODEL, V46, P462, DOI 10.1021/ci050348j HODES L, 1981, J CHEM INF COMP SCI, V21, P132, DOI 10.1021/ci00031a004 HODES L, 1981, J CHEM INF COMP SCI, V21, P128, DOI 10.1021/ci00031a003 Hodes L., 1979, ACS SYM SER, V112, P583 HODES L, 1977, J MED CHEM, V20, P469, DOI 10.1021/jm00214a002 Klon AE, 2004, J MED CHEM, V47, P4356, DOI 10.1021/jm049970d Lagunin A, 2000, BIOINFORMATICS, V16, P747, DOI 10.1093/bioinformatics/16.8.747 Melville JL, 2009, COMB CHEM HIGH T SCR, V12, P332 Mitchell JBO, 2014, WIRES COMPUT MOL SCI, V4, P468, DOI 10.1002/wcms.1183 ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 Plewczynski D, 2009, COMB CHEM HIGH T SCR, V12, P358 Poroikov VV, 2000, J CHEM INF COMP SCI, V40, P1349, DOI 10.1021/ci000383k REDL G, 1974, CHEM SOC REV, V3, P273, DOI 10.1039/cs9740300273 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rogers D, 2005, J BIOMOL SCREEN, V10, P682, DOI 10.1177/1087057105281365 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Truchon JF, 2007, J CHEM INF MODEL, V47, P488, DOI 10.1021/ci600426e VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Xia XY, 2004, J MED CHEM, V47, P4463, DOI 10.1021/jm0303195 NR 34 TC 0 Z9 0 PU AMER CHEMICAL SOC PI WASHINGTON PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA SN 1549-9596 EI 1549-960X J9 J CHEM INF MODEL JI J. Chem Inf. Model. PD FEB PY 2015 VL 55 IS 2 BP 214 EP 221 DI 10.1021/ci500540s PG 8 WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Pharmacology & Pharmacy; Chemistry; Computer Science GA CB9HR UT WOS:000349943100002 PM 25615712 ER PT J AU Lamirel, JC Falk, I Gardent, C AF Lamirel, Jean-Charles Falk, Ingrid Gardent, Claire TI Federating clustering and cluster labelling capabilities with a single approach based on feature maximization: French verb classes identification with IGNGF neural clustering SO NEUROCOMPUTING LA English DT Article DE NLP; Clustering; Incremental learning; Neural networks; Cluster labelling; Verb classification AB Classifications which group together verbs and a set of shared syntactic and semantic properties have proven to be useful in both linguistics and Natural Language Processing tasks. However, most existing approaches for automatically acquiring verb classes fail to associate the verb classes produced with an explicit characterisation of the syntactic and semantic properties shared by the class elements. We propose a novel approach to verb clustering which addresses this shortcoming and permits building verb classifications whose classes group together verbs, subcategorisation frames and thematic grids. Our approach involves the use of a recent neural clustering method called IGNGF (Incremental Growing Neural Gas with Feature maximization). The use of a standard distance measure for determining a winner is replaced in IGNGF by feature maximisation measure relying on the features of the data that are associated with clusters during learning. A main advantage of the method is that maximised features used by IGNGF during learning can also be exploited in a final step for accurately labelling the resulting clusters. In this paper, we exploit IGNGF for the unsupervised classification of French verbs and evaluate the obtained clusters (i.e., verb classes) in two different ways. The first way is a quantitative analysis of the clustering process relying on a usual gold standard and on complementary unbiased clustering quality indexes. The second way is a qualitative analysis of the cluster labelling process. Relying on an adapted gold standard, we evaluate the capacity of the IGNGF clusters labels (i.e., subcategorisation frames and thematic grids) to be exploited for bootstraping a VerbNet-like classification for French. Both analyses clearly highlight the advantages of the approach. (C) 2014 Elsevier B.V. All rights reserved. C1 [Lamirel, Jean-Charles; Gardent, Claire] Synalp LORIA, Nancy, France. [Falk, Ingrid] Univ Strasbourg, Strasbourg, France. RP Lamirel, JC (reprint author), Synalp LORIA, Nancy, France. CR Abeille A., 2003, BUILDING TREEBANK FR Attik M., 2006, DATABASES APPL, P58 Brew C, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P117 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Dang H.T., 1998, COLING ACL MONTR QUE, P293 DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dorr B. J., 1997, Machine Translation, V12, DOI 10.1023/A:1007965530302 Dunn J. C., 1974, Journal of Cybernetics, V4 Fritzke B., 1995, ADV NEURAL INFORMATI, V7, P625 Ghribi M., 2010, EVALUATION METHODES, P15 Gross M., 1975, METHODES SYNTAXE Hebb D., 1949, ORG BEHAV NEUROPSYCH Kassab R., 2008, ARTIF INTELL, P232 Kipper K., 2000, AAAI IAAI, P691 Kipper K., 2006, P LREC Kipper Schuler K, 2006, THESIS U PENNSYLVANI Korhonen A., 2002, ACL WORKSH UNS LEX A Kupsc A, 2008, LECT NOTES COMPUT SC, V4919, P28, DOI 10.1007/978-3-540-78135-6_3 Lamirel J.-C., 2012, P 9 WORKSH SELF ORG, P235 Lamirel J.-C., 2008, AIA IASTED INNBR AUT Lamirel J.-C., 2013, P PAKDD QIMIE Lamirel JC, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P956, DOI 10.1109/IJCNN.2011.6033326 Lamirel J.-C., 2012, P WIS 2012, P102 Levin B., 1993, ENGLISH VERB CLASSES Martinetz T., 1991, ARTIFICIAL NEURAL NE, VI, P397 Merlo P., 2002, ACL, P207 Mouton C., 2010, THESIS U PARIS 11 PA Oishi A., 1997, INT J CORPUS LINGUIS, V2, P65, DOI 10.1075/ijcl.2.1.05ois Prescher D., 2000, 18 INT C COMP LING S, P649 Prudent Y, 2005, IEEE IJCNN, P1211 Randall J.H., 2010, LINKING STUDIES NATU ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 Schulte im Walde S., 2003, THESIS U STUTTGART Sun L., 2010, P 23 INT C COMP LING, P1056 Swier R.S., 2005, HLT EMNLP ASS COMPUT Van de Cruys T., 2012, P COLING 2012 TECNH, P2703 van den Eynde K., 2003, J FRENCH LANGUAGE ST, V13, P63, DOI 10.1017/S0959269503001005 Walde SSI, 2006, COMPUT LINGUIST, V32, P159 NR 40 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 EI 1872-8286 J9 NEUROCOMPUTING JI Neurocomputing PD JAN 5 PY 2015 VL 147 BP 136 EP 146 DI 10.1016/j.neucom.2014.02.060 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA AR1HZ UT WOS:000343337600012 ER PT S AU Saini, B Singh, V AF Saini, Balwinder Singh, Vikram BE Jain, LC Behera, HS Mandal, JK Mohapatra, DP TI Probabilistic Ranking of Documents Using Vectors in Information Retrieval SO COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 1 SE Smart Innovation Systems and Technologies LA English DT Proceedings Paper CT 1st International Conference on Computational Intelligence in Data Mining (ICCIDM) CY DEC 20-21, 2014 CL Burla, INDIA SP Veer Surendra Sai Univ Technol, Dept MCA HO Veer Surendra Sai Univ Technol, Dept Comp Sci & Engn, Informat Technol DE Information retrieval (IR); Ranking/indexing; Tokenization; Clustering AB On the web, electronic form of information is increasing exponentially with the passage of past few years. Also, this advancement creates its own uncertainties. The overload information result is progressive while finding the relevant data with a chance of HIT or Miss Exposure. For improving this, Information Retrieval Ranking, Tokenization and Clustering techniques are suggestive as probable solutions. In this paper, Probabilistic Ranking using Vectors (PRUV) algorithm is proposed, in which tokenization and Clustering of a given documents are used to create more precisely and efficient rank gratify user's information need to execute sharply reduced search, is believed to be a part of IR. Tokenization involves pre-processing of the given documents and generates its respective tokens and then based on probability score cluster are created. Performance of some of existing clustering techniques (K-Means and DB-Scan) is compared with proposed algorithm PRUV, using various parameters, e.g. Time, Accuracy and Number of Tokens Generated. C1 [Saini, Balwinder; Singh, Vikram] NIT Kurukshetra, Dept Comp Engn, Chandigarh, Haryana, India. RP Saini, B (reprint author), NIT Kurukshetra, Dept Comp Engn, Chandigarh, Haryana, India. EM me7saini@gmail.com; viks@nitkkr.ac.in CR Baeza Yates R., 1999, MODERN INFORM RETRIE Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Dong H., 2008, IEEE INT C DIG EC TE, P397 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Karthikeyan M., 2012, J APPL SOFT COMPUT, V13, P959 Lashkari AH, 2009, 2009 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND ENGINEERING, PROCEEDINGS, P385, DOI 10.1109/ICIME.2009.101 Qui J., 2007, P SIGMOD WORKSH INN, P57 Raman S., 2012, IEEE C COMM INF COMP ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1983, INTRO MODERN INFORM Senthesree K., 2010, IEEE C DAT STOR DAT, P220 Wang J., 2011, IEEE 3 INT C COMM SO, P44 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2190-3018 BN 978-81-322-2205-7; 978-81-322-2204-0 J9 SMART INNOV SYST TEC PY 2015 VL 31 BP 613 EP 624 DI 10.1007/978-81-322-2205-7_57 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BD0MK UT WOS:000357388200057 ER PT J AU Chen, YF Guo, HJ Liu, F Manderick, B AF Chen, Yifei Guo, Hongjian Liu, Feng Manderick, Bernard TI Improving protein-protein interaction article classification using biological domain knowledge SO INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS LA English DT Article DE text classification; protein-protein interaction; feature weighting; biological domain knowledge; data mining ID FEATURE-SELECTION; TEXT; INFORMATION AB Interaction Article Classification (IAC) is a specific text classification application in biological domain that tries to find out which articles describe Protein-Protein Interactions (PPIs) to help extract PPIs from biological literature more efficiently. However, the existing text representation and feature weighting schemes commonly used for text classification are not well suited for IAC. We capture and utilise biological domain knowledge, i.e. gene mentions also known as protein or gene names in the articles, to address the problem. We put forward a new gene mention order-based approach that highlights the important role of gene mentions to represent the texts. Furthermore, we also incorporate the information concerning gene mentions into a novel feature weighting scheme called Gene Mention-based Term Frequency (GMTF). By conducting experiments, we show that using the proposed representation and weighting schemes, our Interaction Article Classifier (IACer) performs better than other leading systems for the moment. C1 [Chen, Yifei; Guo, Hongjian] Nanjing Audit Univ, Sch Informat Sci, Nanjing 211815, Jiangsu, Peoples R China. [Liu, Feng; Manderick, Bernard] Vrije Univ Brussel, Computat Modeling Lab, B-1050 Brussels, Belgium. RP Chen, YF (reprint author), Nanjing Audit Univ, Sch Informat Sci, 86 Yushan Rd W, Nanjing 211815, Jiangsu, Peoples R China. EM yifeichen91@nau.edu.cn; g_coolman@nau.edu.cn; fengliu77@gmail.com; bmanderi@vub.ac.be FU National Natural Science Foundation of China [61202135]; Natural Science Foundation of Jiangsu Province [BK2012472]; Natural Science Foundation of the Higher Education Institutions of Jiangsu Province [11KJB520007]; Qing Lan Project FX The authors would like to thank the reviewers for their helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 61202135), the Natural Science Foundation of Jiangsu Province (No. BK2012472), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (No. 11KJB520007) and Qing Lan Project. CR Antunes M, 2011, LECT NOTES COMPUT SC, V6594, P342, DOI 10.1007/978-3-642-20267-4_36 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Chen YF, 2010, IEEE ACM T COMPUT BI, V7, P428, DOI 10.1109/TCBB.2010.47 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Dai H., 2007, P 2 BIOCREATIVE CHAL, P69 Ding J, 2011, INT J DATA MIN BIOIN, V5, P73, DOI 10.1504/IJDMB.2011.038578 Efron B, 1994, INTRO BOOTSTRAP Ehrler F., 2007, P 2 BIOCR CHALL EV W, P199 Huang M., 2007, P 2 BIOCREATIVE CHAL, P237 Jiang YQ, 2011, LECT NOTES COMPUT SC, V6988, P196 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Kim S, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S8-S9 Krallinger M., 2007, P 2 BIOCREATIVE CHAL, P29 Krallinger M, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S8-S3 Lan M, 2009, J BIOMED INFORM, V42, P866, DOI 10.1016/j.jbi.2009.07.004 Lan M., 2007, P 2 BIOCREATIVE CHAL, P183 Lan M., 2006, ASS ADV ARTIFICIAL I, V6, P763 Marcotte EM, 2001, BIOINFORMATICS, V17, P359, DOI 10.1093/bioinformatics/17.4.359 National Library of Medicine, 2011, MEDLINE FACT SHEET Nigam K., 1999, IJCAI 99 WORKSH MACH, V1, P61 Noreen Eric W., 1989, COMPUTER INTENSIVE M Robertson S.E., 1996, P 3 TEXT RETRIEVAL C, P109 Robertson S.E., 1997, SIMPLE PROVEN APPROA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 Suomela BP, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-75 Vapnik VN, 1998, STAT LEARNING THEORY WHITE AP, 1994, MACH LEARN, V15, P321, DOI 10.1007/BF00993349 Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 Xia T., 2011, 6 INT C COMP SCI ED, P210 Zheng FB, 2010, INT J DATA MIN BIOIN, V4, P228 NR 32 TC 0 Z9 0 PU INDERSCIENCE ENTERPRISES LTD PI GENEVA PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND SN 1748-5673 EI 1748-5681 J9 INT J DATA MIN BIOIN JI Int. J. Data Min. Bioinform. PY 2015 VL 12 IS 2 BP 144 EP 166 DI 10.1504/IJDMB.2015.069415 PG 23 WC Mathematical & Computational Biology SC Mathematical & Computational Biology GA CI6RO UT WOS:000354888900003 ER PT J AU Santos, RLT Macdonald, C Ounis, I AF Santos, R. L. T. Macdonald, C. Ounis, I. TI Search Result Diversification SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL LA English DT Article ID PROBABILITY RANKING PRINCIPLE; INFORMATION-RETRIEVAL; WEB-SEARCH; QUERIES; RELEVANCE; IR; ALGORITHMS; NOVELTY; INDEX AB Ranking in information retrieval has been traditionally approached as a pursuit of relevant information, under the assumption that the users' information needs are unambiguously conveyed by their submitted queries. Nevertheless, as an inherently limited representation of a more complex information need, every query can arguably be considered ambiguous to some extent. In order to tackle query ambiguity, search result diversification approaches have recently been proposed to produce rankings aimed to satisfy the multiple possible information needs underlying a query. In this survey, we review the published literature on search result diversification. In particular, we discuss the motivations for diversifying the search results for an ambiguous query and provide a formal definition of the search result diversification problem. In addition, we describe the most successful approaches in the literature for producing and evaluating diversity in multiple search domains. Finally, we also discuss recent advances as well as open research directions in the field of search result diversification. C1 [Santos, R. L. T.] Universidade Federal de Minais Gerais, Dep of Comp Sci, Belo Horizonte, MG, Brazil. [Macdonald, C.; Ounis, I.] Univ Glasgow, Sch of Computing Science, Glasgow G12 8QQ, Lanark, Scotland. RP Santos, RLT (reprint author), Universidade Federal de Minais Gerais, Dep of Comp Sci, Belo Horizonte, MG, Brazil. CR Agrawal R., 2009, P 2 ACM INT C WEB SE, P5, DOI 10.1145/1498759.1498766 Amati G, 2006, LECT NOTES COMPUT SC, V3936, P13 Amati Giambattista, 2003, THESIS U GLASGOW Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 Ashkan A., 2011, P 20 INT C WORLD WID, P407 Aslam J.A., 2005, P 28 ANN INT ACM SIG, P27, DOI 10.1145/1076034.1076042 BaezaYates R, 2004, LECT NOTES COMPUT SC, V3268, P588 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE, Vsecond Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 Belem F., 2013, P 7 ACM C REC SYST H, P141 Bernstein Y., 2005, P 14 ACM INT C INF K, P736, DOI 10.1145/1099554.1099733 Berry D. A., 1985, BANDIT PROBLEMS SEQU Bhatia S., 2011, P 34 INT ACM SIGIR C, P795 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008 Boldi P., 2009, P 2009 WORKSH WEB SE, P56, DOI DOI 10.1145/1507509.1507518 BOLDI P., 2008, P 17 ACM C INF KNOWL, P609, DOI DOI 10.1145/1458082.1458163 Boldi P, 2009, 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P183 Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286 Bouchoucha A., 2013, P 22 ACM INT C INF K, P1861 Broccolo D, 2012, INFORM PROCESS MANAG, V48, P326, DOI 10.1016/j.ipm.2011.07.005 Broder A., 2002, SIGIR Forum, V36 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009132 Capannini G., 2011, P VLDB ENDOW, V4, P451 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Carterette B, 2009, LECT NOTES COMPUT SC, V5766, P200, DOI 10.1007/978-3-642-04417-5_18 Carterette B., 2009, P 18 ACM C INF KNOWL, P1287, DOI 10.1145/1645953.1646116 Carterette B., 2007, P ACM INT C RES DEV, P55, DOI 10.1145/1277741.1277754 Chandar P., 2013, P 36 INT ACM SIGIR C, P413 Chapelle O., 2011, JMLR WORKSH C P, P91 Chapelle O., 2009, P 18 ACM C INF KNOWL, P621, DOI DOI 10.1145/1645953.1646033 Chapelle O, 2011, INFORM RETRIEVAL, V14, P572, DOI 10.1007/s10791-011-9167-7 Chen HH, 2006, IEEE VEH TECHNOL MAG, V1, P2, DOI 10.1109/MVT.2006.307302 Clarke C L, 2008, P 31 ANN INT ACM SIG, P659, DOI 10.1145/1390334.1390446 Clarke C. L. A., 2010, P 19 TEXT RETR C GAI Clarke C. L. A., 2012, P 21 TEXT RETR C GAI Clarke C. L. A., 2011, P 4 ACM INT C WEB SE, P75, DOI 10.1145/1935826.1935847 Clarke C. L. A., 2009, P 18 TEXT RETR C GAI Clarke C. L. A., 2011, P 20 TEXT RETR C GAI Clarke CLA, 2009, LECT NOTES COMPUT SC, V5766, P188, DOI 10.1007/978-3-642-04417-5_17 CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 Clough P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P734, DOI 10.1145/1571941.1572102 Collins-Thompson K., 2013, P 22 TEXT RETR C GAI COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 Cooper W. S., 1971, TECHNICAL REPORT Cormen T., 2001, INTRO ALGORITHMS Craswell N., 2008, P INT C WEB SEARCH W, P87, DOI 10.1145/1341531.1341545 Craswell N., 2007, P 30 ANN INT ACM SIG, P239, DOI 10.1145/1277741.1277784 Cronen-Townsend S., 2002, P 2 INT C HUM LANG T, P104, DOI 10.3115/1289189.1289266 Cutts M., 2012, P SEARCH ENG STRAT S Dang V, 2013, P 36 INT ACM SIGIR C, P603 Dang V, 2012, P SIGIR 2012, P65 Dang V., 2011, P 20 ACM INT C INF K, P2117 Dang V., 2010, P ACM INT C WEB SEAR, P41, DOI 10.1145/1718487.1718493 Demartini G, 2011, LECT NOTES COMPUT SC, V6611, P772, DOI 10.1007/978-3-642-20161-5_91 Demidova E, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P331 Deselaers T., 2009, P ACM INT C IM VID R, P1, DOI 10.1145/1646396.1646443 Diaz F, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P910 Dou Z, 2011, P 4 ACM INT C WEB SE, P475, DOI 10.1145/1935826.1935897 Downey D., 2007, P 30 ACM SIGIR C RES, P847, DOI 10.1145/1277741.1277939 Dundar M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P756 Eiron N., 2003, P 26 ANN INT ACM SIG, P459 Feige U, 1998, J ACM, V45, P634, DOI 10.1145/285055.285059 Fonseca B. M., 2003, J WEB ENG, V2, P215 Fox E. A., 1993, P 2 TEXT RETRIEVAL C, P243 Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451 Geng X, 2008, P 31 ANN INT ACM SIG, P115, DOI 10.1145/1390334.1390356 Gil-Costa Veronica, 2013, Journal of Discrete Algorithms, V18, DOI 10.1016/j.jda.2012.07.004 Gil-Costa V, 2011, LECT NOTES COMPUT SC, V7024, P344, DOI 10.1007/978-3-642-24583-1_34 GOFFMAN W, 1964, INFORM STORAGE RET, V2, P201, DOI 10.1016/0020-0271(64)90004-X Golbus PB, 2013, INFORM RETRIEVAL, V16, P530, DOI 10.1007/s10791-012-9218-8 Gollapudi S., 2009, P 18 INT C WORLD WID, P381, DOI 10.1145/1526709.1526761 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P703, DOI 10.1002/(SICI)1097-4571(199112)42:10<703::AID-ASI3>3.0.CO;2-1 GORDON MD, 1992, J AM SOC INFORM SCI, V43, P1, DOI 10.1002/(SICI)1097-4571(199201)43:1<1::AID-ASI1>3.0.CO;2-5 Harman D., 1993, P 2 TEXT RETR C GAIT HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 He J, 2012, P 35 INT ACM SIGIR C, P851 He JY, 2011, J AM SOC INF SCI TEC, V62, P550, DOI 10.1002/asi.21468 Hersh W. R., 1999, P 8 TEXT RETR C GAIT Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 Hochbaum DS, 1997, APPROXIMATION ALGORI Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Jansen B. J., 1998, SIGIR Forum, V32 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835 Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 Kemeny J G, 1960, FINITE MARKOV CHAINS Kharazmi S., 2014, P 37 INT ACM SIGIR C, P1143 Kharitonov E., 2013, P 22 ACM INT C C INF, P2303 Khuller S, 1999, INFORM PROCESS LETT, V70, P39, DOI 10.1016/S0020-0190(99)00031-9 Kim YJ, 2014, CLIN EXP OTORHINOLAR, V7, P7, DOI 10.3342/ceo.2014.7.1.7 Kraft R., 2004, P 13 INT C WORLD WID, P666, DOI 10.1145/988672.988763 Krestel R., 2011, P IEEE WIC ACM INT C, P138 Kruschwitz U, 2013, J AM SOC INF SCI TEC, V64, P1975, DOI 10.1002/asi.22901 Lagergren E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290986 Lathia N, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P210 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Leelanupab T., 2012, P 21 ACM INT C INF K, P1950 Liang S., 2014, P 37 INT ACM SIGIR C, P303 Limsopatham N., 2014, P 23 ACM INT C INF K, P1639 Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d Ma H., 2010, P 24 AAAI C ART INT Marden J. I., 1996, ANAL MODELING RANK D Markowitz H, 1952, J FINANC, V7, P77, DOI 10.2307/2975974 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mei Q., 2008, P 17 ACM C INF KNOWL, P469, DOI 10.1145/1458082.1458145 Melucci Massimo, 2012, Foundations and Trends in Information Retrieval, V6, DOI 10.1561/1500000023 Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Moffat A., 2008, ACM T INFORM SYST, V27, P1 Murdock V., 2008, SIGIR FORUM, V42, P80, DOI DOI 10.1145/1480506.1480520 NEMHAUSER GL, 1978, MATH PROGRAM, V14, P265, DOI 10.1007/BF01588971 Nguyen T. N., 2014, P 36 EUR C INF RETR, P222 Over P., 1997, P 6 TEXT RETR C, P73 Over P., 1998, P 7 TEXT RETR C GAIT, P33 Ozdemiray A. M., 2013, METUCENG201301 Paramita ML, 2009, LECT NOTES COMPUT SC, V5478, P603, DOI 10.1007/978-3-642-00958-7_56 Peng J, 2010, LECT NOTES COMPUT SC, V5993, P114 Plachouras V., 2011, P WORKSH DIV DOC RET, P63 Plakhov A., 2011, P 1 INT WORKSH ENT O Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Radlinski F., 2010, P 19 INT C WORLD WID, P1171, DOI 10.1145/1772690.1772859 Radlinski F, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P853 Radlinski F, 2008, P 25 INT C MACH LEAR, P784, DOI DOI 10.1145/1390156.1390255 Radlinski F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148320 Radlinski Filip, 2009, SIGIR FORUM, V43, p[2009, 46] Rafiei D, 2010, P 19 INT C WORLD WID, P781, DOI 10.1145/1772690.1772770 Raman K., 2012, P 18 ACM SIGKDD INT Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 Robertson S., 1981, P 3 ANN ACM C RES DE, P35 Robertson S. E., 1994, P 3 TEXT RETR C GAIT ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 Rowe B. R., 2010, EC IMP ASS NISTS TEX Sakai T., 2010, P 3 INT WORKSH EV IN, P42 Sakai T., 2012, INFORM RETRIEVAL Sakai T., 2013, P 36 INT ACM SIGIR C, P921 Sakai T., 2013, P 5 INT WORKSH EV IN, P1 Sakai T, 2012, P 21 INT C WORLD WID, P499 Sakai T., 2007, P 30 ANN INT ACM SIG, P71, DOI DOI 10.1145/1277741.1277756 Sakai T., 2013, P 10 NTCIR WORKSH M Sakai T., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148261 Samuelson P., 2001, MICROECONOMICS Sanderson M., 2008, P 31 ANN INT ACM SIG, P499, DOI 10.1145/1390334.1390420 Sanderson M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P555 Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009 Santos R L, 2010, P 19 ACM INT C INF K, P1179, DOI 10.1145/1871437.1871586 Santos R L, 2011, P 34 ANN INT ACM SIG, P595 Santos R. L. T., 2013, THESIS U GLASGOW GLA Santos R. L. T., 2011, P 1 INT WORKSH DIV D, P37 Santos RLT, 2013, INFORM RETRIEVAL, V16, P429, DOI 10.1007/s10791-012-9211-2 Santos RLT, 2011, LECT NOTES COMPUT SC, V6931, P250, DOI 10.1007/978-3-642-23318-0_23 Santos RLT, 2012, FOUND TRENDS INF RET, V6, P1, DOI 10.1561/1500000026 Santos RLT, 2012, INFORM RETRIEVAL, V15, P478, DOI 10.1007/s10791-011-9180-x Santos RLT, 2010, LECT NOTES COMPUT SC, V5993, P87, DOI 10.1007/978-3-642-12275-0_11 Searcoid M., 2006, SPRINGER UNDERGRADUA Silverstein C., 1999, SIGIR Forum, V33 Silvestri Fabrizio, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000013 Slivkins A., 2010, P 27 ANN INT C MACH, P983 Song R., 2011, P 9 NICIR WORKSH M E Song RH, 2009, INFORM PROCESS MANAG, V45, P216, DOI 10.1016/j.ipm.2008.09.005 Song Y., 2011, P SIGIR, P815 Sparck-Jones K., 2007, SIGIR FORUM, V41, P8, DOI DOI 10.1145/1328964.1328965 Szpektor I., 2011, P 20 INT C WORLD WID, P47 Teevan J., 2007, P 30 ANN INT ACM SIG, P757, DOI 10.1145/1277741.1277894 Tsochantaridis I, 2005, J MACH LEARN RES, V6, P1453 Turtle H. R., 1996, UNCERTAINTY MANAGEME, P189 Vallet D., 2011, P ACM SIGIR WORKSH C Vallet David, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348396 van Leuken R. H., 2009, P 18 INT C WORLD WID, P341, DOI DOI 10.1145/1526709.1526756 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR Vargas S., 2013, P 10 C OP RES AR INF, P69 Vargas S., 2011, P 34 INT ACM SIGIR C, P1211 Vargas S., 2011, P 5 ACM C REC SYST R, P109 Vee E, 2008, PROC INT CONF DATA, P228, DOI 10.1109/ICDE.2008.4497431 VOHRA RV, 1993, DISCRETE APPL MATH, V43, P175, DOI 10.1016/0166-218X(93)90006-A Von Neumann J., 1944, THEORY GAMES EC BEHA Voorhees E. M., 1997, P 6 TEXT RETR C GAIT Voorhees E. M., 1999, P 8 TEXT RETR C GAIT Voorhees E. M., 1998, P 7 TEXT RETR C GAIT Voorhees Ellen M., 2005, TREC EXPT EVALUATION Voorhees EM, 2007, COMMUN ACM, V50, P51, DOI 10.1145/1297797.1297822 Wang J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P115, DOI 10.1145/1571941.1571963 Wang QL, 2013, INFORM RETRIEVAL, V16, P484, DOI 10.1007/s10791-013-9221-8 Wang XZ, 2008, INT J PATTERN RECOGN, V22, P1, DOI 10.1142/S0218001408006065 Welch M. J., 2011, P 20 INT C WORLD WID, P237, DOI DOI 10.1145/1963405.1963441 Woeginger GJ, 2003, LECT NOTES COMPUT SC, V2570, P185 Woodbury M. A., 1950, MR38136 PRINC U STAT Wu F, 2011, J ARTIF INTELL RES, V40, P677 Yin XS, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P901 Yu C, 2009, P 12 INT C EXT DAT T, P368, DOI 10.1145/1516360.1516404 Yue Y, 2008, P 25 INT C MACH LEAR, P1224, DOI 10.1145/1390156.1390310 Zaragoza H., 2004, P 13 TEXT RETR C GAI Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhang Z., 2006, P 15 INT C WORLD WID, P1039, DOI 10.1145/1135777.1136004 Zheng W., 2011, P 20 ACM INT C INF K, P1901 Zheng W., 2011, P 11 ANN INT ACM IEE, P387 Zheng W, 2014, INFORM RETRIEVAL, V17, P52, DOI 10.1007/s10791-013-9228-1 Zheng W., 2012, P 21 ACM INT C INF K, P1844 Zhou T, 2010, P NATL ACAD SCI USA, V107, P4511, DOI 10.1073/pnas.1000488107 Zhu X., 2007, P N AM CHAPT ASS COM, P97 Zhu Y., 2014, P 37 INT ACM SIGIR C, P293 Ziegler C.-N., 2005, P 14 INT C WORLD WID, P22, DOI DOI 10.1145/1060745.1060754 Zobel J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291014 Zuccon G., 2012, P 34 EUR C ADV INF R, P305 Zuccon G, 2010, LECT NOTES COMPUT SC, V5993, P357, DOI 10.1007/978-3-642-12275-0_32 NR 212 TC 0 Z9 0 PU NOW PUBLISHERS INC PI HANOVER PA PO BOX 1024, HANOVER, MA 02339 USA SN 1554-0669 EI 1554-0677 J9 FOUND TRENDS INF RET JI Found. Trends Inf. Retr. PY 2015 VL 9 IS 1 BP 1 EP 90 DI 10.1561/1500000040 PG 90 WC Computer Science, Information Systems SC Computer Science GA CF1OG UT WOS:000352315300001 ER PT J AU Dong, H Hussain, FK AF Dong, Hai Hussain, Farookh Khadeer TI Service-requester-centered service selection and ranking model for digital transportation ecosystems SO COMPUTING LA English DT Article DE Digital ecosystems; Generic services; Service ranking; Service selection; Transport services; Trust ID CERTIFICATION AB Transport services are a fundamental utility that drives human society. A Digital Transportation Ecosystem is a sub-system of the Digital Ecosystem, which uses ICT resources to facilitate transport service transactions. This research focuses on the selection and ranking of online transport service information. The previous research in this area has been unable to achieve satisfactory performance or give sufficient freedom to service requesters to rank services based on their preferences. 'User-centered design' is a broad term to describe how end-users influence system design. In this research, we propose a Service-Requester-Centered Service Selection and Ranking Model, guided by the philosophy of user-centered design. Three major sub-models are involved in this model: a model for assisting service requesters to search appropriate transport service ontology concepts to denote their service requests, a model for enhancing the accuracy of automatic transport service concept recommendation by observing service requesters' click behaviours, and a model for enabling service-requester-preference-based service ranking. Implementations and empirical experiments are conducted to evaluate the three sub-models and the drawn conclusions along with directions for future work are outlined. C1 [Dong, Hai] Curtin Univ Technol, Sch Informat Syst, Curtin Business Sch, Bentley, WA 6102, Australia. [Hussain, Farookh Khadeer] Univ Technol Sydney, Sch Software, Fac Engn & Informat Technol, Decis Support & E Serv Intelligence Lab, Ultimo, NSW 2007, Australia. RP Hussain, FK (reprint author), Univ Technol Sydney, Sch Software, Fac Engn & Informat Technol, Decis Support & E Serv Intelligence Lab, Ultimo, NSW 2007, Australia. EM hai.dong@cbs.curtin.edu.au; farookh.hussain@uts.edu.au RI Dong, Hai/E-9101-2010 OI Dong, Hai/0000-0002-7033-5688 CR Abras C, 2004, BERKSHIRE ENCY HUMAN, V2, P763 Agarwal S, 2005, LECT NOTES COMPUT SC, V3590, P348 Arcieri F., 2003, Proceedings 2003 Symposium on Applications and the Internet, DOI 10.1109/SAINT.2003.1183057 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Chang E., 2006, WSEAS Transactions on Environment and Development, V2 Chinnici R, 2007, SERVICES DESCRIPTION Deerwester S., 1988, US Patent, Patent No. [4,839,853, 4839853] Dini P, 2005, DIGITAL ECOSYSTEMS R Dini P, 2007, SCI FDN DIGITAL ECOS Dong H, 2008, IEEE/SOLI'2008: PROCEEDINGS OF 2008 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS, VOLS 1 AND 2, P820 Dong H, 2011, CONCURR COMP-PRACT E, V23, P505, DOI 10.1002/cpe.1652 Dong H, 2011, IEEE T IND ELECTRON, V58, P2183, DOI 10.1109/TIE.2009.2031186 Dong H, 2010, WORLD WIDE WEB, V13, P75, DOI 10.1007/s11280-009-0081-5 Dong H., 2012, LNCS, V7636, P591 Dong H, 2011, KNOWL-BASED SYST, V24, P761, DOI 10.1016/j.knosys.2011.02.005 Dong H, 2013, WEB INFORM SYSTEMS E, V8180, P267 Dong H, 2013, CONCURR COMP-PRACT E, V25, P961, DOI 10.1002/cpe.2886 Draper SW, 1986, NEW PERSPECTIVES HUM Endsley M.R., 2004, DESIGNING SITUATION, V2nd Garofalakis J, 2006, J WEB ENG, V5, P265 Hussain FK, 2004, COMPUT SYST SCI ENG, V19, P173 Kopecky J, 2007, IEEE INTERNET COMPUT, V11, P60, DOI 10.1109/MIC.2007.134 Leem CS, 2004, TECHNOVATION, V24, P63, DOI 10.1016/S0166-4972(03)00145-7 Ludwig SA, 2005, J PARALLEL DISTR COM, V65, P1533, DOI 10.1016/j.jpdc.2005.05.022 Martin David, 2004, OWL S SEMANTIC MARKU Mathew TV, 2001, INTRO TRANSPORTATION ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roman D, 2005, APPL ONTOL, V1, P77 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Satyanarayanan M, 2001, IEEE PERS COMMUN, V8, P10, DOI 10.1109/98.943998 Serhani M. A., 2005, Proceedings. 2005 IEEE International Conference on Web Service Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 NR 32 TC 0 Z9 0 PU SPRINGER WIEN PI WIEN PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA SN 0010-485X EI 1436-5057 J9 COMPUTING JI Computing PD JAN PY 2015 VL 97 IS 1 SI SI BP 79 EP 102 DI 10.1007/s00607-013-0347-8 PG 24 WC Computer Science, Theory & Methods SC Computer Science GA AY2FS UT WOS:000347404700005 ER PT J AU Pal, D Mitra, M Datta, K AF Pal, Dipasree Mitra, Mandar Datta, Kalyankumar TI Improving Query Expansion Using WordNet SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL AB This study proposes a new way of using WordNet for query expansion (QE). We choose candidate expansion terms from a set of pseudo-relevant documents; however, the usefulness of these terms is measured based on their definitions provided in a hand-crafted lexical resource such as WordNet. Experiments with a number of standard TREC collections WordNet-based that this method outperforms existing WordNet-based methods. It also compares favorably with established QE methods such as KLD and RM3. Leveraging earlier work in which a combination of QE methods was found to outperform each individual method (as well as other well-known QE methods), we next propose a combination-based QE method that takes into account three different aspects of a candidate expansion term's usefulness: (a) its distribution in the pseudo-relevant documents and in the target corpus, (b) its statistical association with query terms, and (c) its semantic relation with the query, as determined by the overlap between the WordNet definitions of the term and query terms. This combination of diverse sources of information appears to work well on a number of test collections, viz., TREC123, TREC5, TREC678, TREC robust (new), and TREC910 collections, and yields significant improvements over competing methods on most of these collections. C1 [Pal, Dipasree; Mitra, Mandar] Indian Stat Inst, Kolkata 700108, India. [Datta, Kalyankumar] Jadavpur Univ, Kolkata 700032, India. RP Pal, D (reprint author), Indian Stat Inst, 203 BT Rd, Kolkata 700108, India. EM dipasree.pal@gmail.com; mandar@isical.ac.in; kalyankumardatta@gmail.com CR Abdul-Jaleel N., 2004, TREC Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Amati G., 2003, PROBABILITY MODELS I Banerjee S., 2003, P 18 INT JOINT C ART, V3, P805 Banerjee S., 2002, P 3 INT C INT TEXT P, P136 Bendersky Michael, 2011, P 34 INT ACM SIGIR C, P605, DOI [DOI 10.1145/2009916.2009998, 10.1145/2009916.2009998] Bendersky Michael, 2012, P 5 ACM INT C WEB SE, P443 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Buckley C., 1995, TREC Cao Guihong, 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377 CARPINETO C, 2012, ACM COMPUT SURV, V44, P50, DOI DOI 10.1145/2071389.2071390 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Carpineto C, 2002, ACM T INFORM SYST, V20, P259, DOI 10.1145/568727.568728 Cui H., 2002, 11 INT C WORLD WID W, P325 DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 Fang H, 2008, P ANN M ASS COMP LIN, P139 JING YF, 1994, RIAO 94 CONFERENCE PROCEEDINGS - INTELLIGENT MULTIMEDIA INFORMATION RETRIEVAL SYSTEMS AND MANAGEMENT, P146 Krikon Eyal, 2010, ACM T INFORM SYST, V29, P3 Lavrenko V., 2001, SIGIR Forum Lesk M, 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Li Y., 2007, P 30 ANN INT ACM SIG, P797, DOI 10.1145/1277741.1277914 Liu S, 2004, P 27 ANN INT ACM SIG, P266, DOI DOI 10.1145/1008992.1009039 Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579 Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Miao J., 2012, P 35 INT ACM SIGIR C, P535 Pal D., 2013, COMPUTING RES RESP C Perez-Aguera JR, 2008, ADV NATURAL LANGUAGE, V33, P177 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Voorhees E.M., 1993, TEXT RETRIEVAL C, P223 Xu J., 1996, SIGIR 96, P4 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Zhang JL, 2009, AST: 2009 INTERNATIONAL E-CONFERENCE ON ADVANCED SCIENCE AND TECHNOLOGY, PROCEEDINGS, P52, DOI 10.1109/AST.2009.24 NR 37 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2330-1635 EI 2330-1643 J9 J ASSOC INF SCI TECH PD DEC PY 2014 VL 65 IS 12 BP 2469 EP 2478 DI 10.1002/asi.23143 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AU2IO UT WOS:000345441600007 ER PT J AU Borg, M Runeson, P Ardo, A AF Borg, Markus Runeson, Per Ardo, Anders TI Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability SO EMPIRICAL SOFTWARE ENGINEERING LA English DT Article DE Traceability; Information retrieval; Software artifacts; Systematic mapping study ID LATENT SEMANTIC ANALYSIS; OBJECT-ORIENTED DESIGN; SOURCE-CODE; REQUIREMENTS TRACEABILITY; CENTRIC TRACEABILITY; IR; LINKS; DOCUMENTATION; RELEVANCE; MODELS AB Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery. C1 [Borg, Markus; Runeson, Per] Lund Univ, Dept Comp Sci, S-22100 Lund, Sweden. [Ardo, Anders] Lund Univ, Dept Elect & Informat Technol, Lund, Sweden. RP Borg, M (reprint author), Lund Univ, Dept Comp Sci, Box 118, S-22100 Lund, Sweden. EM markus.borg@cs.lth.se; per.runeson@cs.lth.se; anders.ardo@eit.lth.se RI Runeson, Per/ OI Runeson, Per/0000-0003-2795-4851 FU Industrial Excellence Center EASE - Embedded Applications Software Engineering FX This work was funded by the Industrial Excellence Center EASE - Embedded Applications Software Engineering.6 Thanks go to our librarian Mats Berglund for working on the search strings, and Lorand Dali for excellent comments on IR details. CR Abadi A, 2008, INT C PROGRAM COMPRE, P103, DOI 10.1109/ICPC.2008.30 Aitchison J., 2000, THESAURUS CONSTRUCTI Ali N, 2011, CONF PROC INT SYMP C, P111, DOI 10.1109/ICPC.2011.42 Ali N, 2011, P 18 WORK C REV ENG, P45 Ali N, 2012, SOFTWARE SYSTEMS TRA Antonelli G., 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), DOI 10.1109/IROS.1999.812994 Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Antoniol G, 1999, PROG COMPREHEN, P151, DOI 10.1109/WPC.1999.777754 Antoniol G, 2000, PROC IEEE INT CONF S, P40, DOI 10.1109/ICSM.2000.883003 Antoniol G, 2000, PROG COMPREHEN, P79, DOI 10.1109/WPC.2000.852482 Assawamekin N, 2010, KNOWL INF SYST, V25, P493, DOI 10.1007/s10115-009-0259-2 Asuncion HU, 2010, P 32 ACM IEEE INT C, P95, DOI DOI 10.1145/1806799.1806817 Ayari K., 2007, P 2007 C CTR ADV STU, P215, DOI 10.1145/1321211.1321234 Bacchelli A, 2010, P ICSE 2010 32 INT C, P375 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE, Vsecond Banko Michele, 2001, P 39 ANN M ASS COMP, P26, DOI 10.3115/1073012.1073017 Ben Charrada E, 2011, P 12 INT WORKSH PRIN, P21 Bianchi A, 2000, PROG COMPREHEN, P149, DOI 10.1109/WPC.2000.852489 Binkley D, 2010, ENCY SOFTWARE ENG Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borg M, 2011, P INT WORKSH MACH LE, P27 Borg M, 2012, P 16 INT C EV ASS SO Borillo M, 1992, P 10 EUR C ART INT, P593 BRAS M, 1993, APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN ENGINEERING VIII, VOL 1, P275 Brereton P, 2007, J SYST SOFTWARE, V80, P571, DOI 10.1016/j.jss.2006.07.009 Canfora G, 2006, P INT WORKSH MIN SOF, P105, DOI DOI 10.1145/1137983.1138009 Capobianco G, 2009, WORK CONF REVERSE EN, P195, DOI 10.1109/WCRE.2009.14 Capobianco G, 2009, INT C PROGRAM COMPRE, P148 Carnegie Mellon Software Engineering Institute, 2010, CMMI DEV VERS 1 3 Castell N, 1994, P 7 INT C IND ENG AP, P37 Chang J, 2010, ANN APPL STAT, V4, P124, DOI 10.1214/09-AOAS309 Charikar M., 1997, P 29 ANN ACM S THEOR, P626, DOI 10.1145/258533.258657 Chen X, 2011, P 33 INT C SOFTW ENG, P912 Chen X, 2010, P INT C AUT SOFTW EN, P505, DOI 10.1145/1858996.1859098 Chen X., 2011, P 26 IEEE ACM ASE 11, P223 Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78 Cleland-Huang J, 2007, COMPUTER, V40, P27, DOI 10.1109/MC.2007.195 Cleland-Huang J, 2008, IEEE T SOFTWARE ENG, V34, P685, DOI 10.1109/TSE.2008.45 CLELANDHUANG J, 2010, P ICSE, P155 Cleland-Huang J, 2003, IEEE T SOFTWARE ENG, V29, P796, DOI 10.1109/TSE.2003.1232285 Cleland-Huang J., 2012, SOFTWARE SYSTEMS TRA Cleland-Huang J, 2006, COETGCT060109 Cleland-Huang J, 2011, P 6 INT WORKSH TRAC Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 Croft B, 1991, P SIGIR 91, P32 Cuddeback D, 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), DOI 10.1109/RE.2010.35 Czauderna A, 2011, P 6 INT WORKSH TRAC, P75 Dag JNO, 2006, EMPIR SOFTW ENG, V11, P303, DOI 10.1007/s10664-006-6405-5 De Lucia A, 2012, SOFTWARE SYSTEMS TRA De Lucia A, 2007, T SOFTW ENG METHODOL, V16 De Lucia A, 2008, P 23 INT C AUT SOFTW, P39, DOI 10.1109/ASE.2008.14 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dekhtyar A, 2007, INT REQUIR ENG CONF, P141, DOI 10.1109/RE.2007.17 Dekhtyar A, 2006, P 22 INT C SOFTW MAI Dekhtyar A, 2011, INT REQUIR ENG CONF, P111, DOI 10.1109/RE.2011.6051649 Dekhtyar A, 2007, P 3 INT WORKSH PRED Dekhtyar A, 2007, P INT S GRAND CHALL De Lucia A, 2006, INT C PROGRAM COMPRE, P317, DOI 10.1109/ICPC.2006.28 De Lucia A, 2008, 2008 FRONTIERS OF SOFTWARE MAINTENANCE, P21, DOI 10.1109/FOSM.2008.4659245 De Lucia A, 2006, PROC IEEE INT CONF S, P274 De Lucia A, 2009, EMPIR SOFTW ENG, V14, P57, DOI 10.1007/s10664-008-9090-8 De Lucia A, 2011, CONF PROC INT SYMP C, P21, DOI 10.1109/ICPC.2011.34 De Lucia A, 2005, NINTH EUROPEAN CONFERENCE ON SOFTWARE MAINTENANCE AND REENGINEERING, PROCEEDINGS, P32, DOI 10.1109/CSMR.2005.7 De Lucia A, 2006, PROC IEEE INT CONF S, P299 De Lucia A, 2006, INT C PROGRAM COMPRE, P307, DOI 10.1109/ICPC.2006.15 De Lucia A, 2004, PROC IEEE INT CONF S, P306, DOI 10.1109/ICSM.2004.1357816 De Lucia A, 2009, PROC IEEE INT CONF S, P371, DOI 10.1109/ICSM.2009.5306317 Di F, 2009, P INT C COMP INT SOF, P1 Di Penta M, 2002, PROG COMPREHEN, P207, DOI 10.1109/WPC.2002.1021342 Dit B, 2011, J SOFTWARE MAINTEN E, V25, P53 Domges R, 1998, COMMUN ACM, V41, P54, DOI 10.1145/290133.290149 Duan C., 2007, P 22 IEEE ACM INT C, P244, DOI [10.1145/1321631.1321668, DOI 10.1145/1321631.1321668] Egyed A, 2002, ASE 2002: 17TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, P163 Eisenbarth T, 2003, IEEE T SOFTWARE ENG, V29, P210, DOI 10.1109/TSE.2003.1183929 Falessi D, 2010, P 4 INT S EMP SOFTW Felizardo K. R., 2011, Proceedings of the 2011 5th International Symposium on Empirical Software Engineering and Measurement (ESEM 2011), DOI 10.1109/ESEM.2011.16 Fiutem R, 1998, PROC IEEE INT CONF S, P94, DOI 10.1109/ICSM.1998.738497 Gay G, 2009, PROC IEEE INT CONF S, P351, DOI 10.1109/ICSM.2009.5306315 Gethers M, 2011, P IEEE ACM INT C AUT, P540 Gibiec M, 2010, P ASE, P245, DOI 10.1145/1858996.1859046 Gotel O, 2012, SOFTWARE SYSTEMS TRA Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), DOI 10.1109/ICRE.1994.292398 Hayes JH, 2008, WORK CONF REVERSE EN, P165, DOI 10.1109/WCRE.2008.36 HAYES JH, 2005, P 3 INT WORKSH TRAC, P20, DOI 10.1145/1107656.1107661 Hayes JH, 2005, INT J SOFTW ENG KNOW, V15, P751, DOI 10.1142/S021819400500252X Hayes J. H., 2003, Proceedings 11th IEEE International Requirements Engineering Conference Hayes JH, 2006, IEEE T SOFTWARE ENG, V32, P4, DOI 10.1109/TSE.2006.3 Heindl M., 2005, P 10 EUR SOFTW ENG C, P60, DOI 10.1145/1081706.1081717 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Huffman Hayes J, 2005, P 2005 INT WORKSH MI, P1, DOI 10.1145/1083142.1083153 Huffman Hayes J, 2011, P 6 INT WORKSH TRAC, P50 Hayes J. H., 2004, Proceedings. 12th IEEE International Requirements Engineering Conference Hayes Jane Huffman, 2007, Innovations in Systems and Software Engineering, V3, DOI 10.1007/s11334-007-0024-1 Ingwersen P., 2005, TURN INTEGRATION INF International Electrotechnical Commission, 2003, 615111 IEC International Organization for Standardization, 2011, 2626212011 ISO Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jedlitschka A, 2008, GUIDE ADV EMPIRICAL, P201, DOI DOI 10.1007/978-1-84800-044-5_8 Jiang H, 2008, P 23 IEEE ACM INT C, P59 Jones K. S., 2000, Information Processing & Management, V36 Katta V, 2011, P COMPL SYST DES MAN Kaushik N., 2011, P 18 WORK C REV ENG, P411 Kekalainen J, 2002, COLIS4: EMERGING FRAMEWORKS AND METHODS, P253 Kitchenham B., 2007, GUIDELINES PERFORMIN Kitchenham BA, 2011, INFORM SOFTWARE TECH, V53, P638, DOI 10.1016/j.infsof.2010.12.011 Kitchenham BA, 2002, IEEE T SOFTWARE ENG, V28, P721, DOI 10.1109/TSE.2002.1027796 Klock S, 2011, P 6 INT WORKSH TRAC, P24 Kong L, 2009, P 21 INT C SOFTW ENG Kong W, 2011, P 1 INT WORKSH EMP R, P45 Kong W, 2011, P CHASE, P32 Kruchten P, 2004, RATIONAL UNIFIED PRO Leuser J, 2010, LECT NOTES COMPUT SC, V6182, P203, DOI 10.1007/978-3-642-14192-8_19 Leuser J, 2009, P 2009 ICSE WORKSH T, P31 Lewis D.D., 1998, MACHINE LEARNING ECM, V1398, P4, DOI 10.1007/BFb0026666 Li Y, 2008, LECT NOTES COMPUT SC, V5007, P100 Liddy E, 2001, NATURAL LANGUAGE PRO Lin J, 2006, P 14 IEEE RE 06, P363 Lindvall M, 2009, P S APPL COMP, P496, DOI 10.1145/1529282.1529387 Lormans M, 2006, P 10 EUR C SOFTW MAI, P45 Lormans M, 2008, EMPIR SOFTW ENG, V13, P727, DOI 10.1007/s10664-008-9078-4 Lormans M, 2006, WORK CONF REVERSE EN, P275 Mahmoud A, 2011, P 6 INT WORKSH TRAC, P3 Mahmoud A, 2010, P INT COMP SOFTW APP, P246 Manning C. D., 2008, INTRO INFORM RETRIEV Marcus A, 2005, INT J SOFTW ENG KNOW, V15, P811, DOI 10.1142/S0218194005002543 Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10 Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McMillan C, 2009, 2009 ICSE WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE), P41, DOI 10.1109/TEFSE.2009.5069582 Natt och Dag J, 2004, P 12 INT REQ ENG C, P283, DOI DOI 10.1109/RE.2004.47 och Dag J. N., 2002, REQUIR ENG, V7, P20 Oliveto R, 2008, THESIS U SALERNO Oliveto Rocco, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.20 Olsson T, 2002, THESIS LUND U Park S, 2000, INFORM SOFTWARE TECH, V42, P429, DOI 10.1016/S0950-5849(99)00102-0 Parvathy Anju G, 2008, Proceedings Tenth International Conference on Enterprise Information Systems. Information Systems Analysis and Specification Petersen K, 2008, P 12 INT C EV ASS SO, P71 Petersen K., 2009, P 3 INT S EMP SOFTW, P401 Pohl K., 2005, SOFTWARE PRODUCT LIN Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Port D, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P701, DOI 10.1145/1985793.1985891 Randolph JJ, 2005, JOENS LEARN INSTR S Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S., 1999, MASTERING REQUIREMEN ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Runeson P, 2007, PROC INT CONF SOFTW, P499 Runeson P., 2012, CASE STUDY RES SOFTW Sabaliauskaite G, 2010, LECT NOTES COMPUT SC, V6182, P128, DOI 10.1007/978-3-642-14192-8_14 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scacchi W., 2002, IEE Proceedings-Software, V149, DOI 10.1049/ip-sen:20020202 Settimi R., 2004, Proceedings. 7th International Workshop on Principles of Software Evolution, DOI 10.1109/IWPSE.2004.1334768 Shull FJ, 2008, EMPIR SOFTW ENG, V13, P211, DOI 10.1007/s10664-008-9060-1 Singhal A, 2001, DATA ENG B, V24, P1 Smeaton AF, 1997, J INFORM SCI, V23, P169, DOI 10.1177/016555159702300208 Spanoudakis G, 2004, J SYST SOFTWARE, V72, P105, DOI 10.1016/S0164-1212(03)00242-5 Spanoudakis G, 2003, P 15 INT C SOFTW ENG Stone A, 2006, P 1 INT C SOFTW DAT, P139 Sultanov H, 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), DOI 10.1109/RE.2010.33 Sundaram S, 2005, P WORKSH PRED MOD SO, P1, DOI 10.1145/1083165.1083169 Sundaram SK, 2010, REQUIR ENG, V15, P313, DOI 10.1007/s00766-009-0096-6 Torchiano M., 2010, P 4 INT S EMP SOFTW, P47 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Van Rompaey B, 2009, EUR CON SFTWR MTNCE, P209, DOI 10.1109/CSMR.2009.39 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wang X, 2009, ELECT NOTES THEOR CO, V243, P121 Winkler S, 2010, SOFTW SYST MODEL, V9, P529, DOI 10.1007/s10270-009-0145-0 Winkler S, 2009, 2009 ICSE WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE), P49, DOI 10.1109/TEFSE.2009.5069583 Wohlin C, 2012, EXPT SOFTWARE ENG PR Yadla S, 2005, INNOVATIONS SYSTEMS, V1, P116, DOI DOI 10.1007/S11334-005-0011-3 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2007, TECHNICAL REPORT Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhao W, 2003, ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE, PROCEEDINGS, P68 Zhou X, 2007, LECT NOTES COMPUT SC, V4422, P412 Zou X, 2008, P INT C SOFTW ENG RE, P157 Zou XC, 2006, P INT COMP SOFTW APP, P265 Zou XC, 2010, EMPIR SOFTW ENG, V15, P119, DOI 10.1007/s10664-009-9114-z NR 181 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1382-3256 EI 1573-7616 J9 EMPIR SOFTW ENG JI Empir. Softw. Eng. PD DEC PY 2014 VL 19 IS 6 BP 1565 EP 1616 DI 10.1007/s10664-013-9255-y PG 52 WC Computer Science, Software Engineering SC Computer Science GA AR9QU UT WOS:000343910700002 ER PT J AU Saglam, RB Temizel, TT AF Saglam, Rahime Belen Temizel, Tugba Taskaya TI Automatic information timeliness assessment of diabetes web sites by evidence based medicine SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE LA English DT Article DE Content-based retrieval; Diabetes; Medical information systems; Timeliness analysis AB Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine. Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites. The proposed method is based on automatic term recognition, relevance feedback and information retrieval techniques in order to generate time-aware structured queries. We tested the framework on diabetes health web sites which were archived between 2006 and 2013 by Archive-it using American Diabetes Association's (ADA) guidelines. The results showed that the proposed framework achieves 65% and 77% accuracy in detecting the timeliness of the web content according to years and pre-determined time intervals respectively. Information seekers and web site owners may benefit from the proposed framework in finding relevant and up-to-date diabetes web sites. (C) 2014 Elsevier Ireland Ltd. All rights reserved. C1 [Saglam, Rahime Belen; Temizel, Tugba Taskaya] Middle E Tech Univ, Inst Informat, Dept Informat Syst, TR-06531 Ankara, Turkey. RP Saglam, RB (reprint author), Univ Mahallesi, ODTU Enformat Enstitusu, Dumlupinar Bulvan 1, TR-06800 Ankara, Turkey. EM rahimebelen@gmail.com CR Alonso O., 2011, 1 TEMP WEB AN WORKSH Alonso O., 2009, 18 ACM C INF KNOWL M [Anonymous], 2010, MEDLINEPLUS GUIDE HL Aronson A.R., 2001, P AMIA S Belen Saglam R., 2014, INFORM HLTH SOC JAN, P1 Bouzeghoub M., 2004, P INT WORKSH INF QUA, P59, DOI 10.1145/1012453.1012464 Griffiths K., 2005, J MED INTERNET RES, V7 HON Health on the Net Foundation, 2010, HLTH NET FDN Kanhabua N, 2009, LECT NOTES ARTIF INT, V5782, P738 Klimov D, 2010, ARTIF INTELL MED, V49, P11, DOI 10.1016/j.artmed.2010.02.001 Knoth P., 2009, C ZNAL 2009 BRNO CZE Lin YK, 2013, J BIOMED INFORM, V46, pS20, DOI 10.1016/j.jbi.2013.07.012 Miyanishi T., 2013, 36 INT ACM SIGIR C R Peralta V., 2006, TR0613 U REP I COMP Porter M., 2006, PORTER STEMMING ALGO Post RE, 2010, ARCH INTERN MED, V170, P1504, DOI 10.1001/archinternmed.2010.289 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sackett DL, 1996, BRIT MED J, V312, P71 Savova GK, 2010, J AM MED INFORM ASSN, V17, P507, DOI 10.1136/jamia.2009.001560 Seidman JJ, 2003, J MED INTERNET RES, V5, DOI 10.2196/jmir.5.4.e30 Shin B., 2003, J ASS INFORM SYST, V4 TheCochrane Collaboration, 2014, DAT ABSTR REV EFF DA Theodoratos D., 1999, DMDW, P15 University of Glasgow School of Computing Science, TERR IR PLATF V3 5 Wang R. Y., 1996, Journal of Management Information Systems, V12 Willett P, 2006, PROGRAM-ELECTRON LIB, V40, P219, DOI 10.1108/00330330610681295 Yuanhua L., 2011, P CIKM 2011 Zhang Z., 2008, P 6 INT C LANG RES E NR 29 TC 0 Z9 0 PU ELSEVIER IRELAND LTD PI CLARE PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND SN 0169-2607 EI 1872-7565 J9 COMPUT METH PROG BIO JI Comput. Meth. Programs Biomed. PD NOV PY 2014 VL 117 IS 2 BP 104 EP 113 DI 10.1016/j.cmpb.2014.07.014 PG 10 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Biomedical; Medical Informatics SC Computer Science; Engineering; Medical Informatics GA AQ8PV UT WOS:000343091400006 PM 25168774 ER PT J AU Glavas, G Snajder, J AF Glavas, Goran Snajder, Jan TI Event graphs for information retrieval and multi-document summarization SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Event extraction; Information extraction; Information retrieval; Multi-document summarization; Natural language processing ID VECTOR-SPACE MODEL; SEMANTICS; ONTOLOGY; TRACKING AB With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Glavas, Goran; Snajder, Jan] Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Zagreb 10000, Croatia. RP Snajder, J (reprint author), Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Unska 3, Zagreb 10000, Croatia. EM goran.glavas@fer.hr; jan.snajder@fer.hr CR ACE, 2005, EV DET REC ACE ENT V Ahn David, 2006, P WORKSH ANN REAS TI, P1, DOI 10.3115/1629235.1629236 Allan J, 2002, TOPIC DETECTION TRAC, V12 ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434 Amati Giambattista, 2003, THESIS U GLASGOW Atkinson J, 2013, EXPERT SYST APPL, V40, P4346, DOI 10.1016/j.eswa.2013.01.017 Atkinson M., 2009, P 18 INT WORLD WID W, P1153, DOI 10.1145/1526709.1526903 Baralis E, 2013, EXPERT SYST APPL, V40, P6976, DOI 10.1016/j.eswa.2013.06.047 Barzilay R., 1999, P 37 ANN M ASS COMP, P550, DOI 10.3115/1034678.1034760 Bejan Cosmin Adrian, 2008, P 6 INT C LANG RES E Bethard S., 2013, 2 JOINT C LEX COMP S, V2 Borgwardt K. M., 2007, THESIS LUDWIG MAXIMI Bramsen P., 2006, P 2006 C EMP METH NA, P189, DOI 10.3115/1610075.1610105 Buttcher S., 2007, P SIGIR 2007, P63, DOI 10.1145/1277741.1277755 Canhasi E, 2014, EXPERT SYST APPL, V41, P535, DOI 10.1016/j.eswa.2013.07.079 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chang A.X., 2012, P 8 INT C LANG RES E Christensen Janara, 2013, P NAACL HLT, P1163 Conroy J. M., 2004, P DOC UND C DUC 2004 Dang H. T., 2008, P TEXT AN C, P1 Daniel N., 2003, P HLT NAACL 03 WORKS, V5, P9, DOI 10.3115/1119467.1119469 Du P., 2010, P ACM INT C INF KNOW, P1757, DOI 10.1145/1871437.1871722 Filatova E., 2004, P ACL WORKSH SUMM, V111). Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885 Gartner T, 2003, LECT NOTES ARTIF INT, V2777, P129, DOI 10.1007/978-3-540-45167-9_11 Ge J., 2003, P 2003 DOC UND WORKS Gennari SP, 2002, COGNITION, V83, P49, DOI 10.1016/S0010-0277(01)00166-4 Glavas G., 2013, P CICLING 2013, P408 Glavas G., 2013, P 51 ANN M ASS COMP, P797 Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 Grishman R., 1996, P INT C COMP LING CO, V96, P466, DOI DOI 10.3115/992628.992709 Grover C., 2010, P 5 INT WORKSH SEM E, P333 Hammack R., 2011, HDB PRODUCT GRAPHS D Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582 He RF, 2012, EXPERT SYST APPL, V39, P2375, DOI 10.1016/j.eswa.2011.08.084 Humphreys K., 1998, P 7 MESS UND C MUC 7 Kawahara D., 2013, P INT JOINT C NAT LA, P37 Kingsbury P., 2002, P 3 INT C LANG RES E, P1989 Kolomiyets O., 2012, P 50 ANN M ASS COMP Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044 Li WJ, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P369 Lin C.-H., 2007, IADIS INT C WWW INT Lin C-Y., 2000, P 18 C COMP LING ASS, V1, P495, DOI 10.3115/990820.990892 Lin C.-Y., 2004, P WORKSH TEXT SUMM B, P74 Llorens H, 2010, P 5 INT WORKSH SEM E, P284 MacCartney B., 2006, P LREC 06, V319, P449 Makkonen J., 2003, P 2003 C N AM CHAPT, P43 Makkonen J, 2004, INFORM RETRIEVAL, V7, P347, DOI 10.1023/B:INRT.0000011210.12953.86 Mayo B., 1950, ANALYSIS, V10, P109, DOI 10.2307/3326687 Menchetti S., 2005, P 22 INT C MACH LEAR, P585, DOI 10.1145/1102351.1102425 Michel JB, 2011, SCIENCE, V331, P176, DOI 10.1126/science.1199644 Nallapati R., 2004, P 13 ACM INT C INF K, P446, DOI 10.1145/1031171.1031258 Nenkova A., 2005, MSRTR2005101 Noh TG, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P492, DOI 10.1145/1571941.1572026 Ouyang Y, 2011, INFORM PROCESS MANAG, V47, P227, DOI 10.1016/j.ipm.2010.03.005 Over P., 2004, P WORKSH AUT SUMM DU Over P., 2002, P WORKSH AUT SUMM DU Page L., 1998, PAGERANK CITATION RA Pai MY, 2013, EXPERT SYST APPL, V40, P2447, DOI 10.1016/j.eswa.2012.10.056 Pan Z., 1993, POLIT COMMUN, V10, P55, DOI DOI 10.1080/10584609.1993.9962963 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Pustejovsky J., 2003, CORPUS LINGUISTICS, V2003, P40 PUSTEJOVSKY J, 1991, COGNITION, V41, P47, DOI 10.1016/0010-0277(91)90032-Y Pustejovsky J., 2003, NEW DIRECTIONS QUEST, V2003, P28 Ramos-Santacruz M., 2000, P 6 APPL NAT LANG PR, P76, DOI 10.3115/974147.974158 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Saggion H., 2004, P DOC UND C, P6 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saric F., 2012, P 6 INT WORKSH SEM E Sarkar IN, 2012, J AM MED INFORM ASSN, V19, P249, DOI 10.1136/amiajnl-2011-000480 Sbattella L, 2013, J SYST SOFTWARE, V86, P1426, DOI 10.1016/j.jss.2013.01.029 Suzuki J., 2005, THESIS NARA I SCI TE Suzuki J., 2004, P 42 ANN M ASS COMP, P119, DOI 10.3115/1218955.1218971 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 UzZaman N., 2010, P 5 INT WORKSH SEM E, P276 UzZaman N., 2013, P 7 INT WORKSH SEM E Van Dijk T. A., 1985, DISCOURSE COMMUN, V10, P69 Verhagen M., 2007, P 4 INT WORKSH SEM E, P75, DOI 10.3115/1621474.1621488 Verhagen M., 2010, P 5 INT WORKSH SEM E, P57 Wei CP, 2007, IEEE T SYST MAN CY A, V37, P273, DOI 10.1109/TSMCA.2006.886377 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Yan R., 2011, P C EMP METH NAT LAN, P433 Yang CC, 2009, IEEE T SYST MAN CY A, V39, P850, DOI 10.1109/TSMCA.2009.2015885 Yang YM, 1999, IEEE INTELL SYST APP, V14, P32, DOI 10.1109/5254.784083 NR 84 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD NOV 1 PY 2014 VL 41 IS 15 BP 6904 EP 6916 DI 10.1016/j.eswa.2014.04.004 PG 13 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AM2PX UT WOS:000339694400032 ER PT J AU Murata, M Nagano, H Mukai, R Kashino, K Satoh, S AF Murata, Masaya Nagano, Hidehisa Mukai, Ryo Kashino, Kunio Satoh, Shin'ichi TI BM25 With Exponential IDF for Instance Search SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE BM25 with exponential IDF; content-based video retrieval (CBVR); instance video search ID RETRIEVAL; SCALE; VIDEO; TEXT; IR AB This paper deals with a novel concept of an exponential IDF in the BM25 formulation and compares the search accuracy with that of the BM25 with the original IDF in a content-based video retrieval (CBVR) task. Our video retrieval method is based on a bag of keypoints (local visual features) and the exponential IDF estimates the keypoint importance weights more accurately than the original IDF. The exponential IDF is capable of suppressing the keypoints from frequently occurring background objects in videos, and we found that this effect is essential for achieving improved search accuracy in CBVR. Our proposed method is especially designed to tackle instance video search, one of the CBVR tasks, and we demonstrate its effectiveness in significantly enhancing the instance search accuracy using the TRECVID2012 video retrieval dataset. C1 [Murata, Masaya; Nagano, Hidehisa; Mukai, Ryo; Kashino, Kunio; Satoh, Shin'ichi] NTT Corp, NTT Commun Sci Labs, Atsugi, Kanagawa 2430198, Japan. [Kashino, Kunio; Satoh, Shin'ichi] Res Org Informat & Syst, Natl Inst Informat, Chiyoda Ku, Tokyo 1018430, Japan. RP Murata, M (reprint author), NTT Corp, NTT Commun Sci Labs, Atsugi, Kanagawa 2430198, Japan. EM murata.masaya@lab.ntt.co.jp CR Aly R, 2013, INFORM RETRIEVAL, V16, P557, DOI 10.1007/s10791-012-9207-y Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Blanco Roi, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348406 Fang H., 2004, P 27 ANN INT ACM SIG, P49, DOI 10.1145/1008992.1009004 Hoi SCH, 2008, IEEE T MULTIMEDIA, V10, P607, DOI 10.1109/TMM.2008.921735 Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lavrenko V., 2003, P 17 ANN C NEUR INF, V16, P553 Le D. D., 2012, P TRECVID WORKSH 201, P276 Liu J., 2007, P 15 INT C MULT, P208, DOI DOI 10.1145/1291233.1291279 Liu YA, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P500, DOI 10.1145/1571941.1572027 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lv Y., 2011, P ACM SIGIR C RES DE, P1103 Matas J., 2002, BRIT MACH VISION COM, V22, P761 Mikolajczyk K, 2004, INT J COMPUT VISION, V60, P63, DOI 10.1023/B:VISI.0000027790.02288.f2 Nister D, 2006, P IEEE C COMP VIS PA, V2, P2161, DOI DOI 10.1109/CVPR.2006.264 Page L., 1998, SIDLWP19990120 Peng Y., 2012, P TRECVID WORKSH 201, P305 Philbin J., 2008, P IEEE C COMP VIS PA, P1 Philbin J., 2007, P IEEE C COMP VIS PA, P1 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S, 2008, J INF SCI, V34, P439, DOI 10.1177/0165551507086989 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 Robertson S. E., 1996, P 4 TEXT RETRIEVAL C, P21 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Siciv J., 2003, P 9 IEEE INT C COMP, P1470 Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 Squire DM, 2000, PATTERN RECOGN LETT, V21, P1193, DOI 10.1016/S0167-8655(00)00081-7 Svore K. M., 2009, P CIKM, P1811, DOI 10.1145/1645953.1646237 van de Sande K. E. A., 2008, P 4 EUR C COL GRAPH, P378 Vries A. P., 2004, P 2004 INT C IM PROC, V4, P2387 Vries A. P., 1998, P 20 ANN BCS IRSG C Vries A. P., 1999, THESIS U TWENTE ENSC Yan R, 2007, INFORM RETRIEVAL, V10, P445, DOI 10.1007/s10791-007-9031-y Zhang W., 2012, P TRECVID WORKSH 201, P420 Zhao Z., 2012, P TRECVID WORKSH 201, P66 Zhou W., 2010, P INT C MULT, P511, DOI 10.1145/1873951.1874019 Zhu C. Z., 2012, P 2 ACM INT C MULT R NR 41 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1520-9210 EI 1941-0077 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD OCT PY 2014 VL 16 IS 6 BP 1690 EP 1699 DI 10.1109/TMM.2014.2323945 PG 10 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA AT1UW UT WOS:000344720200017 ER PT J AU Roitmann, E Eriksson, R Brunak, S AF Roitmann, Eva Eriksson, Robert Brunak, Soren TI Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events SO FRONTIERS IN PHYSIOLOGY LA English DT Article DE adverse events; adverse drugs reactions; electronic medical records; patient stratification; data mining; network analysis ID ELECTRONIC HEALTH RECORDS; COMPLEX WEIGHTED NETWORKS; HUMAN-DISEASE; PHARMACOVIGILANCE; CARE; POPULATION; DICTIONARY; DISORDERS; HOSPITALS AB Purpose: New pharmacovigilance methods are needed as a consequence of the morbidity caused by drugs. We exploit fine-grained drug related adverse event information extracted by text mining from electronic medical records (EMRs) to stratify patients based on their adverse events and to determine adverse event co-occurrences. Methods: We analyzed the similarity of adverse event profiles of 2347 patients extracted from EMRs from a mental health center in Denmark. The patients were clustered based on their adverse event profiles and the similarities were presented as a network. The set of adverse events in each main patient cluster was evaluated. Co-occurrences of adverse events in patients (p-value < 0.01) were identified and presented as well. Results: We found that each cluster of patients typically had a most distinguishing adverse event. Examination of the co-occurrences of adverse events in patients led to the identification of potentially interesting adverse event correlations that may be further investigated as well as provide further patient stratification opportunities. Conclusions: We have demonstrated the feasibility of a novel approach in pharmacovigilance to stratify patients based on fine-grained adverse event profiles, which also makes it possible to identify adverse event correlations. Used on larger data sets, this data-driven method has the potential to reveal unknown patterns concerning adverse event occurrences. C1 [Roitmann, Eva; Eriksson, Robert; Brunak, Soren] Univ Copenhagen, Novo Nordisk Fdn Ctr Prot Res, Fac Med & Hlth Sci, Dept Dis Syst Biol, Copenhagen, Denmark. [Roitmann, Eva; Eriksson, Robert; Brunak, Soren] Tech Univ Denmark, Ctr Biol Sequence Anal, Dept Syst Biol, DK-2800 Lyngby, Denmark. RP Brunak, S (reprint author), Tech Univ Denmark, Dept Syst Biol, Bldg 208, DK-2800 Lyngby, Denmark. EM brunak@cbs.dtu.dk FU Danish Council for Strategic Research; Novo Nordisk Foundation FX The project was supported by grants from the Danish Council for Strategic Research and the Novo Nordisk Foundation. The sponsors had no role on the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. CR Avery AJ, 2011, HEALTH TECHNOL ASSES, V15, P1, DOI 10.3310/hta15200 Barabasi AL, 2011, NAT REV GENET, V12, P56, DOI 10.1038/nrg2918 Barrat A, 2004, P NATL ACAD SCI USA, V101, P3747, DOI 10.1073/pnas.0400087101 Bender S, 2004, PHARMACOPSYCHIATRY, V37, pS46, DOI 10.1055/s-2004-815510 Benjamini Y., 1995, J R STAT SOC B, V57, P289 Brown EG, 1999, DRUG SAFETY, V20, P109, DOI 10.2165/00002018-199920020-00002 Brownstein JS, 2007, PLOS ONE, V2, DOI 10.1371/journal.pone.0000840 Chan LWC, 2011, ENG APPL ARTIF INTEL, V24, P1398, DOI 10.1016/j.engappai.2011.05.013 Chen XJ, 2013, SCI REP-UK, V3, DOI 10.1038/srep01744 Classen DC, 2011, HEALTH AFFAIR, V30, P581, DOI 10.1377/hlthaff.2011.0190 Coloma P. M., 2012, DRUG SAFETY, V21, P611, DOI [10.1002/pds.3197, DOI 10.1002/PDS.3197] Coloma PM, 2011, PHARMACOEPIDEM DR S, V20, P1, DOI 10.1002/pds.2053 Eriksson R, 2013, J AM MED INFORM ASSN, V20, P947, DOI 10.1136/amiajnl-2013-001708 Eriksson R, 2014, DRUG SAFETY, V37, P237, DOI 10.1007/s40264-014-0145-z Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104 Haerian K, 2012, CLIN PHARMACOL THER, V92, P228, DOI 10.1038/clpt.2012.54 Harpaz R, 2013, J AM MED INFORM ASSN, V20, P413, DOI 10.1136/amiajnl-2012-000930 Harpaz R, 2012, CLIN PHARMACOL THER, V91, P1010, DOI 10.1038/clpt.2012.50 Hatton JD, 2012, PROC TECH, V5, P706, DOI 10.1016/j.protcy.2012.09.078 Hazell L, 2006, DRUG SAFETY, V29, P385, DOI 10.2165/00002018-200629050-00003 Hidalgo CA, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000353 Huang LC, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-S5-S11 Jain T., 2011, GER J PSYCHIAT, V14, P26 Jensen PB, 2012, NAT REV GENET, V13, P395, DOI 10.1038/nrg3208 Kalviainen R, 2001, CNS DRUGS, V15, P217 Kierkegaard P, 2011, COMPUT LAW SECUR REV, V27, P503, DOI 10.1016/j.clsr.2011.07.013 Kongkaew C, 2008, ANN PHARMACOTHER, V42, P1017, DOI 10.1345/aph.1L037 Lage K, 2007, NAT BIOTECHNOL, V25, P309, DOI 10.1038/nbt1295 Lage K, 2010, MOL SYST BIOL, V6, DOI 10.1038/msb.2010.36 Landmark CJ, 2012, PHARMACOEPIDEM DR S, V21, P11, DOI 10.1002/pds.2269 LePendu P, 2013, CLIN PHARMACOL THER, V93, P547, DOI 10.1038/clpt.2013.47 McClellan M, 2007, NEW ENGL J MED, V356, P1700, DOI 10.1056/NEJMp078057 Meystre S., 2008, YB MED INFORM, V35, P128 Muench J, 2010, AM FAM PHYSICIAN, V81, P617 Pirmohamed M, 2004, BRIT MED J, V329, P15, DOI 10.1136/bmj.329.7456.15 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roque FS, 2011, PLOS COMPUT BIOL, V7, DOI 10.1371/journal.pcbi.1002141 Rottenkolber D, 2012, VALUE HEALTH, V15, P868, DOI 10.1016/j.jval.2012.05.007 Serrano MA, 2009, P NATL ACAD SCI USA, V106, P6483, DOI 10.1073/pnas.0808904106 The Uppsala Monitoring Centre, 2014, WHO ADV REACT TERM W Trifiro G, 2009, PHARMACOEPIDEM DR S, V18, P1176, DOI 10.1002/pds.1836 Wang XY, 2009, J AM MED INFORM ASSN, V16, P328, DOI 10.1197/jamia.M3028 Yusof W, 2012, TOXICOL MECH METHOD, V22, P184, DOI 10.3109/15376516.2011.623331 NR 43 TC 0 Z9 0 PU FRONTIERS RESEARCH FOUNDATION PI LAUSANNE PA PO BOX 110, LAUSANNE, 1015, SWITZERLAND SN 1664-042X J9 FRONT PHYSIOL JI Front. Physiol. PD SEP 9 PY 2014 VL 5 AR 332 DI 10.3389/fphys.2014.00332 PG 14 WC Physiology SC Physiology GA AX7HM UT WOS:000347087300001 PM 25249979 ER PT J AU Soulier, L Tamine, L Bahsoun, W AF Soulier, Laure Tamine, Lynda Bahsoun, Wahiba TI On domain expertise-based roles in collaborative information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Collaborative information retrieval; Domain expertise; Ranking model; Learning-method ID SEARCH PROCESS; INTERNET USE; HEALTH-CARE; FEEDBACK; STRATEGIES; SEEKING; TERMS; WEB AB Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and providing a ranked list of documents adapted for each collaborator. In this paper, we introduce the first document-ranking model supporting collaboration between two users characterized by roles relying on different domain expertise levels. Specifically, we propose a two-step ranking model: we first compute a document-relevance score, taking into consideration domain expertise-based roles. We introduce specificity and novelty factors into language-model smoothing, and then we assign, via an Expectation-Maximization algorithm, documents to the best-suited collaborator. Our experiments employ a simulation-based framework of collaborative information retrieval and show the significant effectiveness of our model at different search levels. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Soulier, Laure; Tamine, Lynda; Bahsoun, Wahiba] Univ Toulouse 3, IRIT, F-31062 Toulouse 9, France. RP Soulier, L (reprint author), Univ Toulouse 3, IRIT, 118 Route Narbonne, F-31062 Toulouse 9, France. EM soulier@irit.fr; tamine@irit.fr; wbahsoun@irit.fr CR Attfield Simon J, 2006, Health Informatics J, V12, P165, DOI 10.1177/1460458206063811 Allen B., 1991, THE LIBRARY QUARTERL, P188 Attfield S, 2010, INFORM PROCESS MANAG, V46, P632, DOI 10.1016/j.ipm.2009.10.003 Callan J., 2004, HLT NAACL CONFERENCE Castells P., 2011, ECIR CONFERENCE PROC Catledge L. D., 1995, WWW 95, P1065 Collins-Thompson K., 2011, CIKM CONFERENCE PROC, P403 Conrad J. G., 2007, ICAIL CONFERENCE PRO, P321 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DENNING PJ, 2008, COMMUN ACM, V51, P19 ECDPC, 2011, TECHNICAL REPORT Erickson T., 2010, CSCW CONFERENCE PROC Filho F. F., 2010, ICDC CONFERENCE PROC, P89 Foley C, 2010, INFORM PROCESS MANAG, V46, P762, DOI 10.1016/j.ipm.2009.10.010 Foley C., 2008, THESIS Foley C, 2009, LECT NOTES COMPUT SC, V5478, P42, DOI 10.1007/978-3-642-00958-7_7 Fox S., 2008, TECHNICAL REPORT Gianoutsos S., 1996, CSCW CONFERENCE PROC, P14 Goggins S., 2010, INTERNATIONAL WORKSH Golovchinsky G, 2009, COMPUTER, V42, P47, DOI 10.1109/MC.2009.73 Golovchinsky G., 2011, HCIR CONFERENCE PROC Hansen P, 2005, INFORM PROCESS MANAG, V41, P1101, DOI 10.1016/j.ipm.2004.04.016 Harman D., 2002, TEXT RETR C TREC 200, P46 Harman D., 2005, HLT EMNLP, P105 Harrison-Walker L., 2006, BUS HORIZONS, V49, P7 Hembrooke HA, 2005, J AM SOC INF SCI TEC, V56, P861, DOI 10.1002/asi.20180 Holscher C, 2000, COMPUT NETW, V33, P337, DOI 10.1016/S1389-1286(00)00031-1 Horowitz D., 2010, WWW, P431 Hsieh-yee L., 1993, JASIS, V44, P161 ISAACS EA, 1987, J EXP PSYCHOL GEN, V116, P26, DOI 10.1037/0096-3445.116.1.26 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Joho H, 2009, LECT NOTES COMPUT SC, V5478, P66, DOI 10.1007/978-3-642-00958-7_9 Kang RG, 2010, IUI 2010, P329 Kashyap A., 2010, CIKM 2010, P719 Keskustalo H, 2006, LECT NOTES COMPUT SC, V3936, P191 Kim G, 2006, INFORM PROCESS MANAG, V42, P1218, DOI 10.1016/j.ipm.2005.12.004 Kim J. Y., 2012, WSDM CONFERENCE PROC, P213 KUHLTHAU C, 1992, P ASIS ANNU MEET, V29, P67 Marsland S, 2009, CH CRC MACH LEARN PA, P1 McMullan M, 2006, PATIENT EDUC COUNS, V63, P24, DOI 10.1016/j.pec.2005.10.006 Moraveji N., 2011, CHI CONFERENCE PROCE, P1797 Morris M. R., 2006, INTERNATIONAL WORKSH, P97, DOI 10.1109/TABLETOP.2006.32 Morris M. R., 2011, INTERNATIONAL WORKSH, P11 Morris M. R., 2009, COLLABORATIVE WEB SE Morris MR, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P481 Morris MR, 2007, UIST 2007: PROCEEDINGS OF THE 20TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P3 Over P, 2001, INFORM PROCESS MANAG, V37, P369, DOI 10.1016/S0306-4573(00)00053-4 Pickens J., 2008, SIGIR 2008, P315 Podichetty VK, 2006, POSTGRAD MED J, V82, P274, DOI 10.1136/pgmj.2005.040675 Privault Caroline, 2010, Artificial Intelligence and Law, V18, DOI 10.1007/s10506-010-9090-z ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rodriguez Perez J. A., 2011, INTERNATIONAL WORKSH, P29 RUDD J, 1986, COLL RES LIBR, V47, P315 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shah C., 2012, COLLABORATIVE INFORM Shah C., 2012, COAGMENTOA CASE STUD Shah C., 2012, REPORT ON THE SECOND Shah C, 2010, INFORM PROCESS MANAG, V46, P773, DOI 10.1016/j.ipm.2009.10.002 Smeaton A. F., 2006, TABLETOP 06 1 IEEE I, P151 Smirnova E, 2011, LECT NOTES COMPUT SC, V6611, P580, DOI 10.1007/978-3-642-20161-5_58 Soulier L., 2013, AIRS CONFERENCE PROC, P109 TAYLOR RS, 1968, COLL RES LIBR, V29, P178 Toomela A, 2007, INTEGR PSYCHOL BEHAV, V41, P198, DOI 10.1007/s12124-007-9015-x Twidale MB, 1996, ASLIB PROC, V48, P177 Twidale MB, 1997, INFORM PROCESS MANAG, V33, P761, DOI 10.1016/S0306-4573(97)00040-X Vakkari P, 2003, INFORM PROCESS MANAG, V39, P445, DOI 10.1016/S03064573(02)0003l-6 Wald HS, 2007, PATIENT EDUC COUNS, V68, P218, DOI 10.1016/j.pec.2007.05.016 WANG JG, 2010, MAGN FRING FIELDS, P1 White R. W., 2011, CHI CONFERENCE PROCE, P2837 White R. W., 2012, SIGIR CONFERENCE PRO, P1055 White R. W., 2009, WSDM CONFERENCE PROC, P132 White RW, 2005, ACM T INFORM SYST, V23, P325, DOI 10.1145/1080343.1080347 Wielhorski K., 1994, THE PUBLIC ACCESS CO, V5, P5 NR 73 TC 0 Z9 0 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2014 VL 50 IS 5 BP 752 EP 774 DI 10.1016/j.ipm.2014.04.002 PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AN0XJ UT WOS:000340307000010 ER PT J AU Ma, YL Lin, HF AF Ma, Yunlong Lin, Hongfei TI A Multiple Relevance Feedback Strategy with Positive and Negative Models SO PLOS ONE LA English DT Article ID RETRIEVAL; DOCUMENTS AB A commonly used strategy to improve search accuracy is through feedback techniques. Most existing work on feedback relies on positive information, and has been extensively studied in information retrieval. However, when a query topic is difficult and the results from the first-pass retrieval are very poor, it is impossible to extract enough useful terms from a few positive documents. Therefore, the positive feedback strategy is incapable to improve retrieval in this situation. Contrarily, there is a relatively large number of negative documents in the top of the result list, and it has been confirmed that negative feedback strategy is an important and useful way for adapting this scenario by several recent studies. In this paper, we consider a scenario when the search results are so poor that there are at most three relevant documents in the top twenty documents. Then, we conduct a novel study of multiple strategies for relevance feedback using both positive and negative examples from the first-pass retrieval to improve retrieval accuracy for such difficult queries. Experimental results on these TREC collections show that the proposed language model based multiple model feedback method which is generally more effective than both the baseline method and the methods using only positive or negative model. C1 [Ma, Yunlong; Lin, Hongfei] Dalian Univ Technol, Sch Comp Sci & Technol, Informat Retrieval Lab, Dalian, Liaoning, Peoples R China. RP Lin, HF (reprint author), Dalian Univ Technol, Sch Comp Sci & Technol, Informat Retrieval Lab, Dalian, Liaoning, Peoples R China. EM hflin@dlut.edu.cn CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 BUCKLEY C, 1994, TREC 94, P69 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CROFT W, 1996, SIGIR 1996 P 19 ANN, P4 Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 Ji RR, 2012, INT J COMPUT VISION, V96, P290, DOI 10.1007/s11263-011-0472-9 Kankanhalli MS, 2008, P IEEE, V96, P712, DOI 10.1109/JPROC.2008.916383 KARIMZADEHGAN M, 2010, P 19 ACM INT C INF K, P1397, DOI DOI 10.1145/1871437.1871631 KARIMZADEHGAN M, 2011, P 20 ACM INT C INF K, P27, DOI DOI 10.1145/2063576.2063586 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V., 2001, SIGIR Forum Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 NG K, 1999, MAXIMUM LIKELIHOOD R Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Raman K, 2010, LECT NOTES COMPUT SC, V5993, P573, DOI 10.1007/978-3-642-12275-0_50 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, SMART RETRIEVAL SYST, V21, P313 Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Singhal A, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P25, DOI 10.1145/258525.258530 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Strohman T., 2009, SEARCH ENGINES INFOR Wang X., 2008, P 31 ANN INT ACM SIG, P219, DOI 10.1145/1390334.1390374 WANG X, 2007, P CIKM 07, P991, DOI DOI 10.1145/1321440.1321593 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhang P, 2010, LECT NOTES COMPUT SC, V6458, P527 NR 29 TC 0 Z9 0 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD AUG 19 PY 2014 VL 9 IS 8 AR e104707 DI 10.1371/journal.pone.0104707 PG 10 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA AN6XM UT WOS:000340742100043 PM 25137234 ER PT J AU Sadeghi, M Vegas, J AF Sadeghi, Mohammad Vegas, Jesus TI Automatic identification of light stop words for Persian information retrieval systems SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE Information retrieval; information theory; natural language processing; Persian language; stop words ID MATHEMATICAL-THEORY; COMMUNICATION AB Stop word identification is one of the most important tasks for many text processing applications such as information retrieval. Stop words occur too frequently in documents in a collection and do not contribute significantly to determining the context or information about the documents. These words are worthless as index terms and should be removed during indexing as well as before querying by an information retrieval system. In this paper, we propose an automatic aggregated methodology based on term frequency, normalized inverse document frequency and information model to extract the light stop words from Persian text. We define a light stop word' as a stop word that has few letters and is not a compound word. In the Persian language, a complete stop word list can be derived by combining the light stop words. The evaluation results, using a standard corpus, show a good percentage of coincidence between the Persian and English stop words and a significant improvement in the number of index terms. Specifically, the first 32 Persian light stop words have a great impact on the index size reduction and the set of stop words can reduce the number of index terms by about 27%. C1 [Sadeghi, Mohammad; Vegas, Jesus] Univ Valladolid, Dept Comp Sci, E-47011 Valladolid, Spain. RP Sadeghi, M (reprint author), Univ Valladolid, Dept Informat, Campus Miguel Delibes S-N, E-47011 Valladolid, Spain. EM msadeghi@infor.uva.es FU project entitled 'Compresioen y Recuperacioen de Contenidos Multilinges' - National Plan I + D/I + D + I [TIN2009-14009-C02-02] FX This work has been partially supported by the project entitled 'Compresioen y Recuperacioen de Contenidos Multilinges' financed by the National Plan I + D/I + D + I (TIN2009-14009-C02-02). CR Alajmi A, 2012, INT J COMPUTER APPL, V46 AleAhmad A, 2009, KNOWL-BASED SYST, V22, P382, DOI 10.1016/j.knosys.2009.05.002 Davarpanah MR, 2009, LIBR HI TECH, V27, P435, DOI 10.1108/07378830910988559 El-Khair IA, 2006, INT J COMPUTING INFO, V4 Francis W. N., 1982, FREQUENCY ANAL ENGLI Kalbasi I., 1990, J LINGUIST, V7, P61 Kalbasi I., 2001, DERIVATIONAL STRUCTU Korfhage R., 1997, INFORM STORAGE RETRI Lo R., 2005, P 5 DUTCH BELG INF R LUHN HP, 1957, IBM J RES DEV, V1, P309 Megerdoomian K, 2000, MEMORANDA COMPUTER C Myerson RB, 1996, FUNDAMENTALS SOCIAL Neshati M, 2007, P 2007 IEEE ACS INT Qasemizadeh B, 2006, 11 COMP SOC IR COMP Rezaie S., 2001, ARABIC LANGUAGE PROC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHANNON CE, 1948, AT&T TECH J, V27, P623 Taghva K, 2003, 200301 U NEV INF SCI Taghva K, 2003, P SDIUT 03 S DOC IM Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Witten I. H., 1999, MANAGING GIGABYTES C Zipf H. K., 1949, HUMAN BEHAV PRINCIPL Zou F., 2006, P 5 WSEAS INT C APPL, P1010 NR 23 TC 0 Z9 0 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 EI 1741-6485 J9 J INF SCI JI J. Inf. Sci. PD AUG PY 2014 VL 40 IS 4 BP 476 EP 487 DI 10.1177/0165551514530655 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AM8UJ UT WOS:000340152900005 ER PT J AU Dang, EKF Luk, RWP Allan, J AF Dang, Edward K. F. Luk, Robert W. P. Allan, James TI Beyond Bag-of-Words: Bigram-Enhanced Context-Dependent Term Weights SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; TEXT CLASSIFICATION; RELEVANCE FEEDBACK; MODEL; FREQUENCY; PROXIMITY; SYSTEMS AB While term independence is a widely held assumption in most of the established information retrieval approaches, it is clearly not true and various works in the past have investigated a relaxation of the assumption. One approach is to use n-grams in document representation instead of unigrams. However, the majority of early works on n-grams obtained only modest performance improvement. On the other hand, the use of information based on supporting terms or "contexts" of queries has been found to be promising. In particular, recent studies showed that using new context-dependent term weights improved the performance of relevance feedback (RF) retrieval compared with using traditional bag-of-words BM25 term weights. Calculation of the new term weights requires an estimation of the local probability of relevance of each query term occurrence. In previous studies, the estimation of this probability was based on unigrams that occur in the neighborhood of a query term. We explore an integration of the n-gram and context approaches by computing context-dependent term weights based on a mixture of unigrams and bigrams. Extensive experiments are performed using the title queries of the Text Retrieval Conference (TREC)-6, TREC-7, TREC-8, and TREC-2005 collections, for RF with relevance judgment of either the top 10 or top 20 documents of an initial retrieval. We identify some crucial elements needed in the use of bigrams in our methods, such as proper inverse document frequency (IDF) weighting of the bigrams and noise reduction by pruning bigrams with large document frequency values. We show that enhancing context-dependent term weights with bigrams is effective in further improving retrieval performance. C1 [Dang, Edward K. F.; Luk, Robert W. P.] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. [Allan, James] Univ Massachusetts, Ctr Intelligent Informat Retrieval, Sch Comp Sci, Amherst, MA 01003 USA. RP Dang, EKF (reprint author), Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. EM cskfdang@comp.polyu.edu.hk; csrluk@comp.polyu.edu.hk; allan@cs.umass.edu RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 CR Bekkerman R., 2003, IR408 CIIR U MASS Buckley C., 1994, P 17 ANN INT ACM SIG, P292 Buckley C., 1994, 3 TEXT RETR C TREC 3 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Cummins R, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P251, DOI 10.1145/1571941.1571986 Dang EKF, 2010, J AM SOC INF SCI TEC, V61, P2514, DOI 10.1002/asi.21425 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 Kleinbaum DG, 2002, LOGISTIC REGRESSION Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lease M, 2008, LECT NOTES COMPUT SC, V5152, P687, DOI 10.1007/978-3-540-85760-0_87 Lease M., 2008, 17 TEXT RETR C TREC Liu RL, 2010, J AM SOC INF SCI TEC, V61, P300, DOI 10.1002/asi.21260 Peng FC, 2003, LECT NOTES COMPUT SC, V2633, P335 Pickens J., 2006, P 15 ACM INT C INF K, P559, DOI DOI 10.1145/1183614.1183694 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Song RH, 2008, LECT NOTES COMPUT SC, V4956, P346 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P809, DOI 10.1016/S0306-4573(00)00016-9 SPARCKJONES K, 1972, J DOC, V28, P11 Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Tan CM, 2002, INFORM PROCESS MANAG, V38, P529, DOI 10.1016/S0306-4573(01)00045-0 Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794 Tesar R., 2006, P 2006 ACM S DOC ENG, P138, DOI DOI 10.1145/1166160.1166197 Voorhees E. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291017 Wu HC, 2007, INFORM PROCESS MANAG, V43, P1308, DOI 10.1016/j.ipm.2006.10.009 Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 40 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2330-1635 EI 2330-1643 J9 J ASSOC INF SCI TECH PD JUN PY 2014 VL 65 IS 6 BP 1134 EP 1148 DI 10.1002/asi.23024 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AG7FO UT WOS:000335583900004 ER PT J AU Deng, ZH Luo, KH Yu, HL AF Deng, Zhi-Hong Luo, Kun-Hu Yu, Hong-Liang TI A study of supervised term weighting scheme for sentiment analysis SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Sentiment analysis; Term weighting; Supervised learning; Experimentation; Performance ID TEXT CATEGORIZATION; SEARCH TERMS; CLASSIFICATION AB Term weighting is a strategy that assigns weights to terms to improve the performance of sentiment analysis and other text mining tasks. In this paper, we propose a supervised term weighting scheme based on two basic factors: Importance of a term in a document (ITD) and importance of a term for expressing sentiment (175), to improve the performance of analysis. For ITD, we explore three definitions based on term frequency. Then, seven statistical functions are employed to learn the ITS of each term from training documents with category labels. Compared with the previous unsupervised term weighting schemes originated from information retrieval, our scheme can make full use of the available labeling information to assign appropriate weights to terms. We have experimentally evaluated the proposed method against the state-of-the-art method. The experimental results show that our method outperforms the method and produce the best accuracy on two of three data sets. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Deng, Zhi-Hong; Luo, Kun-Hu; Yu, Hong-Liang] Peking Univ, Sch Elect Engn & Comp Sci, Dept Machine Intelligence, Key Lab Machine Percept,Minist Educ, Beijing 100871, Peoples R China. RP Deng, ZH (reprint author), Peking Univ, Sch Elect Engn & Comp Sci, Dept Machine Intelligence, Key Lab Machine Percept,Minist Educ, Beijing 100871, Peoples R China. EM zhdeng@cis.pku.edu.cn FU National Natural Science Foundation of China; [61170091] FX This work is partially supported by Project 61170091, supported by National Natural Science Foundation of China. We would also like to thank the anonymous reviewers for their helpful comments. CR Armstrong T.G., 2009, P 18 ACM C INF KNOWL, P601, DOI DOI 10.1145/1645953.1646031 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE, Vsecond Church K., 1989, P ACL, P76, DOI DOI 10.3115/981623.981633 Das S., 2001, P AS PAC FIN ASS ANN Debole F., 2003, P SAC 03 18 ACM S AP, P784 Deng ZH, 2004, LECT NOTES COMPUT SC, V3007, P588 Hamilton H. J., 2006, ACM COMPUTING SURVEY, V38 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Li S., 2009, P 47 ANN M ACL, P692 Maas A L, 2011, P 49 ANN M ASS COMP, P142 Manning C. D., 2008, INTRO INFORM RETRIEV Martineau J., 2009, P 3 AAAI INT C WEBL, P258 Mladenic D., 1998, P C AUT LEARN DISC C Ng V., 2006, P COLING ACL MAIN C Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Paltoglou G., 2010, P 48 ANN M ASS COMP, P1386 Pang B, 2002, P 2002 C EMP METH NA Pang B., 2008, OPINION MINING SENTI Pang B., 2004, ACL 04 P 42 ANN M AS, V42, P271 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Ren FJ, 2013, INFORM SCIENCES, V236, P109, DOI 10.1016/j.ins.2013.02.029 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 Robertson S. E., 1996, 5 TEXT RETR C TREC 5 Robertson S.E., 1994, TREC 3, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Soucy P, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1130 Tong R. M., 2001, P WORKSH OP TEXT CLA Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 33 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JUN 1 PY 2014 VL 41 IS 7 BP 3506 EP 3513 DI 10.1016/j.eswa.2013.10.056 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AA3UM UT WOS:000331019800035 ER PT J AU Kocabas, I Dincer, BT Karaoglan, B AF Kocabas, Ilker Dincer, Bekir Taner Karaoglan, Bahar TI A nonparametric term weighting method for information retrieval based on measuring the divergence from independence SO INFORMATION RETRIEVAL LA English DT Article DE Information retrieval; Nonparametric index term weighting; Statistical dependence; Pearson's Chi-Square statistics ID PROBABILISTIC MODELS; RELEVANCE; FREQUENCY AB In this article, we introduce an out-of-the-box automatic term weighting method for information retrieval. The method is based on measuring the degree of divergence from independence of terms from documents in terms of their frequency of occurrence. Divergence from independence has a well-establish underling statistical theory. It provides a plain, mathematically tractable, and nonparametric way of term weighting, and even more it requires no term frequency normalization. Besides its sound theoretical background, the results of the experiments performed on TREC test collections show that its performance is comparable to that of the state-of-the-art term weighting methods in general. It is a simple but powerful baseline alternative to the state-of-the-art methods with its theoretical and practical aspects. C1 [Kocabas, Ilker; Karaoglan, Bahar] Ege Univ, Int Comp Inst, Izmir, Turkey. [Dincer, Bekir Taner] Mugla Univ, Dept Stat, Mugla, Turkey. [Dincer, Bekir Taner] Mugla Univ, Dept Comp Engn, Mugla, Turkey. RP Kocabas, I (reprint author), Ege Univ, Int Comp Inst, Izmir, Turkey. EM ilker.kocabas@ege.edu.tr; dtaner@mu.edu.tr FU TUBITAK, The Scientific and Technological Research Council of Turkey [107E192] FX Authors are thankful to anonymous reviewers for their valuable comments and advices that make this a better paper, and also to Craig Macdonald, Giambattista Amati, and Iadh Ounis for their kind helps. Index term weighting by DFI is developed under the project titled "Design of A Statistical Information Retrieval System'', and supported by TUBITAK, The Scientific and Technological Research Council of Turkey, with Project No: 107E192. Any opinions, findings and conclusions or recommendations expressed in this material are the authors' and do not necessarily reflect those of the sponsor. CR Agresti A, 2002, CATEGORICAL DATA ANA, VSecond Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 ARNOLD B. C., 1983, PARETO DISTRIBUTIONS BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 Bradley J., 1968, DISTRIBUTION FREE ST Church K., 1995, P 3 WORKSH VER LARG, P121 Church K. W., 1995, SIGIR Forum Clarke C. L. A., 2010, P 19 TEXT RETR C TRE Clinchant A., 2010, P 33 ANN INT ACM SIG, P234 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Cormack G. V., 2010, EFFICIENT EFFECTIVE CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DAMERAU FJ, 1965, AM DOC, V16, P283, DOI 10.1002/asi.5090160403 Dincer B. T., 2010, P 19 TEXT RETR C TRE Dincer B. T., 2009, P 18 TEXT RETR C TRE Dincer B. T., 2012, P 21 TEXT RETR C TRE FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 He B, 2005, LECT NOTES COMPUT SC, V3408, P200 He B., 2003, P 12 INT C INF KNOWL Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hintikka J., 1970, INFORMATION INFERENC, P3 Hoaglin D. C., 1983, WILEY SERIES PROBABI Hollander M., 1999, NONPARAMETRIC STAT M Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LUHN HP, 1958, IBM J RES DEV, V2, P159 Macdonald C., 2005, P TREC 2005 Margulis E., 1992, P ACM SIGIR COP JUN, P177, DOI 10.1145/133160.133195 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mosteller F., 1977, DATA ANAL REGRESSION Ounis I., 2007, RES DIRECTIONS TERRI Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Popper K., 1959, LOGIC SCI DISCOVERY Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Robertson S. E., 1981, INFORMATION RETRIEVA, P35 Robertson S. E., 1999, NIST SPECIAL PUBLICA, V500, p[242, 253] Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Shannon C.E., 1949, MATH THEORY COMMUNIC, P3 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Wolfowitz J, 1942, ANN MATH STAT, V13, P247, DOI 10.1214/aoms/1177731566 Wong S., 1995, ACM T INFORM SYST, V16, P38 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 46 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2014 VL 17 IS 2 BP 153 EP 176 DI 10.1007/s10791-013-9225-4 PG 24 WC Computer Science, Information Systems SC Computer Science GA AD0ZP UT WOS:000332963700003 ER PT J AU Aly, R Demeester, T Robertson, S AF Aly, Robin Demeester, Thomas Robertson, Stephen TI Probabilistic models in IR and their relationships SO INFORMATION RETRIEVAL LA English DT Article DE Probabilistic models; Probability of relevance; Probability ranking principle; Language models ID INFORMATION-RETRIEVAL; RELEVANCE; SPACE AB A solid research path towards new information retrieval models is to further develop the theory behind existing models. A profound understanding of these models is therefore essential. In this paper, we revisit probability ranking principle (PRP)-based models, probability of relevance (PR) models, and language models, finding conceptual differences in their definition and interrelationships. The probabilistic model of the PRP has not been explicitly defined previously, but doing so leads to the formulation of two actual principles with different objectives. First, the belief probability ranking principle (BPRP), which considers uncertain relevance between known documents and the current query, and second, the popularity probability ranking principle (PPRP), which considers the probability of relevance of documents among multiple queries with the same features. Our analysis shows how some of the discussed PR models implement the BPRP or the PPRP while others do not. However, for some models the parameter estimation is challenging. Finally, language models are often presented as related to PR models. However, we find that language models differ from PR models in every aspect of a probabilistic model and the effectiveness of language models cannot be explained by the PRP. C1 [Aly, Robin] Univ Twente, NL-7500 AE Enschede, Netherlands. [Demeester, Thomas] Univ Ghent, iMinds, B-9000 Ghent, Belgium. [Robertson, Stephen] UCL, London, England. RP Aly, R (reprint author), Univ Twente, POB 217, NL-7500 AE Enschede, Netherlands. EM r.aly@ewi.utwente.nl FU EU Project AXES [FP7-269980] FX The work reported in this paper was funded partly by the EU Project AXES (FP7-269980) and carried out at the University of Twente, in the Netherlands, and partly by Ghent University-iMinds in Flanders. We would like to thank Djoerd Hiemstra, Arjen de Vries and the anonymous reviewers for their comments that greatly helped to improve this work and especially forced us to reflect critically on our motivations and results. CR Aly R, 2011, LECT NOTES COMPUT SC, V6931, P164, DOI 10.1007/978-3-642-23318-0_16 Bishop C. H., 2006, PATTERN RECOGNITION Chen H., 2006, SIGIR 2006, P429 Cooper W. S., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval COX RT, 1946, AM J PHYS, V14, P1, DOI 10.1119/1.1990764 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Fang H., 2005, SIGIR 05, P480 Feller W., 1968, INTRO PROBABILITY TH, V1 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Hiemstra D., 2001, THESIS U TWENTE KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Lafferty J., 2003, PROBABILISTIC RELEVA, V13, P1 Lavrenko Victor, 2003, LANGUAGE MODELING IN, P11 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Liu T., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Luk RWP, 2008, INFORM RETRIEVAL, V11, P539, DOI 10.1007/s10791-008-9062-z Lv Y., 2012, THESIS U ILLINOIS UR Manning C. D., 1999, FDN STAT NATURAL LAN MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roelleke T., 2006, SIGIR 2006, P107, DOI [10.1145/1148170.1148192, DOI 10.1145/11.48170.1148192] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck-Jones K., 2003, LANGUAGE MODELING IN, P57 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wang J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P115, DOI 10.1145/1571941.1571963 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 32 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2014 VL 17 IS 2 BP 177 EP 201 DI 10.1007/s10791-013-9226-3 PG 25 WC Computer Science, Information Systems SC Computer Science GA AD0ZP UT WOS:000332963700004 ER PT J AU Yang, XQ Yang, D Yuan, M Lv, XH AF Yang, Xi-Quan Yang, Dian Yuan, Ming Lv, Xing-Hua BE Watada, J Ito, A Pan, JS Chao, HC Chen, CM TI Scientific literature retrieval model based on weighted term frequency SO 2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014) LA English DT Proceedings Paper CT 10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) CY AUG 27-29, 2014 CL Kitakyushu, JAPAN SP IEEE Comp Soc, IEEE, Natl Kaohsiung Univ Appl Sci, IEEE Tainan Sect, IEEE Signal Proc Soc, Tainan Chapter, Int Soc Management Engineers, JSPS NSFC NRF A3 Foresight Program Ultra Realist Acoust Interact Commun Next Generat Internet HO Waseda Univ DE information retrieval; simulated annealing AB Science and technology literature retrieval system is commonly used by researchers as document retrieval tools. The classic information retrieval models such as Boolean Model [1], Vector Space Model and Probabilistic model neglect the position of query words in every paper. We propose a weighted term frequency model (WTFM) based on term frequency, and through a simulated annealing algorithm to learn the weighted factor. The results of experiments show that our weighted factors model gets better performance than ordinary models. C1 [Yang, Xi-Quan; Lv, Xing-Hua] NE Normal Univ, Sch Comp Sci & Informat Technol, Changchun, Peoples R China. [Yang, Xi-Quan; Lv, Xing-Hua] NE Normal Univ, Coll Humanities & Sci, Changchun, Peoples R China. [Yang, Dian] Shanghai Jiao Tong Univ, Sch Microelect, Shanghai 200030, Peoples R China. [Yuan, Ming] Jilin Teachers Inst Engn & Technol, Changchun, Peoples R China. RP Yang, XQ (reprint author), NE Normal Univ, Sch Comp Sci & Informat Technol, Changchun, Peoples R China. EM Yangxq375@nenu.edu.cn; lucyyd@tju.edu.cn CR BROOKS SP, 1995, STATISTICIAN, V44, P241, DOI 10.2307/2348448 Cutler Michal, 1997, USENIX S INT TECHN S Lancaster Frederick Wilfrid, 1973, INFORM RETRIEVAL Nie Zaiqing, 2005, P 14 INT C WORLD WID Perea-Ortega JM, 2008, LECT NOTES COMPUT SC, V5039, P142 Pereira RA Marques, 2005, SOFT COMPUT, V9.7, P481 Robertson Stephen E., 1996, NIST SPECIAL PUBLICA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton Gerard, 1968, J ACM, V15.1, P8 Song M, 2007, DATA KNOWL ENG, V63, P63, DOI 10.1016/j.datak.2006.10.010 Whissell JS, 2011, INFORM RETRIEVAL, V14, P466, DOI 10.1007/s10791-011-9163-y NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-5390-5 PY 2014 BP 427 EP 430 DI 10.1109/IIH-MSP.2014.113 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BD2HI UT WOS:000358743800107 ER PT B AU Thompson, P AF Thompson, Paul BE Agah, A TI Satisficing or the Right Information at the Right Time: Artificial Intelligence and Information Retrieval, a Comparative Study in Medicine and Law SO MEDICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE LA English DT Article; Book Chapter ID RELEVANCE C1 Dartmouth Coll, Hanover, NH 03755 USA. RP Thompson, P (reprint author), Dartmouth Coll, Hanover, NH 03755 USA. CR ACE, 2009, AUT CONT EXTR ACE EV [Anonymous], 2001, MESS UND C Baron J., 2007, INT C ART INT LAW 20 Baumgartner W. A., 2007, BIOINFORMATICS, V23, P141 Brown P. F., 1990, Computational Linguistics, V16 Buchanan B.G., 1984, RULE BASED EXPERT SY COOPER WS, 1988, INFORM PROCESS MANAG, V24, P243, DOI 10.1016/0306-4573(88)90091-X Dartmouth, 2012, DARTM ART INT C NEXT Elbassuoni S., 2011, THESIS MAX PLANCK I Ferucci D., 2010, ARTIF INTELL MAG, V31, P59 Hersh W, 2009, INFORM RETRIEVAL, V12, P1, DOI 10.1007/s10791-008-9076-6 Jackson P., 2011, STRUKTURIERUNG JURIS, P417 Kleinberg Jon, 1998, P ACM SIAM S DISCR A Lehnert W.G, 1978, PROCESS QUESTION ANS LINDSAY RK, 1993, ARTIF INTELL, V61, P209, DOI 10.1016/0004-3702(93)90068-M Lu ZY, 2009, J AM MED INFORM ASSN, V16, P32, DOI 10.1197/jamia.M2935 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McCray AT, 1999, J AM MED INFORM ASSN, P107 Newell A., 1959, P INT C INF PROC, P256 Page L, 1999, PAGERANK CITATION RA Pearl J., 1988, PROBABILISTIC REASON Ponte J. M., 1998, THESIS U MASSACHUSET Pritchard-Schoch T., 1993, ONLINE, V17 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROGERS FB, 1964, B MED LIBR ASSOC, V52, P150 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM Silobrcic J., 2012, NAT QUAL C SIMON HA, 1956, PSYCHOL REV, V63, P129, DOI 10.1037/h0042769 Springer, 2006, TEXT SPEECH LANG TEC, V32, P1, DOI 10.1007/978-1-4020-4746-6 Tipster, 2000, TIPSTER TEXT PROGR M TREC, 2012, TEXT RETRIEVAL C TRE TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Vasilevsky N, 2012, DATABASE-OXFORD, DOI 10.1093/database/bar067 Westlaw, 2012, KEYN SYST YEST TOD Woods W., 1972, 2378 BBN NR 37 TC 0 Z9 0 PU CRC PRESS-TAYLOR & FRANCIS GROUP PI BOCA RATON PA 6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA BN 978-1-4398-8434-8; 978-1-4398-8433-1 PY 2014 BP 71 EP 78 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Biomedical SC Computer Science; Engineering GA BC6KX UT WOS:000354045800006 ER PT B AU Saad, I Rosenthal-Sabroux, C Gargouri, F AF Saad, Ines Rosenthal-Sabroux, Camille Gargouri, Faiez BE Saad, I RosenthalSabroux, C Gargouri, F TI Relevant Information Management in Microblogs SO INFORMATION SYSTEMS FOR KNOWLEDGE MANAGEMENT LA English DT Article; Book Chapter CR Agarwal N., 2008, P INT C WEB SEARCH W, P207, DOI DOI 10.1145/1341531.134155 AKERMI I., 2012, P INT C WEB INT MIN Amer-Yahia S., 2007, IEEE DATA ENG B, V30, P23 BALOG K., 2008, P 31 ANN INT ACM SIG, P753, DOI 10.1145/1390334.1390486 BARRY S., 2011, C RECH INF APPL CORI BAZIZ M, 2006, CLEF 2005 P ACC MULT, V4022, P238 BEN JABEUR L., 2010, C FRANC RECH DINF AP, P325 BRINI H., 2005, THESIS Cha M., 2010, P 4 INT AAAI C WEBL CHERICHI S., 2013, INT JOINT C KNOWL DI CHERICHI S., 2013, P INT C WEB INT MIN CHERICHI S., 2013, INT C KNOWL MAN INF CHERICHI S., 2012, C INT EXTR GEST CONN DAMAK F., 2012, C RECH DINF APPL COR, P371 DIAKOPOULOS NA, 2010, P 28 INT C HUM FACT, P1195, DOI DOI 10.1145/1753326.1753504 Dong A., 2010, P 19 INT C WORLD WID, P331, DOI DOI 10.1145/1772690.1772725 Duan Yajuan, 2010, COLING, P295 EFRON M, 2010, P 33 INT ACM SIGIR C, P787, DOI DOI 10.1145/1835449.1835616 EVGENY M., 2009, DISSENT, V56, P10, DOI 10.1353/dss.0.0092 GROSSMAN L., 2009, TIME 0617 HIEMSTRA D., 2000, USING LANGUAGE MODEL, p[I, 1] Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 Jansen BJ, 2009, J AM SOC INF SCI TEC, V60, P2169, DOI 10.1002/asi.21149 Java A., 2007, P 9 WEBKDD 1 SNA KDD, P56, DOI DOI 10.1145/1348549.1348556 JONES S., 2000, INFORM PROCESSING MA, V36, P779 KIRSCH M., 2006, P 28 EUR INF RETR EC KOMPAORE N.D.Y., 2008, THESIS P SABATIER U Krishnamurthy B, 2008, P 1 WORKSH ONL SOC N, P19, DOI DOI 10.1145/1397735.1397741 Kwak H., 2010, P 19 INT C WORLD WID, V112, P591, DOI DOI 10.1145/1772690.1772751 LONGUEVILLE B.D., 2009, P 2009 INT WORKSH LO, P73, DOI [10.1145/1629890.1629907, DOI 10.1145/1629890.1629907] MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MASSOUDI K, 2011, 33 EUR C INF RETR EC, V6611, P362 MORNEAU M., 2006, THESIS MUTSCHKE P., 2001, RES ADV TECHN DIG LI Nagmoti R., 2010, P 2010 IEEE WIC ACM, V1, P153 NOLL G., 2009, P 32 INT ACM SIGIR C, P612 OUNIS I., 2010, TEXT RETR C TREC NOV OURDIA R., 2011, THESIS RAMAGE D., 2011, P 4 ACM INT C WEB SE, P35 RAMAGE D., 2010, P 4 INT C WEBL SOC M, P1 ROBERTSON S., 1998, TREC, P199 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rosson M. B., 2009, P ACM 2009 INT C SUP, P243, DOI DOI 10.1145/1531674.1531710 Sakaki T., 2010, P 19 INT C WORLD WID, P851, DOI DOI 10.1145/1772690.1772777 SALTON G., 1971, J AM DOCUMENTATION, V20, P61 Salton G., 1983, INTRO MODERN INFORM Sankaranarayanan J, 2009, P 17 ACM SIGSPATIAL, P42, DOI 10.1145/1653771.1653781 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Shamma D., 2009, P 1 SIGMM WORKSH SOC, P3, DOI DOI 10.1145/1631144.1631148 SONG SY, 2010, P 6 INT C ACT MED TE, V6335, P63 TAMINE L., 2012, C RECH DINF APPL COR, P301 TAMINE L., 2011, C MOD AN RES APPR MA VIEWEG S, 2010, P 28 INT C HUM FACT, P1079, DOI DOI 10.1145/1753326.1753486 WENG J., 2010, P 3 ACM INT C WEB SE, P261, DOI DOI 10.1145/1718487.1718520 Yang Z, 2010, P 19 ACM INT C INF K, P1633, DOI 10.1145/1871437.1871691 ZAIHAN Y., 2010, 9 INT C AD PERS FUS ZHANG J, 2007, ADV DATABASES CONCEP, V4443, P1066, DOI 10.1007/978-3-540-71703-4_106 NR 57 TC 0 Z9 0 PU ISTE LTD PI LONDON PA 27-37 ST GEORGES RD, 3RD FLR, TUITION HOUSE, LONDON, SW19 4EU, ENGLAND BN 978-1-118-92061-9; 978-1-84821-664-8 PY 2014 BP 159 EP 182 D2 10.1002/9781118920664 PG 24 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BC3FK UT WOS:000351609400006 ER PT J AU Khennak, I Drias, H AF Khennak, Ilyes Drias, Habiba GP IEEE TI A new Term-Term similarity measure for selecting expansion features in Big Data SO 2014 International Conference on Advanced Networking Distributed Systems and Applications (INDS 2014) LA English DT Proceedings Paper CT International Conference on Advanced Networking Distributed Systems and Applications (INDS) CY JUN 17-19, 2014 CL Bejaia, ALGERIA DE term co-occurrence; term proximity; query expansion; information retrieval AB The massive growth of information and the exponential increase in the number of documents published and uploaded online each day have led to led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play a central role in retrieving the desired information, it becomes necessary to give more importance to the sites and topics where these new words appear, or rather, to give value to the words that occur frequently with them. For this purpose, in this paper, we propose a new term-term similarity measure based on the co-occurrence and closeness of words. It relies on searching for each query feature the locations where it appears, then selecting from these locations the words which often neighbor and co-occur with the query features, and finally used the selected words in the retrieval process. Our experiments were performed using the OHSUMED test collection and show significant performance enhancement over the state-of-the-art. C1 [Khennak, Ilyes; Drias, Habiba] USTHB, Dept Comp Sci, Artificial Intelligence Res Lab, Algiers, Algeria. RP Khennak, I (reprint author), USTHB, Dept Comp Sci, Artificial Intelligence Res Lab, Algiers, Algeria. CR Ahmad F., 2005, HLT 05, P955 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Bharat K, 1998, COMPUT NETWORKS ISDN, V30, P379, DOI 10.1016/S0169-7552(98)00127-5 Chen Q., 2007, EMNLP CONLL, P181 Eisenstein J., 2012, NIPS 12 Fang H., 2005, SIGIR 05, P480 Khennak I., 2013, CORIA 13, P341 Khennak I., 2013, WORLD CIST 13, P477 Ntoulas A., 2004, WWW 04, P1, DOI DOI 10.1145/988672.988674 Ranganathan P, 2011, COMPUTER, V44, P39, DOI 10.1109/MC.2011.18 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S. E., 1995, TREC 4, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST Song F., 1999, CIKM 1999, P316 Subramaniam L. V., 2009, AND 2009 P 3 WORKSH, P115 Sun H.-m., 2010, STUDIES LIT LANGUAGE, V1, P98 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Williams H. E., 2005, INT J DIGITAL LIB, V5, P99, DOI 10.1007/s00799-003-0050-z Zhu YY, 2009, LECT NOTES ARTIF INT, V5819, P147 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-5178-9 PY 2014 BP 87 EP 92 DI 10.1109/INDS.2014.23 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC5TD UT WOS:000353564300015 ER PT J AU Zheng, C Shen, WM Ghenniwa, H AF Zheng, Cheng Shen, Weiming Ghenniwa, Hamada TI An Adaptive Intent Resolving Scheme for Service Discovery and Integration SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE LA English DT Article DE Service Discovery and Integration; Web Services; Intents; Intent Resolving ID WEB SERVICES; INFORMATION; MODEL AB Service discovery and integration is an important research area with efforts invested to explore the potential advantages of collaborative computing in general and service-oriented computing in particular. However, current technologies still limit their application within the reach of enterprise systems or privately available services. Intents is an emerging and innovative technique aimed to discover and integrate publically available services. In Intents, intent message resolving is a critical step which is deemed to decide the quality of the whole system. However, existing schemes applied in intent resolving adopt the exact-matching strategy which may rule out services desired by the user. This paper brings in information retrieval techniques and applies them to intent resolving. We take an empirical approach through extensive experiments and analyses on a real dataset to obtain guiding principles. Based on the resulting principles, an adaptive intent resolving scheme is designed. Afterwards, we integrate the scheme into the Intents user agent developed in a previous project. C1 [Zheng, Cheng; Shen, Weiming; Ghenniwa, Hamada] Western Univ, London, ON, Canada. RP Zheng, C (reprint author), Western Univ, London, ON, Canada. EM czheng23@uwo.ca; wshen@uwo.ca; hghenniwa@uwo.ca CR Efron Miles, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348405 Espada JP, 2012, J UNIVERS COMPUT SCI, V18, P1410 Fang H., 2007, THESIS U ILLINOIS UR Fellbaum C, 1998, WORDNET ELECT LEXICA FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Imran Hazra, 2009, International Journal of Computer Science & Information Technology, V1 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Manning C. D., 2009, INTRO INFORM RETRIEV McCandless M., 2010, LUCENE ACTION, V2nd Mian AN, 2009, IEEE PERVAS COMPUT, V8, P66, DOI 10.1109/MPRV.2009.2 Neyem A, 2008, J UNIVERS COMPUT SCI, V14, P88 Pan Y, 2011, J UNIVERS COMPUT SCI, V17, P2029 Pedrinaci C, 2010, J UNIVERS COMPUT SCI, V16, P1694 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen WM, 2007, ROBOT CIM-INT MANUF, V23, P315, DOI 10.1016/j.rcim.2006.02.009 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Walker D., 2001, QUERY EXPANSION USIN Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zheng C, 2013, INT C COMP SUPP COOP, P207 Zheng C, 2013, INT C COMP SUPP COOP, P275 Zhu F, 2005, IEEE PERVAS COMPUT, V4, P81, DOI 10.1109/MPRV.2005.87 NR 28 TC 0 Z9 0 PU GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM PI GRAZ PA INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA SN 0948-695X J9 J UNIVERS COMPUT SCI JI J. Univers. Comput. Sci. PY 2014 VL 20 IS 13 BP 1791 EP 1812 PG 22 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA CC2YS UT WOS:000350211700005 ER PT J AU Jun, T Cheng, L Jin, G AF Jun, Tian Cheng, Le Jin, Gao BE Xu, XD Li, B Lu, QM Yan, XY Li, JL TI Algorithm to Promote Efficiency and Accuracy of Advertisement on Social Network SO MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY SE Applied Mechanics and Materials LA English DT Proceedings Paper CT International Conference on Mechatronics Engineering and Computing Technology (ICMECT) CY APR 09-10, 2014 CL Shanghai, PEOPLES R CHINA DE Friendly Value; network community; relationship; Similarity AB Network Community like Sina Weibo platform and Facebook website spend much time and money into provide users with appropriate advertisements. There exits great space to improve the current situation that users receive much news they don't like, and change the situation that server need to push news to all users who are interested in certain product. This algorithm based on Friendly Value enables server to push news to only some of the users and the rest will see them by forwarding among these users. Algorithm clarify the relationship between users. Making it like a graph. Algorithm tested more than 2,000 users from Sina Weibo using crawler, and find that, when we get a group of users who show interests in a certain product, algorithm can help to shrink this group to an accurate number of users, and by these users, the whole group will see the news of this certain product, thus decrease time and resources on server. We invented Friendly Value which reflect user's enthusiasm on Internet in terms of publishing and making comments to help us do the research. Modified similarity is also used to find users who are interested in certain commodity but not important part in this article. C1 [Jun, Tian; Cheng, Le; Jin, Gao] Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China. RP Jun, T (reprint author), Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China. EM mozillafire@bupt.edu.cn; lechengbupt@gmail.com; gaojin1105@icloud.com CR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 1 TC 0 Z9 0 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1660-9336 BN 978-3-03835-115-3 J9 APPL MECH MATER PY 2014 VL 556-562 BP 4612 EP 4616 DI 10.4028/www.scientific.net/AMM.556-562.4612 PG 5 WC Engineering, Electrical & Electronic; Engineering, Mechanical; Materials Science, Multidisciplinary; Mechanics SC Engineering; Materials Science; Mechanics GA BC0RU UT WOS:000349448506101 ER PT J AU Batra, N Kumar, A Singh, D Rajotia, RN AF Batra, Neha Kumar, Ashok Singh, Dheerendra Rajotia, R. N. BE Batra, U Sujata Arpita TI Content Based Hidden web Ranking Algorithm(CHWRA) SO SOUVENIR OF THE 2014 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) SE IEEE International Advance Computing Conference LA English DT Proceedings Paper CT 4th IEEE International Advance Computing Conference (IACC) CY FEB 21-22, 2014 CL Gurgaon, INDIA SP IEEE, ITM Univ, Dept Comp Sci Engn & Informat Technol, IEEE Comp Soc, IEEE Student Branch HO ITM Univ DE Hidden Web; PageRank AB The World Wide Web consists of millions of interconnected web pages that provide information to the user present in any part of the world. The World Wide Web is expanding and growing in size and the complexity of the web pages. That is why it is necessary to retrieve the best or the web pages that are more relevant in terms of information for the query entered by the user in the search engine. To extract the relevancy of a web page, the search engine requires applying retrieval or a ranking module that applies to a ranking algorithm on the web to fetch the web pages in order of the importance of the information entered by the user in the query. The ranking algorithm is much efficient to rank the surface web, i.e. the web pages that can be indexed by the search engine, as well as the hidden web, i.e. the web pages that cannot be indexed by the search engine. This Paper proposed an algorithm consists of: 1) PageRank Algorithm, 2) Term Weighting Technique, 3) Feedback (Likes/Dislikes) and 4) Visitor Count C1 [Batra, Neha] MRIU, Dept IT, Faridabad, India. RP Batra, N (reprint author), MRIU, Dept IT, Faridabad, India. EM nehabatra3@gmail.com; er.ashokgoel@gmaill.com; professordsingh@gmail.com; ravinder.rajotiya@gmail.com CR Amit Singhal Google Inc, MOD INF RETR BRIEF O Ju Shiguang, 2008 INT S INF PROC Khanchana R., EFFICIENT WEB PAGE P Kritikopoulos Apostolos, 2007, 24 BRIT NAT C DAT BN, P92 Kumar Gyanendra, EINTERNATIONAL C COM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sharma Dilip Kumar, 2010, INT J ENG SCI TECHNO, V2, P6097 Xing Wenpu, CANADA WEIGHTED PAGE Yen Chia-Chen, PAGERANK ALGORITHM I Zhou Cailan, IMPROVED PAGERANK AL NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2164-8263 BN 978-1-4799-2571-1 J9 IEEE INT ADV COMPUT PY 2014 BP 586 EP 589 PG 4 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC0ZG UT WOS:000349866100106 ER PT B AU Godert, W Hubrich, J Nagelschmidt, M AF Godert, W Hubrich, J Nagelschmidt, M TI Semantic Knowledge Representation for Information Retrieval SO SEMANTIC KNOWLEDGE REPRESENTATION FOR INFORMATION RETRIEVAL LA English DT Book ID VECTOR-SPACE MODEL; SEARCH; CLASSIFICATION; SERVICES; TERMS; WEB CR Almeida MB, 2013, J AM SOC INF SCI TEC, V64, P1682, DOI 10.1002/asi.22861 American Library Association / Subcommittee on Subject Relationships/ Reference Structures, 1997, FIN REP ALCTS CCS SU [Anonymous], 2006, 132502 ISO [Anonymous], 1974, 2788 ISO [Anonymous], 2005, 87232 BS [Anonymous], 2011, 259641 ISO [Anonymous], 1985, 5963 ISO [Anonymous], 2013, 259642 ISO [Anonymous], 2007, VER 3 4 [Anonymous], 1999, 13250 ISO [Anonymous], 1985, 5964 ISO ANSI/NISO, 2005, GUID CONSTR FORM MAN Antoniou Grigoris, 2008, SEMANTIC WEB PRIMER ASIS& T, 2005, TECHN LIBR Assem M. v., 2006, SEMANTIC WEB RES APP, P95 Austin D., 1976, CLASSIFICATION 1970S, P158 AUSTIN D, 1969, J LIBR, V1, P149, DOI 10.1177/096100066900100302 Austin D., 1987, ENCY LIB INFORMTI S7, V42, P375 Austin D., 2009, ENCY LIB INFORM SCI, P4251 Austin D., 1984, PRECIS MANUAL CONCEP Austin D., 1976, VARIETY LIBRARIANSHI, P19 Austin D., 1969, CATALOGUE INDEX, V14, P11 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 Bayer O., 2005, INFORM SERVICES USE, V25, P181 Bean C. A., 2001, RELATIONSHIPS ORG KN Beckett D., 2004, RDF XML SYNTAX SPECI Begthol C., 1986, LIBR RESOUR TECH SER, V30, P109 Berners-Lee T., 2011, TURTLE TERSE RDF TRI Berners-Lee T., 2001, SCI AM, V284, P35, DOI DOI 10.1038/SCIENTIFICAMERICAN0501-34 Berners-Lee T, 2011, NOTATION3 N3 READABL Bibel W., 1993, WISSENSREPRASENTATIO Bizer C., 2009, INT J SEMANT WEB INF, V5, P1 Boteram F., 2008, SEMANTISCHE RELATION Boteram F, 2009, THEMATISCHE RECHERCH Boteram F., 2011, CONCEPTS IN CONTEXT Boteram F, 2010, KNOWL ORGAN, V37, P216 Bray T., 2009, NAMESPACES XML 1 0 W Bray T., 2008, EXTENSIBLE MARKUP LA Broughton V., 2001, New Review of Hypermedia and Multimedia, V7, DOI 10.1080/13614560108914727 Buchanan B., 1979, THEORY LIB CLASSIFIC BUCKLAND M, 1994, J AM SOC INFORM SCI, V45, P12, DOI 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chan L. M., 2004, J AM SOC INFORM SCI, V55, P377 Chan L. M., 1985, THEORY SUBJECT ANAL, P154 CHAPLAN MA, 1995, LIBR QUART, V65, P39 Classification Research Group, 1969, CLASS INF CONTR PAP Clavel-Merrin G., 2006, REPORT CROSS LANGUAG Coates E. J., 1969, CLASSIFICATION INFOR, P19 Connolly D. W., 2006, WEB NAMING ADDRESSIN Cool C., 2011, INTERACTIVE INFORM S, P1 Cruse DA, 2002, INFO SCI KNOW MANAGE, V3, P3 de Bruijn J., 2003, DERI20031029 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Denton W., MAKE FACETED CLASSIF Dextre Clarke S. G., 2011, ENCY LIB INFORM SCI, P3164 Dextre-Clarke S., 2011, CONCEPTS IN CONTEXT, P91 Dunsire G, 2010, KNOWL ORGAN, V37, P280 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Ellis D., 1996, PROGR PROBLEMS INFOR Euzenat J., 2010, ONTOLOGY MATCHING Fellbaum C, 1998, WORDNET ELECT LEXICA Foskett D. J., 1970, CLASSIFICATION GEN I Fuhr N., 2003, P 26 ANN INT ACM SIG, P290 Garshol L. M., 2002, WHAT ARE TOPIC MAPS Garshol L. M., TOLOG TOPIC MAP QUER Garshol LM, 2004, J INFORM SCI, V30, P378, DOI 10.1177/0165551504045856 Geroimenko V., 2003, DICT XML TECHNOLOGIE Godert W., 2011, INFORMATIONSERSCHLIE Godert W., 2008, NEW PERSPECTIVES SUB, P233 Godert W., 2010, INFORM WISSENSCHAFT, V61, P19 GODERT W, 1987, NACHR DOK, V38, P91 Godert W., 2010, INFORM WISSENSCHAFT, V61, P5 Godert W., 2007, WAHRE INFORM, P233 Green R., 2006, KNOWLEDGE ORG INFORM, P111 Green R., 2008, CULT ID KNOWL ORG P, P8 Green R., 2011, CLASSIFICATION ONTOL, P161 Grossman D. A., 2004, INFORM RETRIEVAL ALG GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Harken S. E., 2006, SUBJECT SEMANTIC INT Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Hodge G.M., 2000, SYSTEMS KNOWLEDGE OR Hoppenbrouwers J., 2001, MACS MULTILINGUAL AC Horridge M., 2012, 11 W3C Hubrich J., 2011, CONCEPTS IN CONTEXT, P69 Hubrich J., 2010, NEXT GENERATION ANGE, P234 Hubrich J., 2013, WISSEN WISSENSCHAFT, P161 Hunting S., 2003, XML TOPIC MAPS CREAT, P81 IFLA Study Group on the Functional Requirements for Bibliographic Records, 2009, FUNCT REQ BIBL REC F IFLA Study Group on the Functional Requirements for Bibliographic Records, 1998, FUNCT REQ BIBL REC F INFODATA Thesaurus, 2000, INFODATA THES VERS O Ingwersen P., 1992, INFORM RETRIEVAL INT Isaac A., 2009, SKOS SIMPLE KNOWLEDG Jacobs J. H., 2011, CONCEPTS IN CONTEXT, P37 Jacobs JH, 2010, ADV KNOW OR, V12, P236 Jahns Y., 2011, CONCEPTS IN CONTEXT, P51 Jouis C, 2002, INFO SCI KNOW MANAGE, V3, P127 Kekalainen J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290978 Kohler J, 2006, KNOWL-BASED SYST, V19, P744, DOI 10.1016/j.knosys.2006.04.015 Kowalski G, 2011, INFORMATION RETRIEVAL ARCHITECTURE AND ALGORITHMS, P1, DOI 10.1007/978-1-4419-7716-8 Landry P., 2000, P WORKSH ADV TECHN D, P34 Lenat D. B., 1990, BUILDING LARGE KNOWL Lenat D. B., 1995, Communications of the ACM, V38 Lepsky K., 2006, ABI-Technik, V26, DOI 10.1515/ABITECH.2006.26.1.18 McCulloch E, 2008, J INF SCI, V34, P70, DOI 10.1177/0165551507079130 MADS/RDF, 2012, MADS RDF PRIM Mandala R., 1998, COLING ACL 98 WORKSH, P31 Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979 Matuszek C., 2006, FOM COMP BACKGR KNOW, P44 Meyer A., 2013, INFORM WISSENSCHAFT, V64, P115 MEYER C. D., 2006, GOOGLES PAGERANK SCI Michel D., 1997, TAXONOMY SUBJECT REL Miles A., 2004, SKOS MAPPING VOCABUL Miles A., 2009, SKOS SIMPLE KNOWLEDG Mills J., 1992, BLISS BIBLIO CLASSIF Mitchell J. S., 2011, CLASSIFICATION ONTOL, P241 Mitchell JS, 2001, INFO SCI KNOW MANAGE, V2, P211 Moens M.-F., 2002, AUTOMATIC INDEXING A Mooers C. N., 1951, AM DOC, V2, P20, DOI 10.1002/asi.5090020107 Motik B., 2012, OWL 2 WEB ONTOLOGY L Murphy M. L., 2008, SEMANTIC RELATIONS L Nicholson D., 2001, New Review of Information Networking, V7 Nicholson D., 2003, International Cataloguing and Bibliographic Control, V32 Nicholson D. M., 2006, Cataloging & Classification Quarterly, V42, DOI 10.1300/J104v42n03_08 Odgen C. K., 1923, MEANING MEANING STUD Panzer M., 2011, CONCEPTS IN CONTEXT, P157 Paralic J., 2003, P 14 INT C INF INT S, P23 Park J., 2003, XML TOPIC MAPS CREAT Pepper S., 2002, TAO TOPIC MAPS FINDI Pepper S., 2006, SURVEY RDF TOPIC MAP Pepper S., 2001, XML TOPIC MAPS XTM 1 Pepper S., 2006, GUIDELINES RDF TOPIC Pepper S., 2010, ENCY LIB INFORM SCI, P5247 PERREAULT J, 1994, KNOWL ORGAN, V21, P189 Peterson D., 2012, XML SCHEMA DEFINIT 2 Petras V., 2009, INT CATALOGUING BIBL, V38, P43 Pribbenow S, 2002, INFO SCI KNOW MANAGE, V3, P35 Probets S., 2010, ASLIB P, V62, P70 Putkey T., 2011, LIB PHILOS PRACTICE Ranganathan S. R., 1987, COLON CLASSIFICATION, V1 Rasmussen E., 2011, INTERACTIVE INFORM S, P95 Rath H., 2003, XML TOPIC MAPS CREAT, P357 Reiner U., 2008, NEW PESPECTIVES SUBJ, P121 Reiner U, 2008, ST CLASS DAT ANAL, P697, DOI 10.1007/978-3-540-78246-9_82 Riesthuis G. J. A., 2009, IFLA PROFESSIONAL RE, V115 Riesthuis G. J. A., 2008, NEW PERSPECTIVES SUB, P257 Rijsbergen C. J. v., 1979, INFORM RETRIEVAL Robertson S. E., 1997, READINGS INFORM RETR, P281 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shiri AA, 2002, J INFORM SCI, V28, P111, DOI 10.1177/0165551024234011 Shudi G., 2012, XML SCHEMA DEFINIT 1 Smiraglia R., 2011, CLASSIFICATION ONTOL, P25 Stock W. G., 2013, HDB INFORM SCI Stock WG, 2010, J AM SOC INF SCI TEC, V61, P1951, DOI 10.1002/asi.21382 Stojanovic N., 2005, THESIS U KARLSRUHE Stuckenschmidt H, 2004, IEEE INTELL SYST, V19, P34, DOI 10.1109/MIS.2004.9 Summers E., 2008, METADATA SEMANTIC SO, P25 Svenonius E., 2000, INTELLECTUAL FDN INF Tudhope D., 2001, J DIGITAL INFORM, V1 Ullrich M., 2003, ONTOPRISE WHITEPAPER Vatant B., 2003, XML TOPIC MAPS CREAT, P67 Vizine-Goetz D., 2004, J DIGITAL INFORM, V4 Weller K., 2008, RECONSIDERING RELATI White R. W., 2009, EXPLORATORY SEARCH Q World Wide Web Consortium, 2007, SEM LAYER CAK World Wide Web Consortium OWL Working Group, 2009, OML 2 ONT LANG OV W3 Zapilko B., 2009, TECHNICAL REPORTS GE, V2009/7 Zeng M., 2009, SEMANTIC INTEROPERAB, P3 Zeng M. L., 2011, FUNCTINAL REQUIREMEN Zeng M. L., 2011, P ISKO UK BIENN C 4 Zumer M., 2011, CONCEPTS IN CONTEXT, P149 Zumer Maja, 2011, FUNCTIONAL REQUIREME NR 172 TC 0 Z9 0 PU WALTER DE GRUYTER GMBH PI BERLIN PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY BN 978-3-11-032970-4; 978-3-11-030477-0 PY 2014 BP 1 EP 294 DI 10.1515/9783110329704 PG 294 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BB9MR UT WOS:000348499800013 ER PT J AU Zheng, C Shen, WM Ghenniwa, HH AF Zheng, Cheng Shen, Weiming Ghenniwa, Hamada H. BE Hou, JL Trappey, AJC Wu, CW Chang, KH Liao, CS Shen, WM Barthes, JP Luo, JZ TI A Study of Intents Resolving for Service Discovery SO PROCEEDINGS OF THE 2014 IEEE 18TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) SE International Conference on Computer Supported Cooperative Work in Design LA English DT Proceedings Paper CT IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD) CY MAY 21-23, 2014 CL Hsinchu, TAIWAN SP IEEE, Natl Tsing Hua Univ, IEEE Systems, Man, & Cybernet Soc DE service discovery; Intents; Web services AB Intents is an emerging framework which is employed for service discovery and integration. Currently the main strategy applied in Intents for resolving an intent message is exactly matching which may miss some valuable service candidates for the user. In order to address this issue, techniques in Information Retrieval (IR) are potential alternatives to find the missing services. This paper makes an empirical study of some classic IR techniques on a practical Intents dataset and demonstrates the findings of interest from the experiments which can be employed in designing matching schemes based on similarity. C1 [Zheng, Cheng; Shen, Weiming; Ghenniwa, Hamada H.] Univ Western Ontario, Dept Elect & Comp Engn, London, ON N6A 3K7, Canada. RP Zheng, C (reprint author), Univ Western Ontario, Dept Elect & Comp Engn, 1151 Richmond St, London, ON N6A 3K7, Canada. EM czheng23@uwo.ca; wshen@uwo.ca; hghenniw@uwo.ca CR Fang H., 2007, THESIS U ILLINOIS UR FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 McCandless M., 2010, LUCENE ACTION, V2nd Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Zheng C, 2013, INT C COMP SUPP COOP, P207 Znai C., 2001, P 24 ANN INT ACM SIG, P334 NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3776-9 J9 INT C COMP SUPP COOP PY 2014 BP 649 EP 654 PG 6 WC Computer Science, Interdisciplinary Applications SC Computer Science GA BB7ZB UT WOS:000346136400115 ER PT S AU Thovex, C Trichet, F AF Thovex, Christophe Trichet, Francky BE Buchmann, R Kifor, CV Yu, J TI Automatic Building of Socio-semantic Networks for Requirements Analysis Model and Business Application SO KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2014 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 7th International Conference on Knowledge Science, Engineering and Management (KSEM) CY OCT 16-18, 2014 CL Sibiu, ROMANIA HO Lucian Blaga Univ Sibiu, Fac Engn DE semantic networks building; social networks analysis ID BETWEENNESS CENTRALITY; FLOW AB Experts teams using large volumes of official documents need to see and understand at a glance how texts regarding a topic are redundant and depend on each other. In line with the strategic line of a consultants company, we present a decision support system for the visual analysis of requirements and regulation texts, based on a new model of semantic social networks analysis. We present our model and a business application. In this work, standard metrics of semantic and linguistic statistics are combined with measures of social networks analysis, in order to display socio-semantic networks of regulation texts supporting experts' decision. C1 [Thovex, Christophe; Trichet, Francky] Univ Nantes, Lab Comp Sci Nantes Atlantique, LINA, UMR CNRS 6241, F-44322 Nantes, France. RP Thovex, C (reprint author), Univ Nantes, Lab Comp Sci Nantes Atlantique, LINA, UMR CNRS 6241, 2 Rue Houssiniere,BP 92208, F-44322 Nantes, France. EM christophe.thovex@univ-nantes.fr; francky.trichet@univ-nantes.fr CR Aime X., 2010, J INTELL INF SYST, V2, P65 Brandes U, 2001, J MATH SOCIOL, V25, P163 Brandes U, 2005, LECT NOTES COMPUT SC, V3404, P533 Ereteo G., 2009, P WEBSCI 2009 SOC ON FREEMAN LC, 1991, SOC NETWORKS, V13, P141, DOI 10.1016/0378-8733(91)90017-N Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P241 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Newman MEJ, 2005, SOC NETWORKS, V27, P39, DOI 10.1016/j.socnet.2004.11.009 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROGERS DJ, 1960, SCIENCE, V132, P1115, DOI 10.1126/science.132.3434.1115 Salton G., 1986, INTRO MODERN INFORM, P201 Thovex C., 2012, EXPLORATORY ANAL DYN, P31 Zaragoza H., 2004, P TEXT RETRIEVAL C T Zhuhadar L., 2011, SOCIAL NETWORK ANAL, V3, P219 NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-12096-6; 978-3-319-12095-9 J9 LECT NOTES ARTIF INT PY 2014 VL 8793 BP 37 EP 48 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Robotics SC Computer Science; Robotics GA BB6UD UT WOS:000345026700004 ER PT S AU Mbarek, R Tmar, M Hattab, H AF Mbarek, Rabeb Tmar, Mohamed Hattab, Hawete BE Gu, Q Hell, P Yang, B TI An Optimal Context for Information Retrieval SO ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT, AAIM 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th International Conference on Algorithmic Aspects of Information and Management (AAIM) CY JUL 08-11, 2014 CL Vancouver, CANADA HO Harbour Ctr, Simon Fraser Univ DE optimal context; relevance feedback; vector space model AB In general, document representation and ranking are dependent on context. In this work, we introduce the notion of optimal context, i.e. a context which gives the best ranking. We develop an algorithm to compute this optimal context and we show that it has an effect of query reformulation. Our approach gives substantial improvements in retrieval performance over known models. C1 [Mbarek, Rabeb; Tmar, Mohamed] Univ Sfax, High Inst Comp Sci & Multimedia, Multimedia Informat Syst & Adv Comp Lab, Sfax, Tunisia. RP Mbarek, R (reprint author), Univ Sfax, High Inst Comp Sci & Multimedia, Multimedia Informat Syst & Adv Comp Lab, Sfax, Tunisia. EM rabeb.hattab@gmail.com; mohamedtmar@yahoo.fr; hattab.hawete@yahoo.fr CR Campos L.M.D., 2001, DOCUMENT INSTANTIATI Croft W.B., 2006, P JOINT DELOS NSF WO, P49 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ide E., 1971, SMART RETRIEVAL SYST, P337 Mbarek R, 2012, LECT NOTES COMPUT SC, V7608, P342, DOI 10.1007/978-3-642-34109-0_36 Melucci M., 2008, ACM T INFORM SYSTEMS, V26, P1 Melucci M., 2005, P ACM C INF KNOWL MA, P808, DOI 10.1145/1099554.1099745 Robertson S.E., 1992, OKAPI TREC TREC, P21 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I., 2002, 4 INT C CONC LIB INF, P199 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-07956-1; 978-3-319-07955-4 J9 LECT NOTES COMPUT SC PY 2014 VL 8546 BP 323 EP 330 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BB4WZ UT WOS:000343424800029 ER PT S AU Mbarek, R Tmar, M Hattab, H AF Mbarek, Rabeb Tmar, Mohamed Hattab, Hawete BE Gelbukh, A TI A New Relevance Feedback Algorithm Based on Vector Space Basis Change SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2014, PART II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing) CY APR 06-12, 2014 CL Kathmandu, NEPAL SP Inst Politecnico Nacl Centro Invest Computac Nat Language &Text Proc Lab, Mexican Soc Artificial Intelligence HO Ctr Commun & Dev DE Vector space basis; Relevance Feedback; Query ID INFORMATION AB The idea of Relevance Feedback is to take the results that are initially returned from a given query and to use information about whether or not those results are relevant to perform a new query. The most commonly used Relevance Feedback methods aim to rewrite the user query. In the Vector Space Model, Relevance Feedback is usually undertaken by re-weighting the query terms without any modification in the vector space basis. With respect to the initial vector space basis (index terms), relevant and irrelevant documents share some terms (at least the terms of the query which selected these documents). In this paper we propose a new Relevance Feedback method based on vector space basis change without any modification on the query term weights. The aim of our method is to build a basis which optimally separates relevant and irrelevant documents. That is, this vector space basis gives a better representation of the documents such that the relevant documents are gathered and the irrelevant documents are kept away from the relevant ones. C1 [Mbarek, Rabeb; Tmar, Mohamed] Univ Sfax, High Inst Comp Sci & Multimedia, Multimedia Informat Syst & Adv Comp Lab, Sfax, Tunisia. RP Mbarek, R (reprint author), Univ Sfax, High Inst Comp Sci & Multimedia, Multimedia Informat Syst & Adv Comp Lab, Sfax, Tunisia. EM rabeb.hattab@gmail.com; mohamedtmar@yahoo.fr; hattab.hawete@yahoo.fr CR Amati G, 2004, LECT NOTES COMPUT SC, V2997, P127 Cao G., 2008, SIGIR 2008, P243 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Croft W.B., 2001, P DELOS NSF WORKSH P, P49 De Campos L.M., 2001, LNCS, V2189, P13 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ide E., 1971, SMART RETRIEVAL SYST, P337 Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579 Manning C. D., 2008, INTRO INFORM RETRIEV Mbarek R, 2012, LECT NOTES COMPUT SC, V7608, P342, DOI 10.1007/978-3-642-34109-0_36 Melucci M., 2008, ACM T INFORM SYSTEMS, V26, P1 Melucci M., 2005, P ACM C INF KNOWL MA, P808, DOI 10.1145/1099554.1099745 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I., 2002, 4 INT C CONC LIB INF, P199 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Sakai T., 2005, ACM T ASIAN LANGUAGE, V4, P111, DOI 10.1145/1105696.1105699 Tao T., 2006, SIGIR 2006, P162 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR Walker S., 1992, TREC, P21 Xu Y, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P59, DOI 10.1145/1571941.1571954 Zhou D, 2012, INFORM RETRIEVAL, V15, P218, DOI 10.1007/s10791-012-9191-2 Zhou D, 2013, EXPERT SYST APPL, V40, P6805, DOI 10.1016/j.eswa.2013.06.030 NR 23 TC 0 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-54902-1; 978-3-642-54903-8 J9 LECT NOTES COMPUT SC PY 2014 VL 8404 BP 355 EP 366 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3PT UT WOS:000342990000030 ER PT J AU Kim, H AF Kim, Heesop TI Retrieval effectiveness of controlled and uncontrolled index terms in INSPEC database SO MALAYSIAN JOURNAL OF LIBRARY & INFORMATION SCIENCE LA English DT Article DE Bibliographic Databases; Controlled vocabulary; Uncontrolled vocabulary; INSPEC database; Information retrieval; Index terms; Precision and recall ID INFORMATION-RETRIEVAL; CONTROLLED VOCABULARY; FREE-TEXT; NATURAL-LANGUAGE; IR EVALUATION; RELEVANCE; SEARCH; SCIENCE; DESIGN AB The purpose of this empirical study is to assess the retrieval effectiveness of controlled and uncontrolled index terms in bibliographic database. Two types of index terms were tested in a web-based environment using the operational large-scale INSPEC database. 15 query types used in the study were both controlled terms and uncontrolled index terms derived from inverse document frequency weights. The retrieval effectiveness was evaluated using Precision. The main finding indicates there are statistically significant differences in precision arising from the two types of index terms; the uncontrolled index terms demonstrate better precision than the controlled index terms. C1 Kyungpook Natl Univ, Dept Lib & Informat Sci, Taegu 702701, South Korea. RP Kim, H (reprint author), Kyungpook Natl Univ, Dept Lib & Informat Sci, Taegu 702701, South Korea. EM heesop@knu.ac.kr CR Aitchison T.M., 1970, INSPEC5 I EL ENG Aitchison T.M., 1969, INSPEC4 I EL ENG Belkin N. J., 2009, P SIGIR 2009 WORKSH, P7 BHATTACH.K, 1974, J DOC, V30, P235, DOI 10.1108/eb026580 Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286 BOYCE BR, 1989, J AM SOC INFORM SCI, V40, P273, DOI 10.1002/(SICI)1097-4571(198907)40:4<273::AID-ASI8>3.0.CO;2-T Bradshaw S, 2003, LECT NOTES COMPUT SC, V2769, P499 Browne G, 2007, INDEXING COMPANION, P1, DOI 10.1017/CBO9781139168595 Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 CALKINS ML, 1980, DATABASE, V3, P53 Carrow D., 1981, INFORMATION MANAGEME, P131 Chu Heting, 2003, INFORM REPRESENTATIO Cleveland D.B., 2001, INTRO INDEXING ABSTR FIDEL R, 1991, J AM SOC INFORM SCI, V42, P501, DOI 10.1002/(SICI)1097-4571(199108)42:7<501::AID-ASI5>3.0.CO;2-V FUGMANN R, 1982, INT CLASSIF, V9, P140 HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q HEINE MH, 1984, INFORM TECHNOL R & D, V3, P95 HENZLER RG, 1978, INT CLASSIF, V5, P21 Jarvelin K, 2009, LECT NOTES COMPUT SC, V5766, P289 Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137 Kim H., 1998, THESIS U SHEFFIELD D Lancaster F. W., 2003, INDEXING ABSTRACTING Muddamalle MR, 1998, J AM SOC INFORM SCI, V49, P881, DOI 10.1002/(SICI)1097-4571(199808)49:10<881::AID-ASI4>3.0.CO;2-Y PAO M. L., 1989, CONCEPTS INFORM RETR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROWLEY J, 1994, J INFORM SCI, V20, P108, DOI 10.1177/016555159402000204 Ruthven I., 2005, NEW DIRECTIONS COGNI Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P1915, DOI 10.1002/asi.20682 Savoy J, 2005, INFORM PROCESS MANAG, V41, P873, DOI 10.1016/j.ipm.2004.01.004 SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 Soergel D, 1985, ORG INFORM PRINCIPLE Sparck Jones K., 1981, INFORM RETRIEVAL EXP SVENONIUS E, 1986, J AM SOC INFORM SCI, V37, P331, DOI 10.1002/asi.4630370509 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 The Institution of Engineering and Technology, 2012, IET INSPECDIRECT US Waffenschmidt S, 2010, HEALTH INFO LIBR J, V27, P262, DOI [10.1111/j.1471-1842.2010.00910.x, 10.1111/J.1471-1842.2010.00910.x] WALLACE PM, 1993, RQ, V33, P239 White Hollie, 2013, Cataloging & Classification Quarterly, V51, DOI 10.1080/01639374.2013.777004 NR 38 TC 0 Z9 0 PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH PI KUALA LUMPUR PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR, 50603, MALAYSIA SN 1394-6234 J9 MALAYS J LIBR INF SC JI Malays. J. Libr. Sci. PY 2014 VL 19 IS 2 BP 103 EP 117 PG 15 WC Information Science & Library Science SC Information Science & Library Science GA AO3MH UT WOS:000341233600007 ER PT J AU Rivas, AR Iglesias, EL Borrajo, L AF Rivas, A. R. Iglesias, E. L. Borrajo, L. TI Study of Query Expansion Techniques and Their Application in the Biomedical Information Retrieval SO SCIENTIFIC WORLD JOURNAL LA English DT Article ID MEDICAL SUBJECT-HEADINGS; SEARCH; TERMS AB Information Retrieval focuses on finding documents whose content matches with a user query from a large document collection. As formulating well-designed queries is difficult for most users, it is necessary to use query expansion to retrieve relevant information. Query expansion techniques are widely applied for improving the efficiency of the textual information retrieval systems. These techniques help to overcome vocabulary mismatch issues by expanding the original query with additional relevant terms and reweighting the terms in the expanded query. In this paper, different text preprocessing and query expansion approaches are combined to improve the documents initially retrieved by a query in a scientific documental database. A corpus belonging to MEDLINE, called Cystic Fibrosis, is used as a knowledge source. Experimental results show that the proposed combinations of techniques greatly enhance the efficiency obtained by traditional queries. C1 [Rivas, A. R.; Iglesias, E. L.; Borrajo, L.] Univ Vigo, Dept Comp Sci, Higher Tech Sch Comp Engn, Orense 32004, Spain. RP Iglesias, EL (reprint author), Univ Vigo, Dept Comp Sci, Higher Tech Sch Comp Engn, Orense 32004, Spain. EM eva@uvigo.es FU European Union [FP7/REGPOT-2012-2013.1, 316265] FX This work has been funded from the European Union Seventh Framework Programme (FP7/REGPOT-2012-2013.1) under Grant agreement no. 316265, BIOCAPS. CR Abdou S, 2005, P 15 TEXT RETR C P T, P863 Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Borrajo L, 2011, J Integr Bioinform, V8, P176, DOI 10.2390/biecoll-jib-2011-176 Brants T., 2004, P CLIN 2004 ANTW BEL, P1 Chang AA, 2006, LARYNGOSCOPE, V116, P336, DOI 10.1097/01.mlg.0000195371.72887.a2 Coletti MH, 2001, J AM MED INFORM ASSN, V8, P317 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 Ebbert JO, 2003, MAYO CLIN PROC, V78, P87 Ellen Voorhees M., 2006, P 15 TEXT RETR C TRE, P500 Gauch S, 1999, ACM T INFORM SYST, V17, P250, DOI 10.1145/314516.314519 Gault LV, 2002, J MED LIBR ASSOC, V90, P173 Greengrass E., 2001, TRR52008001 US DEP D Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989 Hiemstra D., 2002, CREATING DUTCH INFOR Hirschman L, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S1 Karimi S, 2012, INFORM PROCESS MANAG, V48, P94, DOI 10.1016/j.ipm.2011.02.009 Leveling J., 2010, ACM T ASIAN LANGUAGE, V9 Liddy E. D., 1992, TREC, P113 Maning C. D., 2008, INTRO INFORM RETRIEV Miller G A, 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Ramos J., 2003, USING TF IDF DETERMI Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Robertson S. E., 1999, OKAPI KEENBOW TREC 8 Robertson S. E., 1996, OKAPI TREC 3 Robertson S. E., 2001, MICROSOFT CAMBRIDGE ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shin K., 2004, LECT NOTES COMPUTER, V3136, P137 Trotman A., 2004, ARTIFICIAL INTELLIGE Trotman A., 2005, LEARNING RANK INFORM, V8 Yang Z., 2005, TEXT RETRIEVAL C Zhou G. D., P COLING INT JOINT W, P1 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 37 TC 0 Z9 0 PU HINDAWI PUBLISHING CORPORATION PI NEW YORK PA 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA SN 1537-744X J9 SCI WORLD J JI Sci. World J. PY 2014 AR 132158 DI 10.1155/2014/132158 PG 10 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA AC9OV UT WOS:000332864900001 ER PT J AU Melucci, M AF Melucci, Massimo TI Deriving a Quantum Information Retrieval Basis SO COMPUTER JOURNAL LA English DT Article DE probability ranking principle; quantum detection; search engines; information retrieval ID PROBABILITY RANKING PRINCIPLE AB Indexing is a core process of an information retrieval (IR) system (IRS). As indexing can neither be exhaustive nor precise, the decision taken by an IRS about the relevance of the content of a document to an information need is subject to uncertainty. It is our hypothesis that one of the reasons that IRSs are unable to optimally respond to every query is that the document collections and the posting lists are modeled as sets of documents. In contrast, if vector spaces replace sets along the way given by quantum mechanics, it is possible to define a quantum information retrieval basis (QIRB) that at least in principle yields more effective document ranking than the ranking yielded by the current principles, with effectiveness being measured in terms of recall at every level of fallout. To this end, we show that the probability ranking principle and the Neyman-Pearson Lemma (NPL) are equivalent. The rest of the article follows from this result. In particular, we introduce the QIRB, link it to a generalization of the NPL and demonstrate its superiority through a concise mathematical analysis and an empirical study. The challenges posed by this basis and the research directions that would be opened are also discussed. C1 Univ Padua, I-35100 Padua, Italy. RP Melucci, M (reprint author), Univ Padua, I-35100 Padua, Italy. EM melo@dei.unipd.it FU European Union [247490] FX This work was supported by the European Union Seventh Framework Programme (FP7/2007-2013) Marie Curie IRSES [grant number 247490]. CR Bruza P. D., 2005, WE WILL SHOW THEM ES Cooper W., 1991, P 14 ANN INT ACM SIG, P57, DOI 10.1145/122860.122866 Eldar YC, 2001, IEEE T INFORM THEORY, V47, P858, DOI 10.1109/18.915636 Fuhr N, 2008, INFORM RETRIEVAL, V11, P251, DOI 10.1007/s10791-008-9045-0 Griffiths R. B., 2002, CONSISTENT QUANTUM T Harman D, 2009, INFORM RETRIEVAL, V12, P615, DOI 10.1007/s10791-009-9101-4 Helstrom C. W., 1976, QUANTUM DETECTION ES Hughes R. I. G., 1989, STRUCTURE INTERPRETA Kolmogorov A., 1956, FDN THEORY PROBABILI, V2nd KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Melucci M, 2011, INFORM RETRIEVAL SER, V33, P125, DOI 10.1007/978-3-642-20946-8_6 Melucci M, 2011, LECT NOTES COMPUT SC, V6931, P139, DOI 10.1007/978-3-642-23318-0_14 Neyman J, 1933, PHILOS T R SOC LOND, V231, P289, DOI 10.1098/rsta.1933.0009 Piwowarski B., 2010, P CIKM, P59, DOI 10.1145/1871437.1871450 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Van Rijsbergen K., 2004, GEOMETRY INFORM RETR Wang J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P115, DOI 10.1145/1571941.1571963 Widdows Dominic, 2004, GEOMETRY AND MEANING Zuccon G, 2010, LECT NOTES COMPUT SC, V5993, P357, DOI 10.1007/978-3-642-12275-0_32 NR 22 TC 2 Z9 2 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0010-4620 EI 1460-2067 J9 COMPUT J JI Comput. J. PD NOV PY 2013 VL 56 IS 11 BP 1279 EP 1291 DI 10.1093/comjnl/bxs095 PG 13 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 247XA UT WOS:000326655700002 ER PT J AU Wu, IC Chen, GW Hsu, JL Lin, CY AF Wu, I-Chin Chen, Guan-Wei Hsu, Jia-Lien Lin, Chun-Yu TI An entropy-based query expansion approach for learning researchers' dynamic information needs SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Query expansion; Pseudo-relevance feedback; Term weighting; Topic change; Entropy ID RESEARCH-PROJECT; TASK COMPLEXITY; COGNITIVE MODEL; SEARCH TERMS; DOCUMENT USE; RETRIEVAL; SEEKING; REFINEMENT; SELECTION; FEEDBACK AB Literature survey is one of the most important steps in the process of academic research, allowing researchers to explore and understand topics. However, novice researchers without sufficient prior knowledge lack the skills to determine proper keywords for searching topics of choice. To tackle this problem, we propose an entropy-based query expansion with a reweighting (E_QE) approach to revise queries during the iterative retrieval process. We designed a series of experiments that consider the researcher's changing information needs during task execution. Three topic change situations are considered in this work: minor, moderate and dramatic topic changes. The simulation-based pseudo-relevance feedback technique is applied during the search process to evaluate the effectiveness of the proposed approach without the intervention of human effort. We measured the effectiveness of the TFIDF and E_QE approaches for different types of topic change situations. The results show that the proposed E_QE approach achieves better search results than the TFIDF, helping researchers to revise queries. The results also confirm that the E_QE approach is effective when considering the relevant and irrelevant pages during the relevance feedback process at different levels of topic change. (C) 2013 Elsevier B.V. All rights reserved. C1 [Wu, I-Chin; Chen, Guan-Wei] Fu Jen Cathol Univ, Dept Informat Management, Xinbei City 24205, Taiwan. [Hsu, Jia-Lien] Fu Jen Cathol Univ, Dept Comp Sci & Informat Engn, Xinbei City 24205, Taiwan. [Lin, Chun-Yu] Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu 30043, Taiwan. RP Wu, IC (reprint author), Fu Jen Cathol Univ, Dept Informat Management, 510 Chung Cheng Rd, Xinbei City 24205, Taiwan. EM icwu.fju@gmail.com; archie.cylin@gmail.com FU National Science Council of Taiwan; Fu-Jen Catholic University [NSC 97-2410H-030-030-MY2, 409931074078] FX This research was supported by the National Science Council of Taiwan and Fu-Jen Catholic University under the Grant NSC 97-2410H-030-030-MY2 and No. 409931074078, respectively. CR Alejandra Segura N., 2011, KNOWL-BASED SYST, V24, P119, DOI DOI 10.1016/J.KNOSYS.2010.07.012 Allan J., 2012, SIGIR FORUM, V46, P2 Antoniou D, 2012, J SYST SOFTWARE, V85, P1577, DOI 10.1016/j.jss.2012.01.049 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BYSTROM K, 1995, INFORM PROCESS MANAG, V31, P191, DOI 10.1016/0306-4573(94)00041-Z CALLAN J, 2007, 30 INT ACM SIGIR C R, V41, P25 Chang YJ, 2006, INFORM PROCESS MANAG, V42, P453, DOI 10.1016/j.ipm.2005.03.025 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Greenberg J, 2001, J AM SOC INF SCI TEC, V52, P402, DOI 10.1002/1532-2890(2001)9999:9999<::AID-ASI1089>3.3.CO;2-B IDE E, 1971, SMART RETRIEVAL SYST, P37 Ingwersen P., 2005, TURN INTEGRATION INF Jansen M.B.J., 2000, INFORM PROCESSING MA, V36, P207 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Kelly Diane, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000012 Kuhlthau C. C., 1993, SEEKING MEANING PROC LJOSLAND M, 1999, ACM C RES DEV INF RE Manning C. D., 2008, INTRO INFORM RETRIEV Okabe M, 2005, KNOWL-BASED SYST, V18, P117, DOI 10.1016/j.knosys.2004.04.011 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCCHIO JJ, 1966, THESIS HARVARD U CAM SALTON G., 1988, J INFORM PROCESSING, V24, P513 SARACEVIC T, 1997, ACM SIGIR FORUM, V31, P1627 SHANNON CE, 1951, AT&T TECH J, V30, P50 Spink A, 1999, J INFORM SCI, V25, P477, DOI 10.1177/016555159902500604 Tang XY, 2012, J SYST SOFTWARE, V85, P87, DOI 10.1016/j.jss.2011.07.029 Vakkari P, 1998, INFORM PROCESS MANAG, V34, P361, DOI 10.1016/S0306-4573(97)00074-5 Vakkari P., 2000, P 23 ANN INT ACM SIG, P2, DOI 10.1145/345508.345512 Vakkari P, 2003, INFORM PROCESS MANAG, V39, P445, DOI 10.1016/S03064573(02)0003l-6 Vakkari P, 2002, LECT NOTES COMPUT SC, V2291, P110 WANG C, 2009, INT J WEB INFORM SYS, V5, P220 Wang P, 1997, P INT C INF SEEK CON, P307 Wang PL, 1998, J AM SOC INFORM SCI, V49, P115, DOI 10.1002/(SICI)1097-4571(1998)49:2<115::AID-ASI3>3.0.CO;2-1 Wang PL, 1999, J AM SOC INFORM SCI, V50, P98 White RW, 2006, INFORM PROCESS MANAG, V42, P166, DOI 10.1016/j.ipm.2004.08.010 White RW, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P363, DOI 10.1145/1571941.1572005 WILSON T. D., 1996, INFORM BEHAV INTERDI Wilson TD, 1999, J DOC, V55, P249, DOI 10.1108/EUM0000000007145 Wu IC, 2008, INFORM PROCESS MANAG, V44, P1649, DOI 10.1016/j.ipm.2007.11.005 Wu IC, 2011, J DOC, V67, P525, DOI 10.1108/00220411111124578 Xie I., 2008, INTERACTIVE INFORM R Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 47 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 EI 1872-7409 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD NOV PY 2013 VL 52 BP 133 EP 146 DI 10.1016/j.knosys.2013.07.017 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 233WM UT WOS:000325600200011 ER PT J AU Trappey, CV Wang, TM Hoang, S Trappey, AJC AF Trappey, Charles V. Wang, Tong-Mei Hoang, Sean Trappey, Amy J. C. TI Constructing a dental implant ontology for domain specific clustering and life span analysis SO ADVANCED ENGINEERING INFORMATICS LA English DT Article DE Clustering; Key phrase extraction; Dental implant ontology; Life span analysis ID PATENT ANALYSIS; TECHNOLOGY; TOOL AB Dental implant and prosthetics is a growing industry that follows the increasing aged populations that incur a higher percentage of tooth loss [1]. The dental implant sector is one of the most technical oriented fields in dentistry with many new techniques, devices, and materials being invented and put to clinical trials. Most innovations and technologies tend to be protected by intellectual property rights (IPRs) through patents. Thus, this research identifies the life spans of dental implant (DI) key technologies using patent analysis. Key patents and their frequently appearing phrases are analyzed for the construction of the DI ontology. Afterward, the life spans of DI technical clusters are defined based on the ontology schema. This research demonstrates the feasibility of using text mining and data mining techniques to extract key phrases from a set of DI patents with different patent classifications (e.g., UPC, IPC) as the basis for building a domain-specific ontology. The case study of ontological sub-clustering for dental implants demonstrates life span mapping of the technology and the ability to use clusters to represent stages of development and maturity in specific technology life cycles. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Trappey, Charles V.; Hoang, Sean] Natl Chiao Tung Univ, Dept Management Sci, Hsinchu, Taiwan. [Wang, Tong-Mei] Natl Taiwan Univ, Sch Dent, Taipei 10764, Taiwan. [Trappey, Amy J. C.] Natl Tsing Hua Univ, Dept Ind Engn & Engn Management, Hsinchu, Taiwan. RP Wang, TM (reprint author), Natl Taiwan Univ, Sch Dent, Taipei 10764, Taiwan. EM trappey@faculty.nctu.edu.tw; tongmeiwang@ntu.edu.tw; long.hoang87@gmail.com; trappey@ie.nthu.edu.tw RI WANG, TONG-MEI/ OI WANG, TONG-MEI/0000-0002-9885-6899 FU National Science Council FX This research was partially supported by National Science Council research projects. The authors express their gratitude to Dr. Chun-Yi Wu for his assistance in running the case analysis using the IPDSS software tool (www.wheeljet.com.tw/edu/). CR Almeida JAS, 2007, CHEMOMETR INTELL LAB, V87, P208, DOI 10.1016/j.chemolab.2007.01.005 [Anonymous], 1999, GRANSTRAND EC MANAGE Ceramic Industry, 2012, DENT IMPL PROSTH MAR Choi C, 2007, TECHNOL FORECAST SOC, V74, P1296, DOI 10.1016/j.techfore.2006.10.008 Ernst H., 1995, RES POLICY, P143 Ernst H, 1997, SMALL BUS ECON, V9, P361, DOI 10.1023/A:1007921808138 Grilliches Z., 1990, J ECON LIT, P1661 Hammouda KM, 2005, LECT NOTES ARTIF INT, V3587, P265 Han J, 2012, MOR KAUF D, P1 Haupt R, 2007, RES POLICY, V36, P387, DOI 10.1016/j.respol.2006.12.004 HUANG CJ, 2008, P 15 ISPE INT C CONC, P495 Jung RE, 2008, CLIN ORAL IMPLAN RES, V19, P119, DOI 10.1111/j.1600-0501.2007.01453.x Kettenring JR, 2009, APPL STOCH MODEL BUS, V25, P460, DOI 10.1002/asmb.772 Kostoff RN, 2001, TECHNOL FORECAST SOC, V68, P223, DOI 10.1016/S0040-1625(01)00133-0 Lee S, 2009, TECHNOVATION, V29, P481, DOI 10.1016/j.technovation.2008.10.006 Mangano C, 2010, INT J ORAL MAX IMPL, V25, P394 Matsuo Y., 2003, KEYWORD EXTRACTION S, P392 MOGEE ME, 1994, TECHNOL ANAL STRATEG, V6, P485, DOI 10.1080/09537329408524188 Nasukawa T, 2001, IBM SYST J, V40, P967 Nenkova A., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148269 Pritzek S., 2005, ONTOLOGY RFID DOMAIN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rubin DL, 2008, BRIEF BIOINFORM, V9, P75, DOI 10.1093/bib/bbm059 Soo VW, 2006, EXPERT SYST APPL, V31, P766, DOI 10.1016/j.eswa.2006.01.014 Taduri S., 2011, ANN INT C DIG GOV RE Tan A.H., 2011, P PAKDD, V696, P65 Trappey A.J.C., IND MANAGEMENT DATA, V108 Trappey CV, 2010, J SYST SCI SYST ENG, V19, P162, DOI 10.1007/s11518-010-5134-x Trappey CV, 2011, ADV ENG INFORM, V25, P53, DOI 10.1016/j.aei.2010.05.007 Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 Tseng Y.H., 2005, P IACIS PAC 2005 C 2 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 Wanner Leo, 2008, World Patent Information, V30, DOI 10.1016/j.wpi.2007.03.008 Weiss S. M., 2005, TEXT MINING PREDICTI Wheeljet.com, 2012, IPDSS INT PROP DEF S World Health Organization, 2012, ORAL HLTH Yoon B., 2004, J HIGH TECHNOLOGY MA, V15, P37, DOI DOI 10.1016/J.HITECH.2003.09.003 NR 37 TC 1 Z9 1 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 1474-0346 J9 ADV ENG INFORM JI Adv. Eng. Inform. PD AUG PY 2013 VL 27 IS 3 BP 346 EP 357 DI 10.1016/j.aei.2013.04.003 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Multidisciplinary SC Computer Science; Engineering GA 194CF UT WOS:000322607900004 ER PT J AU Togia, T McNeill, F AF Togia, Theodosia McNeill, Fiona TI On-the-fly repair of structural and lexical ontology mismatches in multi-agent systems SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article DE Semantic matching; Ontology repair; Sense and reference AB In a Semantic-Web-like multi-agent environment, ontology mismatch is inevitable: we can't realistically expect agents created at different times and places by different people to commit to one unchanging universal ontology. Ontology matching seems to be the only solution to such a problem. However, standard techniques for aligning heterogeneous ontologies are based on time-consuming, off-line and often semi-automated processes and pre-suppose full access to the interacting agents' ontologies. This is far from ideal in situations where agents meet for the first time, interact quickly and have restricted access to other agents' private information. In this paper we present the Ontology Repair System (ORS), which attempts to match fully-fledged first-order ontologies automatically using incomplete information. Particular emphasis is laid on its semantic matching module, the Semantic Matcher, which provides a solution for lexical mismatches, which are the most common and the most challenging to address. ORS and the Semantic Matcher have been implemented and evaluated, with very promising results. C1 [Togia, Theodosia] Univ Cambridge, Comp Lab, Cambridge CB2 3QG, England. [McNeill, Fiona] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland. RP McNeill, F (reprint author), Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland. EM f.j.mcneill@ed.ac.uk CR CARNAP R., 1947, MEANING NECESSITY Frege G, 1892, Z PHILOS PHILOS KRIT, P563 Garcia-Silva A, 2012, KNOWL ENG REV, V27, P57, DOI 10.1017/S026988891100018X Giunchiglia F, 2003, KNOWL ENG REV, V18, P265, DOI 10.1017/S0269888904000074 Halpin H, 2010, P WEBSCI10 EXT FRONT Halpin Harry, 2009, P WEBSCI 09 SOC ON L Kripke S., 1972, NAMING NECESSITY Laurence S, 1999, CONCEPTS CORE READIN, P2 McNeill F, 2007, INT J SEMANT WEB INF, V3, P1, DOI 10.4018/jswis.2007070101 Miller G, 2005, INTRO WORDNET ON LIN Niles I., 2003, P 2003 INT C INF KNO Qu W, 2006, P 15 INT WORLD WID W, P23 Richards I., 1923, MEANING MEANING ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shvaiko P., 2012, IEEE T KNOWL DATA EN Vander-Wal T., 2005, EXPLAINING SHOWING B Wittgenstein L., 1953, PHILOS INVESTIGATION NR 18 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0269-2821 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD AUG PY 2013 VL 40 IS 2 SI SI BP 175 EP 191 DI 10.1007/s10462-012-9352-0 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA 189MP UT WOS:000322271600004 ER PT J AU Wang, QL Qian, YN Song, RH Dou, ZC Zhang, F Sakai, T Zheng, QH AF Wang, Qinglei Qian, Yanan Song, Ruihua Dou, Zhicheng Zhang, Fan Sakai, Tetsuya Zheng, Qinghua TI Mining subtopics from text fragments for a web query SO INFORMATION RETRIEVAL LA English DT Article DE Query intent; Intent mining; Intents ranking ID SEARCH ENGINE; RELEVANCE; ALGORITHM AB Web search queries are often ambiguous or faceted, and the task of identifying the major underlying senses and facets of queries has received much attention in recent years. We refer to this task as query subtopic mining. In this paper, we propose to use surrounding text of query terms in top retrieved documents to mine subtopics and rank them. We first extract text fragments containing query terms from different parts of documents. Then we group similar text fragments into clusters and generate a readable subtopic for each cluster. Based on the cluster and the language model trained from a query log, we calculate three features and combine them into a relevance score for each subtopic. Subtopics are finally ranked by balancing relevance and novelty. Our evaluation experiments with the NTCIR-9 INTENT Chinese Subtopic Mining test collection show that our method significantly outperforms a query log based method proposed by Radlinski et al. (2010) and a search result clustering based method proposed by Zeng et al. (2004) in terms of precision, I-rec, D-nDCG and D#-nDCG, the official evaluation metrics used at the NTCIR-9 INTENT task. Moreover, our generated subtopics are significantly more readable than those generated by the search result clustering method. C1 [Wang, Qinglei; Qian, Yanan; Zheng, Qinghua] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, SPKLSTN Lab, Xian 710049, Peoples R China. [Song, Ruihua; Dou, Zhicheng; Sakai, Tetsuya] Microsoft Res Asia, Beijing 100080, Peoples R China. [Zhang, Fan] Nankai Univ, Nankai Baidu Joint Lab, Tianjin 300071, Peoples R China. RP Wang, QL (reprint author), Xi An Jiao Tong Univ, Dept Comp Sci & Technol, SPKLSTN Lab, Xian 710049, Peoples R China. EM wangqinglei0116@gmail.com; yanan.qian@stu.xjtu.edu.cn; Song.Ruihua@microsoft.com; zhichdou@microsoft.com; zhangfan555@gmail.com; tetsuyasakai@acm.org; qhzheng@mail.xjtu.edu.cn FU National Science Foundation of China [61173112, 61070072, 61103160, 61103239]; National High Technology Research and Development Program 863 of China [2012AA011003]; Ministry of Education of China Humanities and Social Sciences Project [12YJC880117]; NSFC of China [60903028, 61070014]; Key Projects in the Tianjin Science & Technology Pillar Program [11ZCKFGX01100]; Cheung Kong Scholars Program FX The research was supported in part by National Science Foundation of China under Grant Nos. 61173112, 61070072, 61103160, 61103239; National High Technology Research and Development Program 863 of China under Grant No. 2012AA011003; Cheung Kong Scholars Program; Ministry of Education of China Humanities and Social Sciences Project under Grant No. 12YJC880117. The fifth author is supported by NSFC of China (60903028, 61070014), Key Projects in the Tianjin Science & Technology Pillar Program (11ZCKFGX01100). CR Agrawal R., 2009, P 2 ACM INT C WEB SE, P5, DOI 10.1145/1498759.1498766 Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Chandar P, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P869 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 Clarke C. L., 2009, TECHNICAL REPORT Clarke C. L. A., 2011, P 4 ACM INT C WEB SE, P75, DOI 10.1145/1935826.1935847 Clough P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P734, DOI 10.1145/1571941.1572102 Cutting D.R., 1993, P 16 ANN INT ACM SIG, P126, DOI 10.1145/160688.160706 Ferragina P, 2008, SOFTWARE PRACT EXPER, V38, P189, DOI 10.1002/spe.829 Gale W., 1995, J QUANT LINGUIST, V2, P217, DOI 10.1080/09296179508590051 Geraci F., 2006, P 21 ANN ACM S APPL, P1058, DOI 10.1145/1141277.1141527 GOFFMAN W, 1964, INFORM STORAGE RET, V2, P201, DOI 10.1016/0020-0271(64)90004-X Gollapudi S., 2009, P 18 INT C WORLD WID, P381, DOI 10.1145/1526709.1526761 Hearst M., 1995, AAAI FALL S AI APPL Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Ji X., 2007, DAT MIN WORKSH 2007, P325 Koshman S, 2006, J AM SOC INF SCI TEC, V57, P1875, DOI 10.1002/asi.20408 Leouski A. V., 2005, TECHNICAL REPORT Leuski A., 2000, P RIAO, V2000 Li Xiao, 2008, P 31 ANN INT ACM SIG, P339, DOI 10.1145/1390334.1390393 Osinski S, 2005, IEEE INTELL SYST, V20, P48, DOI 10.1109/MIS.2005.38 Radlinski F., 2010, P 19 INT C WORLD WID, P1171, DOI 10.1145/1772690.1772859 Rafiei D, 2010, P 19 INT C WORLD WID, P781, DOI 10.1145/1772690.1772770 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 Sakai T., 2011, P 34 ANN INT ACM SIG, P1043 Sakai T., 2011, P FOR INF TECHN 2011, V2, P23 Santos R L, 2010, P 19 ACM INT C INF K, P1179, DOI 10.1145/1871437.1871586 Santos R.L., 2010, P 19 INT C WORLD WID, P881, DOI 10.1145/1772690.1772780 SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30 Song R., 2011, P 9 NTCIR WORKSH M E Song R., 2004, P 13 TEXT RETR C P T Strohmaier M., 2009, P 2009 WORKSH WEB SE, P68, DOI 10.1145/1507509.1507520 Wang Xuanhui, 2007, P 30 ANN INT ACM SIG, P87, DOI http://dx.doi.org/10.1145/1277741.1277759 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zheng W., 2011, P DDR Zheng W., 2011, P JCDL, V11 Zheng W., 2010, P TREC, V10 NR 42 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD AUG PY 2013 VL 16 IS 4 SI SI BP 484 EP 503 DI 10.1007/s10791-013-9221-8 PG 20 WC Computer Science, Information Systems SC Computer Science GA 188IY UT WOS:000322187800004 ER PT J AU Hou, YX Zhao, XZ Song, DW Li, WJ AF Hou, Yuexian Zhao, Xiaozhao Song, Dawei Li, Wenjie TI Mining Pure High-Order Word Associations via Information Geometry for Information Retrieval SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Theory; Measurement; Algorithms; Word association; pure high-order dependence; information geometry; text classification; text retrieval ID INDEPENDENCE; MODEL AB The classical bag-of-word models for information retrieval (IR) fail to capture contextual associations between words. In this article, we propose to investigate pure high-order dependence among a number of words forming an unseparable semantic entity, that is, the high-order dependence that cannot be reduced to the random coincidence of lower-order dependencies. We believe that identifying these pure high-order dependence patterns would lead to a better representation of documents and novel retrieval models. Specifically, two formal definitions of pure dependence-unconditional pure dependence (UPD) and conditional pure dependence (CPD)-are defined. The exact decision on UPD and CPD, however, is NP-hard in general. We hence derive and prove the sufficient criteria that entail UPD and CPD, within the well-principled information geometry (IG) framework, leading to a more feasible UPD/CPD identification procedure. We further develop novel methods for extracting word patterns with pure high-order dependence. Our methods are applied to and extensively evaluated on three typical IR tasks: text classification and text retrieval without and with query expansion. C1 [Hou, Yuexian; Zhao, Xiaozhao; Song, Dawei] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. [Li, Wenjie] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. RP Song, DW (reprint author), Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. EM krete1941@gmail.com; 0.25eye@gmail.com; dawei.song2010@gmail.com; cswjli@comp.polyu.edu.hk RI Li, Wenjie/ OI Li, Wenjie/0000-0002-7360-8864 FU Natural Science Foundation of China [61070044, 61111130190, 61272265]; European Union Framework 7 Marie-Curie International Research Staff Exchange Programme [247590] FX This work is partially supported by the Natural Science Foundation of China (grants no. 61070044, 61111130190 and 61272265), and the European Union Framework 7 Marie-Curie International Research Staff Exchange Programme (grant no. 247590). CR Agrawal R., 1993, ACM SIGMOD RECORD, V22, P207, DOI DOI 10.1145/170035.170072 Amari S, 2001, IEEE T INFORM THEORY, V47, P1701, DOI 10.1109/18.930911 Amari S. I., 2000, METHODS INFORM GEOME, V191 Bakirov NK, 2006, J MULTIVARIATE ANAL, V97, P1742, DOI 10.1016/j.jmva.2005.10.005 Bendersky Michael, 2010, P 3 ACM INT C WEB SE, P31, DOI DOI 10.1145/1718487.1718492 Bendersky Michael, 2008, P 31 ANN INT ACM SIG, P491, DOI DOI 10.1145/1390334.1390419 Caropreso M.F., 2001, LEARNER INDEPENDENT, P78 Chickering DM, 2004, J MACH LEARN RES, V5, P1287 Costa S., 2005, P IEEE INF THEOR WOR, P28 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Hofmann T., 1999, P C ADV NEUR INF PRO, P914 JEFFREYS H, 1946, PROC R SOC LON SER-A, V186, P453, DOI 10.1098/rspa.1946.0056 Kirsch A, 2012, J ACM, V59, DOI 10.1145/2220357.2220359 Korkontzelos I, 2008, LECT NOTES ARTIF INT, V5221, P248 Kumaran G, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P564, DOI 10.1145/1571941.1572038 Lang H., 2010, P 19 ACM INT C INF K, P249, DOI 10.1145/1871437.1871473 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Mihalcea R., 2006, P 21 NAT C ART INT A, P775 Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 Nakahara H, 2002, NEURAL COMPUT, V14, P2269, DOI 10.1162/08997660260293238 Niesler TR, 1996, INT CONF ACOUST SPEE, P164, DOI 10.1109/ICASSP.1996.540316 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rao C.R., 1945, Bulletin of the Calcutta Mathematical Society, V37 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Shi L., 2010, P 19 ACM INT C INF K, P1493, DOI 10.1145/1871437.1871655 Sokal RR, 2012, BIOMETRY PRINCIPLES, V4th Song DW, 2008, LECT NOTES COMPUT SC, V4956, P334 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 Taskinen S, 2005, J AM STAT ASSOC, V100, P916, DOI 10.1198/016214505000000097 Tsukiyama S., 1977, SIAM Journal on Computing, V6, DOI 10.1137/0206036 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Xue Xiaobing, 2010, P INT C INF KNOWL MA, P1059, DOI 10.1145/1871437.1871572 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhang S, 2003, PACLIC 17: Language, Information and Computation, Proceedings, P251 Zhong N., 2012, IEEE T KNOWL DATA EN, V24, P1 NR 42 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 EI 1558-2868 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 2013 VL 31 IS 3 AR 12 DI 10.1145/2493175.2493177 PG 32 WC Computer Science, Information Systems SC Computer Science GA 197DP UT WOS:000322829900002 ER PT J AU Macdonald, C Santos, RLT Ounis, I He, B AF Macdonald, Craig Santos, Rodrygo L. T. Ounis, Iadh He, Ben TI About Learning Models with Multiple Query-Dependent Features SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Performance; Experimentation; Learning to rank; samples; field-based weighting models ID STRATEGIES AB Several questions remain unanswered by the existing literature concerning the deployment of query-dependent features within learning to rank. In this work, we investigate three research questions in order to empirically ascertain best practices for learning-to-rank deployments. (i) Previous work in data fusion that pre-dates learning to rank showed that while different retrieval systems could be effectively combined, the combination of multiple models within the same system was not as effective. In contrast, the existing learning-to-rank datasets (e. g., LETOR), often deploy multiple weighting models as query-dependent features within a single system, raising the question as to whether such a combination is needed. (ii) Next, we investigate whether the training of weighting model parameters, traditionally required for effective retrieval, is necessary within a learning-to-rank context. (iii) Finally, we note that existing learning-to-rank datasets use weighting model features calculated on different fields (e. g., title, content, or anchor text), even though such weighting models have been criticized in the literature. Experiments addressing these three questions are conducted on Web search datasets, using various weighting models as query-dependent and typical query-independent features, which are combined using three learning-to-rank techniques. In particular, we show and explain why multiple weighting models should be deployed as features. Moreover, we unexpectedly find that training the weighting model's parameters degrades learned model's effectiveness. Finally, we show that computing a weighting model separately for each field is less effective than more theoretically-sound field-based weighting models. C1 [Macdonald, Craig; Santos, Rodrygo L. T.; Ounis, Iadh] Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland. RP Macdonald, C (reprint author), Univ Glasgow, Sch Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. CR AMATI G., 2008, P 16 TEXT RETRIEVAL Amati Giambattista, 2003, THESIS U GLASGOW Becchetti L., 2006, P 2 INT WORKSH ADV I Beitzel SM, 2004, J AM SOC INF SCI TEC, V55, P859, DOI 10.1002/asi.20012 Bendersky M., 2011, P ACM INT C WEB SEAR, P95, DOI 10.1145/1935826.1935849 Broder Andrei Z., 2003, P 12 INT C INF KNOWL, P426, DOI DOI 10.1145/956863.956944 Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363 CALLAN J., 2009, P 18 TEXT RETRIEVAL Cambazoglu B. B., 2010, P 3 ACM INT C WEB SE, P411, DOI 10.1145/1718487.1718538 Cao Zhe, 2007, P 24 INT C MACH LEAR, P129, DOI 10.1145/1273496.1273513 CHAPELLE O, 2011, JMLR WORKSH C P, V14, P1 Chowdhury A., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CLARKE C. L. A., 2012, P 20 TEXT RETRIEVAL CLARKE C. L. A., 2011, P 19 TEXT RETRIEVAL CLARKE C. L. A., 2010, P 18 TEXT RETRIEVAL Cormack GV, 2011, INFORM RETRIEVAL, V14, P441, DOI 10.1007/s10791-011-9162-z Craswell N., 2010, P 18 TEXT RETRIEVAL Fang H., 2004, P 27 ANN INT ACM SIG, P49, DOI 10.1145/1008992.1009004 Fox E. A., 1993, P 1 TEXT RETR C TREC, P319 Fox Edward A., 1994, P 2 TEXT RETRIEVAL C, V500-215, P243 Ganjisaffar Y., 2011, P 34 ANN INT ACM SIG, P85, DOI DOI 10.1145/2009916.2009932 HE B., 2007, ACM T INFORM SYST, V25, P3 HE B., 2005, P 28 ANN INT ACM SIG, P465, DOI 10.1145/1076034.1076114 He B., 2008, P 31 ANN INT ACM SIG, P67, DOI [10.1145/1390334.1390348, DOI 10.1145/1390334.1390348] HE B, 2007, THESIS U GLASGOW He B, 2007, INFORM PROCESS MANAG, V43, P1294, DOI 10.1016/j.ipm.2006.11.002 He Ben, 2003, P 12 INT C INF KNOWL, P10 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Jones K. S., 2000, Information Processing & Management, V36 KAMPS J., 2006, P 14 TEXT RETRIEVAL Katzer J., 1982, Information Technology: Research and Development, V1 KIRKPATRICK S, 1983, SCIENCE, V220, P671, DOI 10.1126/science.220.4598.671 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041 LI H., 2011, SYNTHESIS LECT HUMAN Liu T., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] MACDONALD C., 2012, P SIGIR WORKSH OP SO MACDONALD C., 2006, P CLEF WORKSH 2005 Macdonald C, 2013, INFORM RETRIEVAL, V16, P584, DOI 10.1007/s10791-012-9209-9 Metzler D, 2007, P 30 ANN INT ACM SIG, P687, DOI 10.1145/1277741.1277859 Metzler D., 2007, P CIKM 2007, P253, DOI 10.1145/1321440.1321478 MOFFAT A., 1996, ACM T INFORM SYST, V14, P4 Ogilvie P., 2003, P 26 ANN INT ACM SIG, P143, DOI DOI 10.1145/860435.860463 Page L., 1998, PAGERANK CITATION RA PENG J, 2007, P 30 ANN INT ACM SIG Plachouras V., 2006, THESIS U GLASGOW Plachouras V, 2005, J WEB ENG, V4, P165 Qin T., 2009, INFORM RETRIEVAL, V13, P347 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 ROBERTSON S. E., 1995, P 4 TEXT RETRIEVAL C Robertson S.E., 1992, P TEXT RETRIEVAL C T, P21 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 SMUCKER M. D., 2005, IR391 U MASS Strohman T., 2009, SEARCH ENGINES INFOR Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H Tyree S., 2011, P 20 INT C WORLD WID, P387 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VOGT C., 1997, P 20 ANN INT ACM SIG Vogt C. C., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290991 WEINBERGER K., 2010, J MACHINE LEARN RES, V14, P1 Wu Q., 2008, MSRTR2008109 Xu J., 2007, P 30 ANN INT ACM SIG, P391, DOI 10.1145/1277741.1277809 ZARAGOZA H., 2004, P 13 TEXT RETRIEVAL Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 NR 66 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 2013 VL 31 IS 3 AR 11 DI 10.1145/2493175.2493176 PG 39 WC Computer Science, Information Systems SC Computer Science GA 197DP UT WOS:000322829900001 ER PT J AU Karimzadehgan, M Zhai, CX AF Karimzadehgan, Maryam Zhai, ChengXiang TI A learning approach to optimizing exploration-exploitation tradeoff in relevance feedback SO INFORMATION RETRIEVAL LA English DT Article DE Interactive retrieval models; Feedback; Diversification; User modeling ID INFORMATION-RETRIEVAL; DIVERSITY; PERFORMANCE AB Relevance feedback is an effective technique for improving search accuracy in interactive information retrieval. In this paper, we study an interesting optimization problem in interactive feedback that aims at optimizing the tradeoff between presenting search results with the highest immediate utility to a user (but not necessarily most useful for collecting feedback information) and presenting search results with the best potential for collecting useful feedback information (but not necessarily the most useful documents from a user's perspective). Optimizing such an exploration-exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback. We formally frame this tradeoff as a problem of optimizing the diversification of search results since relevance judgments on more diversified results have been shown to be more useful for relevance feedback. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experiment results on three representative retrieval test collections show that the proposed learning approach can effectively optimize the exploration-exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation without exploration. C1 [Karimzadehgan, Maryam; Zhai, ChengXiang] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Karimzadehgan, M (reprint author), Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. EM mkarimz2@illinois.edu; czhai@cs.illinois.edu FU National Science Foundation [IIS-0713581, CNS-0834709, CNS 1028381]; NIH/NLM [1 R01 LM009153-01]; Sloan Research Fellowship; Google PhD fellowship FX We thank the anonymous reviewers for their useful comments. This material is based upon work supported by the National Science Foundation under Grant Numbers IIS-0713581, CNS-0834709, and CNS 1028381, by NIH/NLM grant 1 R01 LM009153-01, and by a Sloan Research Fellowship. Maryam Karimzadehgan was supported by the Google PhD fellowship. Any opinions, findings, conclusions, or recommendations expressed in this material are the authors' and do not necessarily reflect those of the sponsors. CR Abbeel P., 2006, NEURAL INFORM PROCES, P1 Agarwal D., 2009, ICDM Agrawal R, 2009, ACM WSDM, P5 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley C., 2004, ACM SIGIR, P584 Burges C, 2005, ICML 05, P89 Cao Z, 2007, LECT NOTES COMPUT SC, V4464, P129 Carbonell J., 1998, ACM SIGIR, P335 Carmel D., 2006, ACM SIGIR, P390 Carterette B., 2006, ACM SIGIR, P629 Carterette B., 2009, ACM CIKM, P1287 Chen H., 2006, ACM SIGIR, P429 Clarke C. L., 2008, ACM SIGIR, P659 Clough P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P734, DOI 10.1145/1571941.1572102 Cohn DA, 1996, J ARTIF INTELL RES, V4, P129 Collins-Thompson K, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P672, DOI 10.1145/1571941.1572071 Cooper W. S., 1992, ACM SIGIR, P198 Cronen-Townsend S., 2002, ACM SIGIR, P299 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Faber V., 1994, LOS ALAMOS SCI, V22, P138 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Fuhr N, 2008, INFORM RETRIEVAL, V11, P251, DOI 10.1007/s10791-008-9045-0 Gey F. C., 1994, ACM SIGIR C RES DEV, P222 GOFFMAN W, 1964, INFORM STORAGE RET, V2, P73, DOI 10.1016/0020-0271(64)90006-3 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Harman D., 2004, SIGIR FORUM, V38, P45 Hastie T, 2001, ELEMENTS STAT LEARNI Hauff C., 2008, ACM CIKM, P439 He B, 2004, LECT NOTES COMPUT SC, V3246, P43 He JY, 2008, LECT NOTES COMPUT SC, V4956, P689 Herbrich R., 2000, ADV LARGE MARGIN CLA, V88, P115 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Jaakkola T., 2001, NIPS Jensen E. C., 2005, ACM SIGIR, P615 Joachims T., 2002, ACM SIGKDD C KNOWL D, P133 Joachims T., 1997, IJCAI Karimzadehgan M., 2010, ACM CIKM, P1397 Karimzadehgan M., 2011, ACM CIKM, P27 Karimzadehgan M., 2008, ACM CIKM, P1113 Kumar PR, 1986, STOCHASTIC SYSTEMS E Laffetry J., 2001, ACM SIGIR, P111 Lavrenko V., 2001, ACM SIGIR, P120 Leuski A., 2001, THESIS U MASSACHUSET Lewis D., 1994, ACM SIGIR C RES DEV, P3 Lv Y., 2009, ACM CIKM, P255 McCallum A., 1998, ICML, P350 Moffat A., 2008, ACM T INFORM SYST, V27, P1 Pandey S., 2005, VLDB C TRONDH NORW, P781 Pandey S., 2007, ICML, P721 Paramita M. L., 2009, CLEF WORKSH Ponte J. M., 1998, ACM SIGIR 98, P275 Radlinski F., 2005, ACM SIGKDD, P239 Radlinski F, 2008, ICML 2008, P784 Radlinski F., 2006, ACM SIGIR C RES DEV, P691 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Roy N., 2001, ICML, P441 Sanderson M, 2009, LECT NOTES COMPUT SC, V5478, P562, DOI 10.1007/978-3-642-00958-7_51 Scholer F, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P640, DOI 10.1145/1571941.1572055 Shen X., 2005, ANN ACM SIGIR C RES, P59 Singh S, 2002, J ARTIF INTELL RES, V16, P105 Song R., 2007, WWW 2007, P1169 Tang JY, 2010, LECT NOTES COMPUT SC, V5993, P179 Tao T., 2006, ACM SIGIR, P162 Thrun S. B., 1992, HDB INTELLIGENT CONT Tong S., 2000, ICML, P999 Vee E, 2008, PROC INT CONF DATA, P228, DOI 10.1109/ICDE.2008.4497431 Voorhees E. M., 2005, NOTEBOOK TREC 2005 Voorhees EM, 2004, TREC Wang X., 2008, ACM SIGIR, P219 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Xu Z., 2008, ACM SIGIR, P227 Xu ZB, 2007, LECT NOTES COMPUT SC, V4425, P246 Yom-Tov E., 2005, ACM SIGIR, P512 Yue Y., 2009, ICML 2009, P1201 Yue Y., 2008, ICML, P1224 Zhai C., 2001, ACM CIKM, P403 Zhai C., 2001, ACM INT SIGIR C SIGI, P334 Zhai C. X., 2003, ACM SIGIR, P10 Zhang W, 1995, IJCAI 95, P1114 Zhang Y., 2003, ICML 2003, P896 Zheng Z., 2007, ACM SIGIR, P287 Zhu X., 2007, NAACL HLT 2007, P97 Ziegler C.-N., 2005, WWW 05, P22 NR 85 TC 0 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2013 VL 16 IS 3 BP 307 EP 330 DI 10.1007/s10791-012-9198-8 PG 24 WC Computer Science, Information Systems SC Computer Science GA 148UP UT WOS:000319273400001 ER PT J AU Iqbal, F Binsalleeh, H Fung, BCM Debbabi, M AF Iqbal, Farkhund Binsalleeh, Hamad Fung, Benjamin C. M. Debbabi, Mourad TI A unified data mining solution for authorship analysis in anonymous textual communications SO INFORMATION SCIENCES LA English DT Article DE Authorship identification; Authorship characterization; Stylometric features; Writeprint; Frequent patterns; Cyber forensics ID DETECTING FAKE WEBSITES; ATTRIBUTION; IDENTIFICATION AB The cyber world provides an anonymous environment for criminals to conduct malicious activities such as spamming, sending ransom e-mails, and spreading botnet malware. Often, these activities involve textual communication between a criminal and a victim, or between criminals themselves. The forensic analysis of online textual documents for addressing the anonymity problem called authorship analysis is the focus of most cybercrime investigations. Authorship analysis is the statistical study of linguistic and computational characteristics of the written documents of individuals. This paper is the first work that presents a unified data mining solution to address authorship analysis problems based on the concept of frequent pattern-based writeprint. Extensive experiments on real-life data suggest that our proposed solution can precisely capture the writing styles of individuals. Furthermore, the writeprint is effective to identify the author of an anonymous text from a group of suspects and to infer sociolinguistic characteristics of the author. (C) 2011 Elsevier Inc. All rights reserved. C1 [Iqbal, Farkhund; Binsalleeh, Hamad; Fung, Benjamin C. M.; Debbabi, Mourad] Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ H3G 1M8, Canada. RP Fung, BCM (reprint author), Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ H3G 1M8, Canada. EM iqbal_f@ciise.concordia.ca; h_binsal@ciise.concordia.ca; fung@ciise.concordia.ca; debbabi@cii-se.concordia.ca RI Fung, Benjamin C. M./; Iqbal, Farkhund/ OI Fung, Benjamin C. M./0000-0001-8423-2906; Iqbal, Farkhund/0000-0001-9081-3598 FU National Cyber-Forensics and Training Alliance Canada (NCFTA Canada); Le Fonds quebecois de la recherche sur la nature et les technologies (FQRNT) FX The research is supported in part by the National Cyber-Forensics and Training Alliance Canada (NCFTA Canada) and Le Fonds quebecois de la recherche sur la nature et les technologies (FQRNT). CR Abbasi A, 2008, J MANAGE INFORM SYST, V25, P49, DOI 10.2753/MIS0742-1222250103 Abbasi A, 2009, COMPUTER, V42, P78, DOI 10.1109/MC.2009.306 Abbasi A, 2010, MIS QUART, V34, P435 Abbasi A., 2008, ACM T INFORM SYSTEMS, V26, P1 Agrawal R., 1993, ACM SIGMOD RECORD, V22, P207, DOI DOI 10.1145/170035.170072 Argamon S., 2003, P 9 ACM SIGKDD INT C, P475 Argamon S, 2009, COMMUN ACM, V52, P119, DOI 10.1145/1461928.1461959 Baayen R.H., 1996, LIT LINGUISTIC COMPU, V2, P110 Buhmann M. D., 2003, RADIAL BASIS FUNCTIO Burrows J. F., 1987, Literary & Linguistic Computing, V2, DOI 10.1093/llc/2.2.61 Carvalho V.R., 2004, P C EM ANT Corney M., 2002, Proceedings 18th Annual Computer Security Applications Conference, DOI 10.1109/CSAC.2002.1176299 de Vel O, 2001, P ACM C COMP SEC WOR de Vel O, 2001, SIGMOD RECORD, V30, P55 Diederich J, 2003, APPL INTELL, V19, P109, DOI 10.1023/A:1023824908771 Fouss F, 2010, INFORM SCIENCES, V180, P2095, DOI 10.1016/j.ins.2010.01.020 Frank E., 2004, P 21 INT C MACH LEAR, P305 Han J., 2000, SIGKDD EXPLORATIONS, V2, P14 Hegland M., 2005, WSPC LECT NOTES SERI, V9 Hu QH, 2008, INFORM SCIENCES, V178, P3577, DOI 10.1016/j.ins.2008.05.024 Hu QH, 2010, INFORM SCIENCES, V180, P4384, DOI 10.1016/j.ins.2010.07.010 Iqbal F, 2010, DIGIT INVEST, P1 Iqbal F., 2008, DIGIT INVEST, V5, P42 Joachims T., 1998, LNCS, V1398, P137 Koppel M, 2009, J AM SOC INF SCI TEC, V60, P9, DOI 10.1002/asi.20961 Koppel M., 2002, Literary & Linguistic Computing, V17, DOI 10.1093/llc/17.4.401 Kucukyilmaz T, 2008, INFORM PROCESS MANAG, V44, P1448, DOI 10.1016/j.ipm.2007.12.009 lqbal F, 2010, P 25 ACM SIGAPP S AP, P1591, DOI 10.1145/1774088.1774428 Mendenhall TC, 1887, SCIENCE, V11, P237 Miller A., 2002, SUBSET SELECTION REG Mosteller F., 1964, APPL BAYESIAN CLASSI Pearl J, 1985, P 7 C COGN SCI SOC, P329 Pillay S. R., 2010, ECRIME RES SUMMIT EC, P1 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Quinlan J.R., 1993, MACH LEARN, P343 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sewell M., 2007, FEATURE SELECTION Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001 Tanbeer SK, 2009, INFORM SCIENCES, V179, P559, DOI 10.1016/j.ins.2008.10.027 Teng G, 2004, P 3 INT C MACH LEARN Wimmer K., 2002, 1 AMENDMENT MEDIA Witten I. H., 2005, DATA MINING PRACTICA Yule G., 1938, BIOMETRIKA, V30, P363 Zaki MJ, 2000, IEEE T KNOWL DATA EN, V12, P372, DOI 10.1109/69.846291 Zhao Y, 2005, LECT NOTES COMPUT SC, V3689, P174 Zheng R., 2006, J AM SOC INFORM SCI, V57, P1532 NR 47 TC 8 Z9 9 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD MAY 10 PY 2013 VL 231 BP 98 EP 112 DI 10.1016/j.ins.2011.03.006 PG 15 WC Computer Science, Information Systems SC Computer Science GA 115UE UT WOS:000316836600008 ER PT J AU Memar, S Affendey, LS Mustapha, N Doraisamy, SC Ektefa, M AF Memar, Sara Affendey, Lilly Suriani Mustapha, Norwati Doraisamy, Shyamala C. Ektefa, Mohammadreza TI An integrated semantic-based approach in concept based video retrieval SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Video retrieval; Semantic knowledge; Content-based analysis; Similarity; Search ID IMAGE; SYSTEM; MODEL; WEB AB Multimedia content has been growing quickly and video retrieval is regarded as one of the most famous issues in multimedia research. In order to retrieve a desirable video, users express their needs in terms of queries. Queries can be on object, motion, texture, color, audio, etc. Low-level representations of video are different from the higher level concepts which a user associates with video. Therefore, query based on semantics is more realistic and tangible for end user. Comprehending the semantics of query has opened a new insight in video retrieval and bridging the semantic gap. However, the problem is that the video needs to be manually annotated in order to support queries expressed in terms of semantic concepts. Annotating semantic concepts which appear in video shots is a challenging and time-consuming task. Moreover, it is not possible to provide annotation for every concept in the real world. In this study, an integrated semantic-based approach for similarity computation is proposed with respect to enhance the retrieval effectiveness in concept-based video retrieval. The proposed method is based on the integration of knowledge-based and corpus-based semantic word similarity measures in order to retrieve video shots for concepts whose annotations are not available for the system. The TRECVID 2005 dataset is used for evaluation purpose, and the results of applying proposed method are then compared against the individual knowledge-based and corpus-based semantic word similarity measures which were utilized in previous studies in the same domain. The superiority of integrated similarity method is shown and evaluated in terms of Mean Average Precision (MAP). C1 [Memar, Sara; Affendey, Lilly Suriani; Mustapha, Norwati; Doraisamy, Shyamala C.; Ektefa, Mohammadreza] Univ Putra Malaysia, Dept Comp Sci, Serdang 43400, Selangor, Malaysia. RP Memar, S (reprint author), Univ Putra Malaysia, Dept Comp Sci, Serdang 43400, Selangor, Malaysia. EM sr.memar@gmail.com CR [Anonymous], 2005, GUIDELINES TRECVID 2 Aytar Y, 2008, IEEE C COMP VIS PATT Bach JR, 1996, P SOC PHOTO-OPT INS, V2670, P76, DOI 10.1117/12.234785 BLAIR DC, 1979, J AM SOC INFORM SCI, V30, P374, DOI 10.1002/asi.4630300621 Campbell M, 2006, TRECVID WORKSH Chang SF, 2006, NIST TRECVID WORKSH Coelho TAS, 2004, IEEE T KNOWL DATA EN, V16, P408, DOI 10.1109/TKDE.2004.1269666 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 Frankel C., 1996, WEBSEER IMAGE SEARCH Frawley William, 1992, LINGUISTIC SEMANTICS Hatzivassiloglou V, 1999, DETECTING TEXT SIMIL Haubold A, 2008, P INT C IM VID RETR, P437, DOI 10.1145/1386352.1386408 Hauptmann A, 2007, IEEE T MULTIMEDIA, V9, P958, DOI 10.1109/TMM.2007.900150 Hauptmann AG, 2008, P IEEE, V96, P602, DOI 10.1109/JPROC.2008.916355 Hopfgartner F, 2008, PVLDB, V1, P1604, DOI 10.1145/1454159.1454233 Islam A., 2008, ACM T KNOWL DISCOV D, V2, P1, DOI DOI 10.1145/1376815.1376819 Jiang Y. G., 2007, P 6 ACM INT C IM VID, P494, DOI DOI 10.1145/1282280.1282352 Jiang Y.-G., 2009, P 17 ACM INT C MULT, P155, DOI DOI 10.1145/1631272.1631296 Jones K. S., 1997, READINGS INFORM RETR Ko Y, 2004, INFORM PROCESS MANAG, V40, P65, DOI 10.1016/S0306-4573(02)00056-0 Lin C.-Y., 2003, P 2003 C N AM CHAPT, V1, P71, DOI 10.3115/1073445.1073465 Liu T, 2005, LECT NOTES COMPUT SC, V3644, P456 Markkula M., 2000, Information Retrieval, V1, DOI 10.1023/A:1009995816485 Mihalcea R., 2006, AAAI 06, V1, P775 Nowak S, 2010, P CIVR, P303, DOI 10.1145/1816041.1816086 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Park EK, 2005, INFORM PROCESS MANAG, V41, P1207, DOI 10.1016/j.ipm.2004.08.002 Pedersen T, 2004, WORDNET SIMILARITY M, V04, P1024 Radev DR, 2004, J ARTIF INTELL RES, V22, P2004 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rodden K, 2001, P SIGCHI C HUM FACT, P190, DOI DOI 10.1145/365024.365097 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smeaton AF, 2007, INFORM SYST, V32, P545, DOI 10.1016/j.is.2006.09.001 Smith JR, 1997, IEEE MULTIMEDIA, V4, P12, DOI 10.1109/93.621578 Snoek C.G.M., 2007, IEEE T MULTIMEDIA, V9 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Wang D., 2007, P ACM INT C MULT AUG, P285, DOI DOI 10.1145/1291233.1291293 Yanagawa A., 2007, COLUMBIA U BASELINE NR 41 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD MAY PY 2013 VL 64 IS 1 BP 77 EP 95 DI 10.1007/s11042-011-0848-4 PG 19 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 114SU UT WOS:000316763600005 ER PT J AU Iruetaguena, A Adeva, JJG Pikatza, JM Segundo, U Buenestado, D Barrena, R AF Iruetaguena, A. Garcia Adeva, J. J. Pikatza, J. M. Segundo, U. Buenestado, D. Barrena, R. TI Automatic retrieval of current evidence to support update of bibliography in clinical guidelines SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Clinical guidelines; Evidence-based medicine; Bibliometrics Information retrieval; Literature-based discovery ID QUALITY; LITERATURES; CONNECTIONS; KNOWLEDGE; SEARCH AB This paper reports on a system developed to support medical experts in the process of updating clinical guidelines by automatically suggesting new articles suitable to the domain under consideration. It follows a comprehensive process based on several consecutive steps in order to (i) identify which articles from the current guideline are eligible to be updated; (ii) retrieve and filter new related articles from MEDUNE; and (iii) select the most relevant resulting articles by applying a scoring algorithm. Extensive validation is based on a set of experiments on 40 guidelines from multiple medical domains. The analysis of results shows a promising prospect as indicated by recall values greater than 90%. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Iruetaguena, A.; Garcia Adeva, J. J.; Pikatza, J. M.; Segundo, U.; Buenestado, D.; Barrena, R.] Univ Basque Country UPV EHU, Fac Informat, Dept Comp Languages & Syst, Donostia San Sebastian 20018, Spain. RP Iruetaguena, A (reprint author), Univ Basque Country UPV EHU, Fac Informat, Dept Comp Languages & Syst, Paseo Manuel de Lardizabal 1, Donostia San Sebastian 20018, Spain. EM ander.iruetaguena@ehu.es RI Iruetaguena, Ander/; Buenestado, David/ OI Iruetaguena, Ander/0000-0002-3440-2505; Buenestado, David/0000-0002-7839-5255 FU Department of Education, Universities and Research of the Basque Government [BFI-09-270]; UPV/EHU [GIU08/27, INF10/58, GIU11/28, UFI11/19]; Gipuzkoa Regional Council [OF53/2011]; Department of Industry, Commerce and Tourism - Basque Government [S-PE09UN60, S-PE11UN115]; Spanish Ministry of Science and Innovation [TIN2009-14 159-C05-03] FX The authors thank J. Lopez-Cuadrado for his comments on the manuscript and doctors J. J. Elorz Lambarri, E. Gonzalez Perez-Yarza, and J. J. Korta Murua for the fruitful discussions. This work was supported by funding received from the Department of Education, Universities and Research of the Basque Government (Grant No. BFI-09-270), the UPV/EHU [GIU08/27, INF10/58, GIU11/28 and UFI11/19], Gipuzkoa Regional Council [OF53/2011], the Department of Industry, Commerce and Tourism - Basque Government [S-PE09UN60 and S-PE11UN115], and the Spanish Ministry of Science and Innovation [TIN2009-14 159-C05-03]. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Field MJ, 1990, CLIN PRACTICE GUIDEL Graham R., 2011, CLIN PRACTICE GUIDEL, P146 Grol Dalhuijsen J., BMJ, V317, P858 Guyatt GH, 2008, BRIT MED J, V336, P995, DOI 10.1136/bmj.39490.551019.BE Ramampiaro H, 2010, LECT NOTES COMPUT SC, V6266, P143, DOI 10.1007/978-3-642-15020-3_14 Robertson S. E., 1999, P 8 TEXT RETR C TREC, P151 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rosenfeld RM, 2009, OTOLARYNG HEAD NECK, V140, pS1, DOI 10.1016/j.otohns.2009.04.015 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Scottish Intercollegiate Guidelines Network, 2008, SIGN 50 GUID DEV HDB SHAPIRO DW, 1993, ANNU REV PUBL HEALTH, V14, P219 Shekelle PG, 1999, BRIT MED J, V318, P593 SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7 SWANSON DR, 1990, PERSPECT BIOL MED, V33, P157 Swanson DR, 2008, MED HYPOTHESES, V71, P178, DOI 10.1016/j.mehy.2008.02.017 SWANSON DR, 1988, PERSPECT BIOL MED, V31, P526 Swanson DR, 1991, P 14 ANN INT ACM SIG, P280, DOI 10.1145/122860.122889 Timmermans S, 2005, HEALTH AFFAIR, V24, P18, DOI 10.1377/hlthaff.24.1.18 van der Weijden T, 2012, J CLIN EPIDEMIOL, V65, P584, DOI 10.1016/j.jclinepi.2011.10.007 Weeber M, 2003, J AM MED INFORM ASSN, V10, P252, DOI 10.1197/jamia.M1158 Woolf SH, 1999, BRIT MED J, V318, P527 Wren JD, 2004, BIOINFORMATICS, V20, P389, DOI 10.1093/bioinformatics/btg421 NR 23 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD MAY PY 2013 VL 40 IS 6 BP 2081 EP 2091 DI 10.1016/j.eswa.2012.10.015 PG 11 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 099FP UT WOS:000315607200017 ER PT J AU Hu, KM Wang, B Yong, JH Paul, JC AF Hu, Kai-Mo Wang, Bin Yong, Jun-Hai Paul, Jean-Claude TI Relaxed lightweight assembly retrieval using vector space model SO COMPUTER-AIDED DESIGN LA English DT Article DE Lightweight; Typical parts; Assembly retrieval; Vector space model; Relaxed retrieval ID TOPOLOGICAL SIMILARITY; DATABASE SYSTEM; REUSE; DESIGN; SEARCH AB Assembly searching technologies are important for the improvement of design reusability. However, existing methods require that assemblies possess high-level information, and thus cannot be applied in lightweight assemblies. In this paper, we propose a novel relaxed lightweight assembly retrieval approach based on a vector space model (VSM). By decomposing the assemblies represented in a watertight polygon mesh into bags of parts, and considering the queries as a vague specification of a set of parts, the resilient ranking strategy in VSM is successfully applied in the assembly retrieval. Furthermore, we take the scale-sensitive similarities between parts into the evaluation of matching values, and extend the original VSM to a relaxed matching framework. This framework allows users to input any fuzzy queries, is capable of measuring the results quantitatively, and performs well in retrieving assemblies with specified characteristics. To accelerate the online matching procedure, a typical parts based matching process, as well as a greedy strategy based matching algorithm is presented and integrated in the framework, which makes our system achieve interactive performance. We demonstrate the efficiency and effectiveness of our approach through various experiments on the prototype system. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Hu, Kai-Mo; Wang, Bin; Yong, Jun-Hai; Paul, Jean-Claude] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China. [Hu, Kai-Mo] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. [Hu, Kai-Mo; Wang, Bin; Yong, Jun-Hai] Minist Educ, Key Lab Informat Syst Secur, Beijing 100084, Peoples R China. [Hu, Kai-Mo; Wang, Bin; Yong, Jun-Hai] Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China. RP Hu, KM (reprint author), Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China. EM hukaimo02@gmail.com FU National Basic Research Program of China [2010CB328001]; National Science Foundation of China [61003096]; National High-tech RD Program [2012AA040902] FX The research was supported by National Basic Research Program of China (2010CB328001), National Science Foundation of China (61003096), and National High-tech R&D Program (2012AA040902). CR Ankerst M, 1999, P ISMB Bai J, 2010, COMPUT AIDED DESIGN, V42, P1069, DOI 10.1016/j.cad.2010.07.002 Bespalov D, 2006, COMPUT AIDED DESIGN, V38, P1020, DOI 10.1016/j.cad.2006.07.005 Bronstein AM, 2011, ACM T GRAPHIC, V30, DOI 10.1145/1899404.1899405 Chao Yong-sheng, 2011, Computer Integrated Manufacturing Systems, V17 Chen DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669 Chen GL, 2006, COMPUT AIDED DESIGN, V38, P531, DOI 10.1016/j.cad.2006.01.012 Chen X, 2012, COMPUT AIDED DESIGN, V44, P554, DOI 10.1016/j.cad.2012.02.001 Cormen TH, 2009, INTRO ALGORITHMS, P561 Deshmukh AS, 2008, COMPUT AIDED DESIGN, V40, P244, DOI 10.1016/j.cad.2007.10.012 El-Mehalawi M, 2003, COMPUT AIDED DESIGN, V35, P95, DOI 10.1016/S0010-4485(01)00178-6 El-Mehalawi M, 2003, COMPUT AIDED DESIGN, V35, P83, DOI 10.1016/S0010-4485(01)00177-4 Funkhouser T, 2006, P EUR S GEOM PROC Gal R, 2007, IEEE T VIS COMPUT GR, V13, P261, DOI 10.1109/TVCG.2007.45 Gal R, 2006, ACM T GRAPHIC, V25, P130, DOI 10.1145/1122501.1122507 Hong T, 2006, COMPUT AIDED DESIGN, V38, P973, DOI 10.1016/j.cad.2006.05.004 HORN BKP, 1984, P IEEE, V72, P1671, DOI 10.1109/PROC.1984.13073 Iyer N, 2005, COMPUT AIDED DESIGN, V37, P509, DOI 10.1016/j.cad.2004.07.002 Jayanti S, 2009, COMPUT AIDED DESIGN, V41, P999, DOI 10.1016/j.cad.2009.07.003 Kazhdan M, 2003, P S GEOM PROC Kim GJ, 1997, COMPUT AIDED DESIGN, V29, P497, DOI 10.1016/S0010-4485(96)00100-5 Liu R, 2007, P CIVR 2007, P379 Liu R, 2005, IEEE INT C VIS INF E, P45 Ma LJ, 2009, COMPUT AIDED DESIGN, V41, P952, DOI 10.1016/j.cad.2009.05.007 Ma LJ, 2010, COMPUT GRAPH-UK, V34, P545, DOI 10.1016/j.cag.2010.06.002 Osada R, 2002, ACM T GRAPHIC, V21, P807, DOI 10.1145/571647.571648 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Ricardo B-Y, 1999, MODERN INFORM RETRIE, P27 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rustamov R.M., 2007, P EUR S GEOM PROC SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Shilane P., 2004, P SHAPE MODELING APP, V2, P167 Wartick S, 1992, INFORMATION RETRIEVA, P264 Zhang H, 2008, COMPUT GRAPH FORUM, V27, P1431, DOI 10.1111/j.1467-8659.2008.01283.x NR 34 TC 3 Z9 3 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0010-4485 J9 COMPUT AIDED DESIGN JI Comput.-Aided Des. PD MAR PY 2013 VL 45 IS 3 BP 739 EP 750 DI 10.1016/j.cad.2012.10.005 PG 12 WC Computer Science, Software Engineering SC Computer Science GA 102GY UT WOS:000315834300016 ER PT J AU Na, SH AF Na, Seung-Hoon TI Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Probabilistic co-relevance; Query-sensitive similarity; Inter-document similarity; Cluster hypothesis; Cluster-based retrieval ID AUTOMATIC DOCUMENT CLASSIFICATION; MODEL AB Interdocument similarities are the fundamental information source required in cluster-based retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees' nearest neighbor test (NNT). (C) 2012 Elsevier Ltd. All rights reserved. C1 Elect & Telecommun Res Inst, Taejon, South Korea. RP Na, SH (reprint author), Elect & Telecommun Res Inst, Taejon, South Korea. EM nash@etri.re.kr CR Abdul-Jaleel Nasreen, 2004, P 13 TEXT RETR C TRE Allan James, 2000, P 9 TEXT RETR C TREC Blanco R, 2008, LECT NOTES COMPUT SC, V4956, P394 Buckley C., 1994, P 3 TEXT RETR C TREC Cartright Marc-Allen, 2010, P 19 ACM INT C INF K, P1573, DOI 10.1145/1871437.1871675 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 El-Hamdouchi A, 1986, P ACM C RES DEV INF, P149, DOI 10.1145/253168.253200 Fuhr N., 2011, INFORM RETRIEVAL, V15, P93 GOFFMAN W, 1968, INFORM STORAGE RET, V4, P361, DOI 10.1016/0020-0271(68)90030-2 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 Hiemstra D., 2001, THESIS U TWENTE Kurland O., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009027 Kurland Oren, 2005, P SIGIR, P306, DOI 10.1145/1076034.1076087 Lafferty J., 2003, KLUWER INT SERIES IN, V13, P258 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V., 2002, P 2 INT C HUM LANG T, P115, DOI 10.3115/1289189.1289268 Lavrenko V., 2010, GENERATIVE THEORY RE Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Losada D. E, 2008, INFORM RETRIEVAL, P11 Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Na SH, 2007, INFORM PROCESS MANAG, V43, P302, DOI 10.1016/j.ipm.2006.07.003 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rijsbergen C. J. V., 1979, INFORM RETRIEVAL, V2nd Rijsbergen C. J. V., 1975, INFORM PROCESSING MA, P171 Rijsbergen C. J. V, 1971, INFORM STORAGE RETRI, V7, P210 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roelleke T., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148192 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Smucker MD, 2009, LECT NOTES COMPUT SC, V5766, P281 Smucker M.D, 2005, INVESTIGATION DIRICH Tao T., 2006, HLT NAACL 2006, P407 Tombros A, 2004, KNOWL INF SYST, V6, P617, DOI 10.1007/s10115-003-0115-8 Tombros A, 2002, INFORM PROCESS MANAG, V38, P559, DOI 10.1016/S0306-4573(01)00048-6 Tombros A., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 Wei F., 2008, P 31 ANN INT ACM SIG, P283, DOI 10.1145/1390334.1390384 WILLETT P, 1985, INT FORUM INFORM DOC, V10, P28 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhou ZH, 2006, IEEE DATA MINING, P1211 NR 47 TC 1 Z9 1 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2013 VL 49 IS 2 BP 558 EP 575 DI 10.1016/j.ipm.2012.10.002 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 083FP UT WOS:000314448400011 ER PT J AU Vanetti, M Binaghi, E Ferrari, E Carminati, B Carullo, M AF Vanetti, Marco Binaghi, Elisabetta Ferrari, Elena Carminati, Barbara Carullo, Moreno TI A System to Filter Unwanted Messages from OSN User Walls SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Online social networks; information filtering; short text classification; policy-based personalization ID SEMANTIC WEB SERVICES; TEXT CATEGORIZATION; INFORMATION; NETWORKS; RETRIEVAL AB One fundamental issue in today's Online Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now, OSNs provide little support to this requirement. To fill the gap, in this paper, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning-based soft classifier automatically labeling messages in support of content-based filtering. C1 [Vanetti, Marco; Binaghi, Elisabetta; Ferrari, Elena; Carminati, Barbara; Carullo, Moreno] Univ Insubria, Dept Comp Sci & Commun DICOM, I-21100 Varese, Italy. RP Vanetti, M (reprint author), Univ Insubria, Dept Comp Sci & Commun DICOM, 5 Via Mazzini, I-21100 Varese, Italy. EM marco.vanetti@uninsubria.it; elisabetta.binaghi@uninsubria.it; elena.ferrari@uninsubria.it; barbara.carminati@uninsubria.it; moreno.carullo@uninsubria.it CR Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 BACLACE PE, 1992, COMMUN ACM, V35, P50, DOI 10.1145/138859.138865 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bizer C, 2009, J WEB SEMANT, V7, P1, DOI 10.1016/j.websem.2008.02.005 Bobicev V., 2008, P 23 NAT C ART INT A, P1444 Bonatti P., 2005, Proceedings. Sixth IEEE International Workshop on Policies for Distributed Systems and Networks Bonchi F., 2010, PRIVACY AWARE KNOWLE Carullo M, 2009, PATTERN RECOGN LETT, V30, P870, DOI 10.1016/j.patrec.2009.04.001 Carullo M., 2008, P 19 INT C PATT REC Chameau J. L., 1987, International Journal of Approximate Reasoning, V1, DOI 10.1016/S0888-613X(87)80003-8 Chau M, 2008, DECIS SUPPORT SYST, V44, P482, DOI 10.1016/j.dss.2007.06.002 Cleverdon C., 1984, Information Services & Use, V4 Denning PJ., 1982, ACM COMMUNICATIONS, V25, P163, DOI [10.1145/358453.358454, DOI 10.1145/358453.358454] Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Fang L., 2010, P 19 INT C WORLD WID, P351, DOI DOI 10.1145/1772690.1772727 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Frakes W. B., 1992, INFORM RETRIEVAL DAT Gediminas Adomavicius A. T., 2005, IEEE T KNOWL DATA EN, V17, P734 Golbeck J, 2006, LECT NOTES COMPUT SC, V4145, P101 Golbeck J.A, 2005, THESIS U MARYLAND CO Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Hartman E, 1990, NEURAL COMPUT, V2, P210, DOI 10.1162/neco.1990.2.2.210 Hayes P. J., 1990, P CAIA 90 6 IEEE C A, P320 JACOBS PS, 1990, COMMUN ACM, V33, P88, DOI 10.1145/92755.92769 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Joachims T., 1998, LNCS, V1398, P137 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Kagal L, 2004, IEEE INTELL SYST, V19, P50, DOI 10.1109/MIS.2004.23 LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310 Laudanna A., 1995, 3 GIORN INT AN STAT, VI, P103 Leekwijck V., 1999, FUZZY SETS SYSTEMS, V108, P159 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis DD, 2004, J MACH LEARN RES, V5, P361 Manning C. D., 2008, INTRO INFORM RETRIEV Moody J., 1989, Neural Computation, V1, DOI 10.1162/neco.1989.1.2.281 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 Nin J, 2009, P INT COMP SOFTW APP, P246, DOI 10.1109/COMPSAC.2009.40 PARK J, 1993, NEURAL COMPUT, V5, P305, DOI 10.1162/neco.1993.5.2.305 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 POLLOCK S, 1988, ACM T INFORM SYST, V6, P232, DOI 10.1145/45945.214327 Powell M. J. D., 1987, ALGORITHMS APPROXIMA, P143 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sriram B, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P841, DOI 10.1145/1835449.1835643 Strater K., 2007, P 3 S US PRIV SEC SO, P157, DOI 10.1145/1280680.1280706 Uszok A, 2004, IEEE INTELL SYST, V19, P32, DOI 10.1109/MIS.2004.31 Vanetti M., 2010, P ECML PKDD WORKSH P Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Zelikovitz S., 2000, P 17 INT C MACH LEAR, P1183 Zhang ., 2001, P 24 ANN INT ACM SIG, P294, DOI 10.1145/383952.384012 NR 54 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD FEB PY 2013 VL 25 IS 2 BP 285 EP 297 DI 10.1109/TKDE.2011.230 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 079RF UT WOS:000314188900006 ER PT S AU Ben Mustapha, N Aufaure, MA AF Ben Mustapha, Nesrine Aufaure, Marie-Aude BE Aufaure, MA Zimanyi, E TI Knowledge Harvesting for Business Intelligence SO BUSINESS INTELLIGENCE, EBISS 2012 SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 2nd European Business Intelligence Summer School (eBISS) CY JUL 15-21, 2012 CL Brussels, BELGIUM SP Ecole Centrale Paris, MAS Lab, Univ Libre Bruxelles, Dept Comp & Decis Engn DE ontology; semantics; ontology learning; knowledge engineering; business intelligence ID SEMANTIC WEB; INFORMATION-RETRIEVAL; DOMAIN ONTOLOGIES AB With the growth rate of information volume, information access and knowledge management in enterprises has become challenging. This paper aims at describing the importance of semantic technologies (ontologies) and knowledge extraction techniques for knowledge management, search and capture in e-business processes. We will present the state of the art of ontology learning approaches from textual data and web environment and their integration in enterprise systems to perform personalized and incremental knowledge harvesting. C1 [Ben Mustapha, Nesrine; Aufaure, Marie-Aude] Ecole Cent Paris, MAS Lab, Paris, France. RP Ben Mustapha, N (reprint author), Ecole Cent Paris, MAS Lab, Paris, France. EM Nesrine.Ben-Mustapha@ecp.fr; Marie-Aude.Aufaure@ecp.fr CR Agirre E., 2000, P ECAI WORKSH ONT LE Alfonseca E., 2002, P 1 INT C GEN WORDNE Allani H, 2006, INT WORLD WID WEB C, P491 Aussenac-Gilles N, 2006, LECT NOTES ARTIF INT, V4248, P158 Bachimont B, 2000, INGENIERIE CONNAISSA Ben-Mustapha N., 2009, 3 INT S INN INF COMM Berners-Lee T, 2001, SCI AM, V284, P34 BONINO D, 2003, ICTAI 2003, P580 Boyce S, 2007, EDUC TECHNOL SOC, V10, P275 Brini AH, 2005, LECT NOTES COMPUT SC, V3772, P271 Camon E, 2004, NUCLEIC ACIDS RES, V32, P262 Chandrasekaran B., 1998, P 11 WORKSH KNOWL AC, P20 Christopher B, 2005, FRONTIERS ARTIFICIAL, V123 Cimiano P., 2006, ONTOLOGY LEARNING PO Corby O, 2004, FR ART INT, V110, P705 Davies J., 2002, WWW 2002 WORKSH RDF, P133 Dong Z., 2006, HOWNET COMPUTATION M Downey D, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2733 Esmaili KS, 2006, I C COMP SYST APPLIC, P171, DOI 10.1109/AICCSA.2006.205086 Etzioni O, 2004, P 13 INT C WORLD WID, P100, DOI 10.1145/988672.988687 Faatz A., 2002, SEM WEB MIN 2 WORKSH Faure D., 1998, LREC WORKS AD LEX CO Ferreira J., 1999, LOCAL MAXIMA METHOD, P369 Finin T.W., 2005, SWOOGLE SEARCHING KN, P1682 Fox MS, 1998, SIMULATING ORGANIZATIONS, P131 Furst F., 2002, EGC EXTRACTION CONNA, V1, P227 Geleijnse G., 2005, P 17 BELG NETH C ART, P120 Gruber T., 1993, SPECIAL ISSUE FORMAL Hahn U., 2001, Proceedings of the First International Conference on Knowledge Capture Halaschek-Wiener C., 2004, 30 INT C VER LARG DA, P1317 Harold B, 2001, P SWWS 2001, P381 Harris Z., 1968, MATH STRUCTURES LANG Hearst M.A., 1998, WORDNET ELECT LEXICA, P132 Heflin Jeff, 2000, ARTIF INTELL, VWS-00-01, P35 Holohan E., 2005, WORKSH APPL SEM WEB Horrocks I., 2004, SWRL SEMANTIC WEB RU Hwang C.H., 1999, P 6 INT WORKSH KNOWL, P14 Jannink J., 1999, P FUS 1999 SUNN CA Karoui L., 2006, IEEE P 2006 IEEE WIC, P420 Karoui L., 2007, FLAIRS 20 20 INT FLA, P460 Kietz J.U., 2000, CEUR WORKSHOP P Kuchmann-Beauger N., 2011, LNCS, V6716, P201 Lebart L., 1998, EXPLORING TEXTUAL DA Lemaire B., 2006, BRAIN COGNITION, V18 Lin C.-Y., 2000, COLING, P495 Lin D., 1998, P 17 INT C COMP LING, P768 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 Maedche A., 2004, HDB ONTOLOGIES, P173 Manzano-Macho D., 2008, P 6 INT LANG RES EV, P28 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Moldovan D., 2000, P 13 INT FLOR ART IN, P224 Nakayama K., 2007, AINA 2007 P IEEE INT, P932 Navigli R, 2008, FRONT ARTIF INTEL AP, V167, P71 Navigli R, 2004, COMPUT LINGUIST, V30, P151, DOI 10.1162/089120104323093276 Nixon L., 2005, KNOWLEDGE WEB NETWOR Papatheodrou C., 2002, EDMEDIA 2002 AACE DE Patel C., 2003, WIDM 2003, P58 Priebe T, 2004, DEM 3 INT SEM WEB C, P33 Resnik P, 1995, INT JOINT CONF ARTIF, P448 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocha C., 2004, P 13 INT C WORLD WID, P374, DOI 10.1145/988672.988723 Rubin Daniel L, 2002, Pac Symp Biocomput, P88 Sanchez D., 2009, KNOWL ENG REV, V24, P413 Sanderson M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312679 Sekiuchi R, 1998, LECT NOTES ARTIF INT, V1531, P194 Sell D., 2008, P 1 INT WORKSH ONT S, P111 Shah U., 2002, 10 INT C INF KNOWL M, P461 Spyns P, 2002, LECT NOTES ARTIF INT, V2569, P189 Stojanovic N., 2002, 17 WORLD COMP C, P141 Suryanto H., 2001, Proceedings of the First International Conference on Knowledge Capture Tamma V, 2004, FR ART INT, V110, P63 Tijerino YA, 2005, WORLD WIDE WEB, V8, P261, DOI 10.1007/s11280-005-0360-8 Turney P.D., 2001, LNCS LNAI, V2167, P491 Uschold M, 1996, KNOWL ENG REV, V11, P93 Velardi P., 2002, P ACM C FORM ONT INF, P270 Veronis J, 2004, COMPUT SPEECH LANG, V18, P223, DOI 10.1016/j.csl.2004.05.002 Wang W., 2008, J COMMUNICATIONS SIW, V3, P76 WordNet, 1989, EL LEX DAT Zhang Y., 2004, P 24 SGAI INT C INN, P81 NR 79 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-36318-4; 978-3-642-36317-7 J9 LECT NOTES BUS INF P PY 2013 VL 138 BP 177 EP 207 PG 31 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB7BR UT WOS:000345323900008 ER PT J AU Li, JH Li, SL Zhu, YK Xiao, B AF Li, Jihan Li, Shanglin Zhu, Yingke Xiao, Bo BE Verikas, A Vuksanovic, B Zhou, J TI Design of Personalized Search Engine Based on User-Webpage Dynamic Model SO SIXTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2013) SE Proceedings of SPIE LA English DT Proceedings Paper CT 6th International Conference on Machine Vision (ICMV) CY NOV 16-17, 2013 CL London, ENGLAND SP Sichuan Univ, Huazhong Normal Univ, Aim Shams Univ, Singapore Inst Elect, Univ Portsmouth, China Commun, Sci & Engn Inst DE Personalized search engine; User-webpage dynamic model; SVD; Extended Page; Rank algorithm AB Personalized search engine focuses on establishing a user-webpage dynamic model. In this model, users' personalized factors are introduced so that the search engine is better able to provide the user with targeted feedback. This paper constructs user and webpage dynamic vector tables, introduces singular value decomposition analysis in the processes of topic categorization, and extends the traditional PageRank algorithm C1 [Li, Jihan; Li, Shanglin; Zhu, Yingke; Xiao, Bo] Beijing Univ Posts & Telecommun, Beijing, Peoples R China. RP Li, JH (reprint author), Beijing Univ Posts & Telecommun, Beijing, Peoples R China. CR Carman M J, 2008, P 2008 ACM WORKSH SE, P27, DOI DOI 10.1145/1458583.1458591 Gemmell J, 2008, P 6 WORKSH INT TECHN, P37 Hu Jijun, 2010, SCI TECHNOLOGY INNOV, P27 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 Liang T. P, 2002, P 35 HAW INT C SYST Page L., 1998, PAGERANK CITATION RA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Weiguo S, 2005, GENETIC K MEDOIDS CL NR 8 TC 0 Z9 0 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 978-0-8194-9996-7 J9 PROC SPIE PY 2013 VL 9067 AR 906723 DI 10.1117/12.2052976 PG 5 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Optics SC Computer Science; Engineering; Optics GA BA9CU UT WOS:000339220200084 ER PT J AU Inches, G Harvey, M Crestani, F AF Inches, Giacomo Harvey, Morgan Crestani, Fabio GP IEEE Comp Soc Acad Sci & Engn TI Finding Participants in a Chat: Authorship Attribution for Conversational Documents SO 2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM) LA English DT Proceedings Paper CT ASE/IEEE International Conference on Social Computing (SocialCom) CY SEP 08-14, 2013 CL Washington, DC SP Acad Sci Engn, IEEE, IEEE Comp Soc ID USER AB In this work we study the problem of Authorship Attribution for a novel set of documents, namely online chats. Although the problem of Authorship Attribution has been extensively investigated for different document types, from books to letters and from emails to blog posts, to the best of our knowledge this is the first study of Authorship Attribution for conversational documents (IRC chat logs) using statistical models. We experimentally demonstrate the unsuitability of the classical statistical models for conversational documents and propose a novel approach which is able to achieve a high accuracy rate (up to 95%) for hundreds of authors. C1 [Inches, Giacomo; Harvey, Morgan; Crestani, Fabio] Univ Lugano USI, Fac Informat, Lugano, Switzerland. RP Inches, G (reprint author), Univ Lugano USI, Fac Informat, Lugano, Switzerland. EM giacomo.inches@usi.ch; morgan.harvey@usi.ch; fabio.crestani@usi.ch CR Argamon S., 2011, CLEF NOTEBOOK PAPERS Argamon S., 2005, P 2005 ACH ALLC C Bache R, 2010, DATA KNOWL ENG, V69, P303, DOI 10.1016/j.datak.2009.10.009 Christopher P. R., 2008, INTRO INFORM RETRIEV Cover T. M., 2006, ELEMENTS INFORM THEO Elsner M, 2010, COMPUT LINGUIST, V36, P389, DOI 10.1162/coli_a_00003 Elsner M., 2008, P ACL 08 HLT, P834 Grieve J., 2007, LIT LINGUISTIC COMPU, V22, P251, DOI DOI 10.1093/LLC/FQM020> Hatcher E, 2004, LUCENE ACTION ACTION HOLMES DI, 1994, COMPUT HUMANITIES, V28, P87, DOI 10.1007/BF01830689 Inches G, 2011, LECT NOTES ARTIF INT, V7022, P198, DOI 10.1007/978-3-642-24764-4_18 Inches G, 2010, LECT NOTES COMPUT SC, V5993, P649, DOI 10.1007/978-3-642-12275-0_68 Inches G., 2012, CLEF ONLINE WORKING Juola P., 2012, CLEF ONLINE WORKING Juola Patrick, 2006, Foundations and Trends in Information Retrieval, V1, DOI 10.1561/1500000005 Juola P., 2003, LIT LINGUISTIC COMPU, P59 Juola P., 1997, P INT WORKSH SIM CAT Juola P., 1998, P NE MLAP3 CONLL98 N, P141, DOI 10.3115/1603899.1603923 Jurafsky D., 2008, SPEECH LANGUAGE PROC Koppel M, 2009, J AM SOC INF SCI TEC, V60, P9, DOI 10.1002/asi.20961 Koppel M., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148304 Kucukyilmaz T, 2006, LECT NOTES COMPUT SC, V4243, P274 Kucukyilmaz T, 2008, INFORM PROCESS MANAG, V44, P1448, DOI 10.1016/j.ipm.2007.12.009 Layton R., 2010, 2 CYB TRUSTW COMP WO, P7 Layton R., 2012, 3 CYB TRUSTW COMP WO, P1 Lo R. T.-W., 2005, J DIGITAL INFORM MAN, P3 Mendenhall TC, 1887, SCIENCE, V11, P237 Peersman C, 2011, P 3 INT WORKSH SEARC, P37 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Savoy J., 2012, ACM T INFORM SYSTEMS, P1 Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001 Wang L., 2009, NAACL 09, P200 Zhao Y, 2006, LECT NOTES COMPUT SC, V4182, P92 Zheng R, 2006, J AM SOC INF SCI TEC, V57, P378, DOI 10.1002/asi.20316 NR 34 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-5137-1 PY 2013 BP 272 EP 279 DI 10.1109/SocialCom.2013.45 PG 8 WC Computer Science, Information Systems SC Computer Science GA BJU40 UT WOS:000330563800040 ER PT J AU Fink, C Bos, N Perrone, A Liu, E Kopecky, J AF Fink, Clay Bos, Nathan Perrone, Alexander Liu, Edwina Kopecky, Jonathon GP IEEE Comp Soc Acad Sci & Engn TI Twitter, Public Opinion, and the 2011 Nigerian Presidential Election SO 2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM) LA English DT Proceedings Paper CT ASE/IEEE International Conference on Social Computing (SocialCom) CY SEP 08-14, 2013 CL Washington, DC SP Acad Sci Engn, IEEE, IEEE Comp Soc DE sentiment analysis; social media; Twitter; survey research; Nigeria AB This paper analyzes a corpus of Nigerian Tweets collected during the run-up to the 2011 Nigerian Presidential election and compares it with official election returns and polling data. We found that counts of the mentions on Twitter of the two major candidates correlated strongly with polling and election results when compared across the country's geopolitical regions, though the same data overrepresented two other candidates who did not fare as well in the polls or at the ballot box. Sentiment extracted from Twitter was less accurate in capturing mean levels of support for the two major candidates and, in particular, showed a strong negativity bias against the incumbent president. Twitter sentiment did mirror regional trends in the polling and election results though not as strongly as seen for mention counts. We demonstrate methodologically how to sample these data, extract sentiment, and compare this sentiment with ground-truth polling data and election results. Although social media clearly capture opinion about contentious issues, our results suggest that the opinion represented there may not always accurately reflect true public opinion. C1 [Fink, Clay; Bos, Nathan; Perrone, Alexander; Liu, Edwina; Kopecky, Jonathon] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA. RP Fink, C (reprint author), Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA. EM clayton.fink@jhuapl.edu CR Ajibade Y., 2012, J ED SOCIAL RES Barbosa Luciano, 2010, P 23 INT C COMP LING Chang C.-C., 2011, ACM T INTELLIGENT SY Chen L., 2012, P 6 INT C WEBL SOC M Guerra P., 2011, P 17 ACM SIGKDD INT Jiang M., 2011, ACL, P151 Jungherr A., 2011, SOCIAL SCI COMPUTER, V30, P229, DOI [10.1177/0894439311404119, DOI 10.1177/0894439311404119] Kouloumpis E., 2012, P 5 INT C WEBL SOC M Manning CD, 1999, FDN STAT NATURAL LAN Metaxas P. T., 2011, 2011 IEEE 3 INT C 20, P165 Norvig Peter, 2009, BEAUTIFUL DATA, P219 O'Connor B, 2010, ICWSM, V11, P122 Pang Bo, 2004, P 42 ANN M ASS COMP Pang Bo, 2002, P ACL 02 C EMP METH, V10 Rajaraman A, 2011, MINING MASSIVE DATAS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Tan C., 2011, P 17 ACM SIGKDD INT Tumasjan A, 2010, ICWSM, V10, P178 Wiebe J, 2005, LANG RESOUR EVAL, V39, P165, DOI 10.1007/s10579-005-7880-9 Zhang L., 2011, DEVELOPMENT NR 20 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-5137-1 PY 2013 BP 311 EP 320 DI 10.1109/SocialCom.2013.50 PG 10 WC Computer Science, Information Systems SC Computer Science GA BJU40 UT WOS:000330563800045 ER PT B AU Sodanil, M Ketmaneechairat, H AF Sodanil, Maleerat Ketmaneechairat, Hathairat GP IEEE TI Information Retrieval Experiment on Subjective Words Query Expansion SO 2013 INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT) LA English DT Proceedings Paper CT International Conference of Information and Communication Technology (ICoICT) CY MAR 20-22, 2013 CL Bandung, INDONESIA SP Inst Teknologi Telkom, IEEE Indonesia Sect, Minist Informat & Commun, PT Telkom Indonesia, PT Telkomsel DE Query expansion; keyword-based query; synonymy words; subjective-words ID USER LOGS; TERMS AB For information retrieval systems, the quality of searching is most important. There are many techniques to improve the quality of search, the query expansion (QE) is considered in order to improve the search result in terms of recall and user satisfaction. This paper proposed the query expansion technique using keyword-based query. These semantic terms are added to the original query in order to reformulate query before sending to the searching process. The experiments were tested on twitter data collection which class is annotated by human. The results show that the retrieval effectiveness is considerably higher than using only original query. Compared to the base-line system, this method provides higher performance in terms of recall and precision. C1 [Sodanil, Maleerat; Ketmaneechairat, Hathairat] King Mongkuts Univ Technol North Bangkok, Fac Informat Technol, Bangkok, Thailand. RP Sodanil, M (reprint author), King Mongkuts Univ Technol North Bangkok, Fac Informat Technol, Bangkok, Thailand. EM msn@kmutnb.ac.th; hathairatk@kmutnb.ac.th CR Audeh Bissan, 2012, EXPT 2 QUERY EXPANSI, P407 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Buckley C., 1992, P 1 TEXT RETR C TREC, P59 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 Cui Hang, 2003, IEEE T KNOWLEDGE DAT, V15 Durao Frederico, 2011, INT C INF SCI APPL I Fonseca BM, 2005, P 14 ACM INT C INF K, P696 Lashkari A.H, ICIME 2009, P385 MIAO YQ, 2011, J PATTERN RECOGNITIO, V32, P375, DOI DOI 10.1016/J.PATREC.2010.09.018 Nagypal Gabor, 2005, MOVE MEANINGFUL INTE, V3762, P780, DOI 10.1007/11575863_98 Peng Xingyuan, 2010, 4 INT UN COMM S 2010, P149 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shiri A, 2006, J AM SOC INF SCI TEC, V57, P462, DOI 10.1002/asi.20319 Strasunskas Darijus, 2007, IRMA INT C, P1048 Vechtomova O, 2007, INFORM PROCESS MANAG, V43, P849, DOI 10.1016/j.ipm.2006.09.004 Wen J., 2004, P 27 ANN INT C RES D, P57, DOI 10.1145/1008992.1009005 Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 Wu Jiewan, 2011, CS201104 Ye Z, 2010, LECT NOTES COMPUT SC, V6458, P151 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-4992-5; 978-1-4673-4990-1 PY 2013 BP 161 EP 165 PG 5 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BHW22 UT WOS:000326825500029 ER PT J AU Lamirel, JC AF Lamirel, Jean-Charles TI Multi-View Data Analysis and Concept Extraction Methods for Text SO KNOWLEDGE ORGANIZATION LA English DT Article; Proceedings Paper CT 13th Meeting of the German ISKO on Theory, Information, and Organization of Knowledge CY MAR 19-20, 2013 CL Potsdam, GERMANY SP ISKO AB In the process of textual information analysis, like in the domain of technological survey through patents analysis, or in the domain of emerging research tracking through research papers analysis, the complexity of the studied concepts and the accuracy of the questions to be answered may often lead the analyst to partition his reasoning into viewpoints. Most of the classical information analysis tools can only manage an analysis of the studied domain in a global way. The information analysis paradigm considered in this paper is an alternative paradigm called multi-view data analysis. This paradigm introduces the dimensions of viewpoints and dynamics into information analysis with its multi-view displays, its online generalization capabilities, and its inter-view communication process. The dynamic information exchange between views can be exploited, either by an analyst or in an unsupervised way, in order to perform cooperative deduction between several different analyzes that have been performed on the same data or on related data. This paper demonstrates the efficiency of a viewpoint-oriented analysis as compared to a global analysis in the domain of technological survey and research evaluation. Both objective and subjective quality criteria are taken into account for quality evaluation. C1 [Lamirel, Jean-Charles] LORIA, Synalp team, Nancy, France. LORIA, INRIA TALARIS Project, F-54600 Nancy, France. RP Lamirel, JC (reprint author), LORIA, Synalp team, 615 Rue Jardin Botanique, Nancy, France. EM lamirel@loria.fr CR Al Shehabi Shadi, 2006, J INFORM MANAGEMENT, V1 Allan James Carbonell, 1998, P DARPA BROADC NEWS Attik Mohammed, 2006, DBA 06 P 24 IASTED I, P50 CAMPANARIO JM, 1995, SCIENTOMETRICS, V33, P23, DOI 10.1007/BF02020773 DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Francois Claire, 2003, IST199920350 EICSTES Frizke Bernd, 1995, ADV NEURAL INFORM PR, P625 Gaber MM, 2005, SIGMOD RECORD, V34, P18, DOI 10.1145/1083784.1083789 Ghribi Maha, 2010, EV METH EXTR CONN DO Glanzel Wolfgang, 2010, SCIENTOMETRICS, V88, P297 HINTON GE, 1989, ARTIF INTELL, V40, P185, DOI 10.1016/0004-3702(89)90049-0 Jouve O., 1999, NOUVELLES TECHNOLOGI Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kohonen T., 1982, BIOL CYBERN, V43, P56 Lamirel JC, 2004, SCIENTOMETRICS, V60, P445, DOI 10.1023/B:SCIE.0000034386.05278.e8 Lamirel Jean-Charles, 2008, AIA 08 P 26 IASTED I, P169 Lamirel Jean-Charles, 1994, P ACM CIKM 94 GAITH, P155, DOI 10.1145/191246.191272 Lamirel Jean-Charles, 2000, C P RIAO, V2, P1657 Lamirel Jean-Charles, 2010, 22 INT C IND ENG OTH Lamitel Jean-Charles, 2004, SCI 2004 ORL FL US J Lamitel Jean-Charles, 1995, THESIS U NANCY NANCY Lamitel Jean-Charles, 2011, 2011 INT JOINT C NEU MacQueen J., 1967, P 5 BERK S MATH STAT, P281, DOI DOI 10.1234/12345678 Prudent Y, 2005, IEEE IJCNN, P1211 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schiebel E, 2010, SCIENTOMETRICS, V83, P765, DOI 10.1007/s11192-009-0137-4 MARTINETZ T, 1991, ARTIFICIAL NEURAL NETWORKS, VOLS 1 AND 2, P397 Thijs Bart, 2010, 6 INT C WEB INF SCI Varsis A., 1992, P 3 INT WORKSH PAR A White Howard D., 1998, STRUCTURES RELATIONS, P57 NR 31 TC 0 Z9 1 PU ERGON-VERLAG PI WURZBURG PA GROMBUHLSTR 7, 97080 WURZBURG, GERMANY SN 0943-7444 J9 KNOWL ORGAN JI Knowl. Organ. PY 2013 VL 40 IS 5 BP 305 EP 319 PG 15 WC Information Science & Library Science SC Information Science & Library Science GA 239XV UT WOS:000326060200003 ER PT S AU Zhang, S Zhang, SX AF Zhang, Shuang Zhang, Shixiong BA Liu, XL BF Liu, XL BE Shao, JP TI Researching in Web Technology Classification based on Improved Support Vector Machine SO MECHANICAL ENGINEERING, MATERIALS SCIENCE AND CIVIL ENGINEERING SE Applied Mechanics and Materials LA English DT Proceedings Paper CT International Conference on Mechanical Engineering, Materials Science and Civil Engineering (ICMEMSCE 2012) CY AUG 18-20, 2012 CL Harbin, PEOPLES R CHINA DE Web Text Mining; Web Text Classification; Information Extraction; Text Categorization; SVM; Decision Tree ID RELEVANCE AB Web as the main platform of information manufacturing, issuing, processing and transacting, has emerged massive isomerous dynamic semi-structural or non-structural information resources [1], so how to extract useful information from the mass Web resources has become a question which need to be solved. This paper introduces the background and status from domestic and abroad, and expounds the related theory and technology of text classification, constructs the Web text classification system model, gives a Web text collection algorithm and collection system. It verified the theoretical method which proposed in this paper by experiment, the result show that the extraction of information and Web classification is more accurate. C1 [Zhang, Shuang; Zhang, Shixiong] Heilongjiang Univ, Sch Informat Sci & Technol, Harbin, Peoples R China. RP Zhang, S (reprint author), Heilongjiang Univ, Sch Informat Sci & Technol, Harbin, Peoples R China. EM angie_2008@yeah.net CR Choon Y, 2000, CLASSFICATION WORLD Kovacevic Milos, 2002, P 10 INT C ART INT M MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Min-Yen Kan J., 2004, WEB PAGE CATEGORIZAT, P17 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Soderland S., 2001, MACH LEARN, P125 Vapnik V., 1999, NATURE STAT LEARNING NR 8 TC 0 Z9 0 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1660-9336 BN 978-3-03785-590-4 J9 APPL MECH MATER PY 2013 VL 274 BP 563 EP 566 DI 10.4028/www.scientific.net/AMM.274.563 PG 4 WC Engineering, Manufacturing; Engineering, Civil; Engineering, Mechanical; Materials Science, Multidisciplinary SC Engineering; Materials Science GA BFJ75 UT WOS:000320140900131 ER PT J AU Machhour, H Kassou, I AF Machhour, Hamid Kassou, Ismail GP IEEE TI Dynamic Text Classifier based on Search Engine Features SO 2013 3RD INTERNATIONAL SYMPOSIUM ISKO-MAGHREB LA English DT Proceedings Paper CT ISKO-Maghreb 3rd International Symposium CY NOV 08-09, 2013 CL Marrakech, MOROCCO SP ISKO DE Search Engine; Text Categorization; Text Indexing; K Nearest Neighbors ID DOCUMENT CATEGORIZATION; MODEL AB Search engines and text categorization are two research areas almost inseparable. Where one is studied, the other is referred sooner or later. Automatic text categorization became more important with the enormous increase of the online information, and text classifiers are often there to help search engines classify indexed documents. The main idea presented in this paper consists of using a search engine as a text classifier. A search engine can take advantage of its scoring performances to categorize a new document without requiring building and using other categorization model. K Nearest Neighbors (KNN) principal based on search engine score as similarity measure was used. This approach is highly dependent on the scoring quality of the used search engine. It is a simple approach but can be competitive to other more complex categorization models. Also, this method is useful as a kind of categorization on the fly when indexing a new document. Through its evolving index, the search engine becomes a dynamic classifier of the fact that any document, recently joining the index, participate in the categorization of other new documents. C1 [Machhour, Hamid; Kassou, Ismail] Mohammed V Souissi Univ, ENSIAS, Dept Comp Sci & Decis Support, Rabat, Morocco. RP Machhour, H (reprint author), Mohammed V Souissi Univ, ENSIAS, Dept Comp Sci & Decis Support, Rabat, Morocco. CR Aggarwal CC, 2004, IEEE T KNOWL DATA EN, V16, P245, DOI 10.1109/TKDE.2004.1269601 Aggarwal C.C., 2012, MINING TEXT DATA Aparicio R., 2013, INT J DATA MINING KN Batal I., 2009, P 18 ACM C INF KNOWL, P2041, DOI 10.1145/1645953.1646296 Bespalov D., 2011, P 20 ACM INT C INF K, P375 Bhatia Nitin, 2010, ARXIV10070085 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Cavnar W., 1995, NIST SPECIAL PUBLICA, P269 Cavnar W B, 1994, N GRAM BASED TEXT CA, V48113, P161 Chakrabarti S., 1997, VLDB 97, P446 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 ESULI A, 2009, ADV INFORM RETRIEVAL, V5766, P29, DOI 10.1007/978-3-642-04417-5_4 Fan LJ, 2003, PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PDCAT'2003, PROCEEDINGS, P472, DOI 10.1109/PDCAT.2003.1236347 Ghiassi M, 2012, EXPERT SYST APPL, V39, P10967, DOI 10.1016/j.eswa.2012.03.027 Han EH, 2000, CENTROID BASED DOCUM Heckerman D., 1998, AAAI 98 WORKSH LEARN Jiang SY, 2012, EXPERT SYST APPL, V39, P1503, DOI 10.1016/j.eswa.2011.08.040 Joachims T., 1996, PROBABILISTIC ANAL R Joachims T., 1998, TEXT CATEGORIZATION Joachims Thorsten, 2001, P 24 ANN INT ACM SIG, P128, DOI 10.1145/383952.383974 Kim H, 2009, PATTERN RECOGN, V42, P1777, DOI 10.1016/j.patcog.2009.01.020 Kulesza T., 2011, ACM T INTERACTIVE IN, V1, P1 Lam S. L., 1999, DASFAA 99 P 6 INT C, P195 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lee LH, 2012, APPL INTELL, V37, P80, DOI 10.1007/s10489-011-0314-z Lewis D., 1994, 3 ANN S DOC AN INF R, V33, P81 Lewis D., 1997, REUTERS 21578 TEXT C Lewis D.D., 1994, ICML, V94, P148 Li X., 2011, BUS MAN EL INF BMEI, P224 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Lin WR, 2011, APPL MECH MATER, V50-51, P700, DOI 10.4028/www.scientific.net/AMM.50-51.700 Machhour H., 2013, INT J DATA MINING KN Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, RELEVANCE FEEDBACK I SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi K., 2011, CLOUD COMP INT SYST, P113 Zhong S, 2005, NEURAL NETWORKS, V18, P790, DOI 10.1016/j.neunet.2005.06.008 Sidorov G., 2013, COMPUTATIONAL LINGUI, V7816, P13 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wang L., 2012, CONS EL COMM NETW CE, P1848 Wei ZH, 2009, INT J COMPUT INT SYS, V2, P365 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Xia T., 2011, COMP SCI ED ICCSE 20, P210 Xu H., 2013, P 2012 INT C INF TEC, P619 Xu ZQ, 2012, PHYSCS PROC, V33, P1986, DOI 10.1016/j.phpro.2012.05.312 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Zhang W, 2011, EXPERT SYST APPL, V38, P2758, DOI 10.1016/j.eswa.2010.08.066 [张孝飞 ZHANG Xiao-Fei], 2009, [ęؔ式čƆ别äøŽäŗŗå·„ę™ŗčƒ½, Pattern Recognition and Artificial Intelligence], V22, P936 Zhou Yong, 2009, Journal of Computers, V4 NR 51 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3392-1 PY 2013 PG 8 WC Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary SC Computer Science; Engineering GA BC1NM UT WOS:000350287800019 ER PT J AU Palomino, MA Taylor, T McBride, G Mortimer, H Owen, R Depledge, M AF Palomino, Marco A. Taylor, Tim McBride, Geoff Mortimer, Hugh Owen, Richard Depledge, Michael BE Ganzha, M Maciaszek, L Paprzycki, M TI Optimising Web-Based Information Retrieval Methods for Horizon Scanning Using Relevance Feedback SO 2013 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS) SE Federated Conference on Computer Science and Information Systems LA English DT Proceedings Paper CT Federated Conference on Computer Science and Information Systems (FedCSIS) CY SEP 08-11, 2013 CL Krakow, POLAND SP Polish Informat Proc Soc, Mazowsze Chapter, AGH Univ Min & Metal, Wroclaw Univ Econ, Polish Acad Sci, Syst Res Inst, IEEE Reg 8, Comp Soc Chapter Poland, Gdansk Comp Soc Chapter, IEEE Computat Intelligence Soc, Polish Chapter, ACM Special Interest Grp Appl Comp, Lodz ACM Chapter, Sci & Engn Res Support Soc, Informat Europe, Asociac Tenicos Informatica, Polish Acad Sci, Comm Comp Sci, Polish Assoc Informat Syst, Polish Soc Business Informat, Polish Chamber Commerce High Technol ID EMERGING HEALTH TECHNOLOGIES; GLOBAL CONSERVATION ISSUES; DENMARK; SYSTEM AB Horizon scanning is being increasingly regarded as an instrument to support strategic decision making. It requires the systematic examination of information to identify potential threats, emerging issues and opportunities to improve resilience and decrease risk exposure. Horizon scanning can use the Web to augment the acquisition of information, though this involves a search for novel and emerging issues without knowing them beforehand. To optimise such a search, we propose the use of relevance feedback, which involves human interaction in the retrieval process so as to improve results. As a proof-of-concept demonstration, we have carried out a horizon scanning exercise which showed that our implementation of relevance feedback was able to maintain the retrieval of relevant documents constant over the length of the experiment, without any reduction. This represents an improvement over previous studies where relevance feedback was not considered. C1 [Palomino, Marco A.; Depledge, Michael] Univ Exeter, Sch Med, Truro, England. RP Palomino, MA (reprint author), Univ Exeter, Sch Med, Truro, England. EM m.palomino@exeter.ac.uk; tjt205@exeter.ac.uk; geoff.mcbride@stfc.ac.uk; hugh.mortimer@stfc.ac.uk; r.j.owen@exeter.ac.uk; m.depledge@exeter.ac.uk CR Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Carlsson P, 1998, INT J TECHNOL ASSESS, V14, P695 Chairman of the Joint Intelligence Committee, 2012, REV CROSS GOV HOR SC Chan SWK, 2011, DECIS SUPPORT SYST, V52, P189, DOI 10.1016/j.dss.2011.07.003 Douw K, 2003, J MED INTERNET RES, V5, DOI 10.2196/jmir.5.1.e6 Douw K, 2006, HEALTH POLICY, V76, P334, DOI 10.1016/j.healthpol.2005.06.016 Douw K, 2004, INT J TECHNOL ASSESS, V20, P342 Gheorghiu R., 2009, EC COMPUTATION EC CY, V43, pp1 Google, 2012, CUST SEARCH Harper D.J., 1980, RELEVANCE FEEDBACK D Jackson Monica, 2013, COMMUNICATION Linstone HA, 2011, TECHNOL FORECAST SOC, V78, P1712, DOI 10.1016/j.techfore.2010.09.011 Lv Y., 2009, CIKM 09 Manning C. D., 2008, INTRO INFORM RETRIEV, P496 O'Malley SP, 2009, INT J TECHNOL ASSESS, V25, P374, DOI 10.1017/S0266462309990031 Page L., PAGERANK CITATION RA Palomino M.A., 2012, COMP SCI INF SYST FE Palomino M.A, 2012, BUSINESS INFORM, V3, P139 Palomino Marco A., 2012, Foresight, V14, DOI 10.1108/14636681211269851 Purcell K., 2012, SEARCH ENGINE USE 20 Q-DETECT, 2013, Q DETECT DEV TOOLS O ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1987, TERM WEIGHTING APPRO Sutherland WJ, 2011, TRENDS ECOL EVOL, V26, P10, DOI 10.1016/j.tree.2010.11.002 Sutherland WJ, 2009, TRENDS ECOL EVOL, V24, P523, DOI 10.1016/j.tree.2009.04.008 Sutherland WJ, 2012, TRENDS ECOL EVOL, V27, P12, DOI 10.1016/j.tree.2011.10.011 Sutherland WJ, 2013, TRENDS ECOL EVOL, V28, P16, DOI 10.1016/j.tree.2012.10.022 Sutherland WJ, 2010, TRENDS ECOL EVOL, V25, P1, DOI 10.1016/j.tree.2009.10.003 Truve S., 2011, BIG DATA FUTURE UNLO Twitter, 2013, TWITT FAST SIMPL WAY Wikipedia, 2013, DERW CAP MARK Wild C, 2008, HEALTH POLICY, V87, P160, DOI 10.1016/j.healthpol.2008.01.002 Wilson J.C., 2008, WHITE PAPER DSTL S T NR 35 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2325-0348 BN 978-1-4673-4471-5 J9 FED CONF COMPUT SCI PY 2013 BP 1139 EP 1146 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BB8OZ UT WOS:000347171500180 ER PT J AU Yadav, S Das, S Rudrapal, D AF Yadav, Sunil Das, S. Rudrapal, D. GP IEEE TI A SYSTEM TO FILTER UNSOLICITED TEXTS FROM SOCIAL LEARNING NETWORKS SO 2013 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS AND NETWORKING TECHNOLOGIES (ICCCNT) LA English DT Proceedings Paper CT 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT) CY JUL 04-06, 2013 CL Tiruchengode, INDIA DE online social networks; posts; blogs; message filtering; pattern matching ID INFORMATION; CATEGORIZATION AB In the present day scenario online social networks (OSN) are very trendy and one of the most interactive medium to share, communicate and exchange numerous types of information like text, image, audio, video etc. All these publicly shared information are explicitly viewed by connected people in the blog or networks and having an enormous social impact in human mind. Posting or commenting on particular public/private areas called wall, may include superfluous messages or sensitive data. Information filtering can therefore have a solid influence in online social networks and it can be used to give users the facility to organize the messages written on public areas by filtering out unwanted wordings. In this paper, we have proposed a system which may allow OSN users to have a direct control on posting or commenting on their walls with the help of information filtering. This is achieved through text pattern matching system, that allows users to filter their open space and a privilege to add new words treated as unwanted. For experimental analysis a test social learning website is designed and some unwanted words/texts are kept as blacklisted vocabulary. To provide control to the user, pattern matching of texts are done with the blacklisted vocabulary. If it passes then only text can be posted on someone's wall, otherwise text will be blurred or encoded with special symbols. Analysis of experimental results shows high accuracy of the proposed system. C1 [Yadav, Sunil; Das, S.; Rudrapal, D.] Natl Inst Technol, Dept Comp Sc & Engn, Jirania, Tripura W, India. RP Yadav, S (reprint author), Natl Inst Technol, Dept Comp Sc & Engn, Jirania, Tripura W, India. EM snl.dv2@gmail.com; dwijen.rudrapal@gmail.com CR Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Chau M, 2008, DECIS SUPPORT SYST, V44, P482, DOI 10.1016/j.dss.2007.06.002 DENNING PJ, 1982, COMMUN ACM, V25, P163, DOI 10.1145/358453.358454 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Gediminas Adomavicius A. T., 2005, IEEE T KNOWL DATA EN, V17, P734 JACOBS PS, 1990, COMMUN ACM, V33, P88, DOI 10.1145/92755.92769 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Vanetti M., 2010, P ECML PKDD WORKSH P Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Zhang ., 2001, P 24 ANN INT ACM SIG, P294, DOI 10.1145/383952.384012 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3925-1 PY 2013 PG 5 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BB8FT UT WOS:000346508600225 ER PT J AU Thovex, C Trichet, F AF Thovex, Christophe Trichet, Francky BE Ozyer, T Carrington, P TI Opinion Mining and Semantic Analysis of Touristic Social Networks SO 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) LA English DT Proceedings Paper CT IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) CY AUG 25-28, 2013 CL Niagara Falls, CANADA SP IEEE, Assoc Comp Machinery, Assoc Comp Machinery SIGKDD, IEEE Comp Soc, IEEE TCDE, Global Univ, Hellen American Univ, Springer, Microsoft, EXtra, Springer Verlag, Assoc Comp Machinery SIGMOD DE Knowledge engineering; Semantic analysis of social networks; Opinion mining; Touristic information system; Business intelligence ID BETWEENNESS; MODEL AB In the context of a Regional Platform of Innovation for future tourism, we define a model and a decision support system for semantic analysis of social networks embedding networks of opinions, aiming at representing and understanding territorial uses coming out from digital marks. The territorial and touristic observatory we develop behind a digital platform is designed to complete usual tools of economic intelligence, helping in governance and in the identification of touristic products and services of tomorrow, so as to foster the rise of territorial economy. In the early stage of the project, the contributions we present are (1) a social semantic graph structure, (2) the definition of semantic degree centrality, (3) a process and definitions for interfacing semantic networks of opinions with social semantic networks of uses, and (4) a first use case experimentation. Public deployment is planned for 2013 and economical impacts will be measured on next years. C1 [Thovex, Christophe; Trichet, Francky] Univ Nantes, UR CNRS 6241, LINA, French Lab Comp Sci, F-44322 Nantes, France. RP Thovex, C (reprint author), Univ Nantes, UR CNRS 6241, LINA, French Lab Comp Sci, 2 Rue Houssiniere,BP 92208, F-44322 Nantes, France. EM christophe.thovex@univ-nantes.fr; francky.trichet@univ-nantes.fr CR Adnan M., 2011, SOCIAL NETWORK ANAL, V1, P173, DOI 10.1007/s13278-010-0015-3 Bhattacharyya P., 2011, SOCIAL NETWORK ANAL, V3, P143 Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008 Ereteo G., 2011, THESIS U NICE SOPHIA Freeman L., 1989, RES METHODS SOCIAL N FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543 Jin Y., 2012, SOCIAL NETWORK ANAL, V2, P217 Mika P, 2005, LECT NOTES COMPUT SC, V3729, P522 Moreno J., 1934, WHO SHALL SURVIVE TR Newman MEJ, 2005, SOC NETWORKS, V27, P39, DOI 10.1016/j.socnet.2004.11.009 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P779, DOI 10.1016/S0306-4573(00)00015-7 Surowiecki J., 2005, WISDOM CROWDS Thovex C., 2012, LECT NOTES COMPUTER Thovex C., 2012, SOCIAL NETWORK ANAL, V3, P35 Thovex C, 2010, LECT NOTES ARTIF INT, V6291, P567, DOI 10.1007/978-3-642-15280-1_53 Thovex C., 2012, EXPLORATORY ANAL DYN, P31 Utsumi A, 2000, J PRAGMATICS, V32, P1777, DOI 10.1016/S0378-2166(99)00116-2 Vernier M., 2009, REV NOUVELLES TECHNO, P45 Zagibalov T., 2003, P 22 INT C COMP LING, V1, P1073 Zaragoza H., 2004, P TEXT RETRIEVAL C T NR 21 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4503-2240-9 PY 2013 BP 1155 EP 1160 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC5YC UT WOS:000353639700171 ER PT J AU Yoon, SH Kim, JS Kim, SW Lee, C AF Yoon, Seok-Ho Kim, Ji-Su Kim, Sang-Wook Lee, Choonhwa TI TL-Rank: A Blend of Text and Link Information for Measuring Similarity in Scientific Literature Databases SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS LA English DT Article DE similarity score; text-based measure; link-based measure; keyword set expansion ID RETRIEVAL AB This paper presents a novel similarity measure that computes similarity scores among scientific research papers. The text of a given paper in online scientific literature is often found to be incomplete in terms of its potential to be compared with others, which likely leads to inaccurate results. Our solution to this problem makes use of both text and link information of a paper in question for similarity scores in that the comparison text of the paper is strengthened by adding that of papers related to it. More accurate similarity scores can be computed by reinforcing the input with the citations of the paper as well as the citations included within the paper. The efficacy of the proposed measure is validated through our extensive performance evaluation study which demonstrates a substantial gain. C1 [Yoon, Seok-Ho; Kim, Ji-Su; Kim, Sang-Wook; Lee, Choonhwa] Hanyang Univ, Dept Elect & Comp Engn, Seoul 133791, South Korea. RP Yoon, SH (reprint author), Hanyang Univ, Dept Elect & Comp Engn, 222 Wangsimni Ro, Seoul 133791, South Korea. EM wook@hanyang.ac.kr CR Amsler R., 1972, 7214 U TEX AUST LING Baeza-Yates R. A., 1999, MODERN INFORM RETRIE FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Han J., 2006, DATA MINING CONCEPTS Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126 Karypis G, 1999, COMPUTER, V32, P68, DOI 10.1109/2.781637 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 Wong S.K.M., 1995, ACM T INFORM SYSTEMS, V13, P39 Yoon S.-H., 2010, P 19 INT WORLD WID W, P1213, DOI 10.1145/1772690.1772880 Zhao P., 2009, P 18 ACM C INF KNOWL, P553, DOI DOI 10.1145/1645953.1646025 NR 16 TC 1 Z9 1 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG MINATO-KU SHIBAKOEN 3 CHOME, TOKYO, 105, JAPAN SN 0916-8532 J9 IEICE T INF SYST JI IEICE Trans. Inf. Syst. PD OCT PY 2012 VL E95D IS 10 BP 2556 EP 2559 DI 10.1587/transinf.E95.D.2556 PG 4 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 028BL UT WOS:000310397700021 ER PT J AU Townsend, JA Glen, RC Mussa, HY AF Townsend, Joe A. Glen, Robert C. Mussa, Hamse Y. TI Note on Naive Bayes Based on Binary Descriptors in Cheminformatics SO JOURNAL OF CHEMICAL INFORMATION AND MODELING LA English DT Article ID DISCRIMINATION; CLASSIFIER; DISCOVERY AB A plethora of articles on naive Bayes classifiers, where the chemical compounds to be classified are represented by binary valued (absent or present type) descriptors, have appeared in the cheminformatics literature over the past decade. The principal goal of this paper is to describe how a naive Bayes classifier based on binary descriptors (NBCBBD) can be employed as a feature selector in an efficient manner suitable for cheminformatics. In the process, we point out a fact well documented in other disciplines that NBCBBD is a linear classifier and is therefore intrinsically suboptimal for classifying compounds that are nonlinearly separable in their binary descriptor space. We investigate the performance of the proposed algorithm on classifying a subset of the MDDR data set, a standard molecular benchmark data set, into active and inactive compounds. C1 [Townsend, Joe A.; Glen, Robert C.; Mussa, Hamse Y.] Univ Cambridge, Dept Chem, Unilever Ctr Mol Sci Informat, Cambridge CB2 1EW, England. RP Mussa, HY (reprint author), Univ Cambridge, Dept Chem, Unilever Ctr Mol Sci Informat, Lensfield Rd, Cambridge CB2 1EW, England. EM hym21@cam.ac.uk FU Unilever; Microsoft Research Division FX The authors acknowledge the useful discussions on this work with Dr J. B. O. Mitchell and Dr. B. Brooks. We would also like to thank Mr. R. L. Marchese Robinson for providing the datasets employed in this work The authors would like to thank Unilever (H.Y.M. and R.C.G.) and Microsoft Research Division (J.A.T.) for financial support CR Abdo A, 2010, J CHEM INF MODEL, V50, P1012, DOI 10.1021/ci100090p Abdo A, 2011, J CHEM INF MODEL, V51, P25, DOI 10.1021/ci100232h AITCHISON J, 1976, BIOMETRIKA, V63, P413, DOI 10.1093/biomet/63.3.413 Angelopoulos N, 2009, J CHEM INF MODEL, V49, P1547, DOI 10.1021/ci900046u [Anonymous], 2009, MOE MOL OP ENV VERS Bender A, 2011, METHODS MOL BIOL, V672, P175, DOI 10.1007/978-1-60761-839-3_7 Bishop C. H., 2006, PATTERN RECOGNITION Cannon EO, 2007, J COMPUT AID MOL DES, V21, P269, DOI 10.1007/s10822-007-9113-3 Duda R., 1973, PATTERN CLASSIFICATI Durant JL, 2002, J CHEM INF COMP SCI, V42, P1273, DOI 10.1021/ci010132r Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010 Good I. J., 1965, ESTIMATION PROBABILI Hert J, 2004, ORG BIOMOL CHEM, V2, P3256, DOI 10.1039/b409865j Klon AE, 2009, COMB CHEM HIGH T SCR, V12, P469 Lowe R, 2011, J CHEM INF MODEL, V51, P1539, DOI 10.1021/ci200128w Mussa H. Y., STAT PROBAB IN PRESS Nigsch F, 2008, J CHEM INF MODEL, V48, P306, DOI 10.1021/ci700350n PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214/aoms/1177704472 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Wilton DJ, 2006, J CHEM INF MODEL, V46, P471, DOI 10.1021/ci050397w NR 20 TC 4 Z9 4 PU AMER CHEMICAL SOC PI WASHINGTON PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA SN 1549-9596 J9 J CHEM INF MODEL JI J. Chem Inf. Model. PD OCT PY 2012 VL 52 IS 10 BP 2494 EP 2500 DI 10.1021/ci200303m PG 7 WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Pharmacology & Pharmacy; Chemistry; Computer Science GA 023OH UT WOS:000310043800002 PM 22900941 ER PT J AU Lamirel, JC AF Lamirel, Jean-Charles TI A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research SO SCIENTOMETRICS LA English DT Article; Proceedings Paper CT Joint Meeting of the 7th International Conference on Webometrics, Informetrics and Scientometrics / 12th COLLNET Meeting CY SEP 20-23, 2011 CL Istanbul, TURKEY SP COLLNET, Istanbul Bilgi Univ, Comp Sci Dept HO Istanbul Bilgi Univ DE Diachronic analysis; Clustering; Multiple viewpoint analysis; Unsupervised learning; Bayesian reasoning; Neural networks AB The objective of this paper is to propose a new unsupervised incremental approach in order to follow the evolution of research themes for a given scientific discipline in terms of emergence or decline. Such behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspects of information by opening the way to the detection of convergences and divergences of research themes at a large scale. C1 INRIA TALARIS Project, LORIA, F-54600 Villers Les Nancy, France. RP Lamirel, JC (reprint author), INRIA TALARIS Project, LORIA, 615 R Jardin Bot, F-54600 Villers Les Nancy, France. EM lamirel@loria.fr CR Al Shehabi S., 2004, P ICTTA DAM SYR APR Al Shehabi Shadi, 2006, J INFORM MANAGEMENT, V1 Allan J., 1998, P DARPA BROADC NEWS Attik M., 2006, P IASTED INT C DAT A DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Francois Claire, 2003, IST199920350 EICSTES Frizke B., 1995, ADV NEURAL INFORMATI, V7, P625 Gaber M., 2005, SIGMOD RECORD, V34 Ghribi M., 2010, EVALECD2010 WORKSH H Glanzel Wolfgang, 2010, SCIENTOMETRICS, V88, P297 Lamirel J.-C, 2011, P IJCNN 2011 SAN JOS Lamirel JC, 2004, SCIENTOMETRICS, V60, P445, DOI 10.1023/B:SCIE.0000034386.05278.e8 Lamirel J.-C., 2004, SCI 2004 ORL FL US J Lamirel J.-C., 1994, P ACM CIKM 94 GAITH Lamirel J.-C., 2010, P IEA AIE 2010 CORD Lamirel J.-C., 2008, IASTED INT C ART INT MacQueen J., 1967, P 5 BERK S MATH STAT, P281, DOI DOI 10.1234/12345678 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schiebel E, 2010, SCIENTOMETRICS, V83, P765, DOI 10.1007/s11192-009-0137-4 Thijs Bart, 2010, 6 INT C WEB INF SCI VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X NR 22 TC 2 Z9 2 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD OCT PY 2012 VL 93 IS 1 BP 151 EP 166 DI 10.1007/s11192-012-0771-0 PG 16 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 014EJ UT WOS:000309357600012 ER PT J AU Song, SK Myaeng, SH AF Song, Sa-kwang Myaeng, Sung Hyon TI A novel term weighting scheme based on discrimination power obtained from past retrieval results SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Term weighting; Evidential weight; Discrimination power; Language model; Probabilistic model ID INFORMATION AB Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term's role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term's evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Song, Sa-kwang; Myaeng, Sung Hyon] Korea Adv Inst Sci & Technol, Div Web Sci & Technol, Taejon 305701, South Korea. [Song, Sa-kwang] Korea Inst Sci & Technol Informat, Taejon 305806, South Korea. RP Myaeng, SH (reprint author), Korea Adv Inst Sci & Technol, Div Web Sci & Technol, 291 Daehak Ro, Taejon 305701, South Korea. EM esmallj@kisti.re.kr; myaeng@kaist.ac.kr RI Myaeng, Sung-Hyon/C-1773-2011 FU WCU (World Class University) program under the National Research Foundation of Korea; Ministry of Education, Science and Technology of Korea [R31-30007] FX This research was supported by WCU (World Class University) program under the National Research Foundation of Korea and funded by the Ministry of Education, Science and Technology of Korea (Project No.: R31-30007). CR Allan J., 1996, P ACM C RES DEV INF Amati G., 2003, PROBABILISTIC MODELS Belkin N. J., 1996, TREC 4 P 4 TEXT RETR, P181 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cao G, 2007, P ACM C RES DEV INF ChengXiang Z., 2001, P 10 ACM INT C INF K CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Cummins R, 2006, INFORM RETRIEVAL, V9, P311, DOI 10.1007/s10791-006-1682-6 Duh K. K., 2009, THESIS U WASHINGTON Hiemstra D., 2001, THESIS U TWENTE Iadh Ounis D. J., 2005, P 27 EUR C INF RETR IWAYAMA M., 2000, P 23 ANN INT ACM SIG, P10, DOI 10.1145/345508.345538 Joachims T., 2003, P ACM C KNOWL DISC D Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lempel R., 2003, P 12 INT C WORLD WID, P19 Manning C. D., 2008, INTRO INFORM RETRIEV Metzler W. B., 2004, INFORM PROCESSING MA, V40, P735 Perez J., 2010, P 3 INT SEM SEARCH W, P1, DOI DOI 10.1145/1863879.1863881 Richardson M., 2006, P 15 INT C WORLD WID, P707, DOI DOI 10.1145/1135777.1135881 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RODGERS JL, 1988, AM STAT, V42, P59, DOI 10.2307/2685263 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Witten I. H., 1994, MANAGING GIGABYTES, P72 Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 Yeh J-Y, 2007, P ACM C RES DEV INF NR 29 TC 3 Z9 3 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2012 VL 48 IS 5 BP 921 EP 932 DI 10.1016/j.ipm.2012.03.004 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 991DQ UT WOS:000307682100009 ER PT J AU Lee, HY Chen, CP Lee, LS AF Lee, Hung-yi Chen, Chia-ping Lee, Lin-shan TI Integrating Recognition and Retrieval With Relevance Feedback for Spoken Term Detection SO IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING LA English DT Article DE Relevance feedback; spoken term detection ID INFORMATION-RETRIEVAL; SYSTEMS; SEARCH; AUDIO; WORD AB Recognition and retrieval are typically viewed as two cascaded independent modules for spoken term detection (STD). Retrieval techniques are assumed to be applied on top of automatic speech recognition (ASR) output, with performance depending on ASR accuracy. We propose a framework that integrates recognition and retrieval and consider them jointly in order to yield better STD performance. This can be achieved either by adjusting the acoustic model parameters (model-based) or by considering detected examples (example-based) using relevance information provided by the user (user relevance feedback) or inferred by the system (pseudo-relevance feedback), either for a given query (short-term context) or by taking into account many previous queries (long-term context). Such relevance feedback approaches have long been used in text information retrieval, but are rarely considered and cannot be directly applied to the retrieval of spoken content. The proposed relevance feedback approaches are specific to spoken content retrieval and are hence very different from those developed for text retrieval, which are applied only to text symbols. We present not only these relevance feedback scenarios and approaches for STD, but also propose a framework to integrate them all together. Preliminary experiments showed significant improvements in each case. C1 [Lee, Hung-yi; Chen, Chia-ping] Natl Taiwan Univ, Dept Commun Engn, Taipei 10617, Taiwan. [Lee, Lin-shan] Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan. RP Lee, HY (reprint author), Natl Taiwan Univ, Dept Commun Engn, Taipei 10617, Taiwan. EM tlkagkb93901106@gmail.com; coward7652@yahoo.com.tw; lslee@gate.sinica.edu.tw FU National Science Council, China [NSC 97-2221-E-002-134-MY3]; Ministry of Education [99R80303] FX Manuscript received September 11, 2011; revised January 31, 2012; accepted April 11, 2012. Date of publication April 25, 2012; date of current version June 11, 2012. This work was supported in part by the National Science Council, China, under Contract NSC 97-2221-E-002-134-MY3 and in part by the Ministry of Education under Contract 99R80303. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gokhan Tur. CR Alberti C, 2009, INT CONF ACOUST SPEE, P4873, DOI 10.1109/ICASSP.2009.4960723 Cao G., 2008, P 31 ANN INT ACM SIG Chelba C., 2005, P 43 ANN M ASS COMP Chelba C., 2005, P ACL Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992 Chelba C, 2007, COMPUT SPEECH LANG, V21, P458, DOI 10.1016/j.csl.2006.09.001 Chen C.-P., 2010, P INT Chien JT, 2009, IEEE T AUDIO SPEECH, V17, P267, DOI 10.1109/TASL.2008.2008366 Croft W. B., 1996, P 19 ANN INT ACM SIG Fu Q., 2007, P ASRU Garcia A, 2006, INT CONF ACOUST SPEE, P949 Garofolo J. S., 2000, P TEXT RETR C TREC, V8 Glass J., 2007, P INT Goto M., 2007, P INT Hazen T., 2009, P ASRU Hong P., 2000, P INT C IM PROC Hori T, 2007, INT CONF ACOUST SPEE, P73 Itoh Y., 2007, P INT Juang BH, 1997, IEEE T SPEECH AUDI P, V5, P257 Kelly D., 2004, P 27 ANN INT ACM SIG KONG SY, 2009, P ICASSP, P4709 Kurland O., 2005, P 28 ANN INT ACM SIG Lavrenko V., 2001, P 24 ANN INT ACM SIG Lee H.-Y., 2010, P INT Lee H.-Y., 2010, P SLT Lee HY, 2010, INT CONF ACOUST SPEE, P5290, DOI 10.1109/ICASSP.2010.5494967 Lee K. S., 2008, P 31 ANN INT ACM SIG Lee LS, 2005, IEEE SIGNAL PROC MAG, V22, P42 Lee SW, 2005, INT CONF ACOUST SPEE, P505 Liang B.-Y., 1998, THESIS NATL TAIWAN U Lin W.-H., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) Logan B., 2000, P ICSLP Logan B, 2005, IEEE T MULTIMEDIA, V7, P899, DOI 10.1109/TMM.2005.854429 Lv Y., 2010, P 33 INT ACM SIGIR C Lv Y., 2009, P 18 ACM C INF KNOWL MacArthur S., 2000, P IEEE WORKSH CONT B Mamou J., 2007, P 30 ANN INT ACM SIG Meng S, 2008, INT CONF ACOUST SPEE, P4345 Miller D. R.H., 2007, P INT Nanjo H, 2005, INT CONF ACOUST SPEE, P1053 Ogata J., 2009, P INT Pan H.-L. C. Y.-C., 2007, P ASRU Pan Y.-C., 2007, P INT Pan Y.-C., 2007, P ASRU Parada C., 2009, P IEEE WORKSH AUT SP Parlak S, 2008, INT CONF ACOUST SPEE, P5244, DOI 10.1109/ICASSP.2008.4518842 Povey D., 2003, THESIS CAMBRIDGE U C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART SYSTEM EXPT AU Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Saraclar M., 2004, P HLT Shao J., 2008, P INT Shen X., 2005, P SIGIR Shichiri T, 2008, INT CONF ACOUST SPEE, P1557, DOI 10.1109/ICASSP.2008.4517920 Srinivasan S., 2000, P SIGIR Tao T., 2006, P 29 ANN INT ACM SIG Kelly D., 2003, SIGIR Forum, V37 Thorsten J., 2002, P KDD Turunen V. T., 2008, P INT Vasconcelos N., 2000, P IEEE WORKSH CONT B Vergyri D., 2007, P INT Wallace R, 2010, INT CONF ACOUST SPEE, P5298, DOI 10.1109/ICASSP.2010.5494969 Wallace R., 2007, P INT Wang D, 2008, INT CONF ACOUST SPEE, P4969 Xin J., 2003, P JOINT C 4 INT C IN Yan R., 2003, P CIVR Yan R., 2003, P 11 ACM INT C MULT Yang B., 2007, P 6 ACM INT C IM VID Yu P., 2005, P C HUM LANG TECHN E Yu S., 2003, P 12 INT C WORLD WID Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhang Y., 2009, P ASRU Zhang YD, 2010, INT CONF ACOUST SPEE, P4366, DOI 10.1109/ICASSP.2010.5495637 Zhou XS, 2003, MULTIMEDIA SYST, V8, P536, DOI 10.1007/s00530-002-0070-3 NR 74 TC 3 Z9 4 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1558-7916 J9 IEEE T AUDIO SPEECH JI IEEE Trans. Audio Speech Lang. Process. PD SEP PY 2012 VL 20 IS 7 BP 2095 EP 2110 DI 10.1109/TASL.2012.2196514 PG 16 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA 960NK UT WOS:000305395000003 ER PT J AU Gery, M Largeron, C AF Gery, Mathias Largeron, Christine TI BM25t: a BM25 extension for focused information retrieval SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Probabilistic information retrieval model; Structured information retrieval; XML; Tags; Weighting scheme; BM25 ID XML RETRIEVAL; RELEVANCE AB This paper addresses the integration of XML tags into a term-weighting function for focused XML information retrieval (IR). Our model allows us to consider a certain kind of structural information: tags that represent a logical structure (e.g., title, section, paragraph, etc.) as well as other tags (e.g., bold, italic, center, etc.). We take into account the influence of a tag by estimating the probability for this tag to distinguish relevant terms from the others. Then, these weights are integrated in a term-weighting function. Experiments on a large collection from the INEX 2008 XML IR evaluation campaign showed improvements on focused XML retrieval. C1 [Gery, Mathias; Largeron, Christine] Univ Lyon, F-42023 St Etienne, France. [Gery, Mathias; Largeron, Christine] CNRS, Lab Hubert Curien, UMR 5516, F-42000 St Etienne, France. [Gery, Mathias; Largeron, Christine] Univ St Etienne, F-42000 St Etienne, France. RP Gery, M (reprint author), Univ Lyon, F-42023 St Etienne, France. EM mathias.gery@univ-st-etienne.fr; christine.largeron@univ-st-etienne.fr FU Web Intelligence project (region Rhone-Alpes) FX The authors would like to thank Franck Thollard who greatly contributed to this work. The work was supported by the Web Intelligence project (region Rhone-Alpes, cf. http://www.web-intelligence-rhone-alpes.org). CR Baeza-Yates R, 2006, ACM T INFORM SYST, V24, P405, DOI 10.1145/1185877.1185878 Boyan J, 1996, AAAI WORKSH INT BAS Carmel D, 2001, SIGMOD REC, V30, P62 Denoyer L., 2006, SIGIR Forum, V40 Fuhr N, 2004, P 2 WORKSH INITIATIV Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 Fuhr N, 2005, LECT NOTES COMPUTER, V3493 Fuhr N, 2008, LECT NOTES COMPUTER, V4862 Fuller M, 1993, P 16 ANN INT ACM SIG, P204, DOI 10.1145/160688.160720 Gery M., 2008, P 2008 IEEE WIC ACM, P763 Geva S, 2009, LECT NOTES COMPUTER, V5631 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Kamps J., 2005, P 14 ACM INT C INF K, P4, DOI 10.1145/1099554.1099559 Kamps J, 2005, INFORM RETRIEVAL, V8, P631, DOI 10.1007/s10791-005-0750-7 Kamps Jaap, 2008, SIGIR FORUM, V42, P59, DOI DOI 10.1145/1480506.1480517 Kazai G., 2007, P 1 INT C THEOR INF, P247 Konopnicki D, 1995, P 21 INT C VER LARG, P54 Kotsakis E., 2002, P 2002 ACM S APPL CO, P663 Lalmas M, 2009, ENCY DATABASE SYSTEM, P3616 Lalmas M, 2009, ENCY DATABASE SYSTEM, P2862 Lalmas M, 2009, XML RETRIEVAL SYNTHE Lu W, 2006, LECT NOTES COMPUT SC, V3977, P161 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Rapela J, 2001, P 3 INT WORKSH WEB I, P61 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1986, INTRO MODERN INFORM Schlieder T, 2002, J AM SOC INF SCI TEC, V53, P489, DOI 10.1002/asi.10060 Sun YHK, 2000, P 9 TEXT RETR C TREC, P392 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 Taha K, 2010, KNOWL INF SYST, V23, P153, DOI 10.1007/s10115-009-0210-6 Tamine-Lechani L, 2010, KNOWL INF SYST, V24, P1, DOI 10.1007/s10115-009-0231-1 Taylor M., 2006, P 15 ACM INT C INF K, P585, DOI DOI 10.1145/1183614.118369 Trotman A, 2007, SIGIR FORUM, V41, P97 Trotman A, 2005, INFORM PROCESS MANAG, V41, P243, DOI 10.1016/j.ipm.2003.10.003 van Zwol R, 2006, LECT NOTES COMPUT SC, V3936, P314 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 Wolff J. E., 2000, Proceedings IEEE Advances in Digital Libraries 2000, DOI 10.1109/ADL.2000.848377 Zaragoza H, 2004, P 13 TEXT RETR C TRE Zhu JH, 2010, KNOWL INF SYST, V23, P29, DOI 10.1007/s10115-009-0202-6 NR 42 TC 4 Z9 4 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD JUL PY 2012 VL 32 IS 1 BP 217 EP 241 DI 10.1007/s10115-011-0426-0 PG 25 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 964KB UT WOS:000305692000009 ER PT J AU Hu, QM Huang, JX Hu, XH AF Hu, Qinmin Huang, Jimmy Xiangji Hu, Xiaohua TI Modeling and mining term association for improving biomedical information retrieval performance SO BMC BIOINFORMATICS LA English DT Article; Proceedings Paper CT IEEE International Conference on Bioinformatics and Biomedicine CY NOV 12-15, 2011 CL Atlanta, GA SP IEEE AB Background: The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. Results: We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. Conclusions: First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages. C1 [Hu, Qinmin; Huang, Jimmy Xiangji] York Univ, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada. [Hu, Qinmin] York Univ, Dept Comp Sci & Engn, Toronto, ON M3J 1P3, Canada. [Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. [Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. RP Huang, JX (reprint author), York Univ, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada. EM jhuang@yorku.ca CR Allan J, 2004, NIST SPECIAL PUBLICA Beaulieu M., 1997, NIST SPECIAL PUBLICA, P143 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 GREFENSTETTE G, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P89 Hersh W, 2007, NIST SPECIAL PUBLICA Hersh W, 2005, NIST SPECIAL PUBLICA Hersh W, 2006, NIST SPECIAL PUBLICA Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Hu QM, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S5-S6 Hu Q, 2008, ISMIS 2008 FDN INT S, P434 Hu QM, 2010, J INTELL INF SYST, V34, P249, DOI 10.1007/s10844-009-0097-4 Huang JX, 2009, SIGIR, P307 Huang X, 2005, P 28 ANN INT ACM SIG, P613, DOI 10.1145/1076034.1076154 Huang X, 2005, P 14 TEXT RETR C Huang X., 2006, P 29 ANN INT ACM SIG, P728, DOI 10.1145/1148170.1148341 Kaji H., 2000, P 18 INT C COMP LING, P404 Machado AMC, 2003, COMP GRAPH IM PROC B, V0, P191 Mandl T, 1999, P 7 EUR C INT TECHN Manna S, 2008, P IEEE ISI 2008 PAIS, P14 Manning C, 1999, HDB RES MACHINE LEAR Mehta B, 2006, P 21 NAT C AAAI Olivas ES, 2009, HDB RES MACHINE LEAR R Srikant, 1996, P 5 INT C EXT DAT TE, ppp Reyment R, 1996, APPL FACTOR ANAL NAT, P2 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Subbaraoand C, 1995, ENVIRON GEOL, V28, P175 Wei J, 2000, P 1 INT C WEB INF SY, V1, P366 Yin X, 2010, ECIR, P495 Zhong M., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148336 NR 32 TC 2 Z9 2 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD JUN 11 PY 2012 VL 13 SU 9 AR S2 DI 10.1186/1471-2105-13-S9-S2 PG 18 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 970NY UT WOS:000306139700002 PM 22901087 ER PT J AU Selvaretnam, B Belkhatir, M AF Selvaretnam, Bhawani Belkhatir, Mohammed TI Natural language technology and query expansion: issues, state-of-the-art and perspectives SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE Query expansion systems; Linguistic analysis; Term dependency; Knowledge based processing ID INFORMATION-RETRIEVAL; RELEVANCE; SEARCH; WEB; ENVIRONMENTS AB The availability of an abundance of knowledge sources has spurred a large amount of effort in the development and enhancement of Information Retrieval techniques. Users' information needs are expressed in natural language and successful retrieval is very much dependent on the effective communication of the intended purpose. Natural language queries consist of multiple linguistic features which serve to represent the intended search goal. Linguistic characteristics that cause semantic ambiguity and misinterpretation of queries as well as additional factors such as the lack of familiarity with the search environment affect the users' ability to accurately represent their information needs, coined by the concept "intention gap". The latter directly affects the relevance of the returned search results which may not be to the users' satisfaction and therefore is a major issue impacting the effectiveness of information retrieval systems. Central to our discussion is the identification of the significant constituents that characterize the query intent and their enrichment through the addition of meaningful terms, phrases or even latent representations, either manually or automatically to capture their intended meaning. Specifically, we discuss techniques to achieve the enrichment and in particular those utilizing the information gathered from statistical processing of term dependencies within a document corpus or from external knowledge sources such as ontologies. We lay down the anatomy of a generic linguistic based query expansion framework and propose its module-based decomposition, covering topical issues from query processing, information retrieval, computational linguistics and ontology engineering. For each of the modules we review state-of-the-art solutions in the literature categorized and analyzed under the light of the techniques used. C1 [Belkhatir, Mohammed] Univ Lyon, Lyon Inst Technol, Fac Informat Technol, Lyon, France. [Selvaretnam, Bhawani] Monash Univ, Fac Informat Technol, Sunway, Malaysia. RP Belkhatir, M (reprint author), Univ Lyon, Lyon Inst Technol, Fac Informat Technol, Lyon, France. EM bhawani.selvaretnam@monash.edu; mohammed.belkhatir@univ-lyon1.fr CR Abdelali A, 2007, INFORM PROCESS MANAG, V43, P705, DOI 10.1016/j.ipm.2006.06.007 Arampatzis Avi, 2008, P 31 ANN INT ACM SIG, P811, DOI 10.1145/1390334.1390517 Aula A., 2003, P IADIS INT C WWW IN, P403 Avi A., 2008, P 31 ANN INT ACM SIG, P811 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bai J, 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725 Bendersky M., 2009, ACM SIGIR, P810 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Bozzon A., 2007, P 30 ANN INT ACM SIG, P739, DOI 10.1145/1277741.1277885 Broder A., 2002, SIGIR Forum, V36 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Cao G, 2005, P 28 ANN INT ACM SIG, P298 Chirita P.-A., 2007, P 30 ANN INT ACM SIG, P7, DOI DOI 10.1145/1277741.1277746 Collins-Thompson Kevyn, 2005, P 14 ACM INT C INF K, P704, DOI DOI 10.1145/1099554.1099727 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 Dong H., 2008, 2 IEEE INT C DIG EC, p397 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Frakes W. B., 1992, INFORM RETRIEVAL DAT Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Goldsmith J, 2001, COMPUT LINGUIST, V27, P153, DOI 10.1162/089120101750300490 Hollink L, 2004, INT J HUM-COMPUT ST, V61, P601, DOI 10.1016/j.ijhcs.2004.03.002 Huston S., 2010, ACM SIGIR, V291, P298 Jansen BJ, 2008, INFORM PROCESS MANAG, V44, P1251, DOI 10.1016/j.ipm.2007.07.015 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 Kim S, 2004, P 27 ANN INT ACM SIG, P258, DOI 10.1145/1008992.1009038 Lang H., 2008, EVALUATION ANAL INCO, P602 Lau EP, 2006, INFORM PROCESS MANAG, V42, P1316, DOI 10.1016/j.ipm.2006.02.003 Lin D., 1999, P 37 ANN M ASS COMP, P317, DOI 10.3115/1034678.1034730 Lioma C, 2008, INFORM PROCESS MANAG, V44, P143, DOI 10.1016/j.ipm.2006.12.005 Liu S, 2004, P 27 ANN INT ACM SIG, P266, DOI DOI 10.1145/1008992.1009039 Liu S, 2005, P 14 ACM INT C INF K, P525, DOI 10.1145/1099554.1099696 Manning C. D., 2008, INTRO INFORM RETRIEV Mastora A., 2008, P 12 EUR C RES ADV T, P427 McCarthy D, 2003, COMPUT LINGUIST, V29, P639, DOI 10.1162/089120103322753365 McCarthy D, 2007, COMPUT LINGUIST, V33, P553, DOI 10.1162/coli.2007.33.4.553 Mena E, 2000, INT J COOP INF SYST, V9, P403, DOI 10.1142/S0218843000000193 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Mihalcea R., 2007, USING WIKIPEDIA AUTO, P196 Miller G A, 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Moreau F., 2007, 29 EUR C IR RES ROM, P222 Morenberg M., 2001, DOING GRAMMAR Nanas N., 2004, Proceedings. 15th International Workshop on Database and Expert Systems Applications, DOI 10.1109/DEXA.2004.1333442 Patwardhan S., 2007, SEMEVAL 07, P390 Pedersen T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P1024 Peng Fuchun, 2007, P 30 ANN INT ACM SIG, P639, DOI 10.1145/1277741.1277851 Phan Nina, 2007, P 30 ANN INT ACM SIG, P709, DOI 10.1145/1277741.1277870 Punyakanok V, 2008, COMPUT LINGUIST, V34, P257, DOI 10.1162/coli.2008.34.2.257 Radhouani S, 2006, IEEE INT C MULT EXP, P1853 Rieh SY, 2006, INFORM PROCESS MANAG, V42, P751, DOI 10.1016/j.ipm.2005.05.005 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Salton G., 1990, J AM SOC INFORM SCI, V41, P355 Song R., 2008, 30 EUR C IR RES, P346 Spink A, 1998, INFORM PROCESS MANAG, V34, P599, DOI 10.1016/S0306-4573(98)00025-9 Stevenson M, 2001, COMPUT LINGUIST, V27, P321, DOI 10.1162/089120101317066104 Turney PD, 2006, COMPUT LINGUIST, V32, P379, DOI 10.1162/coli.2006.32.3.379 Vechtomova O., 2009, COLLECTION ENCY DATA, P2254 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 White R., 2007, P 30 ANN INT ACM SIG, P255, DOI 10.1145/1277741.1277787 Wolfram D, 2008, INFORM PROCESS MANAG, V44, P1279, DOI 10.1016/j.ipm.2007.07.010 Xu X., 2006, IEEE INT C SYST MAN, V4, P3441 Yinghao L., 2007, IMPROVING WEAK AD HO NR 65 TC 0 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD JUN PY 2012 VL 38 IS 3 BP 709 EP 740 DI 10.1007/s10844-011-0174-3 PG 32 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 943DJ UT WOS:000304100400007 ER PT J AU Sanderson, M Croft, WB AF Sanderson, Mark Croft, W. Bruce TI The History of Information Retrieval Research SO PROCEEDINGS OF THE IEEE LA English DT Article DE History; information retrieval; ranking algorithms ID PROBABILITY RANKING PRINCIPLE; SYSTEMS; IR AB This paper describes a brief history of the research and development of information retrieval systems starting with the creation of electromechanical searching devices, through to the early adoption of computers to search for items that are relevant to a user's query. The advances achieved by information retrieval researchers from the 1950s through to the present day are detailed next, focusing on the process of locating relevant information. The paper closes with speculation on where the future of information retrieval lies. C1 [Sanderson, Mark] RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic 3001, Australia. [Croft, W. Bruce] Univ Massachusetts, Dept Comp Sci, Amherst, MA 01003 USA. RP Sanderson, M (reprint author), RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic 3001, Australia. EM mark.sanderson@rmit.edu.au; croft@cs.umass.edu RI Sanderson, Mark/ OI Sanderson, Mark/0000-0003-0487-9609 CR [Anonymous], 1954, Chem. Eng. News, V32, P891, Patent No. 1838389291931 [Anonymous], 1954, Chem. Eng. News, V32, P866, Patent No. 001351692311920 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE, Vsecond BJORNER S, 2003, SEARCHER, V11, P36 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BROWNSON HL, 1960, SCIENCE, V132, P1922, DOI 10.1126/science.132.3444.1922 Buckland Michael, 2006, E GOLDBERG HIS KNOWL Bush V., 1945, ATLANTIC MONTHLY, P101 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Cleverdon C. W., 1959, P INT C SCI INF, P687 Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 2009, SEARCH ENGINES INFOR, V1st Cucerzan Silviu, 2004, P EMNLP 04, P293 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENNIS BK, 1962, J CHEM DOC, V2, P234, DOI 10.1021/c160007a014 Dumais S., 2003, P 26 ANN INT ACM SIG, P72 Eliot Simon, 2009, COMPANION HIST BOOK FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Goldberg E., 1931, CHEM ENG NEWS, Patent No. U.S. Patent 183 838 929-1931 Hearst M. A., 2009, SEARCH USER INTERFAC, V1 Hiemstra D, 1998, LNCS, V1513, P569 Holmstrom J. E., 1948, P ROY SOC SCI INF C, P77 Jahoda G., 1961, STATE LIB ART, V4, P139 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1958, IBM J RES DEV, V2, P159 Manning C. D., 2008, INTRO INFORM RETRIEV Maron M. E., 1959, 3 THOMPS RAM WOOLDR McBryan O., 1994, P 1 INT WORLD WID WE Meister D., 1967, CR918 NASA BUNK RAM0 Mitchell H. F., 1953, AM DOC, V4, P16, DOI 10.1002/asi.5090040105 Mooers C. N., 1950, P ASS COMP MACH C Mooers C. N., 1959, P W JOINT COMP C, P81, DOI 10.1145/1457838.1457853 NANUS B, 1960, B MED LIBR ASSOC, V48, P278 Peng Fuchun, 2007, P 30 ANN INT ACM SIG, P639, DOI 10.1145/1277741.1277851 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Radlinski F., 2005, P 11 ACM SIGKDD INT, P239, DOI 10.1145/1081870.1081899 ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J Robertson S. E., 1990, NIST SPECIAL PUBLICA, P109 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J. J., 1965, ISR9 HARV U SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1973, 73173 TR CORN U DEP Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAW RS, 1949, J DOC, V5, P164, DOI 10.1108/eb026148 Soper H. E., 1920, CHEM ENG NEWS, Patent No. U.S. Patent US00 135 169 231-1920 Sparck Jones K., 1981, INFORM RETRIEVAL EXP SPARCKJONES K, 1972, J DOC, V28, P11 Stevens M. E., 1964, P S STAT ASS METH ME Switzer P., 1963, VECTOR IMAGES DOCUME Taube M., 1952, AM DOC, V3, P213, DOI 10.1002/asi.5090030404 Turtle H., 1994, P 17 ANN INT ACM SIG, P212 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VERHOEFF J, 1961, COMMUN ACM, V4, P557, DOI 10.1145/366853.366861 Voorhees E. M., 2001, NAT LANG ENG, V7, P361 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Walter C., 2005, INSIGHTS KRYDERS LAW NR 64 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 0018-9219 J9 P IEEE JI Proc. IEEE PD MAY PY 2012 VL 100 SI SI BP 1444 EP 1451 DI 10.1109/JPROC.2012.2189916 PG 8 WC Engineering, Electrical & Electronic SC Engineering GA 020TR UT WOS:000309838000032 ER PT J AU Ferguson, P Smeaton, AF AF Ferguson, Paul Smeaton, Alan F. TI Index ordering by query-independent measures SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Indexing; Efficiency/effectiveness tradeoffs; Query-independent search ID RETRIEVAL; RELEVANCE; WEB AB Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most "important" documents within the collection, and sort documents within inverted file lists in order of this "importance". In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Ferguson, Paul; Smeaton, Alan F.] Dublin City Univ, CLARITY Ctr Sensor Web Technol, Dublin 9, Ireland. RP Ferguson, P (reprint author), Dublin City Univ, CLARITY Ctr Sensor Web Technol, Dublin 9, Ireland. EM paul.ferguson@dcu.ie FU Science Foundation Ireland as part of the CLARITY CSET [03/IN.3/1361, 07/CE/I1147] FX This work was funded by Science Foundation Ireland as part of the CLARITY CSET, under Grant Numbers 03/IN.3/1361 and 07/CE/I1147. CR Amento B., 2000, SIGIR 00, P296 Anh V., 2009, LECT NOTES COMPUTER, V5280, P51 Anh V. N., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Anh VN, 2001, SIGIR, P35 Anil V. N., 2006, TREC 2006 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Boser B. E., 1992, COMPUTATIONAL LEARNI, P144 Broder A. Z., 2003, CIKM, P426 Buckley C., 1985, SIGIR 85, P97 Buttcher S., 2006, TREC 2006 Buttcher S., 2006, CIKM 06 P 15 ACM INT, P182 Carmel D., 2004, TREC 2004 Carmel D., 2006, TREC 2006 Carmel D., 2001, SIGIR Forum Chakrabarti S, 1999, COMPUTER, V32, P60, DOI 10.1109/2.781636 Cool C., 1993, P 14 NAT ONL M MEDF, P77 ELIAS P, 1975, IEEE T INFORM THEORY, V21, P194, DOI 10.1109/TIT.1975.1055349 Fagni T, 2006, ACM T INFORM SYST, V24, P51, DOI 10.1145/1125857.1125859 Ferguson P., 2005, ECIR 2005, P520 Ferguson P., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Garcia S., 2004, CRPIT 2004 P 27 C AU, P7 Garcia S., 2006, TREC 2006 GOLOMB S., 1966, IEEE T INFORM THEORY, V12, P3 Hauff C., 2008, CIKM 2008, P1419 Jansen B.J, 2003, 4 INT C INT COMP LAS, P65 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Joachims Thorsten, 2002, KDD 02, P133, DOI DOI 10.1145/775047.775067 JOSE JM, 1998, SIGIR P 21 ANN INT C, P232 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lempel R., 2003, WWW 2003 Long X., 2003, VLDB 2003, V29 Moffat A, 1996, ACM T INFORM SYST, V14, P349, DOI 10.1145/237496.237497 Mothe J., 2007, IEEE SITIS 2007 Ntoulas A., 2007, SIGIR, P191 Ntoulas A., 2006, WWW 06 P 15 INT C WO, P83 Page L., 1998, PAGERANK CITATION RA PARK TK, 1993, LIBR QUART, V63, P318 Persin M, 1996, J AM SOC INFORM SCI, V47, P749, DOI 10.1002/(SICI)1097-4571(199610)47:10<749::AID-ASI3>3.3.CO;2-U Persin M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Plachouras V, 2005, INFORM RETRIEVAL, V8, P197, DOI 10.1007/s10791-005-5659-7 Probost F., 2000, AAAI 2000 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Skobeltsyn G., 2008, SIGIR, P131 Smeaton A. F., 1981, SIGIR 1981, P83 The World Wide Web Consortium, 2011, WORLD WID WEB CONS W Vapnik V. N., 1995, NATURE STAT LEARN TH Wang PL, 1999, J AM SOC INFORM SCI, V50, P98 Witten I. H., 1999, MANAGING GIGABYTES C Xue G. -R., 2005, SIGIR 2005 Zhai C., 2001, SIGIR 2001, P334 Zhang J., 2008, WWW 08, P387 Zhu X., 2000, SIGIR 00, P288 Zipf G., 1932, SELECTIVE STUDIES PR NR 53 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2012 VL 48 IS 3 BP 569 EP 586 DI 10.1016/j.ipm.2011.10.003 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 931MI UT WOS:000303223600015 ER PT J AU Akritidis, L Katsaros, D Bozanis, P AF Akritidis, Leonidas Katsaros, Dimitrios Bozanis, Panayiotis TI Improved retrieval effectiveness by efficient combination of term proximity and zone scoring: A simulation-based evaluation SO SIMULATION MODELLING PRACTICE AND THEORY LA English DT Article DE Web; Search engines; Inverted index; Simulation; Evaluation ID VECTOR-SPACE MODEL; SEARCH; COMPRESSION; RANKING AB During the past few years, the commercial Web search engines have augmented their underlying index structures by significantly enriching the information which describes the appearance of a word within a document Dean (2009) [7]. This enriched information is now used in complex and effective functions which rank documents by taking into consideration hundreds of features, with respect to a user query. Despite the evolution of the search engines, the past research has mainly concentrated on improving plain Web indexes storing typical data only. In this work we study the problem of organizing an inverted index storing additional information. In particular, we examine how the physical locations of a document, called zones, can be efficiently integrated with such an index structure. We introduce TZP, an encoder which compresses these zones in combination to the positions of a word in a document, by employing a fixed number of bits for each portion of a word's inverted list. We demonstrate that our method allows direct access to the compressed zones and positions without expensive look-ups, avoids decoding any unnecessary information, while its overall index size is analogous or even better when compared against state-of-the art schemes. Moreover, we examine how the word positions can be combined to the zones to improve retrieval effectiveness. We introduce BM25TOPF, a scheme which incorporates term proximity and zone weighting into a single ranking formula. Unlike other term proximity approaches, BM25TOPF also takes into account the ordering of the query terms by rewarding the documents containing them in the correct order. Our experiments with the Web Adhoc Task of TREC 2009 and a set of own queries show that BM25TOPF outperforms the current state-of-the-art approaches by a margin between 6% and 11%. (C) 2011 Elsevier B.V. All rights reserved. C1 [Akritidis, Leonidas; Katsaros, Dimitrios; Bozanis, Panayiotis] Univ Thessaly, Dept Comp & Commun Engn, Volos, Greece. RP Katsaros, D (reprint author), Univ Thessaly, Dept Comp & Commun Engn, Volos, Greece. EM dkatsar@inf.uth.gr CR Anh VN, 2006, LECT NOTES COMPUT SC, V4209, P304 Anh VN, 2010, SOFTWARE PRACT EXPER, V40, P131, DOI 10.1002/spe.948 Boldi P, 2005, LECT NOTES COMPUT SC, V3772, P25 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Buttcher S., 2006, P 29 ANN INT ACM SIG, P622 Chierichetti F., 2009, P 18 INT C WORLD WID, P451, DOI 10.1145/1526709.1526770 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Dean J., 2009, WSDM 09, P1 Ding S., 2009, WWW 09, P421 Heman S., 2005, THESIS U AMSTERDAM A Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Lu W, 2006, LECT NOTES COMPUT SC, V3977, P161 Manning C. D., 2008, INTRO INFORM RETRIEV Moffat A, 1996, ACM T INFORM SYST, V14, P349, DOI 10.1145/237496.237497 Navarro G, 2000, INFORM RETRIEVAL, V3, P49, DOI 10.1023/A:1009934302807 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1975, COMMUN ACM, V18, P620 Sayood K, 2000, INTRO DATA COMPRESSI, V2nd Schenkel R, 2007, LECT NOTES COMPUT SC, V4726, P287 Silvestri F., 2010, P 19 ACM INT C INF K, P1219, DOI DOI 10.1145/1871437.1871592 Soboroff I., 2009, OVERVIEW TREC 2009 W Tran VX, 2009, SIMUL MODEL PRACT TH, V17, P1378, DOI 10.1016/j.simpat.2009.06.010 Transier F., 2010, ACM T INFORM SYSTEMS, V29, P2 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H Witten I. H., 1999, MANAGING GIGABYTES C Yan H., 2009, WWW, P401 Yan H., 2009, SIGIR 09, P147 Zhang J., 2008, WWW 08, P387 ZOBEL J, 2006, ACM COMPUT SURV, V38, P2, DOI DOI 10.1145/1132956.1132959 Zukowski M., 2006, ICDE 06, P59 NR 32 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1569-190X J9 SIMUL MODEL PRACT TH JI Simul. Model. Pract. Theory PD MAR PY 2012 VL 22 BP 74 EP 91 DI 10.1016/j.simpat.2011.12.002 PG 18 WC Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering SC Computer Science GA 905WU UT WOS:000301307700006 ER PT J AU Blanco, R Lioma, C AF Blanco, Roi Lioma, Christina TI Graph-based term weighting for information retrieval SO INFORMATION RETRIEVAL LA English DT Article DE Information retrieval; Graph theory; Natural language processing ID SMALL-WORLD NETWORKS; SCALE-FREE NETWORKS; METABOLIC NETWORKS; COMPLEX NETWORKS; COMMUNITY STRUCTURE; ORGANIZATION; MODEL; TEXT; WEB; CONNECTIVITY AB A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures. C1 [Lioma, Christina] Univ Stuttgart, Dept Comp Sci, Stuttgart, Germany. [Blanco, Roi] Univ A Coruna, Dept Comp Sci, La Coruna, Spain. RP Lioma, C (reprint author), Univ Stuttgart, Dept Comp Sci, Stuttgart, Germany. EM rblanco@udc.es; liomaca@ims.uni-stuttgart.de CR Agirre E., 2009, EACL, P33 Albert R., 1999, CONDMAT9907038 CORR Albert R., 2001, CONDMAT0106096 CORR Albert R, 2005, J CELL SCI, V118, P4947, DOI 10.1242/jcs.02714 Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47 Allan J., 2009, P 32 ANN INT ACM SIG, pJuly ANH V., 2005, P 28 ANN INT ACM SIG, P226, DOI 10.1145/1076034.1076075 Antiqueira L., 2007, INTELIGENCIA ARTIFIC, V11, P51, DOI DOI 10.4114/IA.V11I36.891 Antiqueira L, 2009, INFORM SCIENCES, V179, P584, DOI 10.1016/j.ins.2008.10.032 Baeza-Yates R. A., 2005, SIGIR 2005 P 28 ANN Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barabasi AL, 2002, PHYSICA A, V311, P590, DOI 10.1016/S0378-4371(02)00736-7 Barrat A, 2004, P NATL ACAD SCI USA, V101, P3747, DOI 10.1073/pnas.0400087101 Bekkerman R., 2007, IJCAI, P2280 Belew R. K, 2005, ABSCS0504036 CORR Belkin N. J., 1989, SIGIR 89 12 INT C RE Berlow EL, 1999, NATURE, V398, P330, DOI 10.1038/18672 Blanco R., 2007, SIGIR, P829 Blondel VD, 2004, SIAM REV, V46, P647, DOI 10.1137/S0036144502415960 Boccaletti S, 2006, PHYS REP, V424, P175, DOI 10.1016/j.physrep.2005.10.009 Bollobas B., 1985, RANDOM GRAPHS Bollobas B., 1979, GRAPH THEORY INTRO C Bookstein A., 1991, P 14 ANN INT ACM SIG Bordag S, 2003, LECT NOTES COMPUT SC, V2877, P10 Caldeira S. M. G., 2005, NETWORK CONCEPTS WRI Cancho RFI, 2007, INT J BIFURCAT CHAOS, V17, P2453 Cao G., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Chakrabarti S, 1998, COMPUT NETWORKS ISDN, V30, P65, DOI 10.1016/S0169-7552(98)00087-7 Chakrabarti S., 2007, P 16 INT C WORLD WID, P571, DOI 10.1145/1242572.1242650 Choudhury M., 2007, P 2 WORKSH TEXTGR GR, P81 Christensen C, 2007, INT J BIFURCAT CHAOS, V17, P2201, DOI 10.1142/S021812740701835X Cramer P., 1968, WORD ASS Craswell N., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Crestani F, 1998, ACM T INFORM SYST, V16, P225, DOI 10.1145/290159.290161 Deese J., 1965, STRUCTURE ASS LANGUA Dorogovtsev SN, 2002, ADV PHYS, V51, P1079, DOI 10.1080/00018730110112519 Dorogovtsev SN, 2001, P ROY SOC B-BIOL SCI, V268, P2603, DOI 10.1098/rspb.2001.1824 DOSZKOCS TE, 1990, ANNU REV INFORM SCI, V25, P209 Eisner J., 2005, P INT WORKSH PARS TE, P30, DOI 10.3115/1654494.1654498 Erdos P., 1960, PUBL MATH I HUNG, V5, P17 Erdos P., 1961, B I INT STATIST TOKY, V38, P343 Erdos PRA., 1959, I PUBL MATH, V6, P290 Erkan G, 2004, J ARTIF INTELL RES, V22, P457 Esuli A., 2007, ASS COMPUTER LINGUIS Faloutsos M., 1999, SIGCOMM, P251 Feinberg M., 1980, DYNAMICS MODELLING R, P59 Ferrer i Cancho R, 2005, PROBLEMS QUANTITATIV, P60 Ferrer i Cancho R., 2004, PHYS REV E, V69, P915, DOI DOI 10.1103/PHYSREVE.69.051915 Ferrer-i Cancho R., 2001, J QUANT LINGUIST, V8, P165 Firth John R., 1968, SELECTED PAPERS JR F, P168 Gamon M., 2006, P 1 WORKSH GRAPH BAS, P17, DOI 10.3115/1654758.1654762 Gaume B, 2008, INT J INTELL SYST, V23, P848, DOI 10.1002/int.20275 Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799 Goldberg A.B., 2006, P 1 WORKSH GRAPH BAS, P45, DOI 10.3115/1654758.1654769 Guimera R, 2005, P NATL ACAD SCI USA, V102, P7794, DOI 10.1073/pnas.0407994102 Halliday M. A. K., 1976, COHESION ENGLISH HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P Hassan S., 2006, P TEXTGRAPS 2 WORKSH, P53, DOI 10.3115/1654758.1654770 Ho N. D., 2004, RIVF, P193 Hoey M., 1991, PATTERNS LEXIS TEXT HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554 HOPFIELD JJ, 1986, SCIENCE, V233, P625, DOI 10.1126/science.3755256 Huang W. Y., 1987, NEURAL INFORM PROCES, P387 Hughes T, 2007, P 2007 JOINT C EMP M, P581 Jeong H, 2000, NATURE, V407, P651 Jespersen O., 1929, PHILOS GRAMMAR Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Joyce T, 2008, LECT NOTES ARTIF INT, V4938, P116, DOI 10.1007/978-3-540-78159-2_12 Jung J., 2008, ICALT, P228 Kleinberg J. M, 2006, SIGIR 06, P210 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Knospe W, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.056133 Kozareva Z., 2008, P ACL 08 HLT, P1048 Kozima H, 1993, EACL, P232 Kraaij W., 2007, SIGIR 2007 P 30 ANN, pJuly Krapivsky PL, 2000, PHYS REV LETT, V85, P4629, DOI 10.1103/PhysRevLett.85.4629 Krovetz RT, 2000, ARTIF INTELL, V118, P277, DOI 10.1016/S0004-3702(99)00101-0 Latora V, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.198701 Latora V, 2003, EUR PHYS J B, V32, P249, DOI 10.1140/epjb/e2003-00095-5 Leicht E., 2006, PHYS REV E Lemke N, 2004, BIOINFORMATICS, V20, P115, DOI 10.1093/bioinformatics/btg386 Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041 Li W, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.046106 Lioma C, 2009, LECT NOTES COMPUT SC, V5478, P412, DOI 10.1007/978-3-642-00958-7_37 Lioma C., 2008, RFLA, V8, P9 Ma'ayan A., 2004, ANN REV GIOPHYS BIOM, P319 Ma'ayan A, 2005, SCIENCE, V309, P1078, DOI 10.1126/science.1108876 MACLEOD KJ, 1991, INFORM PROCESS MANAG, V27, P337, DOI 10.1016/0306-4573(91)90088-4 Manning C. D., 1999, FDN STAT LANGUAGE PR Masucci AP, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.026102 McCann K, 1998, NATURE, V395, P794, DOI 10.1038/27427 Mehler A., 2007, CORPUS LINGUISTICS I Mihalcea Rada, 2004, EMNLP, P404 Milo R, 2004, SCIENCE, V303, P1538, DOI 10.1126/science.1089167 Minkov E., 2008, EMNLP, P907 Minsky M.L., 1969, SEMANTIC INFORM PROC Moore C, 2000, PHYS REV E, V61, P5678, DOI 10.1103/PhysRevE.61.5678 Motter A. E., 2011, PHYS REV E, V65 Muller Philippe, 2006, P HLT NAACL 2006 WOR, P65, DOI 10.3115/1654758.1654773 Nastase V., 2006, AAAI Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480 Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898 Ounis I., 2007, RES DIRECTIONS TERRI Ozmutlu S, 2004, INFORM PROCESS MANAG, V40, P319, DOI 10.1016/S0306-4573(03)00044-X Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Page L., 1998, PAGERANK CITATION RA Pastor-Satorras R, 2001, PHYS REV LETT, V86, P3200, DOI 10.1103/PhysRevLett.86.3200 Pearl J., 1988, PROBABILISTIC REASON Pedersen T., 2004, WORDNET SIMILARITY M, P1024 Plaza L., 2008, COLING 2008, P53 Polis GA, 1998, NATURE, V395, P744, DOI 10.1038/27323 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Popescu A.M., 2005, HLT EMNLP Ramage D., 2009, P 2009 WORKSH GRAPH, P23, DOI 10.3115/1708124.1708131 Reynal V. F., 2005, ANN OPER RES, V23, P37 Robertson S., 1995, NIST SPECIAL PUBLICA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruge G., 1995, SIGIR Forum Scellato S., 2005, EUROPEAN J P B UNPUB, V50, P221 Schenkel R., 2008, SIGIR, P523 Schmid Helmut, 1994, INT C NEW METH LANG, P44 Schutze H., 1995, S DOC AN INF RETR LA, P161 Sigman M, 2002, P NATL ACAD SCI USA, V99, P1742, DOI 10.1073/pnas.022341799 Sigurd B, 2004, STUD LINGUISTICA, V58, P37, DOI 10.1111/j.0039-3193.2004.00109.x Sinclair J., 1991, CORPUS CONCORDANCE C Singhal A., 1996, SIGIR Forum Singhal A., 2001, IEEE DATA ENG B, V24, P35 Sinha S., 2009, P 2009 WORKSH GRAPH, P5, DOI 10.3115/1708124.1708128 Soares MM, 2005, PHYSICA A, V355, P678, DOI 10.1016/j.physa.2005.03.017 Somasundaran S., 2009, P 2009 WORKSH GRAPH, P66, DOI 10.3115/1708124.1708138 Sporns O, 2002, BEHAV BRAIN RES, V135, P69, DOI 10.1016/S0166-4328(02)00157-2 Sporns Olaf, 2002, Complexity, V8, P56, DOI 10.1002/cplx.10047 Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3 Takamura H., 2007, HLT NAACL, P292 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Veronis J, 1990, COLING 90, P389 Vitevitch M. S., 2005, J MULTILINGUAL COMMU, V3, P64, DOI DOI 10.1080/14769670400027332 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wagner A, 2001, P ROY SOC B-BIOL SCI, V268, P1803 Wasserman S., 1994, SOCIAL NETWORK ANAL Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Widdows D., 2002, COLING Zhou D., 2004, NIPS NR 143 TC 4 Z9 5 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD FEB PY 2012 VL 15 IS 1 BP 54 EP 92 DI 10.1007/s10791-011-9172-x PG 39 WC Computer Science, Information Systems SC Computer Science GA 887IN UT WOS:000299920200003 ER PT J AU Zhong, MJ AF Zhong Minjuan GP IEEE TI Selecting Good Expansion Terms for Improving XML Retrieval Performance SO 2012 INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING AND COMMUNICATION TECHNOLOGY (ICCECT 2012) LA English DT Proceedings Paper CT International Conference on Control Engineering and Communication Technology (ICCECT) CY DEC 07-09, 2012 CL Shenyang, PEOPLES R CHINA SP IEEE Comp Soc DE Pseudo-Relevance Feedback; XML query expansion; tag semantic weight; node level AB in this paper, we study how to perform XML query expansion effectively from the high quality pseudo-relevance documents. A solution for selecting good expansion information is presented, in which various features impacting weight, such as term element frequency, term inverse element frequency, semantic weight of tag and level information, are analyzed and those term with high weigh value are selected as expansion term. Experiment results show that proposed expansion method is feasible. Compared to original query and traditional expansion method with no structure features considered, our method achieves better retrieval performance. C1 Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang, Peoples R China. RP Zhong, MJ (reprint author), Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang, Peoples R China. EM lucyzmj@sina.com CR Cao Guihong, 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377 Carnegie Mellon University and the University of Massachusetts, 2010, INDRI LANG MOD MEETS Kaushik R., 2002, P 2002 ACM SIGMOD IN, P133 Li Luping, 2010, J COMPUTER ENG APPL, V46, P131 Li Q., 2001, P 27 INT C VER LARG, P361 Liu D., 2010, P 19 ACM C INF KNOWL, P1289, DOI 10.1145/1871437.1871603 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schenkel R, 2006, LECT NOTES COMPUT SC, V3896, P331 Schenkel R, 2006, LECT NOTES COMPUT SC, V3936, P326 van Rijsbergen, 1989, P ACM C RES DEV INF, P194 Wan Chang-Xuan, 2008, Journal of Software, V19, DOI 10.3724/SP.J.1001.2008.02611 Zhong Minjuan, 2012, J CHINA SOC SCI TECH, V31, P15 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-4499-9 PY 2012 BP 480 EP 483 DI 10.1109/ICCECT.2012.176 PG 4 WC Automation & Control Systems; Operations Research & Management Science SC Automation & Control Systems; Operations Research & Management Science GA BEF33 UT WOS:000316387300116 ER PT J AU Tsai, MF Wang, CJ AF Tsai, Ming-Feng Wang, Chuan-Ju BE Chan, JH Tan, AH TI Post-Modern Portfolio Theory for Information Retrieval SO PROCEEDINGS OF THE INTERNATIONAL NEURAL NETWORK SOCIETY WINTER CONFERENCE (INNS-WC2012) SE Procedia Computer Science LA English DT Proceedings Paper CT 3rd International Winter Conference of the Neural-Network-Society (INNS-WC) / 11th International Conference on Bioinformatics (InCoB) / 3rd International Conference on Computational Systems-Biology and Bioinformatics (CSBio) CY OCT 03-05, 2012 CL Bangkok, THAILAND SP King Mongkuts Univ Technol Thonburi (KMUTT), IEEE Student Branch, Natl Ctr Genet Engn & Biotechnol (BIOTEC), Int Neural Network Soc (INNS), ACM, Thailand Chapter, IEEE Computat Intelligence Soc, INNS Thailand Reg Chapter DE retrieval models; optimization; semivariance AB Information Retrieval (IR) aims to discover relevant information according to a user's information need. The classic Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic IR models. This ranking principle, however, neglects the uncertainty introduced through the estimations from retrieval models. Inspired by the Post-Modern Portfolio Theory (PMPT), this paper proposes a mean-semivariance framework to handle the uncertainty. The proposed framework not only deals with the uncertainty but has the ability to distinguish bad surprises (downside uncertainty) and good surprises (upside uncertainty) when optimizing a ranking list. The experimental results shows that the proposed method improves the IR performance over the PRP baseline in terms of most of IR evaluation metrics; moreover, the results suggest that the mean-semivariance framework can further boost the top-position ranking quality. (C) 2012 Published by Elsevier B. V. Selection and/or peer-review under responsibility of Program Committee of INNS-WC 2012 C1 [Tsai, Ming-Feng] Natl Chengchi Univ, Dept Comp Sci, Taipei 116, Taiwan. RP Wang, CJ (reprint author), Taipei Municipal Univ Educ, Dept Comp Sci, Taipei 100, Taiwan. EM mftsai@nccu.edu.tw; cjwang@tmue.edu.tw CR Cooper W., INADEQUACY PROBABILI Damodaran A., 2003, INVESTMENT PHILOS SU, V185 Estrada J., 2007, J APPL FINANCE, V18, P57 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Markowitz H, 1952, J FINANC, V7, P77, DOI 10.2307/2975974 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rom B. M., 1994, J INVESTING, V3, P11, DOI 10.3905/joi.3.3.11 Sortino F., 2001, MANAGING DOWNSIDE RI, V1 Wang J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P115, DOI 10.1145/1571941.1571963 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 ZHU JH, 2009, ADV INFORM RETRIEVAL, V5478, P17, DOI 10.1007/978-3-642-00958-7_5 Zhu JH, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P99, DOI 10.1145/1571941.1571961 NR 12 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1877-0509 J9 PROCEDIA COMPUT SCI PY 2012 VL 13 BP 80 EP 85 DI 10.1016/j.procs.2012.09.116 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BDU98 UT WOS:000315031700008 ER PT S AU Mbarek, R Tmar, M AF Mbarek, Rabeb Tmar, Mohamed BE CalderonBenavides, L GonzalezCaro, C Chavez, E Ziviani, N TI Relevance Feedback Method Based on Vector Space Basis Change SO STRING PROCESSING AND INFORMATION RETRIEVAL: 19TH INTERNATIONAL SYMPOSIUM, SPIRE 2012 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 19th International Symposium on String Processing and Information Retrieval (SPIRE) / 8th Latin American Web Congress (LA-WEB) CY OCT 21-25, 2012 CL Cartagena de Indias, COLOMBIA DE Basis change; Query; Relevance feedback AB The idea of relevance feedback (RF) is to involve the user in the retrieval process to improve the final result set by reformulating the query. The most commonly used methods in RF aim to rewrite the user query. In the vector space model, RF is usually undertaken by re-weighting the query terms without any modification in the vector space basis. In this paper we propose a RF method based on vector space basis change without any modification on the query term weights. The aim of our method is to find a basis which gives a better representation of the documents such that the relevant documents are gathered and the irrelevant ones are kept away from the relevant documents. C1 [Mbarek, Rabeb; Tmar, Mohamed] Univ Sfax, Multimedia Informat Syst & Adv Comp Lab, High Inst Comp Sci & Multimedia, Sfax, Tunisia. RP Mbarek, R (reprint author), Univ Sfax, Multimedia Informat Syst & Adv Comp Lab, High Inst Comp Sci & Multimedia, Sfax, Tunisia. EM rabeb.hattab@gmail.com; mohamedtmar@yahoo.fr CR Campos L.M.D., 2001, DOCUMENT INSTANTIATI Croft W.B., 2006, CIKM 2006, P49 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Croft W.B., 2001, P JOINT DELOS NSF WO DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ide E., 1971, SMART RETRIEVAL SYST, P337 Melucci M., 2008, ACM T INFORM SYSTEMS, V26, P1 Melucci M., 2005, P ACM C INF KNOWL MA, P808, DOI 10.1145/1099554.1099745 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 1992, TEXT RETR C, P21 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I., 2002, 4 INT C CONC LIB INF, P199 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR Voorhees E. M, 2006, P 14 ANN TEXT RETR C NR 14 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-34108-3 J9 LECT NOTES COMPUT SC PY 2012 VL 7608 BP 342 EP 347 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCM88 UT WOS:000310731700036 ER PT J AU Carpineto, C Romano, G AF Carpineto, Claudio Romano, Giovanni TI A Survey of Automatic Query Expansion in Information Retrieval SO ACM COMPUTING SURVEYS LA English DT Article DE Algorithms; Experimentation; Measurement; Performance; Query expansion; query refinement; search; word associations; pseudo-relevance feedback; document ranking ID RELEVANCE FEEDBACK; WEB SEARCH; SYSTEMS; PERFORMANCE; DICTIONARY; ALGORITHM; CONTEXT; ISSUES; RULES; TERMS AB The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user's original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in laboratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed. Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)? C1 [Carpineto, Claudio; Romano, Giovanni] Fdn Ugo Bordoni, I-00142 Rome, Italy. RP Carpineto, C (reprint author), Fdn Ugo Bordoni, Via Baldassarre Castiglione 59, I-00142 Rome, Italy. EM carpinet@fub.it CR AGICHTEIN E., 2004, ACM T INTERNET TECHN, V4, P1299 AGIRRE E., 2009, P CLEF Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 AKELLA R., 2007, P 29 EUR C IR RES EC, P246 Allan J, 1996, P 19 ANN INT ACM SIG, P270, DOI 10.1145/243199.243274 AMATI G., 2001, NIST SPECIAL PUBLICA, P182 AMATI G., 2003, THESIS U GLASGOW UK AMATI G., 2004, P 25 EUR C INF RETR, P127 AMATI G., 2003, P 4 WORKSH CROSS LAN, P310 ANDERSON JR, 1983, J VERB LEARN VERB BE, V22, P261, DOI 10.1016/S0022-5371(83)90201-3 Arguello Jaime, 2008, P 2 INT C WEBL SOC M, P10 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bai J, 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725 Bai J., 2007, P 30 ANN INT ACM SIG, P15, DOI 10.1145/1277741.1277747 BAI J., 2006, P 2006 C EMP METH NA, P551, DOI 10.3115/1610075.1610152 Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 Baluja S., 2007, P 9 INT C HUM COMP I, P405, DOI 10.1145/1377999.1378046 Bast H., 2007, P 16 C INF KNOWL MAN, DOI 10.1145/1321440.1321560 Bast H., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148234 Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BERNARDINI A, 2009, P IEEE WIC ACM INT C, P206 BERNARDINI A., 2008, P TREC 2008 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Billerbeck B., 2005, P 10 AUSTR DOC COMP, P34 Billerbeck B., 2003, P 26 ANN INT ACM SIG, P387 Billerbeck B., 2004, P AUSTR DAT C DUN NZ, V27, P69 BILLERBECK B., 2004, P STRING PROC INF RE, P30 BILLERBECK B., 2005, THESIS RMIT U MELBOU Billerbeck Bodo, 2003, P 12 INT C INF KNOWL, P2 Bilotti M.W., 2004, P INF RETR QUEST ANS Bodoff D, 1998, J AM SOC INFORM SCI, V49, P1254, DOI 10.1002/(SICI)1097-4571(1998)49:14<1254::AID-ASI4>3.0.CO;2-O Broder A., 2009, P 18 INT C WORLD WID, P511, DOI 10.1145/1526709.1526778 Broder A., 2002, SIGIR Forum, V36 Buckley C., 1995, NIST SPECIAL PUBLICA, V500-225, P69 BUCKLEY C., 2003, PUBLISHING IND CHINA, P1 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buscher Georg, 2008, P 31 ANN INT ACM SIG, P387, DOI 10.1145/1390334.1390401 CAO G., 2007, P 16 C INF KNOWL MAN Cao Guihong, 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377 Carmel D., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/564376.564427 CARPINETO C., 2009, ACM COMPUT SURV, V41, P3 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Carpineto C, 2002, ACM T INFORM SYST, V20, P259, DOI 10.1145/568727.568728 Chang YJ, 2006, INFORM PROCESS MANAG, V42, P453, DOI 10.1016/j.ipm.2005.03.025 CHEN L., 2004, P IEEE INT C E TECHN, P317 Chirita P.-A., 2007, P 30 ANN INT ACM SIG, P7, DOI DOI 10.1145/1277741.1277746 CHURCH K., 2007, P INT C INT US INT I, P112, DOI 10.1145/1216295.1216320 Collins-Thompson K., 2009, P 18 ACM C INF KNOWL, P837, DOI 10.1145/1645953.1646059 Collins-Thompson K., 2007, P SIGIR 2007, P303, DOI 10.1145/1277741.1277795 Collins-Thompson Kevyn, 2005, P 14 ACM INT C INF K, P704, DOI DOI 10.1145/1099554.1099727 CRABTREE D., 2007, P 13 ACM SIGKDD INT, P191, DOI 10.1145/1281192.1281216 CRESTANI, 1997, ARTIF INTELL REV, V11, P6 CROFT W. B., 2007, P 29 EUR C INF RETR, P52 Cronen-Townsend S., 2002, P 2 INT C HUM LANG T, P104, DOI 10.3115/1289189.1289266 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 Custis T., 2007, P 30 ANN INT ACM SIG, P575, DOI 10.1145/1277741.1277840 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Hu JN, 2006, INT C PATT RECOG, P703 Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200 DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 Efron M, 2008, INFORM PROCESS MANAG, V44, P163, DOI 10.1016/j.ipm.2006.12.008 EFTHIMIADIS E. N., 1993, P 16 ANN INT ACM SIG, P146, DOI 10.1145/160688.160710 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 Fitzpatrick L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P306, DOI 10.1145/258525.258597 FLEMMINGS R., 2009, P CLEF Fujii A, 2008, P 17 INT C WORLD WID, P337, DOI 10.1145/1367497.1367544 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Gauch S, 1999, ACM T INFORM SYST, V17, P250, DOI 10.1145/314516.314519 GONG Z., 2006, P 17 INT C DAT EXP S, P379 GONZALO J., 1998, P COLINGACL WORKSH U, P647 Graupmann J., 2005, Proceedings. International Workshop on Challenges in Web Information Retrieval and Integration Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Church K. W., 1990, Computational Linguistics, V16 HARABAGIU S., 2004, P WORKSH PRAGM QUEST, P1 Harabagiu S., 2001, P 39 ANN M ASS COMP, P282, DOI 10.3115/1073012.1073049 Harman D., 1992, INFORMATION RETRIEVA, P241 HARPER G. W., 1978, J DOC, V34, P189 Hauff C., 2008, P 17 ACM C INF KNOWL, P1419, DOI 10.1145/1458082.1458311 He B., 2009, P 18 ACM C INF KNOWL, P2011, DOI 10.1145/1645953.1646289 He B, 2007, INFORM PROCESS MANAG, V43, P1294, DOI 10.1016/j.ipm.2006.11.002 HIDALGO J. M. G., 2005, P 10 INT C APPL NAT, P298 HUANG C.-C., 2005, 2005 IEEEW INT C, P193 Huang CK, 2003, J AM SOC INF SCI TEC, V54, P638, DOI 10.1002/asi.10256 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 Ide E., 1971, SMART RETRIEVAL SYST, P337 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Joachims T, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1229179.1229181 Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835 JONES S, 1993, J INFORM SCI, V19, P167, DOI 10.1177/016555159301900301 JONES S, 1995, J AM SOC INFORM SCI, V46, P52, DOI 10.1002/(SICI)1097-4571(199501)46:1<52::AID-ASI6>3.0.CO;2-1 Kanaan Ghassan, 2008, American Journal of Applied Sciences, V5 Kekalainen J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290978 Kherfi ML, 2004, ACM COMPUT SURV, V36, P35, DOI 10.1145/1013208.1013210 Koehn P., 2010, STAT MACHINE TRANSLA Kraaij W, 2003, COMPUT LINGUIST, V29, P381, DOI 10.1162/089120103322711587 Kraft R., 2004, P 13 INT C WORLD WID, P666, DOI 10.1145/988672.988763 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 Kurland O., 2005, P 28 ANN INT ACM SIG, P19, DOI 10.1145/1076034.1076041 KWOK K. L., 2004, P 13 TEXT RETRIEVAL Lam-Adesina AM, 2001, P 24 ANN INT ACM SIG, P1, DOI 10.114/383952.383953 LATIRI C. C., 2004, P 4 INT C JOURN INF Lau R. Y. K., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009017 Lau T, 1999, CISM COUR L, P119 Lavelli A, 2004, P 13 ACM INT C INF K, P615, DOI 10.1145/1031171.1031284 LAVRENKO V., 2006, 473 U MASS Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lee K., 2008, P 31 ANN INT ACM SIG, P235, DOI DOI 10.1145/1390334.1390376 LESK M. E., 1969, AM DOC, V20, P8 LESK M. E., 1988, P 4 ANN C U WAT CTR, P1 Liu S, 2004, P 27 ANN INT ACM SIG, P266, DOI DOI 10.1145/1008992.1009039 MACDONALD C., 2007, P 16 C INF KNOWL MAN Mandala R., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312677 Manning C. D., 2008, INTRO INFORM RETRIEV MAO W., 2002, COMM I INFO COMPUT M, V5, P2 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McNamee P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Melucci M, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361687 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Miller G., 1990, INT J LEXICOGR, V3, P235 Milne D, 2007, P 16 ACM C INF KNOWL, P445, DOI 10.1145/1321440.1321504 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Montague M., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management MURRAY G. C., 2005, P 28 ANN INT ACM SIG, P635, DOI 10.1145/1076034.1076165 NALLAPATI R., 2006, 521 U MASS Natsev A. P., 2007, P ACM INT C MULT, P991, DOI 10.1145/1291233.1291448 NAVIGLI R., 2003, P ECML PKDD 2003 WOR Navigli R, 2005, IEEE T PATTERN ANAL, V27, P1075, DOI 10.1109/TPAMI.2005.149 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Osinski S, 2005, IEEE INTELL SYST, V20, P48, DOI 10.1109/MIS.2005.38 OUNIS I., 2009, P EUR C INF RETR, P611 Palleti P, 2007, PROCEEDING OF THE 2007 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS, P83 PARK L. A. F., 2007, P 11 PAC AS C KNOWL, P224 Perugini S., 2006, IT Professional, V8, DOI 10.1109/MITP.2006.91 Pirkola A, 2001, INFORM RETRIEVAL, V4, P209, DOI 10.1023/A:1011994105352 Porter M. F., 1997, READINGS INFORM RETR, P313 Porter M. F., 1982, Information Technology: Research and Development, V1 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Riezler S, 2007, P 45 ANN M ASS COMP, P464 ROBERTSON S. E., 2000, NIST SPECIAL PUBLICA, P361 ROBERTSON S. E., 1998, NIST SPECIAL PUBLICA, P253 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Romano G., 2004, CONCEPT DATA ANAL TH Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Ruthven L, 2003, P 26 ANN INT ACM SIG, P213 Sahlgren M, 2005, P METH APPL SEM IND Sakai T., 2005, ACM T ASIAN LANGUAGE, V4, P111, DOI 10.1145/1105696.1105699 Salton G., 1983, INTRO MODERN INFORM Sanderson M., 2000, INFORMATION RETRIEVA, V2, P49, DOI 10.1023/A:1009933700147 Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 Savoy J., 2005, ACM T ASIAN LANGUAGE, V4, P163, DOI 10.1145/1105696.1105701 SCHLAEFER N., 2007, NIST SPECIAL PUBLICA, P332 Schtitze H., 1995, P 4 ANN S DOC AN INF, P161 SCHUTZE H., 1997, INFO PROCESS MANAGE, V33, P307 SEMERARO G, 2009, P 21 INT JOINT C ART, P1543 Shen X., 2005, P 28 ANN INT ACM SIG, P59, DOI 10.1145/1076034.1076047 Shokouhi M, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P427, DOI 10.1145/1571941.1572015 SHOKOUHI M., 2009, P 31 EUR C INF RETR, P362 Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 Song M, 2006, P 6 ACM IEEE CS JOIN, P202, DOI 10.1145/1141753.1141800 Song M, 2007, DATA KNOWL ENG, V63, P63, DOI 10.1016/j.datak.2006.10.010 Sun R., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148237 Suryanto M.A., 2007, P ACM 1 WORKSH CYBER, P47, DOI 10.1145/1317353.1317364 Theobald M., 2004, P 30 INT C VER LARG, P648, DOI 10.1016/B978-012088469-8/50058-9 Theobald Martin, 2005, P 28 ANN INT ACM SIG, P242, DOI 10.1145/1076034.1076077 Tokunaga Takenobu, 1998, P COLING ACL WORKSH, P31 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Vechtomova O, 2006, INFORM PROCESS MANAG, V42, P191, DOI 10.1016/j.ipm.2004.10.006 VECHTOMOVA O., 2009, ENCY DATABASE SYSTEM, P2254 Veronis J, 2004, COMPUT SPEECH LANG, V18, P223, DOI 10.1016/j.csl.2004.05.002 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 VOORHEES E., 2004, NIST SPECIAL PUBLICA VOORHEES E., 1998, NIST SPECIAL PUBLICA, P1 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 WANG HF, 2009, P 32 ANN INT ACM SIG, P51 Wang X., 2008, P 31 ANN INT ACM SIG, P219, DOI 10.1145/1390334.1390374 White R.W., 2005, P 28 ANN INT ACM SIG, P35, DOI 10.1145/1076034.1076044 Winaver M., 2007, P SIGIR, P729, DOI 10.1145/1277741.1277880 Witten I. H., 1999, MANAGING GIGABYTES C WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 Wong WS, 2008, INFORM PROCESS MANAG, V44, P1086, DOI 10.1016/j.ipm.2007.12.002 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Xu Y, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P59, DOI 10.1145/1571941.1571954 Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 Yu S., 2003, P 12 INT C WORLD WID, P11 Zelikovitz S., 2000, P 17 INT C MACH LEAR, P1183 Zha Z.-J., 2009, P 17 ACM INT C MULT, P15, DOI 10.1145/1631272.1631278 Zhai C., 2009, P 18 ACM C INF KNOWL, P255, DOI 10.1145/1645953.1645988 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 ZHANG P., 2008, P INT S INF PROC, P31 ZIMMER C., 2008, P SIGIR, P323, DOI 10.1145/1390334.1390390 NR 203 TC 34 Z9 40 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD JAN PY 2012 VL 44 IS 1 AR 1 DI 10.1145/2071389.2071390 PG 50 WC Computer Science, Theory & Methods SC Computer Science GA 892YS UT WOS:000300322300001 ER PT J AU Balog, K Fang, Y de Rijke, M Serdyukov, P Si, L AF Balog, Krisztian Fang, Yi de Rijke, Maarten Serdyukov, Pavel Si, Luo TI Expertise Retrieval SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL LA English DT Article AB People have looked for experts since before the advent of computers. With advances in information retrieval technology and the large-scale availability of digital traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest, and a wealth of results, in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains, such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research. C1 [Balog, Krisztian] NTNU, Dept Comp & Informat Sci, N-7491 Trondheim, Norway. [Fang, Yi] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. [de Rijke, Maarten] Univ Amsterdam, Intelligent Syst Lab Amsterdam, NL-1098 XII Amsterdam, Netherlands. [Serdyukov, Pavel] Yandex, Moscow 119021, Russia. [Si, Luo] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. RP Balog, K (reprint author), NTNU, Dept Comp & Informat Sci, Sem Saelandsvei 7-9, N-7491 Trondheim, Norway. EM krisztian.balog@idi.ntnu.no; yfang.purdue@gmail.com; derijke@uva.nl; pavser@yandex-team.ru; lsi@purdue.edu RI Balog, Krisztian/ OI Balog, Krisztian/0000-0003-2762-721X FU COMIDOR project - Norwegian Research Council; NSF [IIS-0746830, CNS-1012208, IIS-1017837]; Purdue Cyber Center; Center for Science of Information (CSoI), an NSF Science and Technology Center [CCF-0939370]; European Union's ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme, CIP ICT-PSP [250430]; European Community's Seventh Framework Programme (FP7/2007-2013) [258191]; (LiMoSINe project) [288024]; Netherlands Organisation for Scientific Research (NWO) [612.061.814, 612.061.815, 640.004.802, 380-70-011, 727.011.005, 612.001.116]; Center for Creation, Content and Technology (CCCT); Hyperlocal Service Platform project - Service Innovation ICT program; WAHSP; BILAND projects - CLARIN-nl program; Dutch national program COMMIT; ESF Research Network Program ELIAS FX Krisztian Balog was supported by the COMIDOR project, funded by the Norwegian Research Council.The research of Yi Fang and Luo Si was partially supported by the NSF research grants IIS-0746830, CNS-1012208, IIS-1017837 and research grants from Purdue Cyber Center as well as Google. This work is also partially supported by the Center for Science of Information (CSoI), an NSF Science and Technology Center, under grant agreement CCF-0939370.Maarten de Rijke was partially supported by the European Union's ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme, CIP ICT-PSP under grant agreement nr 250430 (Galateas), the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements nr 258191 (PROMISE Network of Excellence) and 288024 (LiMoSINe project), the Netherlands Organisation for Scientific Research (NWO) under project nrs 612.061.814, 612.061.815, 640.004.802, 380-70-011, 727.011.005, 612.001.116, the Center for Creation, Content and Technology (CCCT), the Hyperlocal Service Platform project funded by the Service Innovation & ICT program, the WAHSP and BILAND projects funded by the CLARIN-nl program, the Dutch national program COMMIT, and the ESF Research Network Program ELIAS. CR Ackerman M. S., 2000, Information Systems Frontiers, V2, DOI 10.1023/A:1026572712714 Adafre S., 2005, P 3 INT WORKSH LINK, P90, DOI 10.1145/1134271.1134284 Adafre S. Fissaha, 2007, REC ADV NAT LANG PRO Adamic LA, 2008, P 17 INT C WORLD WID, P665, DOI [10.1145/1367497.1367587, DOI 10.1145/1367497.1367587] AHO AV, 1975, COMMUN ACM, V18, P333, DOI 10.1145/360825.360855 Amati Giambattista, 2003, THESIS U GLASGOW [Anonymous], 1999, ECSCW 99 WORKSH KNOW Artiles J., 2005, P 28 ANN INT ACM SIG, P569, DOI 10.1145/1076034.1076132 Artiles J., 2010, CROSS LANGUAGE EVALU Artiles J., 2009, WEB PEOPL SEARCH EV Artiles Javier, 2007, P 4 INT WORKSH SEM E, P64, DOI 10.3115/1621474.1621486 Asmussen S, 1987, APPL PROBABILITY QUE Bailey P., 2007, ACM SIGIR FORUM, V41, P42, DOI 10.1145/1328964.1328969 Bailey P., 2008, P TEXT RETRIEVAL C T Balog K., 2008, THESIS U AMSTERDAM Balog K, 2009, INFORM PROCESS MANAG, V45, P1, DOI 10.1016/j.ipm.2008.06.003 BALOG K, 2008, P EUR C IR RES ECIR, V4956, P296 Balog K., 2007, P 30 ANN INT ACM SIG, P551, DOI DOI 10.1145/1277741.1277836 Balog K., 2011, ACM T INFORM SYSTEMS, V29 Balog K., 2007, P SIGIR 07, P821, DOI 10.1145/1277741.1277926 Balog K., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148181 BALOG K., 2008, P 31 ANN INT ACM SIG, P753, DOI 10.1145/1390334.1390486 Balog K., 2007, P TEXT RETRIEVAL C T Balog K., 2006, P 15 INT C WORLD WID, P1035, DOI DOI 10.1145/1135777.1136002 Balog K., 2011, P TEXT RETRIEVAL C T Balog K., 2010, P TEXT RETRIEVAL C T Balog K., 2008, P TEXT RETRIEVAL C T Balog K, 2009, WEAVING SERVICES AND PEOPLE ON THE WORLD WIDE WEB, P301, DOI 10.1007/978-3-642-00570-1_15 Balog K, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2657 Balog K., 2009, P TEXT RETRIEVAL C T Balog K., 2008, P 17 ACM C INF KNOWL, P489, DOI DOI 10.1145/1458082.1458148 Balog Krisztian, 2008, P 31 ANN INT ACM SIG, P371, DOI 10.1145/1390334.1390399 Bansal B., 2011, TALK Bao S., 2007, P TEXT RETRIEVAL C T Baumard P., 2001, TACIT KNOWLEDGE ORG Becerra-Fernandez I., 2006, ACM Transactions on Internet Technology, V6, DOI 10.1145/1183463.1183464 Becerra-Fernandez I, 2000, KNOWL-BASED SYST, V13, P315, DOI 10.1016/S0950-7051(00)00091-5 Bennett J., 2007, KDD 07, P35 Berendsen R., 2012, P EUR C INF RETR ECI Berendsen R., 2012, CLEF 2012 C LABS EV Blanco R., 2011, P INT WORKSH ENT OR, P65 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borgatti SP, 2003, MANAGE SCI, V49, P432, DOI 10.1287/mnsc.49.4.432.14428 Bron M., 2011, P INT C THEOR PRACT Bron M., 2010, P 19 ACM INT C INF K, P1079, DOI 10.1145/1871437.1871574 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Callan J., 2002, ADV INFORM RETRIEVAL, V7, P127, DOI DOI 10.1007/0-306-47019-5_ Campbell C. S., 2003, P 12 INT C INF KNOWL, P528, DOI DOI 10.1145/956863.956965 Cao Y., 2006, P TEXT RETRIEVAL C T Cetintas S., 2011, P INT ACM SIGIR C RE, P1209 Chapelle O, 2010, INFORM RETRIEVAL, V13, P216, DOI 10.1007/s10791-009-9110-3 Charlin L., 2011, P C UNC ART INT UAI, P86 Chen H., 2006, P TEXT RETRIEVAL C T Constant D, 1996, ORGAN SCI, V7, P119, DOI 10.1287/orsc.7.2.119 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 Craswell N., 2003, TREC 02, P78 Craswell N, 2005, P 28 ANN INT ACM SIG, P416, DOI 10.1145/1076034.1076106 Craswell N., 2001, AUSW P Craswell N., 2006, P TEXT RETRIEVAL C T Craswell N., 2002, P TEXT RETRIEVAL C P Csomai A, 2008, IEEE INTELL SYST, V23, P34, DOI 10.1109/MIS.2008.86 Cucerzan S., 2007, P 2007 JOINT C EMP M, P708 CUMMINS R, 2010, P EUR C ART INT ECAI, V215, P535 Das S., 2011, INT WORKSH ENT OR SE, P53 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Demartini G., 2010, INEX 09, P254 DEMARTINI G, 2009, P IN EV XML RETR INE, V5631, P243 Demartini G., 2008, P INT SEM WEB C ISWC Demartini G, 2009, LECT NOTES COMPUT SC, V5478, P189, DOI 10.1007/978-3-642-00958-7_19 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Deng H, 2009, P 18 ACM C INF KNOWL, P1733, DOI 10.1145/1645953.1646217 Deng HB, 2008, IEEE DATA MINING, P163, DOI 10.1109/ICDM.2008.29 Dennis J.E., 1996, NUMERICAL METHODS UN DERIJKE M, 2010, P 2010 C MULT MULT I, V6360, P94 DEVRIES AP, 2008, P IN EV XML RETR INE, V4862, P245 Doan A, 2005, AI MAG, V26, P83 Dom B., 2003, P 8 ACM SIGMOD WORKS, P42, DOI DOI 10.1145/882082.882093 Dong X., 2005, P ACM SIGMOD INT C M, P85, DOI 10.1145/1066157.1066168 Duan H., 2007, P TEXT RETRIEVAL C T EHRLICH K, 2008, P ANN SIGCHI C HUM F, P1093, DOI 10.1145/1357054.1357224 EHRLICH K, 2007, P 2007 INT ACM C SUP, P117, DOI 10.1145/1316624.1316642 Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 FANG H, 2007, P EUR C IR RES ECIR, V4425, P418 Fang Y, 2011, INFORM RETRIEVAL, V14, P158, DOI 10.1007/s10791-010-9139-3 Fang Y., 2009, SIGIR WORKSH LEARN R Fang Y., 2008, SERCTR294 PURD U DEP Fang Y, 2010, INFORM RETRIEVAL, V13, P618, DOI 10.1007/s10791-010-9127-7 Fang Yang, 2011, Proceedings of the 2011 9th World Congress on Intelligent Control and Automation (WCICA 2011), DOI 10.1109/WCICA.2011.5970704 FANG Y, 2010, P ANN INT ACM SIGIR, P683, DOI 10.1145/1835449.1835563 Fox EA, 1994, TEXT RETRIEVAL C TRE, V500-215, P243 Fu YP, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P77 Guan Z., 2012, IEEE T KNOWLEDGE DAT HALPIN H., 2010, P INT WORKSH EV SEM Heath T., 2006, P CHI2006 WORKSH REI Herbrich R., 2000, ADV LARGE MARGIN CLA, V88, P115 Hertzum M., 2000, New Review of Information Behaviour Research, V1 Hertzum M, 2000, INFORM PROCESS MANAG, V36, P761, DOI 10.1016/S0306-4573(00)00011-X Hofmann K, 2010, J AM SOC INF SCI TEC, V61, P994, DOI 10.1002/asi.21292 Hofmann K., 2008, P SIGIR WORKSH FUT C Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Hogan A., 2007, P INT EXPERTFINDER W Horowitz D., 2010, P 19 INT C WORLD WID, P431, DOI DOI 10.1145/1772690.1772735 Jiang J., 2007, P TEXT RETRIEVAL C T Jiang J., 2008, P SIGIR WORKSH FUT C JIANG JP, 2009, P IN EV XML RETR INE, V5631, P264 Jijkoun V., 2008, P 2 WORKSH AN NOIS U, P23, DOI 10.1145/1390749.1390755 KAPTEIN R, 2009, P IN EV XML RETR INE, V5631, P273 Kaptein Rianne, 2010, P 19 ACM C INF KNOWL, P69, DOI 10.1145/1871437.1871451 Karimzadehgan M., 2008, P 17 ACM C INF KNOWL, P1113, DOI 10.1145/1458082.1458230 Karimzadehgan M., 2009, CIKM, P1697 KARIMZADEHGAN M, 2009, P EUR C IR RES ECIR, V5478, P177 Khalid M., 2008, P EUR C IR RES ECIR, P705 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Ko J., 2007, P 30 ANN INT ACM SIG, P343, DOI 10.1145/1277741.1277801 Kukla G, 2012, COMPUT J, V55, P258, DOI 10.1093/comjnl/bxr093 Kuncheva LI, 2003, MACH LEARN, V51, P181, DOI 10.1023/A:1022859003006 Lappas T., 2009, P 15 ACM SIGKDD INT, P467, DOI 10.1145/1557019.1557074 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lee J. H., 1995, SIGIR 95 P 18 ANN IN, P180 Li J.-Z., 2007, P WWW 2007, P1271, DOI 10.1145/1242572.1242803 Liebregts R., 2009, P 31 EUR C IR RES AD, P587 Liker J., 2007, TOYOTA TALENT DEV YO Lin B., 2011, P INT WORKSH ENT OR, P14 Liu T., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Liu X., 2005, P 14 ACM INT C INF K, P315, DOI DOI 10.1145/1099554.1099644 MACDONALD C, 2007, P EUR C IR RES ECIR, V4425, P431 Macdonald C., 2009, THESIS U GLASGOW Macdonald Craig, 2010, SIGIR Forum, V44, DOI 10.1145/1842890.1842899 Macdonald C., 2009, P ACM INT C INF KNOW, P1983 Macdonald C., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148345 Macdonald C, 2009, COMPUT J, V52, P729, DOI 10.1093/comjnl/bxm112 Macdonald C, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P816, DOI 10.1145/1571941.1572143 Macdonald C., 2008, P 17 ACM C INF KNOWL, P1043, DOI 10.1145/1458082.1458221 Macdonald C., 2010, INFORM PROCESSING MA, V74, P376 MACDONALD C, 2011, P EUR C IR RES ECIR, V6611, P517 MACDONALD C, 2008, P EUR C IR RES ECIR, V4956, P283 Macdonald C., 2007, P ACM INT C INF KNOW, P341, DOI 10.1145/1321440.1321490 Macdonald C., 2006, P 15 ACM INT C INF K, P387, DOI 10.1145/1183614.1183671 MACDONALD C, 2008, P EUR C IR RES ECIR, V4956, P555 Maclin R., 1999, J ARTIFICIAL INTELLI, V11, P169 Mann G. S., 2003, P 7 C NAT LANG LEARN, P33 Manning C. D., 2008, INTRO INFORM RETRIEV Maybury M., 2006, MTR06B000040 McDonald D. W., 2001, P ACM C SUPP GROUP W, P214 McDonald D. W., 2000, P ACM C COMP SUPP CO, P231, DOI DOI 10.1145/358916.3589941 Meij E, 2011, J WEB SEMANT, V9, P418, DOI 10.1016/j.websem.2011.04.001 Meij E., 2009, P INT SEM WEB C ISWC, P415 Meij E., 2012, P ACM INT C WEB SEAR MENZEL H, 1966, ANNU REV INFORM SCI, V1, P41 Menzel H., 1960, REV STUDIES FLOW INF Mihalcea Rada, 2007, P 16 ACM C INF KNOWL, P233, DOI DOI 10.1145/1321440.1321475 Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150 MIMNO D, 2007, P 13 ACM SIGKDD INT, P500, DOI 10.1145/1281192.1281247 Mockus A, 2002, ICSE 2002: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P503 Moreira C., 2011, THESIS TU LISBON Moreira C, 2011, LECT NOTES ARTIF INT, V7026, P431, DOI 10.1007/978-3-642-24769-9_32 Morris M. R., 2010, P 4 INT AAAI C WEBL, P291 Nallapati R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009006 Neumayer R., 2012, EUR C INF RETR ECIR, P133 NG A, 2001, P ADV NEUR INF PROC, P841 Noll MG, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P612, DOI 10.1145/1571941.1572046 Page L, 1999, PAGERANK CITATION RA Pal A., 2012, P INT AAAI C WEBL SO, P274 Peacock M., 2009, CMSWIRE Pearl J., 1988, PROBABILISTIC REASON Pehcevski J, 2008, LECT NOTES COMPUT SC, V4956, P258 Petkova D, 2006, PROC INT C TOOLS ART, P599 Petkova D., 2007, P 16 ACM C INF KNOWL, P731, DOI 10.1145/1321440.1321542 Plachouras V., 2011, P WORKSH DIV DOC RET, P63 Pound J., 2010, P 19 INT C WORLD WID, P771, DOI DOI 10.1145/1772690.1772769 Prusak L, 2000, WORKING KNOWLEDGE OR Riahi F., 2012, P 21 INT C COMP WORL, P791 Richardson M., 2011, P INT WORLD WID WEB, P755 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, RELEVANCE FEEDBACK I ROSENBER.V, 1967, INFORM STORAGE RET, V3, P119, DOI 10.1016/0020-0271(67)90005-8 Rosen-Zvi M., 2004, P 20 C UNC ART INT, P487 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009 Santos R. L. T., 2010, AD PERS FUS HET INF, P1 Santos RLT, 2011, INFORM PROCESS MANAG, V47, P467, DOI 10.1016/j.ipm.2010.11.009 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SCHWARTZ MF, 1993, COMMUN ACM, V36, P78, DOI 10.1145/163381.163402 Seid D., 2000, DEMOIR HYBRID ARCHIT SERDYUKOV P, 2007, P EUR C IR RES ECIR, V4425, P737 Serdyukov P., 2009, P TEXT RETRIEVAL C T SERDYUKOV P, 2011, P EUR C IR RES ECIR, V6611, P399 Serdyukov P., 2008, P 17 ACM C INF KNOWL, P1133, DOI 10.1145/1458082.1458232 SERDYUKOV P, 2008, P EUR C IR RES ECIR, V4956, P309 Serdyukov P., 2008, P 31 ANN INT ACM SIG, P17 Shami S. N., 2008, P ACM C HUM FACT COM, P1089 Shami S. N., 2007, P ACM C SUPP GROUP W, P379 Shokouhi M., 2011, FDN TRENDS INFORM RE, V5 SMIRNOVA E, 2011, P EUR C IR RES ECIR, V6611, P580 Soboroff I., 2007, P TEXT RETRIEVAL C T Song D, 2003, J AM SOC INF SCI TEC, V54, P321, DOI 10.1002/asi.10213 Song Y, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P342, DOI 10.1145/1255175.1255243 Sorg P., 2011, P INT C KNOWL DISC I Sorg P., 2010, WORKSH CROSS LANG EV Su H., 2012, P PAC AS C KNOWL DIS Sutton C., 2007, INTRO STAT RELATIONA, P93 TANG J, 2009, P 15 ACM SIGKDD INT, P807, DOI DOI 10.1145/1557019.1557108>(2009) Tang J., 2008, P 14 ACM SIGKDD INT, P990, DOI 10.1145/1401890.1402008 Tang J, 2011, MACH LEARN, V82, P211, DOI 10.1007/s10994-010-5212-9 Tang WB, 2012, NEUROCOMPUTING, V76, P71, DOI 10.1016/j.neucom.2011.04.039 Tax DMJ, 2000, PATTERN RECOGN, V33, P1475, DOI 10.1016/S0031-3203(99)00138-7 Terveen L., 2005, ACM Transactions on Computer-Human Interaction, V12, DOI 10.1145/1096737.1096740 Tsikrika T., 2008, INEX 07, P306 Tu Y, 2010, P 23 INT C COMP LING, P1265 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Vapnik V, 2000, NATURE STAT LEARNING Weerkamp W., 2008, P ASS COMP LING HUM, P923 Weerkamp W, 2011, INFORM RETRIEVAL, V14, P515, DOI 10.1007/s10791-011-9165-9 WENG J., 2010, P 3 ACM INT C WEB SE, P261, DOI DOI 10.1145/1718487.1718520 Wiig KM, 1997, EXPERT SYST APPL, V13, P1, DOI 10.1016/S0957-4174(97)00018-3 Winkler W., 1999, TECHNICAL REPORT Woudstra L, 2008, INFORM PROCESS MANAG, V44, P1267, DOI 10.1016/j.ipm.2007.07.004 Xue Y., 2008, P TEXT RETRIEVAL C T YAHYAEI S, 2008, P EUR C IR RES ECIR, V4956, P406 Yang Zhang, 2009, Proceedings of the 2009 International Conference on Computational Intelligence and Security (CIS 2009), DOI 10.1109/CIS.2009.263 Yarosh S., 2012, P 2012 ACM ANN C HUM, P2247 Yimam-Seid D, 2003, J ORG COMP ELECT COM, V13, P1, DOI 10.1207/S15327744JOCE1301_1 Yom-Tov Elad, 2005, P 28 ANN INT ACM SIG, P512, DOI 10.1145/1076034.1076121 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang J., 2007, P 16 INT C WORLD WID, P221, DOI DOI 10.1145/1242572.1242603 Zhang J, 2007, LECT NOTES COMPUT SC, V4443, P1066 ZHANG J, 2008, P ADV KNOWL DISC DAT, V5012, P466 Zhang J., 2005, P 2005 INT ACM SIGGR, P71, DOI 10.1145/1099203.1099214 Zhang M., 2002, P TEXT RETR C TREC, P586 Zhu J., 2008, P CIKM 2008, P1421, DOI 10.1145/1458082.1458312 Zhu J., 2007, P TEXT RETRIEVAL C T Zhu JH, 2010, KNOWL INF SYST, V23, P29, DOI 10.1007/s10115-009-0202-6 Zhu J., 2008, P 10 ACM WORKSH WEB, P25, DOI 10.1145/1458502.1458507 ZHU JH, 2008, P IN EV XML RETR INE, V4862, P336 Zhu JH, 2009, J AM SOC INF SCI TEC, V60, P694, DOI 10.1002/asi.21012 NR 239 TC 12 Z9 13 PU NOW PUBLISHERS INC PI HANOVER PA PO BOX 1024, HANOVER, MA 02339 USA SN 1554-0669 EI 1554-0677 J9 FOUND TRENDS INF RET JI Found. Trends Inf. Retr. PY 2012 VL 6 IS 2-3 BP 127 EP 256 DI 10.1561/1500000024 PG 130 WC Computer Science, Information Systems SC Computer Science GA V32VI UT WOS:000208978300001 ER PT J AU Halpin, H Lavrenko, V AF Halpin, Harry Lavrenko, Victor TI Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation SO JOURNAL OF WEB SEMANTICS LA English DT Article DE Semantic search; Query logs; Information retrieval; Relevance feedback; Evaluation; Linked Data AB We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a 'virtuous cycle' between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous 'Cranfield' evaluation of Semantic Web search. (C) 2011 Elsevier B.V. All rights reserved. C1 [Halpin, Harry; Lavrenko, Victor] Univ Edinburgh, Edinburgh EH8 9AB, Midlothian, Scotland. RP Halpin, H (reprint author), Univ Edinburgh, 10 Crichton St, Edinburgh EH8 9AB, Midlothian, Scotland. EM H.Halpin@ed.ac.uk; vlavrenk@inf.ed.ac.uk FU Microsoft FX Support provided by Microsoft "Beyond Search'' award. CR Allan James, 2000, P 9 TEXT RETR C TREC Baeza-Yates R., 2008, P 5 EUR SEM WEB C TE Baeza-Yates R.A., 2007, P C KNOWL DISC DAT M Blanco R., 2011, P 34 INT ACM SIGIR C, P923 Blanco R., 2011, P 1 INT WORKSH ENT O Brickley D., 2004, RDF VOCABULARY DESCR Cheng G., 2008, P WORLD WID WEB C Craswell N., 2005, P 7 TEXT RETRIEVAL C Croft W. B., 1996, P 19 ANN INT ACM SIG Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 Ding L., 2006, P INT SEM WEB C ISWC FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619 Guha R., 2003, P INT C WORLD WID WE Halpin H., 2009, P LINK DAT WORLD WID Hawking D., 2000, P TEXT RETRIEVAL C T Hayes P., INT J SEMANTIC WEB I, V4 Klyne G., 2004, RESOURCE DESCRIPTION Lavrenko V., 2008, GENERATIVE THEORY RE Lavrenko V., 2001, P 24 ANN INT ACM SIG LENAT DB, 1990, COMMUN ACM, V33, P30, DOI 10.1145/79173.79176 Mangold Christoph, 2007, International Journal of Metadata, Semantics and Ontologies, V2, DOI 10.1504/IJMSO.2007.015073 Mikheev A., 1998, 7 MESS UND C P C Ponte J. M., 1998, THESIS U MASSACHUSET Robertson S., 1994, P 3 TEXT RETR C TREC ROBERTSON S., 2004, P ACM INT C INF KNOW ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Silverstein C., 1999, SIGIR Forum, V33 NR 28 TC 3 Z9 3 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1570-8268 J9 J WEB SEMANT JI J. Web Semant. PD DEC PY 2011 VL 9 IS 4 BP 474 EP 489 DI 10.1016/j.websem.2011.10.001 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 890UI UT WOS:000300170600008 ER PT J AU Kim, E Pyo, S Park, E Kim, M AF Kim, Eunhui Pyo, Shinjee Park, Eunkyung Kim, Munchurl TI An Automatic Recommendation Scheme of TV Program Contents for (IP)TV Personalization SO IEEE TRANSACTIONS ON BROADCASTING LA English DT Article DE Collaborative filtering; content based filtering; TV personalization; TV program recommendation AB Due to the rapid increase of contents available under the convergence of broadcasting and Internet, efficient access to personally preferred contents has become an important issue. In this paper, an automatic recommendation scheme based on collaborative filtering is presented for intelligent personalization of (IP)TV services. The proposed scheme does not require TV viewers (users) to make explicit ratings on their watched TV program contents. Instead, it implicitly infers the users' interests on the watched TV program contents. For the recommendation of user preferred TV program contents, our proposed recommendation scheme first clusters TV users into similar groups based on their preferences on the content genres from the user's watching history of TV program contents. For the personalized recommendation of TV program contents to an active user, a candidate set of preferred TV program contents is obtained via collaborative filtering for the group to which the active user belongs. The candidate TV programs for recommendation are then ranked by a proposed novel ranking model. Finally, a set of top-N ranked TV program contents is recommended to the active user. The experimental results show that the proposed TV program recommendation scheme yields about 77% of average precision accuracy and 0.135 value of ANMRR (Average Normalized Modified Retrieval Rank) with top five recommendations for 1,509 people. C1 [Kim, Eunhui; Park, Eunkyung; Kim, Munchurl] Korea Adv Inst Sci & Technol, Dept Elect Engn, Taejon 305701, South Korea. [Pyo, Shinjee] Korea Adv Inst Sci & Technol, Dept Informat & Commun Engn, Taejon 305701, South Korea. RP Kim, E (reprint author), Korea Adv Inst Sci & Technol, Dept Elect Engn, Taejon 305701, South Korea. EM lins77@kaist.ac.kr; sjpyo@kaist.ac.kr; epark@kaist.ac.kr; mkim@ee.kaist.ac.kr RI Kim, Munchurl/C-1759-2011 FU MKE/IITA [A1100-0801-3015] FX Manuscript received August 31, 2010; revised February 11, 2011; accepted June 27, 2011. Date of publication August 08, 2011; date of current version August 24, 2011. This work was supported by the R&D program of MKE/IITA (A1100-0801-3015, Development of Open-IPTV Technologies for Wired and Wireless Networks). CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Brake R., 2002, USER MODEL USER-ADAP, V12, P331 COTTER P, 2000, AM ASS AI, P957 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 DUDA R, 2001, PATTERN CLASSIFICATI, P542 GOOD N, 1999, AM ASS AI Hoon M.J.L. De, 2004, BIOINFOMATICS, P781 KAMAN W, 2004, IEEE INT C AC SPEECH, V3, P433 LAFFERTY J, 2002, LANGUAGE MODELING IN Manjunath BS, 2001, IEEE T CIRC SYST VID, V11, P703, DOI 10.1109/76.927424 MANNING CHRISTOPHER D., 2008, INTRO INFORM RETRIEV, P151 Montaner M, 2003, ARTIF INTELL REV, V19, P285, DOI 10.1023/A:1022850703159 Ndjiki-Nya P., 2000, ISO WG11 MPEG M GEN Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Resnick P., 1994, ACM C COMP SUPP COOP, P175 ROBERTSON SE, 1995, 4 TEXT RETR C TREC 4, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SERGIOS T, 2006, PATTERN RECOGN, P572 VETROV DP, 2006, IEEE T PAMI, V28, P1798 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval WANG J, 2007, MULTIMED TOOLS APPL, V36, P89 XUE G, 2005, ACM SIGIR AUG, P114 NR 22 TC 10 Z9 11 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 0018-9316 J9 IEEE T BROADCAST JI IEEE Trans. Broadcast. PD SEP PY 2011 VL 57 IS 3 BP 674 EP 684 DI 10.1109/TBC.2011.2161409 PG 11 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA 810ZA UT WOS:000294168900006 ER PT J AU Roque, FS Jensen, PB Schmock, H Dalgaard, M Andreatta, M Hansen, T Soeby, K Bredkjaer, S Juul, A Werge, T Jensen, LJ Brunak, S AF Roque, Francisco S. Jensen, Peter B. Schmock, Henriette Dalgaard, Marlene Andreatta, Massimo Hansen, Thomas Soeby, Karen Bredkjaer, Soren Juul, Anders Werge, Thomas Jensen, Lars J. Brunak, Soren TI Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts SO PLOS COMPUTATIONAL BIOLOGY LA English DT Article ID HEALTH RECORDS; MEDICAL-RECORDS; CELIAC-DISEASE; INFORMATION EXTRACTION; GENETIC-DISORDERS; CLINICAL-RESEARCH; HEART-FAILURE; CARE; NETWORK; TRIAL AB Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks. C1 [Roque, Francisco S.; Andreatta, Massimo; Brunak, Soren] Tech Univ Denmark, Ctr Biol Sequence Anal, Dept Syst Biol, DK-2800 Lyngby, Denmark. [Jensen, Peter B.; Jensen, Lars J.; Brunak, Soren] Univ Copenhagen, NNF Ctr Prot Res, Copenhagen, Denmark. [Schmock, Henriette; Hansen, Thomas; Bredkjaer, Soren; Werge, Thomas] Copenhagen Univ Hosp, Inst Biol Psychiat, Mental Hlth Ctr Sct Hans, Roskilde, Denmark. [Dalgaard, Marlene; Juul, Anders] Rigshosp, Dept Growth & Reprod GR, DK-2100 Copenhagen, Denmark. [Soeby, Karen] Copenhagen Univ Hosp, Dept Clin Biochem, Hvidovre Hosp, Hvidovre, Denmark. [Bredkjaer, Soren] Psychiat Reg Sealand, Ringsted, Denmark. RP Roque, FS (reprint author), Tech Univ Denmark, Ctr Biol Sequence Anal, Dept Syst Biol, DK-2800 Lyngby, Denmark. EM brunak@cbs.dtu.dk RI Juul, Anders /F-5864-2013; Hvam, Lars/O-4313-2014; Hansen, Thomas/O-5965-2014; Jensen, Lars Juhl/ OI Hansen, Thomas/0000-0001-6703-7762; Jensen, Lars Juhl/0000-0001-7885-715X FU Villum Kann Rasmussen Foundation; Novo Nordisk Foundation; Danish Strategic Research Council FX The work carried out in this study was supported by the Villum Kann Rasmussen Foundation: www.vkr-fondene.dk, the Novo Nordisk Foundation: http://www.novonordiskfonden.dk/ and the Danish Strategic Research Council: www.fi.dk/raad-og-udvalg/det-strategiske-forskningsraad. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Ananiadou S, 2006, TRENDS BIOTECHNOL, V24, P571, DOI 10.1016/j.tibtech.2006.10.002 Aronson A R, 2001, Proc AMIA Symp, P17 Becker D, 2005, J HEALTH ECON, V24, P189, DOI 10.1016/j.jhealeco.2004.07.002 BENJAMINI Y, 1995, J ROY STAT SOC B MET, V57, P289 Bushara KO, 2005, GASTROENTEROLOGY, V128, pS92, DOI 10.1053/j.gastro.2005.02.018 Cao Hui, 2005, AMIA Annu Symp Proc, P106 Chapman W, 2007, CONTEXT ALGORITHM ID, P81 Chapman WW, 2001, J BIOMED INFORM, V34, P301, DOI 10.1006/jbin.2001.1029 Colson NJ, 2004, NEUROGENETICS, V5, P129, DOI 10.1007/s10048-004-0181-4 Crammer KDM, 2007, ACL 07 WORKSH BIOL T, P129 Denny JC, 2010, BIOINFORMATICS, V26, P1205, DOI 10.1093/bioinformatics/btq126 DesRoches CM, 2008, NEW ENGL J MED, V359, P50, DOI 10.1056/NEJMsa0802005 Eaton WW, 2004, BRIT MED J, V328, P438, DOI 10.1136/bmj.328.7437.438 Embi Peter J, 2005, AMIA Annu Symp Proc, P231 Fessatou S, 2003, J PAEDIATR CHILD H, V39, P152, DOI 10.1046/j.1440-1754.2003.00116.x Friedman C, 2004, J AM MED INFORM ASSN, V11, P392, DOI 10.1197/jamia.M1552 Galanter WL, 2005, J AM MED INFORM ASSN, V12, P269, DOI 10.1197/jamia.M1727 Ganesan P, 2003, ACM T INFORM SYSTEMS, V21 Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104 Greenhalgh T, 2010, BRIT MED J, V340, DOI 10.1136/bmj.c3111 Haas JP, 2005, AM J INFECT CONTROL, V33, P439, DOI 10.1016/j.ajic.2005.06.008 Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514 Haux R, 2002, INT J MED INFORM, V66, P3, DOI 10.1016/S1386-5056(02)00030-8 Hayrinen K, 2008, INT J MED INFORM, V77, P291, DOI 10.1016/j.ijmedinf.2007.09.001 Hersh W R, 1996, Proc AMIA Annu Fall Symp, P159 Hettne KM, 2010, J BIOMED SEMANT, V1, DOI 10.1186/2041-1480-1-5 Hidalgo CA, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000353 Hoffman MA, 2007, J BIOMED INFORM, V40, P44, DOI 10.1016/j.jbi.2006.02.010 Hoffman S, 2010, AM J BIOETHICS, V10, P19, DOI 10.1080/15265161.2010.492894 Honigman B, 2001, J AM MED INFORM ASSN, V8, P254 Ikeda A, 1997, J NEUROL NEUROSUR PS, V63, P549, DOI 10.1136/jnnp.63.4.549 Jaspers M W M, 2006, Yearb Med Inform, P29 Jensen LJ, 2006, NAT REV GENET, V7, P119, DOI 10.1038/nrg1768 Knaup Petra, 2007, Yearb Med Inform, P34 Krasowska D, 2007, J AM ACAD DERMATOL, V56, P848, DOI 10.1016/j.jaad.2006.10.020 Kulikowski CA, 2009, METHOD INFORM MED, V48, P4, DOI 10.3414/ME9135 Lage K, 2007, NAT BIOTECHNOL, V25, P309, DOI 10.1038/nbt1295 Lage K, 2010, MOL SYST BIOL, V6, DOI 10.1038/msb.2010.36 Lage K, 2008, P NATL ACAD SCI USA, V105, P20870, DOI 10.1073/pnas.0810772105 Liao KP, 2010, ARTHRIT CARE RES, V62, P1120, DOI 10.1002/acr.20184 Long William, 2005, AMIA Annu Symp Proc, P470 Ma HY, 2010, CANCER RES, V70, P575, DOI 10.1158/0008-5472.CAN-09-3460 Manning C. D., 2009, INTRO INFORM RETRIEV Mercke Y, 2000, Ann Clin Psychiatry, V12, P35, DOI 10.3109/10401230009147085 Meystre S M, 2008, Yearb Med Inform, P128 Mullins IM, 2006, COMPUT BIOL MED, V36, P1351, DOI 10.1016/j.compbiomed.2005.08.003 Muzina DJ, 2002, EPILEPSY RES, V50, P195, DOI 10.1016/S0920-1211(02)00080-3 Pakhomov S, 2007, AM J MANAG CARE, V13, P281 Pakhomov SV, 2005, J BIOMED INFORM, V38, P145, DOI 10.1016/j.jbi.2004.11.016 Paller AS, 2003, J INVEST DERMATOL, V121, P430, DOI 10.1046/j.1523-1747.2003.12370.x Park J, 2009, MOL SYST BIOL, V5, DOI 10.1038/msb.2009.16 Patrick J, 2010, J AM MED INFORM ASSN, V17, P524, DOI 10.1136/jamia.2010.003939 Plovnick RM, 2010, AM J PSYCHIAT, V167, P498, DOI 10.1176/appi.ajp.2009.09101473 Prather J C, 1997, Proc AMIA Annu Fall Symp, P101 Prokosch HU, 2009, METHOD INFORM MED, V48, P38, DOI 10.3414/ME9132 Ritchie MD, 2010, AM J HUM GENET, V86, P560, DOI 10.1016/j.ajhg.2010.03.003 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rual JF, 2005, NATURE, V437, P1173, DOI 10.1038/nature04209 Savova GK, 2010, J AM MED INFORM ASSN, V17, P507, DOI 10.1136/jamia.2009.001560 Sax U, 2005, METHOD INFORM MED, V44, P546 Serden L, 2003, HEALTH POLICY, V65, P101, DOI 10.1016/S0168-8510(02)00208-7 Spasic I, 2010, J AM MED INFORM ASSN, V17, P532, DOI 10.1136/jamia.2010.003657 Sutherland JM, 2009, HEALTH CARE MANAG SC, V13, P65 Suzuki T, 2008, METHOD INFORM MED, V47, P541, DOI 10.3414/ME9128 Tremblay MC, 2009, INFORM TECHNOL MANAG, V10, P253, DOI 10.1007/s10799-009-0061-6 Ullman-Cullere MH, 2011, HUM MUTAT, V32, P512, DOI 10.1002/humu.21456 Uzuner O, 2008, J AM MED INFORM ASSN, V15, P14, DOI 10.1197/jamia.M2408 WHITTY CWM, 1966, LANCET, V1, P856 Wright A, 2010, J BIOMED INFORM, V43, P891, DOI 10.1016/j.jbi.2010.09.009 Zeng Qing T, 2006, BMC Med Inform Decis Mak, V6, P30, DOI 10.1186/1472-6947-6-30 NR 70 TC 40 Z9 40 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1553-7358 J9 PLOS COMPUT BIOL JI PLoS Comput. Biol. PD AUG PY 2011 VL 7 IS 8 AR e1002141 DI 10.1371/journal.pcbi.1002141 PG 10 WC Biochemical Research Methods; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Mathematical & Computational Biology GA 812NH UT WOS:000294299700020 PM 21901084 ER PT J AU Dong, H Hussain, FK AF Dong, Hai Hussain, Farookh Khadeer TI Semantic service matchmaking for Digital Health Ecosystems SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Digital Ecosystems; Digital Health Ecosystems; Health service metadata; Health service ontology; Semantic search; Semantic service matchmaking; Service evaluation and ranking ID WEB SEARCH; INFORMATION; RELEVANCE AB The vision of Digital Ecosystems was initiated by the European Commission, with the purpose of constructing an information and communication technology environment to facilitate the sustainable development of small and medium enterprises. As a key sub-domain of Digital Ecosystems, Digital Health Ecosystems provide crucial services to maintain the health of the main participants of Digital Ecosystems. We are concerned with the large-scale, ambiguous, heterogeneous, and untrustworthy nature of health service information in Digital Health Ecosystems. An intensive survey found that current research cannot support accurate and trustworthy matchmaking between health service requests and health service advertisements in Digital Health Ecosystems. Therefore, in this paper, we propose a framework for a semantic service matchmaker that takes into account the ambiguous, heterogeneous nature of service information in Digital Health Ecosystems. This framework is designed to make four major contributions, which are health service domain knowledge modeling, online health service information disambiguation, health service query disambiguation and health service quality evaluation and ranking. In order to thoroughly evaluate this framework, we implement a prototype - a Semantic Health Service Search Engine, and conduct a series of experiments on the prototype using a functional testing and simulation approach. (C) 2011 Elsevier B.V. All rights reserved. C1 [Dong, Hai; Hussain, Farookh Khadeer] Curtin Univ, Digital Ecosyst & Business Intelligence Inst, Enterprise Unit 4, Bentley, WA 6102, Australia. RP Hussain, FK (reprint author), Curtin Univ, Digital Ecosyst & Business Intelligence Inst, Enterprise Unit 4, De Laeter Way,Technol Pk, Bentley, WA 6102, Australia. EM hai.dong@cbs.curtin.edu.au; farookh.hussain@cbs.curtin.edu.au RI Dong, Hai/E-9101-2010 OI Dong, Hai/0000-0002-7033-5688 CR Andreassen HK, 2007, BMC PUBLIC HEALTH, V7, DOI 10.1186/1471-2458-7-53 BERGENTI F, 2007, WORKSH SEM WEB UB HE Berland GK, 2001, JAMA-J AM MED ASSOC, V285, P2612, DOI 10.1001/jama.285.20.2612 Caceres C, 2006, IEEE INTELL SYST, V21, P11, DOI 10.1109/MIS.2006.107 Can AB, 2007, COMPUT METH PROG BIO, V86, P73, DOI 10.1016/j.cmpb.2007.01.007 Champin PA, 2010, KNOWL-BASED SYST, V23, P287, DOI 10.1016/j.knosys.2009.11.011 Chang E., 2006, P IIWAS YOG IND, P3, DOI 10.1145/1178677.1178679 Chang E., 2006, WSEAS Transactions on Environment and Development, V2 Deerwester S., 1988, US Patent, Patent No. [4,839,853, 4839853] Dini P, 2005, DIGITAL ECOSYSTEMS R Dong H, 2011, J COMPUT SYST SCI, V77, P687, DOI 10.1016/j.jcss.2010.02.009 Dong H, 2009, INT J WEB GRID SERV, V5, P261 Dong H, 2011, IEEE T IND ELECTRON, V58, P2183, DOI 10.1109/TIE.2009.2031186 Dong H, 2010, WORLD WIDE WEB, V13, P75, DOI 10.1007/s11280-009-0081-5 DUMAS M, 2004, ELECTRON COMMER R A, V3, P95, DOI 10.1016/j.elerap.2003.09.003 Fenza G, 2008, INT J APPROX REASON, V48, P808, DOI 10.1016/j.ijar.2008.01.005 Grimes S., 2010, INFORMATIONWEEK HADZIC M, 2008, 2 IEEE INT C DIG EC Hawking D, 2006, COMPUTER, V39, P88, DOI 10.1109/MC.2006.286 Hevner AR, 2004, MIS QUART, V28, P75 Holi M, 2006, LECT NOTES COMPUT SC, V4185, P351 HYVONEN E, 2007, 5 INT SEM WEB C ISWC, P778 Jin H, 2008, KNOWL-BASED SYST, V21, P655, DOI 10.1016/j.knosys.2008.03.040 KANNAN SS, 2010, KNOWLEDGE BASED SYST Klusch M, 2009, J WEB SEMANT, V7, P121, DOI 10.1016/j.websem.2008.10.001 KUSTER U, 2007, 16 INT WORLD WID WEB, P1033 Nachira F., 2007, DIGITAL BUSINESS ECO, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 Suominen O, 2007, LECT NOTES COMPUT SC, V4519, P356 VALLE ED, 2005, W3C WORKSH FRAM SEM, P1 VALLE ED, 2005, 1 INT WORKSH MED SEM, P35 NR 34 TC 9 Z9 10 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD AUG PY 2011 VL 24 IS 6 BP 761 EP 774 DI 10.1016/j.knosys.2011.02.005 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 785JH UT WOS:000292224800004 ER PT J AU Hu, QM Huang, JX Miao, J AF Hu, Qinmin Huang, Jimmy Xiangji Miao, Jun TI A robust approach to optimizing multi-source information for enhancing genomics retrieval performance SO BMC BIOINFORMATICS LA English DT Article; Proceedings Paper CT IEEE International Conference on Bioinformatics and Biomedicine CY DEC 18-21, 2010 CL Hong Kong, PEOPLES R CHINA SP IEEE AB Background: The users desire to be provided short, specific answers to questions and put them in context by linking original sources from the biomedical literature. Through the use of information retrieval technologies, information systems retrieve information to index data based on all kinds of pre-defined searching techniques/functions such that various ranking strategies are designed depending on different sources. In this paper, we propose a robust approach to optimizing multi-source information for improving genomics retrieval performance. Results: In the proposed approach, we first consider a common scenario for a metasearch system that has access to multiple baselines with retrieving and ranking documents/passages by their own models. Then, given selected baselines from multiple sources, we investigate three modified fusion methods in the proposed approach, reciprocal, CombMNZ and CombSUM, to re-rank the candidates as the outputs for evaluation. Our empirical study on both 2007 and 2006 genomics data sets demonstrates the viability of the proposed approach for obtaining better performance. Furthermore, the experimental results show that the reciprocal method provides notable improvements on the individual baseline, especially on the passage2-level MAP and the aspect-level MAP. Conclusions: From the extensive experiments on two TREC genomics data sets, we draw the following conclusions. For the three fusion methods proposed in the robust approach, the reciprocal method outperforms the CombMNZ and CombSUM methods obviously, and CombSUM works well on the passage2-level when compared with CombMNZ. Based on the multiple sources of DFR, BM25 and language model, we can observe that the alliance of giants achieves the best result. Meanwhile, under the same combination, the better the baseline performance is, the more contribution the baseline provides. These conclusions are very useful to direct the fusion work in the field of biomedical information retrieval. C1 [Hu, Qinmin; Huang, Jimmy Xiangji; Miao, Jun] York Univ, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada. [Hu, Qinmin; Miao, Jun] York Univ, Dept Comp Sci & Engn, Toronto, ON M3J 1P3, Canada. [Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. RP Huang, JX (reprint author), York Univ, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada. EM jhuang@yorku.ca CR Amati Giambattista, 2003, THESIS U GLASGOW Beaulieu M, 1997, P TREC 5, P143 Chatzichristofis SA, 2010, P 33 INT ACM SIGIR C, P825, DOI 10.1145/1835449.1835635 Demner-Fushman D, 2007, COMBINING RESOURCES Fautsch C., 2007, TREC Fox EA, 1994, TREC 2 Hearst MA, 2005, P 14 TEXT RETRIEVAL Hersh W., 2006, P 15 TEXT RETRIEVAL Hersh W, 2007, P 16 TEXT RETRIEVAL Hu Q, 2008, ISMIS 2008 FDN INT S, P434 Hu Q, 2010, BIBM, P669 Hu QM, 2010, J INTELL INF SYST, V34, P249, DOI 10.1007/s10844-009-0097-4 Huang X, 2007, TREC Huang X., 2009, P 32 ANN INT ACM SIG Itakura KY, 2009, SIGIR 2009 P 32 INT, P822 Jimeno A, 2007, TREC Lee J. H., 1995, SIGIR 95 P 18 ANN IN, P180 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruiz ME, 2007, TREC SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Stokes N, 2007, TREC Tari L, 2007, TREC Wan R, 2007, TREC Yang Z, 2007, TREC Zhang R, 2009, P N AM CHAPT ASS COM Zhou W, 2007, TREC NR 28 TC 1 Z9 1 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD JUL 27 PY 2011 VL 12 SU 5 AR S6 DI 10.1186/1471-2105-12-S5-S6 PG 9 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 940ZF UT WOS:000303930900006 PM 21989123 ER PT J AU He, B Huang, JX Zhou, XF AF He, Ben Huang, Jimmy Xiangji Zhou, Xiaofeng TI Modeling term proximity for probabilistic information retrieval models SO INFORMATION SCIENCES LA English DT Article DE Proximity; Probabilistic models; BM25 ID PERSPECTIVE; WEB AB Proximity among query terms has been found to be useful for improving retrieval performance. However, its application to classical probabilistic information retrieval models, such as Okapi's BM25, remains a challenging research problem. In this paper, we propose to improve the classical BM25 model by utilizing the term proximity evidence. Four novel methods, namely a window-based N-gram Counting method, Survival Analysis over different statistics, including the Poisson process, an exponential distribution and an empirical function, are proposed to model the proximity between query terms. Through extensive experiments on standard TREC collections, our proposed proximity-based BM25 model, called BM25P, is compared to strong state-of-the-art evaluation baselines, including the original unigram BM25 model, the Markov Random Field model, and the positional language model. According to the experimental results, the window-based N-gram Counting method, and Survival Analysis over an exponential distribution are the most effective among all four proposed methods, which lead to marked improvement over the baselines. This shows that the use of term proximity considerably enhances the retrieval effectiveness of the classical probabilistic models. It is therefore recommended to deploy a term proximity component in retrieval systems that employ probabilistic models. Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved. C1 [He, Ben; Huang, Jimmy Xiangji; Zhou, Xiaofeng] York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 2R7, Canada. RP Huang, JX (reprint author), York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 2R7, Canada. EM benhe@yorku.ca; jhuang@yorku.ca; bluesky@mathstat.yorku.ca FU Natural Sciences and Engineering Research Council (NSERC) of Canada; Early Researcher Award/Premier's Research Excellence Award FX This research is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Early Researcher Award/Premier's Research Excellence Award. The authors thank the anonymous reviewers for their useful and constructive comments on improving the paper. CR Amati Giambattista, 2003, THESIS U GLASGOW Bazan J, 2003, LECT NOTES ARTIF INT, V2871, P160 BEAULIEU M, SPECIAL PUBLICATION, P143 BUTTCHER S, 2005, SPECIAL PUBLICATION, P1 Buttcher S., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148285 Cai D, 2009, EXPERT SYST APPL, V36, P1860, DOI 10.1016/j.eswa.2007.12.072 Crestani F, 2009, ENCY DATABASE SYSTEM, P1652 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 Doob J. L., 1953, STOCHASTIC PROCESSES Elandt-Johnson RC, 1999, SURVIVAL MODELS DATA Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 GAO J, 2000, P 6 INT C SPOK LANG, P1 GEVA S, 2009, REVISED SELECTED PAP HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 Hawking D., 1995, P 4 TEXT RETR C TREC, P131 HUANG X, 2001, ICDM WORKSH TEXT MIN, P1 HUANG X, 2006, P 6 IEEE INT C DAT M, P295 Huang XJ, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P307, DOI 10.1145/1571941.1571995 Huang XJ, 2000, COMPUT INTELL, V16, P552, DOI 10.1111/0824-7935.00126 Katz S.M., 1996, NAT LANG ENG, V2, P15, DOI 10.1017/S1351324996001246 KEEN M, 1992, J INF SCI, V18, P89 KEEN M, 1991, J DOC, V47, P1 KIRKPATRICK S, 1983, SCIENCE, V220, P671, DOI 10.1126/science.220.4598.671 Korenius T, 2007, INFORM SCIENCES, V177, P4893, DOI 10.1016/j.ins.2007.05.027 LETSCHE T, 1997, INFORM SCI, V100, P671 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X LV Y, 2009, P 32 C SPEC INT GROU, P299 Manning C. D., 1999, FDN STAT NATURAL LAN Mishne G, 2005, LECT NOTES COMPUT SC, V3408, P502 Mood M., 1974, INTRO THEORY STAT OUNIS I, 2006, SPECIAL PUBLICATION, P17 Paltoglou G, 2010, INFORM SCIENCES, V180, P2763, DOI 10.1016/j.ins.2010.03.020 Paltoglou G, 2011, INFORM PROCESS MANAG, V47, P18, DOI 10.1016/j.ipm.2010.02.004 Picard D, 2012, INFORM SCIENCES, V192, P71, DOI 10.1016/j.ins.2010.03.003 Plachouras V, 2007, LECT NOTES COMPUT SC, V4425, P28 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1971, SMART RETRIEVAL SYST Song RH, 2008, LECT NOTES COMPUT SC, V4956, P346 Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd van Gils B, 2007, INFORM SCIENCES, V177, P4566, DOI 10.1016/j.ins.2007.05.035 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Vechtomova O, 2010, INFORM PROCESS MANAG, V46, P71, DOI 10.1016/j.ipm.2009.06.005 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Ye Z, 2011, EXPERT SYST APPL, V38, P7569, DOI 10.1016/j.eswa.2010.12.108 Ye Z, 2009, P 18 TEXT RETR C GAI, P1 Yin XS, 2011, INFORM PROCESS MANAG, V47, P53, DOI 10.1016/j.ipm.2010.03.010 YU CT, 1983, GEN TERM DEPENDENCE, P1 ZHAO J, 2009, P 32 C SPEC INT GROU, P299 NR 56 TC 13 Z9 13 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD JUL 15 PY 2011 VL 181 IS 14 BP 3017 EP 3031 DI 10.1016/j.ins.2011.03.007 PG 15 WC Computer Science, Information Systems SC Computer Science GA 768XO UT WOS:000290973100007 ER PT J AU Wang, CM Chiang, MC Yang, CS Tsai, CW AF Wang, Chih-Ming Chiang, Ming-Chao Yang, Chu-Sing Tsai, Chun-Wei TI AIDED CONTENTS SUPPORTING SERVICE FOR E-LEARNING SYSTEMS SO INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL LA English DT Article DE Information retrieval; e-Learning; Aided contents ID ALGORITHM; MODEL AB In this paper, we present a novel method for automatically augmenting the learning contents of an e-Learning system, called Aided Contents Supporting Service (or ACSS for short). The proposed method starts off with the examination subsystem of which the exam items are the main learning contents. Then, motivated by the desire to provide the users of such a system with more contents, ACSS will automatically extract concepts associated with each exam item when it is being read. The key concepts associated with the exam items can even be sent as queries to content search engines to get more contents to aid learning. Moreover, the aided contents can be accumulated, ranked, and shared by the users of such a system. Our simulation results indicate that the proposed method can effectively find and integrate many more relevant contents into an e-Learning system. Besides, as a test bed, we not only apply the proposed method to an e-Learning system we develop, but we also use the grid computing technology to speed up the performance of the proposed method. C1 [Wang, Chih-Ming; Chiang, Ming-Chao] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan. [Yang, Chu-Sing; Tsai, Chun-Wei] Natl Cheng Kung Univ, Dept Elect Engn, Tainan 70101, Taiwan. [Tsai, Chun-Wei] Chia Nan Univ Pharm & Sci, Dept Appl Geoinformat, Tainan 71710, Taiwan. RP Wang, CM (reprint author), Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, 70 Lienhai Rd, Kaohsiung 80424, Taiwan. EM wangcm@cse.nsysu.edu.tw; mcchiang@cse.nsysu.edu.tw; csyang@ee.ncku.edu.tw; cwtsai87@gmail.com FU National Science Council, Taiwan [NSC98-2811-E-006-078, NSC98-2219-E-006-002] FX The authors would like to thank the editor and anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by National Science Council, Taiwan, under Contact Nos. NSC98-2811-E-006-078 and NSC98-2219-E-006-002. CR BAEZE-YATES R., 1999, MODERN INFORM RETRIE CHANG SY, 2005, THESIS NATL TAIWAN N DOWNES S, 2005, E LEARN MAGAZINE Duval E, 2008, IEEE T LEARN TECHNOL, V1, P229, DOI 10.1109/TLT.2009.6 Fu F, 2008, PHYSICA A, V387, P675, DOI 10.1016/j.physa.2007.10.006 *GLOB ALL, GRIDFTP *GLOB ALL, GRAM HAYTHORNTHWAITE C, 2005, E LEARN SEM HOEGG R, 2006, P GENEME Joachims T, 1996, P 14 INT C MACH LEAR, P143 KUO PX, 2005, P 2005 IEEE INT C E, P582 LIAW S, 2003, P 2003 INT C COMP AS, P24 LU YH, 2002, THESIS NATL CHUNG CH Matsumoto Y, 2009, INT J INNOV COMPUT I, V5, P4885 Morita Y, 2009, INT J INNOV COMPUT I, V5, P3565 O'Reilly T., 2005, WHAT IS WEB 2 0 DESI PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PURWITASARI D, 2008, P 16 INT C COMP ED, P557 Richardson W., 2006, BLOGS WIKIS PODCASTS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAFRAN C, 2007, P WORLD C E LEARN CO Safran C., 2007, P INT C INT COMP AID SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shih WC, 2009, FUTURE GENER COMP SY, V25, P687, DOI 10.1016/j.future.2009.01.005 Su Z, 2009, INT J INNOV COMPUT I, V5, P4275 Sun X, 2009, INT J INNOV COMPUT I, V5, P4523 Tsai CW, 2008, J INTERNET TECHNOL, V9, P297 Tsai CW, 2010, INT J INNOV COMPUT I, V6, P1843 Tseng JCR, 2009, INT J INNOV COMPUT I, V5, P567 Tseng SP, 2010, INT J INNOV COMPUT I, V6, P4319 WANG CM, 2009, INT C DISTR FRAM APP CHINESE WORDS SEGMEN WIKI TF IDF NR 33 TC 3 Z9 3 PU ICIC INT PI KUMAMOTO PA TOKAI UNIV, 9-1-1, TOROKU, KUMAMOTO, 862-8652, JAPAN SN 1349-4198 J9 INT J INNOV COMPUT I JI Int. J. Innov. Comp. Inf. Control PD JUL PY 2011 VL 7 IS 7A BP 4005 EP 4026 PG 22 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 790YN UT WOS:000292626800031 ER PT J AU Cao, YB Duan, HZ Lin, CY Yu, Y AF Cao, Yunbo Duan, Huizhong Lin, Chin-Yew Yu, Yong TI Re-Ranking Question Search Results by Clustering Questions SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID WEB SEARCH; DOCUMENTS AB In this article, we address the problem of question clustering and study its use for re-ranking question search results. In question clustering we have to organize question search results into certain meaningful and condensed groups. Specifically, we propose to use a data structure consisting of question topic and question focus for modeling questions, and then cluster questions on the basis of the data structure. Experimental results show that our approach to question clustering improves the performance of question search significantly better than the approach not utilizing the topic focus structure. C1 [Cao, Yunbo; Yu, Yong] Shanghai Jiao Tong Univ, Dept Comp Sci, Shanghai 200240, Peoples R China. [Cao, Yunbo; Lin, Chin-Yew] Microsoft Res Asia, Web Intelligence Grp, Beijing 100190, Peoples R China. [Duan, Huizhong] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Cao, YB (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci, Shanghai 200240, Peoples R China. EM yunbo.cao@microsoft.com RI Cong , Gao/A-3726-2011 CR Aslam J. A., 2004, J GRAPH ALGORITHMS A, V8, P95 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Berger A, 2000, P 23 ANN INT ACM SIG, P192, DOI 10.1145/345508.345576 Bian J, 2008, P 17 INT C WORLD WID, P467, DOI 10.1145/1367497.1367561 Broder A. Z., 1997, P COMPR COMPL SEQ, P21, DOI DOI 10.1109/SEQUEN.1997.666900 Broder AZ, 2000, LECT NOTES COMPUT SC, V1848, P1 Burke R.D., 1997, QUESTION ANSWERING F Cao X, 2010, P 19 INT C WORLD WID, P201, DOI 10.1145/1772690.1772712 Cao Y., 2002, P 19 INT C COMP LING, P127 Charikar M. S., 2002, P 34 ANN ACM S THEOR, P380 Demartini G, 2008, LECT NOTES COMPUT SC, V4956, P564 Duan Huizhong, 2008, P 46 ANN M ASS COMP, P156 Fellbaum C, 1998, WORDNET ELECT LEXICA FREDKIN E, 1960, COMMUN ACM, V3, P490, DOI 10.1145/367390.367400 Henzinger M., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148222 Hoad TC, 2003, J AM SOC INF SCI TEC, V54, P203, DOI 10.1002/asi.10170 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jeon J., 2005, P 28 ANN INT ACM SIG, P617, DOI 10.1145/1076034.1076156 Jeon J., 2005, P 14 ACM INT C INF K, P84, DOI DOI 10.1145/1099554.1099572.ISBN:1-59593-140-6 Jijkoun V., 2005, P 14 ACM INT C INF K, P76, DOI 10.1145/1099554.1099571 LAI YS, 2002, P 2002 C MULT SUMM Q, P1 Li H, 1998, COMPUT LINGUIST, V24, P217 MANBER U, 1994, PROCEEDINGS OF THE WINTER 1994 USENIX CONFERENCE, P1 Manku G. S., 2007, P 16 INT C WORLD WID, P141, DOI 10.1145/1242572.1242592 Page L., 1998, PAGERANK CITATION RA Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 RIEZLER S, 2007, P 45 ANN M ASS COMP, P461 Rissanen J., 1978, AUTOMATICA, V14, P445 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIVAKUMAR N, 1995, 2 ANN C THEOR PRACT Wang K., 2009, P 32 ANN INT ACM SIG, P187 Wang Xuanhui, 2007, P 30 ANN INT ACM SIG, P87, DOI http://dx.doi.org/10.1145/1277741.1277759 Xue Xiaobing, 2008, P 31 ANN INT ACM SIG, P475, DOI DOI 10.1145/1390334.1390416 Yunbo Cao, 2008, P 17 INT C WORLD WID, P81, DOI 10.1145/1367497.1367509 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 NR 37 TC 3 Z9 3 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2011 VL 62 IS 6 BP 1177 EP 1187 DI 10.1002/asi.21529 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 770XW UT WOS:000291122800015 ER PT J AU Ye, Z Huang, JXJ Lin, HF AF Ye, Zheng Huang, Jimmy Xiangji Lin, Hongfei TI Incorporating rich features to boost information retrieval performance: A SVM-regression based re-ranking approach SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Information retrieval; Query expansion; Regression model; Re-ranking model AB Document ranking is an essential problem in the field of information retrieval (IR). Traditional weighting models such as BM25 and Language model can only take advantage of query terms. IR is a complex process that may be affected by a series of heterogeneous features. It is necessary to refine first-pass retrieval results by taking rich features into account. Traditional heuristic re-ranking approaches can only take advantage of a small number of homogeneous features that may affect information retrieval performance. In this paper, we propose and evaluate a regression-based document re-ranking approach for IR, in which we use SVM regression model to learn a re-ranking function automatically. Under this regression-based framework, we can take advantage of rich features to re-rank the firs-pass retrieved documents by traditional weighting models. We conduct a series of experiments on four standard IR collections in two different languages. The experimental results show that our proposed approach can significantly improve the retrieval performance over the first-pass retrieval. Moreover, by refining the first-pass retrieved document set, the traditional pseudo relevant feedback approaches can also be enhanced. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved. C1 [Ye, Zheng; Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. [Ye, Zheng; Lin, Hongfei] Dalian Univ Technol, Dept Comp Sci & Engn, Dalian 116023, Peoples R China. RP Huang, JX (reprint author), York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. EM zye@mail.dlut.edu.cn; jhuang@yorku.ca; hflin@dlut.edu.c FU NSERC of Canada; Early Researcher/Premier's Research Excellence Award; Natural Science Foundation of China [60673039, 60973068]; National High Tech Research and Development Plan of China [2006AA01Z151]; National Social Science Foundation of China [08BTQ025]; Scientific Research Foundation for the Returned Overseas Chinese Scholars; State Education Ministry; Research Fund for the Doctoral Program of Higher Education [20090041110002] FX This research is jointly supported by NSERC of Canada, the Early Researcher/Premier's Research Excellence Award, the Natural Science Foundation of China (Nos. 60673039 and 60973068), the National High Tech Research and Development Plan of China (No. 2006AA01Z151), National Social Science Foundation of China (No. 08BTQ025), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002). CR Balinski J, 2005, INFORM PROCESS MANAG, V41, P759, DOI 10.1016/j.ipm.2004.01.006 Beaulieu M, 1997, P TREC 5, P143 Bellegarda JR, 2000, P IEEE, V88, P1279, DOI 10.1109/5.880084 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 HUANG X, 2005, P 28 ACM SIGIR HUANG X, 2006, P 29 ACM SIGIR HUANG X, 2004, NIST SPECIAL PUBLICA Huang XJ, 2003, INFORM RETRIEVAL, V6, P333, DOI 10.1023/A:1026028229881 KISHIDA K, 2004, 4 NTCI WORKSH M EV I, P480 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Kullback S., 1951, ANN MATH STAT, V22, P7986 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1983, INTRO MODERN INFORM Shevade S., 2000, IEEE T NEURAL NETWOR, V11 Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P3, DOI 10.1016/S0306-4573(99)00043-6 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 XU QY, 2000, P NTCIR WORKSH, V2, P151 YANG LP, 2004, P COLING 2004, P480 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhang R, 2009, P N AM CHAPT ASS COM ZHONG M, 2006, P 29 ACM SIGIR NR 24 TC 2 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JUN PY 2011 VL 38 IS 6 BP 7569 EP 7574 DI 10.1016/j.eswa.2010.12.108 PG 6 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 734OJ UT WOS:000288343900131 ER PT J AU Fang, H Tao, T Zhai, CX AF Fang, Hui Tao, Tao Zhai, Chengxiang TI Diagnostic Evaluation of Information Retrieval Models SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Algorithms; Experimentation; Measurement; Retrieval heuristics; constraints; formal models; TF-IDF weighting; diagnostic evaluation ID PROBABILISTIC MODELS AB Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is necessary to understand the deficiencies of the current retrieval models and the relative strengths of each of them. In this article, we propose a general methodology to analytically and experimentally diagnose the weaknesses of a retrieval function, which provides guidance on how to further improve its performance. Our methodology is motivated by the empirical observation that good retrieval performance is closely related to the use of various retrieval heuristics. We connect the weaknesses and strengths of a retrieval function with its implementations of these retrieval heuristics, and propose two strategies to check how well a retrieval function implements the desired retrieval heuristics. The first strategy is to formalize heuristics as constraints, and use constraint analysis to analytically check the implementation of retrieval heuristics. The second strategy is to define a set of relevance-preserving perturbations and perform diagnostic tests to empirically evaluate how well a retrieval function implements retrieval heuristics. Experiments show that both strategies are effective to identify the potential problems in implementations of the retrieval heuristics. The performance of retrieval functions can be improved after we fix these problems. C1 [Fang, Hui] Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA. [Tao, Tao] Microsoft Corp, Redmond, WA 98052 USA. [Zhai, Chengxiang] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Fang, H (reprint author), Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA. EM hfang@ece.udel.edu; taotao@microsoft.com; czhai@cs.uiuc.edu FU National Science Foundation [IIS-0347933, IIS-0713581]; Alfred P. Sloan Research Fellowship FX This article is based upon work supported in part by the National Science Foundation under grants IIS-0347933 and IIS-0713581, and by an Alfred P. Sloan Research Fellowship. CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 CARTERETTE B, 2006, P ACM SIGIR C RES DE CARTERETTE B, 2005, P 14 INT C INF KNOWL CORMACK GV, 1998, P ACM SIGIR C RES DE FANG H, 2008, P 46 ANN M ASS COMP FANG H, 2005, P ACM SIGIR C RES DE FANG H, 2004, P ACM SIGIR C RES DE FANG H, 2006, P ACM SIGIR C RES DE FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Fuhr N., 2001, P WORKSH LANG MOD IN, P6 Harman D., 2004, SIGIR FORUM, V38, P45 HE B, 2005, P ACM SIGIR C RES DE Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Jones K. S., 1997, READINGS INFORM RETR Lafferty J., 2003, LANGUAGE MODELING IN Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 LOPRESTI D, 1996, P S DOC AN INF RETR Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1983, INTRO MODERN INFORM SANDERSON M, 2004, P ACM SIGIR C RES DE Shi S., 2005, P 28 ANN INT ACM SIG, P488, DOI 10.1145/1076034.1076117 Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 Singhal A., 1996, Proceedings. Fifth Annual Symposium on Document Analysis and Information Retrieval Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 SINGHAL A, 1998, P 7 TEXT RETR C TREC, P186 SOBOROFF I, 2001, P ACM SIGIR C RES DE TAO T, 2007, P ACM SIGIR C RES DE TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJBERGEN CJ, 1977, J DOC, P106 VANRIJSBERGEN CJ, 1986, COMPUT J, V29 Voorhees EM, 2007, COMMUN ACM, V50, P51, DOI 10.1145/1297797.1297822 Wong KF, 2001, ACM T INFORM SYST, V19, P337, DOI 10.1145/502795.502796 WONG SKM, 1995, ACM T INFORM SYST, V13, P69 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhou Y., 2006, P 15 ACM INT C INF K, P567, DOI 10.1145/1183614.1183696 Zobel J., 1998, SIGIR Forum, V32 ZOBEL J, 1998, P ACM SIGIR C RES DE NR 45 TC 3 Z9 3 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 2011 VL 29 IS 2 AR 7 DI 10.1145/1961209.1961210 PG 42 WC Computer Science, Information Systems SC Computer Science GA 759NI UT WOS:000290252900001 ER PT J AU Boyack, KW Newman, D Duhon, RJ Klavans, R Patek, M Biberstine, JR Schijvenaars, B Skupin, A Ma, NAL Borner, K AF Boyack, Kevin W. Newman, David Duhon, Russell J. Klavans, Richard Patek, Michael Biberstine, Joseph R. Schijvenaars, Bob Skupin, Andre Ma, Nianli Boerner, Katy TI Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches SO PLOS ONE LA English DT Article ID LATENT SEMANTIC ANALYSIS; INFORMATION-RETRIEVAL; SCIENCE; SEARCH; DECOMPOSITION; MODELS; GRAPH AB Background: We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents. Methodology: We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models - BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE. Conclusions: PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts. C1 [Boyack, Kevin W.] SciTech Strategies Inc, Albuquerque, NM USA. [Newman, David] Univ Calif Irvine, Irvine, CA USA. [Newman, David] NICTA Victorian Res Lab, Melbourne, Australia. [Duhon, Russell J.; Biberstine, Joseph R.; Ma, Nianli; Boerner, Katy] Indiana Univ, Sch Lib & Informat Sci, Bloomington, IN 47401 USA. [Klavans, Richard; Patek, Michael] SciTech Strategies Inc, Berwyn, PA USA. [Schijvenaars, Bob] Collexis Inc, Geldermalsen, Netherlands. [Skupin, Andre] San Diego State Univ, Dept Geog, San Diego, CA 92182 USA. RP Boyack, KW (reprint author), SciTech Strategies Inc, Albuquerque, NM USA. EM kboyack@mapofscience.com RI Schijvenaars, Bob/; Boyack, Kevin/; Skupin, Andre/ OI Schijvenaars, Bob/0000-0002-8508-3225; Boyack, Kevin/0000-0001-7814-8951; Skupin, Andre/0000-0002-8398-8119 FU National Institutes of Health (NIH) SBIR [HHSN268200900053C]; James S. McDonnell Foundation; Australian Government; Australian Research Council through the ICT Centre of Excellence FX This work was supported by National Institutes of Health (NIH) SBIR Contract HHSN268200900053C and a James S. McDonnell Foundation grant. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Ahlgren P, 2009, J INFORMETR, V3, P49, DOI 10.1016/j.joi.2008.11.003 Ahlgren P, 2008, SCIENTOMETRICS, V76, P273, DOI 10.1007/s11192-007-1935-1 Aphinyanaphongs Y, 2005, J AM MED INFORM ASSN, V12, P207, DOI 10.1197/jamia.M1641 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borner K, 2009, J INFORMETR, V3, P161, DOI 10.1016/j.joi.2009.03.008 Boyack KW, 2010, J AM SOC INF SCI TEC, V61, P2389, DOI 10.1002/asi.21419 Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6 Boyack KW, 2009, 12 INT C INT SOC SCI, P730 Calado P, 2006, J AM SOC INF SCI TEC, V57, P208, DOI 10.1002/asi.20266 Cao MD, 2005, LECT NOTES ARTIF INT, V3809, P143 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 Couto T, 2006, OPENING INFORMATION HORIZONS, P75 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gorrell G., 2005, 9 EUR C SPEECH COMM, P1325 Harman DK, 2006, ANNU REV INFORM SCI, V40, P113, DOI 10.1002/aris.1440400111 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Hjaltason GR, 2003, ACM T DATABASE SYST, V28, P517, DOI 10.1145/958942.958948 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 JANACEK AGK, 2010, TEXT MINING APPL THE, P57 Janssens F., 2006, INSCIT2006 CURRENT R, VI, P615 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 JIANG EP, 2010, TEXT MINING APPL THE, P37, DOI 10.1002/9780470689646.ch3 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Kassab Randa, 2008, Proceedings of the IASTED International Conference on Artificial Intelligence and Applications Klavans R, 2006, J AM SOC INF SCI TEC, V57, P251, DOI 10.1002/asi.20274 KOHONEN T, 1996, 31 HELS U TECHN LAB Kohonen T., 1995, SELF ORGANIZING MAPS Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 Landauer TK, 2004, P NATL ACAD SCI USA, V101, P5214, DOI 10.1073/pnas.0400341101 Lewis J, 2006, BIOINFORMATICS, V22, P2298, DOI 10.1093/bioinformatics/btl388 Lin J, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-423 LIN JH, 1991, IEEE T INFORM THEORY, V37, P145, DOI 10.1109/18.61115 MARTIN S, 2011, C VIS DAT AN 2011 SA Muller HM, 2004, PLOS BIOL, V2, P1984, DOI 10.1371/journal.pbio.0020309 Newman DJ, 2006, J AM SOC INF SCI TEC, V57, P753, DOI 10.1002/asi.20342 Poulter GL, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-108 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Theodosiou T, 2008, BIOINFORMATICS, V24, P1935, DOI 10.1093/bioinformatics/btn318 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 Vanteru BC, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-S1-S10 VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X Yamamoto Y, 2007, J BIOMED INFORM, V40, P114, DOI 10.1016/j.jbi.2006.07.004 NR 47 TC 22 Z9 22 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 185 BERRY ST, STE 1300, SAN FRANCISCO, CA 94107 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD MAR 17 PY 2011 VL 6 IS 3 AR e18029 DI 10.1371/journal.pone.0018029 PG 11 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 736SO UT WOS:000288514600022 PM 21437291 ER PT J AU Ribeiro, LA Harder, T AF Ribeiro, Leonardo Andrade Haerder, Theo TI Generalizing prefix filtering to improve set similarity joins SO INFORMATION SYSTEMS LA English DT Article DE Advanced query processing; Set similarity join ID ALGORITHMS AB Identification of all pairs of objects in a dataset whose similarity is not less than a specified threshold is of major importance for management, search, and analysis of data. Set similarity joins are commonly used to implement this operation; they scale to large datasets and are versatile to represent a variety of similarity notions. Most methods proposed so far present two main phases at a high level of abstraction: candidate generation producing a set of candidate pairs and verification applying the actual similarity measure to the candidates and returning the correct answer. Previous work has primarily focused on the reduction of candidates, where candidate generation presented the major effort to obtain better pruning results. Here, we propose an opposite approach. We drastically decrease the computational cost of candidate generation by dynamically reducing the number of indexed objects at the expense of increasing the workload of the verification phase. Our experimental findings show that this trade-off is advantageous: we consistently achieve substantial speed-ups as compared to known algorithms. (C) 2010 Elsevier B.V. All rights reserved. C1 [Ribeiro, Leonardo Andrade; Haerder, Theo] Univ Kaiserslautern, Dept Comp Sci, AG DBIS, D-67663 Kaiserslautern, Germany. RP Ribeiro, LA (reprint author), Univ Kaiserslautern, Dept Comp Sci, AG DBIS, D-67663 Kaiserslautern, Germany. EM ribeiro@cs.uni-kl.de; haerder@cs.uni-kl.de FU CAPES/Brazil [BEX1129/04-0] FX Work partially supported by CAPES/Brazil; grant BEX1129/04-0. CR Arasu A., 2006, P 32 INT C VER LARG, P918 Arasu A, 2008, PROC INT CONF DATA, P40, DOI 10.1109/ICDE.2008.4497412 BARBARA D, 1997, DATA ENG B, V20, P3 Bayardo R.J., 2007, P 16 INT C WORLD WID, P131, DOI 10.1145/1242572.1242591 Broder A. Z., 1997, P COMPR COMPL SEQ, P21, DOI DOI 10.1109/SEQUEN.1997.666900 Bryan B, 2008, PROC INT CONF DATA, P346, DOI 10.1109/ICDE.2008.4497443 Chakrabarti K., 2008, P ACM SIGMOD INT C M, P805, DOI 10.1145/1376616.1376697 Chandel A., 2007, P ACM SIGMOD INT C M, P353, DOI 10.1145/1247480.1247521 Charikar M. S., 2002, P 34 ANN ACM S THEOR, P380 Chaudhuri S., 2006, P 22 INT C DAT ENG I, P5 Chavez E, 2001, ACM COMPUT SURV, V33, P273, DOI 10.1145/502807.502808 Clauset A, 2009, SIAM REV, V51, P661, DOI 10.1137/070710111 Cohen E, 2001, IEEE T KNOWL DATA EN, V13, P64, DOI 10.1109/69.908981 Cohen W. W., 1998, P ACM SIGMOD INT C M, P201, DOI 10.1145/276304.276323 Faloutsos C., 1995, P 1995 ACM SIGMOD IN, P163, DOI 10.1145/223784.223812 Gionis A, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P518 Gravano L., 2001, Proceedings of the 27th International Conference on Very Large Data Bases GRAY J, 1994, QUICKLY GENERATING B, P243 Guha S, 2006, ACM T DATABASE SYST, V31, P161, DOI 10.1145/1132863.1132868 Hadjieleftheriou M, 2008, PROC INT CONF DATA, P267, DOI 10.1109/ICDE.2008.4497435 Jacox EH, 2007, ACM T DATABASE SYST, V32, DOI 10.1145/1206049.1206056 Jacox EH, 2008, ACM T DATABASE SYST, V33, DOI 10.1145/1366102.1366104 Jin L, 2003, EIGHTH INTERNATIONAL CONFERENCE ON DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PROCEEDINGS, P137 KARP RM, 1987, IBM J RES DEV, V31, P249 Lee H., 2009, PVLDB, V2, P658 Li C, 2008, PROC INT CONF DATA, P257, DOI 10.1109/ICDE.2008.4497434 Lieberman MD, 2008, PROC INT CONF DATA, P1111, DOI 10.1109/ICDE.2008.4497520 Mamoulis N., 2003, P ACM SIGMOD INT C M, P157, DOI DOI 10.1145/872757.872778 RIBEIRO LA, 2009, P 13 E EUR C ADV DAT, P88 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sarawagi S., 2004, P ACM SIGMOD INT C M, P743, DOI 10.1145/1007568.1007652 Silva YN, 2010, PROC INT CONF DATA, P892, DOI 10.1109/ICDE.2010.5447873 Spertus E., 2005, P 11 ACM SIGKDD INT, P678, DOI 10.1145/1081870.1081956 Theobald M., 2008, P 31 ANN INT ACM SIG, P563, DOI 10.1145/1390334.1390431 Weber R., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases Xiao C., 2008, P 17 INT C WORLD WID, P131, DOI 10.1145/1367497.1367516 Xiao C., 2008, PVLDB, V1, P933 Xiao CA, 2009, PROC INT CONF DATA, P916 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 39 TC 1 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD MAR PY 2011 VL 36 IS 1 SI SI BP 62 EP 78 DI 10.1016/j.is.2010.07.003 PG 17 WC Computer Science, Information Systems SC Computer Science GA 676JJ UT WOS:000283906900005 ER PT J AU Dai, KS Kanoulas, E Pavlu, V Aslam, JA AF Dai, Keshi Kanoulas, Evangelos Pavlu, Virgil Aslam, Javed A. TI Variational bayes for modeling score distributions SO INFORMATION RETRIEVAL LA English DT Article DE Score distributions; Gaussian mixtures; Variational inference; Recall-precision curves ID RELEVANCE; NUMBER AB Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications. In this work, we propose modeling the relevant documents' scores by a mixture of Gaussians and the non-relevant scores by a Gamma distribution. Applying Variational Bayes we automatically trade-off the goodness-of-fit with the complexity of the model. We test our model on traditional retrieval functions and actual search engines submitted to TREC. We demonstrate the utility of our model in inferring precision-recall curves. In all experiments our model outperforms the dominant exponential-Gaussian model. C1 [Kanoulas, Evangelos] Univ Sheffield, Dept Informat Studies, Sheffield S1 4DP, S Yorkshire, England. [Dai, Keshi; Pavlu, Virgil; Aslam, Javed A.] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA. RP Kanoulas, E (reprint author), Univ Sheffield, Dept Informat Studies, 211 Portobello St, Sheffield S1 4DP, S Yorkshire, England. EM daikeshi@ccs.neu.edu; e.kanoulas@sheffield.ac.uk; vip@ccs.neu.edu; jaa@ccs.neu.edu FU NSF [IIS-0533625, IIS-0534482]; European Commission [FP7-ICT-248347] FX We would like to thank Avi Arampatzis, Jaap Kamps and Stephen Robertson for many useful discussions. Further, we gratefully acknowledge the support provided by NSF grants IIS-0533625 and IIS-0534482 and by the European Commission who funded parts of this research within the Accurat project under contract number FP7-ICT-248347. CR AKAIKE H, 1974, IEEE T AUTOMAT CONTR, VAC19, P716, DOI 10.1109/TAC.1974.1100705 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Amati Giambattista, 2003, THESIS U GLASGOW Arampatzis A. T., 2009, ICTIR, P139 Arampatzis A., 2001, SIGIR Forum Attias H, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P21 Attias H, 2000, ADV NEUR IN, V12, P209 Baumgarten C, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P246 BENNETT P, 2003, SIGIR 03, P111, DOI DOI 10.1145/860435.860457 Bishop C. H., 2006, PATTERN RECOGNITION BOOKSTEIN A, 1977, INFORM PROCESS MANAG, V13, P377, DOI 10.1016/0306-4573(77)90057-7 Bozdogan H., 1993, INFORM CLASSIFICATIO, P40 Celeux G, 1996, J CLASSIF, V13, P195, DOI 10.1007/BF01246098 COLLINSTHOMPSON K, 2003, P 11 TEXT RETR C Hiemstra D., 2001, THESIS U TWENTE KANOULAS E, 2010, P 33 ANN IN IN PRESS Manmatha R., 2001, SIGIR Forum OARD DW, 2009, P 17 TEXT RETR C Ounis I., 2007, NOVATICA UPGRADE SPE, V8, P49 Richardson S, 1997, J ROY STAT SOC B MET, V59, P731, DOI 10.1111/1467-9868.00095 RISSANEN J, 1987, J ROY STAT SOC B MET, V49, P223 ROBERTSON ES, 1994, SIGIR 94 P 17 ANN IN, P232 Robertson S, 2007, LECT NOTES COMPUT SC, V4425, P40 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136 SPITTERS M, 2000, P TDT WORKSH 2000 GA, P101 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 Voorhees Ellen M., 2005, TREC EXPT EVALUATION ZHANG Y, 2001, SIGIR 2001 P 24 ANN, P294, DOI DOI 10.1145/383952.384012 NR 30 TC 3 Z9 3 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD FEB PY 2011 VL 14 IS 1 SI SI BP 47 EP 67 DI 10.1007/s10791-010-9156-2 PG 21 WC Computer Science, Information Systems SC Computer Science GA 721CU UT WOS:000287329900004 ER PT S AU Blank, D Fuhr, N Henrich, A Mandl, T Rolleke, T Schutze, H Stein, B AF Blank, Daniel Fuhr, Norbert Henrich, Andreas Mandl, Thomas Roelleke, Thomas Schuetze, Hinrich Stein, Benno BE Efthimiadis, E FernandezLuna, JM Heute, JF MacFarlane, A TI Teaching IR: Curricular Considerations SO TEACHING AND LEARNING IN INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Article; Book Chapter ID MODELING INFORMATION-RETRIEVAL; SEARCH INTERFACE; DESIGN C1 [Blank, Daniel; Henrich, Andreas] Univ Bamberg, Bamberg, Germany. [Fuhr, Norbert] Univ Duisburg Essen, Duisburg, Germany. [Mandl, Thomas] Univ Hildesheim, D-31141 Hildesheim, Germany. [Roelleke, Thomas] Queen Mary Univ London, London, England. [Schuetze, Hinrich] Univ Stuttgart, Stuttgart, Germany. [Stein, Benno] Bauhaus Univ Weimar, D-99423 Weimar, Germany. RP Blank, D (reprint author), Univ Bamberg, Bamberg, Germany. EM daniel.blank@uni-bamberg.de CR Al-Maskari A., 2007, P 30 ANN INT ACM SIG, P773, DOI 10.1145/1277741.1277902 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BATES MJ, 1990, INFORM PROCESS MANAG, V26, P575, DOI 10.1016/0306-4573(90)90103-9 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 Bawden D, 2007, 1 INT BCS WORKSH TEA Belew R.K., 2000, FINDING OUT COGNITIV BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Buckley C, 2005, DIGITAL LIB ELECT PU, P53 Buckley C., 1985, P 8 ANN INT ACM SIGI, P97, DOI 10.1145/253495.253515 Cacheda F, 2008, 2 INT BCS WORKSH TEA Croft B, 2003, INFORM RETRIEVAL SER, V13 Croft B, 1995, D LIB MAG, V1, P5 ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 Fernandez-Luna J, 2009, INFORM RETRIEVAL, V12, P201, DOI 10.1007/s10791-009-9089-9 Grossman DA, 2004, INFORM RETRIEVAL SER, V15 Hearst M. A., 2009, SEARCH USER INTERFAC, V1 Ingwersen P., 1992, INFORM RETRIEVAL INT Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jurafsky D., 2008, SPEECH LANGUAGE PROC KUHLTHAU CC, 1988, RQ, V28, P232 Lieberman H, 1995, INT JOINT CONF ARTIF, P924 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Mandl Thomas, 2008, Informatica, V32 Mann TM, 2002, THESIS U CONSTANCE Manning C. D., 2008, INTRO INFORM RETRIEV Manning C. D., 1999, FDN STAT NATURAL LAN Melucci M, 2006, INFORM RETRIEVAL, V9, P119, DOI 10.1007/s10791-006-7145-2 Middleton Christian, 2007, COMPARISON OPEN SOUR Moffat A, 2005, SIGIR FORUM, V39, P3 Riggs KR, 2002, J COMPUT SCI COLL, V17, P179 Rijsbergen C. J. v., 1979, INFORM RETRIEVAL Robertson S., 1994, NIST SPECIAL PUBLICA, P109 Robertson S, 2008, J INF SCI, V34, P439, DOI 10.1177/0165551507086989 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rolleke T, 2006, INFORM PROCESS MANAG, V42, P4, DOI 10.1016/j.ipm.2004.11.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schaefer A, 2005, 9 EUR C DIG LIB VIEN, P414 Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 Shneiderman B., 1998, DESIGNING USER INTER, V3rd Shneiderman B, 1997, ACM INTERACTIONS, V4, P42 Sparck Jones K, 1997, MORGAN KAUFMANN SERI Stein B., 2007, P 30 ANN INT ACM SIG, P527, DOI 10.1145/1277741.1277832 Strohman T., 2009, SEARCH ENGINES INFOR Witten I. H., 1999, MANAGING GIGABYTES C WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 47 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-642-22511-6 J9 INFORM RETRIEVAL SER PY 2011 VL 31 BP 31 EP 46 DI 10.1007/978-3-642-22511-6_3 D2 10.1007/978-3-642-22511-6 PG 16 WC Computer Science, Information Systems SC Computer Science GA BAG48 UT WOS:000304080200003 ER PT S AU Kang, IS Na, SH AF Kang, In-Su Na, Seung-Hoon BE Kim, TH Adeli, H Ma, JH Fang, WC Kang, BH Park, BJ Sandnes, FE Lee, KC TI Disambiguating Author Names Using Automatic Relevance Feedback SO U- AND E-SERVICE, SCIENCE AND TECHNOLOGY SE Communications in Computer and Information Science LA English DT Proceedings Paper CT International Conference on u-Service and e-Service, Science and Technology (UNESST 2011) CY DEC 08-10, 2011 CL Jeju Isl, SOUTH KOREA DE Author Disambiguation; Automatic Relevance Feedback; Web Evidence AB Author disambiguation is to associate author-name instances with author identifiers corresponding to real-world authors. Bibliographic records, which normally contain author-name occurrences to be disambiguated, are not sufficient to provide author-resolving features. This study proposed a feedback-based method for author disambiguation to tackle the insufficiency of author representation. Our method is focused on enhancing author's topical representation by adding topically-related terms obtained from feedback documents. Experiments showed the positive effect of the method on author resolution by improving the performance. C1 [Kang, In-Su] Kyungsung Univ, Pusan 608736, South Korea. RP Kang, IS (reprint author), Kyungsung Univ, Pusan 608736, South Korea. EM dbaisk@ks.ac.kr; nash@comp.nus.edu.sg CR Aswani N, 2006, LECT NOTES COMPUT SC, V4273, P329 Kane I.S., 2011, J INFORM MANAGEMENT, V42, P167 Kang I.S., 2008, J KOREA CONTENTS ASS, V8, P261 Pereira DA, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P49 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Smalheiser NR, 2009, ANNU REV INFORM SCI, V43, P287 Song Y., 2007, P ACM IEEE JOINT C D, P18 Tan Y. F., 2006, P ACM IEEE JOINT C D, P314, DOI 10.1145/1141753.1141826 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-27209-7 J9 COMM COM INF SC PY 2011 VL 264 BP 239 EP 244 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BCL27 UT WOS:000310547700030 ER PT S AU Aly, R Demeester, T AF Aly, Robin Demeester, Thomas BE Amati, G Crestani, F TI Towards a Better Understanding of the Relationship between Probabilistic Models in IR SO ADVANCES IN INFORMATION RETRIEVAL THEORY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 3rd International Conference on the Theory of Information Retrieval (ICTIR 2011) CY SEP 12-14, 2011 CL Bertinoro, ITALY SP Fondazione Ugo Bordoni, Univ Lugano, Almawave, Microsoft Res, Yahoo Res HO Univ Residential Ctr Bertinoro ID INFORMATION-RETRIEVAL AB Probability of relevance (PR) models are generally assumed to implement the Probability Ranking Principle (PRP) of IR, and recent publications claim that PR models and language models are similar. However, a careful analysis reveals two gaps in the chain of reasoning behind this statement. First, the PRP considers the relevance of particular documents, whereas PR models consider the relevance of any query-document pair. Second, unlike PR models, language models consider draws of terms and documents. We bridge the first gap by showing how the probability measure of PR models can be used to define the probabilistic model of the PRP. Furthermore, we argue that given the differences between PR models and language models, the second gap cannot be bridged at the probabilistic model level. We instead define a new PR model based on logistic regression, which has a similar score function to the one of the query likelihood model. The performance of both models is strongly correlated, hence providing a bridge for the second gap at the functional and ranking level. Understanding language models in relation with logistic regression models opens ample new research directions which we propose as future work. C1 [Aly, Robin] Univ Twente, Enschede, Netherlands. RP Aly, R (reprint author), Univ Twente, Enschede, Netherlands. EM r.aly@ewi.utwente.nl; thomas.demeester@intec.ugent.be RI Demeester, Thomas/I-6382-2012 CR Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Hiemstra D., 2001, THESIS U TWENTE ENSC Hosmer D, 2000, APPL LOGISTIC REGRES Lafferty J., 2003, PROBABILISTIC RELEVA, P1 Lavrenko Victor, 2003, LANGUAGE MODELING IN, P11 Luk RWP, 2008, INFORM RETRIEVAL, V11, P539, DOI 10.1007/s10791-008-9062-z Manning C. D., 1999, FDN STAT NATURAL LAN Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roelleke T., 2008, SIGIR, P435, DOI DOI 10.1145/1390334.1390409 Roelleke T., 2006, SIGIR 2006, P107, DOI [10.1145/1148170.1148192, DOI 10.1145/11.48170.1148192] Sparck-Jones K., 2003, LANGUAGE MODELING IN, P57 Yan R., 2006, THESIS CANEGIE MELLO Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 20 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23317-3 J9 LECT NOTES COMPUT SC PY 2011 VL 6931 BP 164 EP 175 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBJ57 UT WOS:000307081000016 ER PT S AU Di Buccio, E Di Nunzio, GM AF Di Buccio, Emanuele Di Nunzio, Giorgio Maria BE Amati, G Crestani, F TI Distilling Relevant Documents by Means of Dynamic Quantum Clustering SO ADVANCES IN INFORMATION RETRIEVAL THEORY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 3rd International Conference on the Theory of Information Retrieval (ICTIR 2011) CY SEP 12-14, 2011 CL Bertinoro, ITALY SP Fondazione Ugo Bordoni, Univ Lugano, Almawave, Microsoft Res, Yahoo Res HO Univ Residential Ctr Bertinoro AB Dynamic Quantum Clustering (DQC) is a recent clustering technique based on physical intuition from quantum mechanics. Clusters are identified as the minima of the potential function of the Schrodinger equation. In this poster, we apply this technique to explore the possibility to select highly relevant documents relative to a query of a user. In particular, we analyze the clusters produced by DQC with a standard test collection. C1 [Di Buccio, Emanuele; Di Nunzio, Giorgio Maria] Univ Padua, Dept Informat Engn, I-35131 Padua, Italy. RP Di Buccio, E (reprint author), Univ Padua, Dept Informat Engn, Via Gradenigo 6-A, I-35131 Padua, Italy. EM emanuele.dibuccio@unipd.it; giorgiomaria.dinunzio@unipd.it CR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Varshavsky R, 2005, LECT NOTES COMPUT SC, V3759, P159 Weinstein M, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.066117 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 NR 4 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23317-3 J9 LECT NOTES COMPUT SC PY 2011 VL 6931 BP 360 EP 363 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBJ57 UT WOS:000307081000039 ER PT S AU Di Buccio, E Di Nunzio, GM AF Di Buccio, Emanuele Di Nunzio, Giorgio Maria BE Song, D Melucci, M Frommholz, I Zhang, P Wang, L Arafat, S TI Envisioning Dynamic Quantum Clustering in Information Retrieval SO QUANTUM INTERACTION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 5th International Symposium on Quantum Interaction CY JUN 26-29, 2011 CL Aberdeen, SCOTLAND SP Scottish Informat & Comp Sci Alliance AB Dynamic Quantum Clustering is a recent clustering technique which makes use of Parzen window estimator to construct a potential function whose minima are related to the clusters to be found. The dynamic of the system is computed by means of the Schrodinger differential equation. In this paper, we apply this technique in the context of Information Retrieval to explore its performance in terms of the quality of clusters and the efficiency of the computation. In particular, we want to analyze the clusters produced by using datasets of relevant and non-relevant documents given a topic. C1 [Di Buccio, Emanuele; Di Nunzio, Giorgio Maria] Univ Padua, Dept Informat Engn, I-35131 Padua, Italy. RP Di Buccio, E (reprint author), Univ Padua, Dept Informat Engn, Via Gradenigo 6-A, I-35131 Padua, Italy. EM emanuele.dibuccio@unipd.it; giorgiomaria.dinunzio@unipd.it CR Becker Barry, 2001, INFORM VISUALIZATION, p237 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Horn D., 2002, Physical Review Letters, V88, DOI 10.1103/PhysRevLett.88.018702 Lamprier S., 2010, P SAC 2010, P1774, DOI 10.1145/1774088.1774465 Nasios N, 2007, PATTERN RECOGN, V40, P875, DOI 10.1016/j.patcog.2006.08.011 Premalatha K, 2010, Information Technology Journal, V9 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 Weinstein M, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.066117 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24970-9 J9 LECT NOTES COMPUT SC PY 2011 VL 7052 BP 211 EP 216 PG 6 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Physics, Multidisciplinary SC Computer Science; Physics GA BBI79 UT WOS:000306990400022 ER PT S AU Gao, N Deng, ZH Jiang, JJ Lv, SL Yu, H AF Gao, Ning Deng, Zhi-Hong Jiang, Jia-Jian Lv, Sheng-Long Yu, Hang BE Geva, S Kamps, J Schenkel, R Trotman, A TI Combining Strategies for XML Retrieval SO COMPARATIVE EVALUATION OF FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2010 CY DEC 13-15, 2010 CL Vught, NETHERLANDS DE INEX; Ad Hoc; Data Centric; Relevance Feedback AB This paper describes Peking University's approaches to the Ad Hoc, Data Centric and Relevance Feedback track. In Ad Hoc track, results for four tasks were submitted, Efficiency, Restricted Focused, Relevance In Context and Restricted Relevance In Context. To evaluate the relevance between documents and a given query, multiple strategies, such as Two-Step retrieval, MAXLCA query results, BM25, distribution measurements and learn-to-optimize method are combined to form a more effective search engine. In Data Centric track, to gain a set of closely related nodes that are collectively relevant to a given keyword query, we promote three factors, correlation, explicitnesses and distinctiveness. In Relevance Feedback track, to obtain useful information from feedbacks, our implementation employs two techniques, a revised Rocchio algorithm and criterion weight adjustment. C1 [Gao, Ning; Deng, Zhi-Hong; Jiang, Jia-Jian; Lv, Sheng-Long; Yu, Hang] Peking Univ, Key Lab Machine Percept, Minist Educ, Sch Elect Engn & Comp Sci, Beijing 100190, Peoples R China. RP Deng, ZH (reprint author), Peking Univ, Key Lab Machine Percept, Minist Educ, Sch Elect Engn & Comp Sci, Beijing 100190, Peoples R China. EM ninggao@pku.edu.cn; zhdeng@pku.edu.cn; jiangjiajian@pku.edu.cn; davidfracs@gmail.com; pkucthh@gmail.com CR Carmel D., 2003, SIGIR, P151 Gao N., MBASASOFT GEDIT WIND Gao N., 2010, PAKDD 2010 Geva S, 2010, LECT NOTES COMPUT SC, V6203, P4, DOI 10.1007/978-3-642-14556-8_4 Guo L., 2003, SIGMOD, P16 Huang Y., 2008, VLDB 2008, P1392 Ide E., 1971, SMART RETRIEVAL SYST, P337 Jiang J., MBASASOFT GEDIT WIND Lavrenko V., 2001, SIGIR Forum Liu Z., 2007, SIGMOD C, P329 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Theobald M., 2005, VLDB, P625 van Rijsbergen CJ, 1979, INFORM RETIREVAL XU JX, 2000, TOIS 2000, V18, P79 Xu Y., 2005, SIGMOD C, P537 Zhai C., 2001, CIKM 2001, P403, DOI 10.1145/502585.502654 ZHAO JL, 2009, SIGIR 2009, P291 NR 18 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23576-4 J9 LECT NOTES COMPUT SC PY 2011 VL 6932 BP 319 EP 331 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBD22 UT WOS:000306498300030 ER PT S AU Wang, SL Liang, F Yang, JW AF Wang, Songlin Liang, Feng Yang, Jianwu BE Geva, S Kamps, J Schenkel, R Trotman, A TI PKU at INEX 2010 XML Mining Track SO COMPARATIVE EVALUATION OF FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2010 CY DEC 13-15, 2010 CL Vught, NETHERLANDS DE XML Document; Classification; Clustering; Frequent Subtree; Structured Link Vector Model (SLVM) AB This paper presents our participation in the INEX 2010 XML Mining track. Our classification and clustering solutions for XML documents have used both the structure and content information, where the frequent subtrees as structural units are used for content extraction from the XML document. In addition, we used the WordNet and the link information for better performance, and applied the structured link vector model for classification. C1 [Wang, Songlin; Liang, Feng; Yang, Jianwu] Peking Univ, Inst Comp Sci & Tech, Beijing 100871, Peoples R China. RP Yang, JW (reprint author), Peking Univ, Inst Comp Sci & Tech, Beijing 100871, Peoples R China. EM Wangsonglin@icst.pku.edu.cn; liangfeng@icst.pku.edu.cn; yangjianwu@icst.pku.edu.cn CR Berry M., 2003, SURVEY TEXT MINING C Chi Y., 2004, 8 PAC AS C KNOWL DIS Chi Y., 2005, FREQUENT SUBTREE MIN Joachims T., 1998, LNCS, V1398 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1983, INTRO MODERN INFORM Shin K, 2003, LECT NOTES COMPUT SC, V2588, P619 Xie W., 2007, ICIS Yang J., 2007, PREPR 6 WORKSH IN EV Yang JW, 2010, LECT NOTES COMPUT SC, V6203, P441 Yang JW, 2002, J COMPUT SCI TECH-CH, V17, P603, DOI 10.1007/BF02948828 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23576-4 J9 LECT NOTES COMPUT SC PY 2011 VL 6932 BP 383 EP 395 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBD22 UT WOS:000306498300037 ER PT S AU Lasek, I AF Lasek, Ivo BE Aroyo, L Welty, C Alani, H Taylor, J Bernstein, A Kagal, L Noy, N BlomQvist, E TI DC Proposal: Model for News Filtering with Named Entities SO SEMANTIC WEB - ISWC 2011, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th International Semantic Web Conference CY OCT 23-27, 2011 CL Bonn, GERMANY SP AI Journal, Elsevier, Fluid Operat, OASIS, THESEUS, Microsoft Res, Ontoprise, Ontotext, PoolParty, Soft Plant, Yahoo Res, Comp Commun Consortium, IBM Res, IEEE Intelligent Syst, IGI Global, IOS Press, LATC, Monnet, Planet Data, RENDER, Springer DE Information Filtering; User Modelling; Evaluation Methods AB In this paper we introduce the project of our PhD thesis. The subject is a model for news articles filtering. We propose a framework combining information about named entities extracted from news articles with article texts. Named entities are enriched with additional attributes crawled from semantic web resources. These properties are then used to enhance the filtering results. We described various ways of a user profile creation, using our model. This should enable news filtering covering any specific user needs. We report on some preliminary experiments and propose a complex experimental environment and different measures. C1 Czech Tech Univ, Fac Informat Technol, Prague, Czech Republic. RP Lasek, I (reprint author), Czech Tech Univ, Fac Informat Technol, Prague, Czech Republic. EM lasekivo@fit.cvut.cz CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Billsus D., 1999, HYBRID USER MODEL NE Billsus D, 2000, USER MODEL USER-ADAP, V10, P147, DOI 10.1023/A:1026501525781 Bogers T., 2007, P 2007 ACM C REC SYS, P141, DOI 10.1145/1297231.1297256 Carreira R., 2004, P 9 INT C INT US INT, P206 Das A. S., 2007, P 16 INT C WORLD WID, P271, DOI 10.1145/1242572.1242610 Fiser D., 2011, SEMANTICKA ANOTACE D Ganter B., 1999, FORMAL CONCEPT ANAL Han EH, 2000, LECT NOTES COMPUT, V1910, P424 Kobilarov G, 2009, LECT NOTES COMPUT SC, V5554, P723, DOI 10.1007/978-3-642-02121-3_53 Krajci S, 2007, P FUZZ IEEE 2007 IEE, P1 Lasek I., 2011, 4 INT SEM SEARCH WOR Liu J, 2010, IUI 2010, P31 Lv Y., 2011, P 20 INT C WORLD WID, P57 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Maruscak D., 2009, P 8 ANN C ZNAL 2009 Pazzani M., 2007, LNCS, V4321, P325, DOI DOI 10.1007/978-3-540-72079-9_10 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Robert I., 2010, P 9 INT SEM WEB C IS Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 22 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25092-7 J9 LECT NOTES COMPUT SC PY 2011 VL 7032 BP 309 EP 316 PN II PG 8 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BBC77 UT WOS:000306458200023 ER PT S AU Al-Shboul, B Myaeng, SH AF Al-Shboul, Bashar Myaeng, Sung-Hyon BE Salem, MVM Shaalan, K Oroumchian, F Shakery, A Khelalfa, H TI Query Phrase Expansion Using Wikipedia in Patent Class Search SO INFORMATION RETRIEVAL TECHNOLOGY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th Asia Information Retrieval Societies Conference (AIRS 2011) CY DEC 18-20, 2011 CL Dubai, U ARAB EMIRATES SP Dubai Knowledge Village, Microsoft Middle E DE Pseudo-Relevance Feedback; Patent Information Retrieval; Wikipedia Categories; Query Expansion; Phrase-based Query Expansion ID INFORMATION-RETRIEVAL; LEXICAL COHESION; TERMS; RELEVANCE AB Relevance Feedback methods generally suffer from topic drift caused by words ambiguity and synonymous uses of words. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with context disambiguating phrases. Focusing on the patent domain, especially on patent search where patents are classified into a hierarchy of categories, we attempt to understand the roles of phrases and words in query expansion in determining the relevance of documents and examine their contributions to alleviating the query drift problem. Our approach is compared against Relevance Model, a state-of-the-art, to show its superiority in terms of MAP on all levels of the classification hierarchy. C1 [Al-Shboul, Bashar; Myaeng, Sung-Hyon] Korea Adv Inst Sci & Technol, Taejon 305701, South Korea. RP Al-Shboul, B (reprint author), Korea Adv Inst Sci & Technol, 373-1 Guseong Dong, Taejon 305701, South Korea. EM bashar@kaist.ac.kr; myaeng@kaist.ac.kr CR Al-Shboul B., 2010, P NTCIR 8 Arampatzis AT, 1998, INFORM PROCESS MANAG, V34, P693, DOI 10.1016/S0306-4573(98)00030-2 Arguello J., 2008, P ICWSM 2008 Azzopardi L., 2010, P SIGIR 2010 Bai J, 2008, INFORM PROCESS MANAG, V44, P1901, DOI 10.1016/j.ipm.2008.07.006 Banerjee S., 2007, P SIGIR 2007 Cao G., 2008, P SIGIR 2008 Croft B., 2008, P SIGIR 2008 Croft B, 2001, P SIGIR 2001 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 Ganesh S., 2009, RECENT ADV NATURAL L Kapalavayi N., 2009, IEEE ACS INT C COMP Koster C., 2009, P PAIR 2009 Kwok K., 1998, P SIGIR 1998 Lang H., 2010, P CIKM 2010 Lewis D., 1990, P SIGIR 1990 Lewis D., 1989, P SIGIR 1990 Li Y., 2007, P SIGIR 2007 Lv Y., 2009, P CIKM 2009 Maxwell K., 2008, P JURIX 2008 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Navigli R., 2003, 14 EUR C MACH LEARN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Vechtomova O, 2006, INFORM PROCESS MANAG, V42, P1230, DOI 10.1016/j.ipm.2006.01.008 Vechtomova O, 2007, INFORM PROCESS MANAG, V43, P849, DOI 10.1016/j.ipm.2006.09.004 Voorhees E, 1994, P SIGIR 1994 Xu J., 1996, P SIGIR 1996 Xu Y., 2009, P SIGIR 2009 Xue X., 2009, P SIGIR 2009 Yin ZJ, 2009, LECT NOTES COMPUT SC, V5478, P362 NR 30 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25630-1 J9 LECT NOTES COMPUT SC PY 2011 VL 7097 BP 115 EP 126 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BZY36 UT WOS:000303361100011 ER PT S AU Fu, Y Pan, R Yang, Q Gao, W AF Fu, Yan Pan, Rong Yang, Qiang Gao, Wen BE Chen, J Wang, JX Zelikovsky, A TI Query-Adaptive Ranking with Support Vector Machines for Protein Homology Prediction SO BIOINFORMATICS RESEARCH AND APPLICATIONS SE Lecture Notes in Bioinformatics LA English DT Proceedings Paper CT 7th International Symposium on Bioinformatics Research and Applications (ISBRA) CY MAY 27-29, 2011 CL Changsha, PEOPLES R CHINA HO Cent S Univ DE Protein homology prediction; information retrieval; ranking function; machine learning; support vector machine AB Protein homology prediction is a crucial step in template-based protein structure prediction. The functions that rank the proteins in a database according to their homologies to a query protein is the key to the success of protein structure prediction. In terms of information retrieval, such functions are called ranking functions, and are often constructed by machine learning approaches. Different from traditional machine learning problems, the feature vectors in the ranking-function learning problem are not identically and independently distributed, since they are calculated with regard to queries and may vary greatly in statistical characteristics from query to query. At present, few existing algorithms make use of the query-dependence to improve ranking performance. This paper proposes a query-adaptive ranking-function learning algorithm for protein homology prediction. Experiments with the support vector machine (SVM) used as the benchmark learner demonstrate that the proposed algorithm can significantly improve the ranking performance of SVMs in the protein homology prediction task. C1 [Fu, Yan] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China. [Fu, Yan] Chinese Acad Sci, Key Lab Intelligent Informat Processing, Beijing 100190, Peoples R China. [Pan, Rong] Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510275, Guangdong, Peoples R China. [Yang, Qiang] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China. [Gao, Wen] Peking Univ, Inst Digital Media, Beijing 100871, Peoples R China. RP Fu, Y (reprint author), Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China. EM yfu@ict.ac.cn; panr@mail.sysu.edu.cn; qyang@cse.ust.hk; wgao@pku.edu.cn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baker D, 2001, SCIENCE, V294, P93, DOI 10.1126/science.1065659 Caruana R., 2004, SIGKDD EXPLORATIONS, V6, P95 Chapelle O, 2010, INFORM RETRIEVAL, V13, P201, DOI 10.1007/s10791-009-9109-9 Cohen WW, 1999, J ARTIF INTELL RES, V10, P243 Cooper W., 1993, 1 NIST TEXT RETR C N, P73 Crammer K, 2002, ADV NEUR IN, V14, P641 Foussette C., 2004, SIGKDD EXPLORATIONS, V6, P128 Fu Y., 2007, THESIS I COMPUTING T Fu Y., 2004, SIGKDD EXPLORATIONS, V6, P120 FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 Gey F., 1994, 17 ANN ACM SIGIR C B, P222 Ginalski K, 2006, CURR OPIN STRUC BIOL, V16, P172, DOI 10.1016/j.sbi.2006.02.003 Herbrich R, 2000, ADV NEUR IN, P115 Joachims T., 1999, ADV KERNEL METHODS S, P115 Joachims Thorsten, 2002, 8 ACM SIGKDD INT C K, P133 McFee B., 2010, 27 INT C MACH LEARN Nallapati R., 2004, 27 ANN INT ACM C RES, P64 Pfahringer B., 2004, SIGKDD EXPLORATIONS, V6, P1117 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Soding J, 2005, BIOINFORMATICS, V21, P951, DOI 10.1093/bioinformatics/bti125 Tang YC, 2005, ARTIF INTELL MED, V35, P121, DOI 10.1016/j.artmed.2005.02.003 Teodorescu O, 2004, PROTEINS, V54, P41, DOI 10.1002/prot.10474 Tobi D., 2000, PROTEIN-STRUCT FUNCT, V41, P16 Vapnik V, 1995, NATURE STAT LEARNING Zhang Y, 2008, CURR OPIN STRUC BIOL, V18, P342, DOI [10.1016/j.sbi.2008.02.004, 10.1016/i.sbi.2008.02.004] Zhang Y, 2005, P NATL ACAD SCI USA, V102, P1029, DOI 10.1073/pnas.0407152101 NR 27 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-21259-8 J9 LECT N BIOINFORMAT JI Lect. Notes Bioinforma. PY 2011 VL 6674 BP 320 EP 331 PG 12 WC Biochemical Research Methods; Computer Science, Information Systems; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Computer Science; Mathematical & Computational Biology GA BXP90 UT WOS:000296688700031 ER PT S AU Gao, S Li, HZ AF Gao, Sheng Li, Haizhou BE Lee, KT Tsai, WH Liao, HYM Chen, T Hsieh, JW Tseng, CC TI Effective Large Scale Text Retrieval via Learning Risk-Minimization and Dependency-Embedded Model SO ADVANCES IN MULTIMEDIA MODELING, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 17th International Multimedia Modeling Conference, MMM 2011 CY JAN 05-07, 2011 CL Taipei, TAIWAN SP Microsoft Res, Ind Techn Res Inst, Inst Informat Ind, Natl Taiwan Sci Educ Ctr, Natl Taiwan Ocean Univ, Bureau Foreign Trade, Natl Sci Council, Natl Taiwan Ocean Univ, Dept Comp Sci & Engn HO Natl Taiwan Ocean Univ, Dept Comp Sci & Engn DE Risk minimization; Diversity search; Language Model ID INFORMATION-RETRIEVAL AB In this paper we present a learning algorithm to estimate a risk-sensitive and document-relation embedded ranking function so that the ranking score can reflect both the query-document relevance degree and the risk of estimating relevance when the document relation is considered. With proper assumptions, an analytic form of the ranking function is attainable with a ranking score being a linear combination among the expectation of relevance score, the variance of relevance estimation and the covariance with the other documents. We provide a systematic framework to study the roles of the relevance, the variance and the covariance in ranking documents and their relations with the different performance metrics. The experiments show that incorporating the variance in ranking score improves both the relevance and diversity. C1 [Gao, Sheng; Li, Haizhou] ASTAR, Inst Infocomm Res I2R, Singapore 138632, Singapore. RP Gao, S (reprint author), ASTAR, Inst Infocomm Res I2R, Singapore 138632, Singapore. EM gaosheng@i2r.a-star.edu.sg; hli@i2r.a-star.edu.sg CR ALLAN J, 2002, ACM T INFORM SYSTEMS, V20, P357 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Carbonell J., 1998, P SIGIR 1998 CHEN H, 2006, P SIGIR 2006 Croft B, 2001, P SIGIR 2001 GOLLAPUDI S, 2009, P WWW 2009 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Lafferty J., 2001, P SIGIR 2001 PONTE JM, 1998, P SIGIR 1998 RADLINSKI F, 2008, P ICML 2008 Robertson S. E., 1997, READINGS INFORM RETR, P281 ROBERTSON SE, 1995, P TEXT RETR C TREC ROBERTSON SE, 1994, P SIGIR 1994 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WANG J, 2009, P SIGIR 2009 ZARAGOZA H, 2003, P SIGIR 2003 ZELLNER A, 1986, J AM STAT ASSOC, V81, P446, DOI 10.2307/2289234 ZHAI C, 2003, P SIGIR 2003 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 ZHU JH, 2009, P SIGIR 2009 NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-17828-3 J9 LECT NOTES COMPUT SC PY 2011 VL 6524 BP 99 EP 110 PN II PG 12 WC Computer Science, Theory & Methods SC Computer Science GA BXM19 UT WOS:000296363300010 ER PT J AU Kocabas, I Dincer, BT Karaoglan, B AF Kocabas, Ilker Dincer, Bekir Taner Karaoglan, Bahar TI Investigation of Luhn's claim on information retrieval SO TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES LA English DT Article DE Luhn; information retrieval; term weighting; indexing ID PROBABILISTIC APPROACH; MODELS; RELEVANCE; ALGORITHM AB In this study, we show how Luhn's claim about the degree of importance of a word in a document can be related to information retrieval. His basic idea is transformed into z -scores as the weights of terms for the purpose of modeling terra frequency (If) within documents. The Luhn-based models represented in this paper are considered as the TF component of proposed TF x IDF weighing schemes. Moreover, the final term weighting functions appropriate for the TF x IDF weighting scheme are applied to TREC-6, -7, and -8 databases. The experimental results show relevance to Luhn's claim by having high mean average precision (MAP) for the terms with frequencies around the mean frequency of terms within a document. On the other hand, the weighting, which significantly discriminates the importance between low/high frequencies and medium frequencies, degrades the retrieval performance. Therefore, any weighting scheme (TF) that is directly proportional to If has a probability of high retrieval performance, if this can optimally indicate the difference of the importance regarding tf values and also optimally eliminate the terms that have high frequencies. C1 [Kocabas, Ilker; Karaoglan, Bahar] Ege Univ, Int Comp Inst, TR-35100 Izmir, Turkey. [Dincer, Bekir Taner] Mugla Univ, Dept Stat, TR-48100 Mugla, Turkey. RP Kocabas, I (reprint author), Ege Univ, Int Comp Inst, TR-35100 Izmir, Turkey. EM ilker.kocabas@ege.edu.tr; dtaner@mu.edutr; bahar.karaoglan@ege.edu.tr FU Scientific and Technological Research Council of Turkey (TUBITAK) [107E192] FX This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) within the scope of Project No. 107E192. The authors thank TUBITAK for supporting this project. CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HIEMSTRA D, 2000, TRCTIT0009 TWENT U Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 2000, Information Processing & Management, V36 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1958, IBM J RES DEV, V2, P159 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MINKER J, 2007, J AM SOC INFORM SCI, V24, P246 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S., 1999, NIST SPECIAL PUBLICA, P253 Robertson S. E., 1980, P 3 ANN ACM C RES DE, P35 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1970, SCIENCE, V168, P335, DOI 10.1126/science.168.3929.335 TURTLE HR, 1992, COMPUT J, V35, P279, DOI 10.1093/comjnl/35.3.279 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 24 TC 1 Z9 1 PU TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY PI ANKARA PA ATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA, 00000, TURKEY SN 1300-0632 J9 TURK J ELECTR ENG CO JI Turk. J. Electr. Eng. Comput. Sci. PY 2011 VL 19 IS 6 BP 993 EP 1004 DI 10.3906/elk-1003-448 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 828JS UT WOS:000295497900013 ER PT B AU Liu, B AF Liu, Bing BA Liu, B BF Liu, B TI Supervised Learning SO WEB DATA MINING: EXPLORING HYPERLINKS, CONTENTS, AND USAGE DATA, SECOND EDITION SE Data-Centric Systems and Applications LA English DT Article; Book Chapter C1 Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA. RP Liu, B (reprint author), Univ Illinois, Dept Comp Sci, 851 S Morgan St, Chicago, IL 60607 USA. EM liub@cs.uic.edu; liub@cs.uic.edu CR Agrawal R, 2000, LECT NOTES COMPUT SC, V1777, P365 Antonie M.-L., 2002, P IEEE INT C DAT MIN Boser B. E., 1992, P 5 ANN WORKSH COMP Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350 Breiman L., 1984, CLASSIFICATION REGRE Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 BRUNK C, 1991, P INT WORKSH MAC LEA Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Clark P., 1989, Machine Learning, V3, DOI 10.1007/BF00116835 Cohen W., 1995, P INT C MACH LEARN I CONG G, 2004, P ACM SIGMOD C MAN D Cristianini N., 2000, INTRO SUPPORT VECTOR DESHPANDE M, 2002, P ACM INT C INF KNOW DIETTERICH T, 1995, J ARTIFICIAL INTELLI, P2 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 DOUGHERTY J, 1995, P INT C MACH LEARN I Duda RO, 2001, PATTERN CLASSIFICATI, V2nd FAN W, 2004, P NAT C ART INT AAAI FAYYAD U, 1993, P INT JOINT C ART IN Freund Y., 1996, P INT C MACH LEARN I FURNKRANZ J, 1994, P INT C MACH LEARN I Gehrke J, 2000, DATA MIN KNOWL DISC, V4, P127, DOI 10.1023/A:1009839829793 Good I. J., 1965, ESTIMATION PROBABILI Han J., 2006, DATA MINING CONCEPTS Hand D, 2001, PRINCIPLES DATA MINI Hyafil L., 1976, Information Processing Letters, V5, DOI 10.1016/0020-0190(76)90095-8 Jindal N., 2006, P ACM SIGIR C RES DE Kass G. V., 1980, APPL STATIST, V29, P119, DOI DOI 10.2307/2986296 Kohavi R., 1997, P EUR C MACH LEARN E LANGLEY P, 1992, P NAT C ART INT AAAI LESH N, 1999, P ACM SIGKDD INT C K LEWIS D, 1992, P ACM SIGIR C RES DE Lewis D., 1994, P ACM SIGIR C RES DE LI H, 1997, P ANN M ASS COMP LIN Li JY, 2004, MACH LEARN, V54, P99, DOI 10.1023/B:MACH.0000011804.08528.7d Li W., 2001, P IEEE INT C DAT MIN Lidstone George James, 1920, T FACULTY ACTUARIES, V8, P182 Lin WY, 2002, DATA MIN KNOWL DISC, V6, P83, DOI 10.1023/A:1013284820704 LIU B, 2006, P ACM SIGKDD INT C K LIU B, 1998, P ACM SIGKDD INT C K LIU B, 2001, CLASSIFICATION USING McCallum A, 1998, P AAAI 98 WORKSH LEA MERETAKIS D, 1999, P ACM SIGKDD INT C K MICHALSKI R, 1986, P NAT C ART INT AAAI Mitchell T.M., 1997, MACHINE LEARNING MOBASHER B, 2001, P ACM WORKSH WEB INF PAZZANI M, 1991, P INT WORKSH MACH LE QUINLAN J, 1996, P NAT C ART INT AAAI QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Rivest R. L., 1987, Machine Learning, V2, DOI 10.1007/BF00058680 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHAPIRE RE, 1990, MACH LEARN, V5, P197, DOI 10.1023/A:1022648800760 Scholkopf B., 2002, LEARNING KERNELS Shannon E. C., 1948, BELL SYST TECH J, V27, P379 Tan P., 2006, INTRO DATA MINING Vapnik V, 1995, NATURE STAT LEARNING WANG K, 2000, P ACM SIGKDD INT C K Witten I. H., 2005, DATA MINING PRACTICA WOLPERT DH, 1992, NEURAL NETWORKS, V5, P241, DOI 10.1016/S0893-6080(05)80023-1 Yang Q, 2004, DATA MIN KNOWL DISC, V8, P253, DOI 10.1023/B:DAMI.0000023675.04946.f1 Yang Y., 1999, P ACM SIGIR C RES DE Yin X., 2003, P SIAM INT C DAT MIN ZAKI M, 2003, P ACM SIGKDD INT C K NR 64 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY BN 978-3-642-19459-7 J9 DATA CENTRIC SYST AP PY 2011 BP 63 EP 132 DI 10.1007/978-3-642-19460-3_3 D2 10.1007/978-3-642-19460-3 PG 70 WC Computer Science, Artificial Intelligence SC Computer Science GA BWH21 UT WOS:000293867700003 ER PT B AU Kowalski, G AF Kowalski, Gerald TI Information Retrieval Architecture and Algorithms SO INFORMATION RETRIEVAL ARCHITECTURE AND ALGORITHMS LA English DT Article; Book ID PROBABILITY RANKING PRINCIPLE; STRING SEARCHING ALGORITHM; PARTIAL-MATCH RETRIEVAL; DOCUMENT-RETRIEVAL; BROADCAST NEWS; TEXT-RETRIEVAL; PERFORMANCE EVALUATION; COMPUTER DETECTION; SPELLING-ERRORS; LANGUAGE CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 *ACSIOM, 1995, INF KSTEM DOC FIL DI ADAMS ES, 1992, THESIS G WASHINGTON ADAMSON G, 1974, INFORM STORAGE RETRI, P253 AHLBERG C, P CHI 94 APR 1994 BO, P313 Ahlberg C., 1995, Proceedings. Information Visualization (Cat. No.95TB100000), DOI 10.1109/INFVIS.1995.528688 AHO AV, 1975, COMMUN ACM, V18, P333, DOI 10.1145/360825.360855 AITCHISON J, 1972, THESAURUS CONSTRUCTI Allan J, 1996, P 19 ANN INT ACM SIG, P270, DOI 10.1145/243199.243274 ALLAN J, 1995, TR951414 CORN U DEP Amheim R., 1969, VISUAL THINKING AMHEIM R, 1986, NEW ESSAYS PSYCHOL A ANGELL RC, 1983, INFORM PROCESS MANAG, V19, P255, DOI 10.1016/0306-4573(83)90022-5 Apte C., 1994, P 17 ANN INT ACM SIG, P23 Avram H. D., 1975, MARC ITS HIST IMPLIC Bach JR, 1996, P SOC PHOTO-OPT INS, V2670, P76, DOI 10.1117/12.234785 BAEZAYATES R, 1990, LECT NOTES COMPUT SC, V447, P332 BAEZAYATES R, 1992, COMMUN ACM, V35, P74, DOI 10.1145/135239.135243 BAEZAYATES R, 1989, LECT NOTES COMPUTER, P332 BAEZAYATES RA, 1992, INFORMATION RETRIEVA, P219 BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J BELKIN N, 1989, ANNU REV INFORM SCI, P109 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 Bergman M.K., 2001, J ELECT PUBLISHING, V7 BERNSTEIN TM, 1984, CAREFUL WRITER THEOD, P366 BERRA P, 1989, IEEE T KNOWL DATA EN, P111 Bikel D. M., 1997, 5 C APPL NAT LANG PR, P194 BIRD RM, 1978, P 4 WORKSH COMP ARCH, P42, DOI 10.1145/800128.804166 BIRD RM, 1977, P 3 NONN WORKSH SYR, P8, DOI 10.1145/800180.810247 BIRD RM, 1979, Patent No. 4152762 BLUM T, 1997, INTELLIGENT MULTIMED, P113 BOYER RS, 1977, COMMUN ACM, V20, P762, DOI 10.1145/359842.359859 Broder A., 1997, P 6 INT WORLD WID WE, P391 BROOKSTEIN A, 1980, J ASIS, V31, P240 BROOKSTEIN A, 1978, J ASIS, V29, P156 BROOKSTEIN A, 1995, P 18 ANN INT ACM SIG, P319 BROWN JR, 1996, COMPUTER GRAPHICS AP, V16, P61 BUCKLEY C, 1996, NIST SPECIAL PUBLICA Buckley C., 1994, P 17 ANN INT AC SIGI, P293 Buckley C., 1995, NIST SPECIAL PUBLICA, P69 BUSH V, 1991, MEMEX HYPERTEXT VANN, P197 Bush V., 1945, ATLANTIC MONTHLY, P101 CAID W, 1993, NIST SPECIAL PUBLICA, P107 CALLAN J, 2005, NEAR DUPLICATE DETEC Callan James P., 1994, P 17 ANN INT ACM SIG, P302 CAN F, 1995, INFORM SCIENCES, V84, P101, DOI 10.1016/0020-0255(94)00111-N CARD K, 1996, CHI 96 ACM C HUM FAC CARD K, 1996, IEEE COMPUT GRAPH, V16, P63 CATARCI T, 1996, COMPUTER GRAPHICS AP, V16, P67 Chalmers Matthew, 1992, P 15 ANN INT ACM SIG, P330, DOI DOI 10.1145/133160.133215 CHO JN, 1999, P ACM SIGMOD C DAT M, P355 Chowdhury A, 2002, ACM T INFORM SYST, V20, P171, DOI 10.1145/506309.506311 Chuah M. C., 1997, INTELLIGENT MULTIMED, P83 Cleverdon C. W., 1962, REPORT TESTING ANAL CLEVERDON CW, 1966, FACTORS DETERMINING, P2 COHEN JD, 1995, J AM SOC INFORM SCI, V46, P162, DOI 10.1002/(SICI)1097-4571(199504)46:3<162::AID-ASI2>3.0.CO;2-6 Commentz-Walter B., 1979, LECTURE NOTES COMPUT, V71, P118 *CONQ SEM NETW, 1995, CONQUEST SOFTW MAN C CONRAD J, 2004, P ACM SIGIR 04 SHEFF COOPER W, 1978, J ASSOC COMPUT MACH, P67 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 COOPER WS, 1994, P 17 ANN INT ACM C R, P242 COOPER WS, 1994, NIST SPECIAL PUBLICA, P57 COPELAND G, 1973, P 1 ANN S COMP ARCH, P121, DOI 10.1145/800123.803977 CREW B, 1967, Patent No. 3358270 CROFT WB, 1993, NIST PUBLICATION CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 CROFT WB, 1979, DOCUMENTATION, V3, P285 CULLUM JK, 1985, LANCZOS ALGORITHMS L, V1, pCH5 CUTTING D, 13 INT C RES DEV INF DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 DAMERAU FJ, 1964, COMMUN ACM, V7, P171, DOI 10.1145/363958.363994 DAWSON JL, 1974, ALLC B, P33 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DENNIS SF, 1967, INFORMATION RETRIEVA, P67 Deppisch Uwe, 1986, P ACM C RES DEV INF, P77, DOI 10.1145/253168.253189 DUMAIS S, 1995, NIST SPECIAL PUBLICA Dumais S., 1993, NIST SPECIAL PUBLICA, P105 EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 ELHAMDOUCHI A, 1989, COMPUT J, V32, P220, DOI 10.1093/comjnl/32.3.220 FAIRTHOME RA, 1985, INT ACM SIGIR C RES FAIRTHORNE RA, 1964, PARAMETERS INFORMATI, P343 Faloutsos C., 1992, INFORMATION RETRIEVA, P44 FALOUTSOS C, 1985, COMPUT SURV, V17, P49 Faloutsos C., 1988, Proceedings of the Fourteenth International Conference on Very Large Databases FALOUTSOS C, 1987, ACM T INFORM SYST, V5, P237, DOI 10.1145/27641.28057 Feiner S., 1990, UIST. Third Annual Symposium on User Interface Software and Technology. Proceedings of the ACM SIGGRAPH Symposium Flickner M., 1997, INTELLIGENT MULTIMED, P7 FORSYTH R, 1986, MACHINE LEARNING APP, P198 FOSTER M, 1980, P 7 ANN S COMP ARCH, V8, P300 FOX EA, 1993, TR9395 COMP SCI DEP FOX EA, 1983, 85561 CORN U COMP SC FOX EA, 1986, TR861 DEP COMP SCI FOX EA, 1993, J AM SOC INFORM SCI, V44, P480, DOI 10.1002/(SICI)1097-4571(199309)44:8<480::AID-ASI7>3.0.CO;2-B FOX EA, 1996, P 1 ACM INT C DIG LI FRAKES WB, 1992, INFORM RETRIEVEL DAT FRIEDER O, 2009, BMC BIOINFORMATIC S3, V10, pS3, DOI DOI 10.1186/1471-2105-10-S#-S3 Friedhoff R. M., 1989, 2 COMPUTER REVOLUTIO FRIEDMAN JH, 1989, J AM STAT ASSOC, V84, P165, DOI 10.2307/2289860 FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 FUNG R, 1995, COMMUNICATIONS ACM, V58 Furui S, 2000, COMMUN ACM, V43, P71, DOI 10.1145/328236.328150 Furuta R., 1989, Electronic Publishing: Origination, Dissemination and Design, V2 GALIL Z, 1979, COMMUN ACM, V22, P505, DOI 10.1145/359146.359148 GARCIAMOLINA H, 2008, VLDB J Gauvain JL, 2000, COMMUN ACM, V43, P64, DOI 10.1145/328236.328148 GERSHON N, 1995, IEEE SPECTRUM, V32, P38, DOI 10.1109/6.469330 GERSHON ND, 1996, COMPUTER GRAPHICS AP, V16, P72 Gey FC, 1994, P 17 ANN INT ACM SIG, P222 GIBSON E, 1960, SCI AM APR, P140 GILBERT H, 1979, 5481 U CAMBR COMP LA GILDEA D, 1999, P 6 EUR C SPEECH COM GNANADESIKAN R, 1979, METHODS STAT DATA AN GOLDSTEIN J, 2000, ANLP NAACL 2000 WORK, P4 GONNET, 1992, INFORM RETRIEVAL DAT, P66 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P703, DOI 10.1002/(SICI)1097-4571(199112)42:10<703::AID-ASI3>3.0.CO;2-1 GORDON MD, 1992, J AM SOC INFORM SCI, V43, P1, DOI 10.1002/(SICI)1097-4571(199201)43:1<1::AID-ASI1>3.0.CO;2-5 GREFFENSTETTE G, 1994, EXPLORATIONS AUTOMAT GROSSMAN D, 2004, INFORM RETRIEVAL ALG, P204 GUSTAFSON RA, 1971, APR P S INF STOR RET, P163 HAFER MA, 1974, INFORM STORAGE RET, V10, P371, DOI 10.1016/0020-0271(74)90044-8 HAGLER R, 1991, BIBLIO RECORD TECHNO HAHN H, 1994, INTERNET COMPLETE RE, P476 Halasz F., 1987, P SIGCHI GI C HUM FA, P45, DOI 10.1145/29933.30859 HARMAN D, NIST SPECIAL PUBLICA HARMAN D, 1993, NIST SPECIAL PUBLICA, P1 Harman D., 1995, NIST SPECIAL PUBLICA, P1 HARMAN D, 1986, ACM C RES DEV INFORM HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, THESIS JESUS COLL HARRISON MC, 1971, COMMUN ACM, V14, P777, DOI 10.1145/362919.362934 HASAN M, 1995, P 1995 C CTR ADV STU, P218 Hascoet-Zizi M., 1996, Proceedings of the 1st ACM International Conference on Digital Libraries HASKIN R, 1983, ACM TRANSACTIONS DAT, V8 Hearst M., 1998, WORDNET ELECT LEXICA Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 HEILMANN K, INTELLIGENT AGENTS T Hemmje M., 1994, P 17 ANN INT ACM SIG, P249 Hendley R. J., 1995, Proceedings. Information Visualization (Cat. No.95TB100000), DOI 10.1109/INFVIS.1995.528691 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hindle D., 1990, P 28 ANN M ASS COMP, P268, DOI 10.3115/981823.981857 HINTON GE, CMUCS84157 Hofmann T., 1999, P 16 INT JOINT C ART HOLLAAR L, 1984, Patent No. 4450520 HOLLAAR LA, 1992, INFORMATION RETRIEVA, P443 HOLLAAR LA, 1979, COMPUTER, V12, P40 HORSPOOL RN, 1980, SOFTWARE PRACT EXPER, V10, P501, DOI 10.1002/spe.4380100608 Hosmer D, 1989, APPL LOGISTIC REGRES Howard RA, 1981, READINGS PRINCIPLES, P721 HUFFMAN S, 1995, NIST SPECIAL PUBLICA, P305 HULL D, 1995, THESIS STANFORD U Hull D. A., 1994, P 17 ACM SIGIR C, P282 Hull D. A., 1996, P 19 ANN INT ACM SIG, P279, DOI 10.1145/243199.243275 HYLAND R, 1999, AFCEA FEDERAL DATA M Hyman Richard Joseph, 1982, SHELF ACCESS LIB HYMAN RJ, 1989, INFORM ACCESS *IBM, MULT MAN PROF ED OS IDE E, ISR15 CORN U NAT SCI Ide E., 1971, SMART RETRIEVAL SYST, P337 Ingwersen P., 1992, INFORM RETRIEVAL INT IYENGAR SS, 1980, APPL MATH COMPUT, V6, P123, DOI 10.1016/0096-3003(80)90037-5 Jing Y., 1994, P RIAO 94, P146 JOHNSON, 1991, IEEE VIS 91 C P IEEE, P284 Jolliffe I.T., 2002, SERIES SPRINGER SERI, VXXIX JONES GJF, 1997, INTELLIGENT MULTIMED, P191 Jones K., 1971, AUTOMATIC KEYWORD CL JONES KS, 1979, 5553 U CAMBR COMP LA Kanungo T., 2002, P 18 ANN ACM S COMP, P10, DOI 10.1145/513400.513402 KARP RM, 1987, IBM J RES DEV, V31, P249 KASKIEL M, 1999, P 20 ANN ACM SIGIR C, P178 Katzer J., 1982, Information Technology: Research and Development, V1 KEEN E, 1971, SMART RETRIEVAL SYST, pCH5 Keen M., 1966, FACTORS DETERMINING, V1 Kellogg R. B., 1996, Proceedings of the 1st ACM International Conference on Digital Libraries, DOI 10.1145/226931.226959 KNAUS D, 1995, NIST SPECIAL PUBLICA, P233 Knuth D. E., 1977, SIAM Journal on Computing, V6, DOI 10.1137/0206024 KOWALSKI G, 1983, THESIS G WASHINGTON KRACSONY P, 1981, INFORM RETRIEVAL RES, P268 KRETSER O, 1999, P 22 ANN ACM SIGIR C, P113 KROHN U, 1995, P WORKSH NEW PAR INF, P26 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 KUBALA F, 1997, P EUR 97 RHOD GREEC, P1455 Kubala F, 2000, COMMUN ACM, V43, P48, DOI 10.1145/328236.328146 KUBALA F, 1999, ACM COMPUTING SURVEY, V31 Kubala F., 1998, P DARPA BROADC NEWS KUNZE JJ, 1995, LISTER HILL NAT CTR Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333 KWOK K, 1996, OV 4 TEXT RETR C TRE KWOK KL, 1995, NIST PUBLICATION, P247 Lamping J., 1995, CHI 95, P401 LANCE GN, 1967, COMPUT J, V9, P373 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S., 2000, Intelligence, V11, DOI 10.1145/333175.333181 LEE DL, 1989, ACM T INFORM SYST, V7, P158, DOI 10.1145/65935.65937 LEE DL, 1985, THESIS U TORONTO LEE DL, 1990, IEEE T COMPUT, V39, P111, DOI 10.1109/12.46285 LEE WC, 1988, TR8827 DEP COMP SCI LEEK T, 1999, P 22 ANN ACM SIGIR C, P214 LEHNERT W, 1991, AI MAG, V12, P81 LENNON M, 1981, J INFORM SCI, V3, P177, DOI 10.1177/016555158100300403 LEVINE JR, 1994, IDG BOOKS, P261 LEWIS D, NIST SPECIAL PUBLICA LEWIS D, 1994, P 7 ANN ACM SIGIR C, P11 LEWIS D, 1994, S DOC ANAL INF RETR Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis D. D., 1995, SIGIR Forum LIDDY ED, 1993, NIST SPECIAL PUBLICA, P113 LIN X, 1991, J AM SOC INFORM SCI, V42, P469, DOI 10.1002/(SICI)1097-4571(199108)42:7<469::AID-ASI2>3.0.CO;2-D Lin X., 1992, Proceedings. Visualization '92 (Cat. No.92CH3201-1), DOI 10.1109/VISUAL.1992.235198 Lin X., 1996, P 1 ACM INT C DIG LI, P45, DOI 10.1145/226931.226941 LIN Z, 1988, CSTR2146 U MAR DEP C LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 LOVINS JB, 1968, MECH TRANSL, V11, P22 LUHN HP, 1958, IBM J RES DEV, V2, P159 Makhoul J, 1998, PATTERN RECOGN, V31, P1285, DOI 10.1016/S0031-3203(97)00152-0 Mandala R., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312677 MANI I, 1997, INTELLIGENT MULTIMED, P241 MARCHIONINI G, 1988, COMPUTER, V21, P70, DOI 10.1109/2.222119 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Masand B., 1992, P 15 ANN INT ACM SIG, P59, DOI 10.1145/133160.133177 MAYBURY M, 2000, COMMUN ACM, V43, P33 MAYBURY M, 1997, ACM INT MULT C SEATT, P381 Maybury M. T., 1998, READINGS INTELLIGENT Maybury M.T., 1997, INTELLIGENT MULTIMED MAYPER V, 1980, Patent No. 4241402 MCCULLAGH P, 1989, GENERALIZED LINEAR M, P101 McCulloch Warren S., 1943, BULL MATH BIOPHYS, V5, P115, DOI 10.1007/BF02459570 Mcllroy M.D., 1982, IEEE T COMMUN, V30, P91 MERLINO A, AUTOMATED TEXT SUMMA, P391 METTLER M, 1993, NIST500207 SPEC PUBL, P309 MIIKE S, 1994, P 17 ANN INT ACM SIG, P152 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MINKER J, 1977, ACM SIGIR FORUM, V12, P1 Minsky M., 1969, INTRO COMPUTATIONAL MITKAS P, P SIGIR 89 MITTENDORF E, 1994, 22 INT C RES DEV INF, P318 MOLLERNIELSEN P, 1984, INFORM PROCESS LETT, V18, P129, DOI 10.1016/0020-0190(84)90015-2 MOOERS C, 1949, BULLETIN, V31 Morris A., 1992, INFORM SYST RES, V3, P17, DOI 10.1287/isre.3.1.17 MORRIS R, 1975, IEEE T PROF COMMUN, VPC18, P54 MUKHERJEA S, 1995, COMPUT NETWORKS ISDN, V27, P1075, DOI 10.1016/0169-7552(95)00023-Z MUNZER T, 1995, VISUALIZING STRUCTUR MURTAGH F, 1983, COMPUT J, V26, P354 MURTAGH F, 1985, COMP STAT LECT, V4 Nelson T., 1974, COMPUTER LIB DREAM M Nelson T., 1965, P 1965 20 NAT C, P84, DOI DOI 10.1145/800197.806036 NIBLACK W, 1995, P IS T SPIE C STOR R, V2420 NIBLACK W, 1993, P IS T SPIE C STOR R, V1908 Niblack W., 1993, SPIE, P173 NIBLACK W, 1994, P IS T SPIE C STOR R, V2185 Nordlie R., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312618 Norman Donald, 1990, ART HUMAN COMPUTER I, P209 NORRIS DM, 1969, HIST CATALOGUING CAT NOWELL L, 1996, P 19 ANN ACM SIGIR C, P66 OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 *ORION, 1993, ORION WHIT PAP WID A PAICE C, 1993, 16 ANN INT ACM SIGIR, P69 PAICE C, 1983, INFORM TECHNOLOGY RE, V3, P33 Paice C. D., 1990, SIGIR Forum, V24 Paice C.D., 1994, P 17 ANN INT ACM SIG, P42 *PAR INC, BIOL TOOL KIT SOFTW Pearl J., 1988, PROBABILISTIC REASON PENTLAND A, 1994, P SOC PHOTO-OPT INS, V2185, P34, DOI 10.1117/12.171786 PENTLAND A, 1997, INTELLIGENT MULTIMED, P175 PETERSON JL, 1980, COMMUN ACM, V23, P676, DOI 10.1145/359038.359041 PICARD J, 1997, AFFECTIVE COMPUTING PIROLLI P, 1996, P ACM SGCHI C HUM FA PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PRATT F, 1942, BLUE RIBBON BOOKS, P50 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 RATHER LJ, 1977, LIBR TRENDS, V25, P625 REARICK TC, 1991, HYPERTEXT HYPERMEDIA, P113 REIMER U, 1988, IEEE C AI APPL, P338 Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 RIVEST RL, 1977, SIAM J COMPUT, V6, P669, DOI 10.1137/0206048 RIVLIN Z, 2000, COMMUN ACM, V43, P57 ROBERTS CS, 1979, P IEEE, V67, P1624, DOI 10.1109/PROC.1979.11543 ROBERTS DC, 4 WORKSH COMP ARCH N, V10, P51 ROBERTS DC, 4 WORKSH COMP ARCH N, V13, P51 ROBERTS DC, 4 WORKSH COMP ARCH N, V7, P51 ROBERTSON GG, 1993, COMMUN ACM, V36, P57, DOI 10.1145/255950.153577 ROBERTSO.SE, 1969, J DOC, V25, P1, DOI 10.1108/eb026462 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCK I, 1990, SCI AM, V263, P84 ROSE R, 1995, P1000 SCI TECHNOLOGY ROSE R, 1996, P1000 SCI TECHNOLOGY ROSELER M, 1994, ONLINE JUL, P19 RUGE G, 1992, INFORM PROCESS MANAG, V28, P317, DOI 10.1016/0306-4573(92)90078-E Rumelhart DE, 1995, BACK PROPAGATION THE RUSH JE, 1971, J AM SOC INFORM SCI, V22, P260, DOI 10.1002/asi.4630220405 RYTTER W, 1980, SIAM J COMPUT, V9, P509, DOI 10.1137/0209037 SACKSDAVIS R, 1983, INFORM SYST, V8, P273, DOI 10.1016/0306-4379(83)90013-3 SACKSDAVIS R, ACM T DATABASE SYSTE, V12, P655 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1975, DYNAMIC INFORM LIB P SALTON G, J DOCUMENTATION, V29, P351 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G, 1983, INTRO MODEM INFORM R SALTON G, 1972, INFORMATION PROCESSI, V71, P115 SALTON GE, 1989, AUTOMATIC TEXT PROCE, P260 Sanderson M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312679 Saracevic T., 1995, P 18 ANN INT ACM SIG, P138, DOI 10.1145/215206.215351 SARACEVIC T, 1991, P ASIS ANNU MEET, V28, P82 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C SCHEK HJ, 1978, REFERENCE STRING IND SCHUEGRAF EJ, 1976, INFORM PROCESS MANAG, V12, P283, DOI 10.1016/0306-4573(76)90066-2 SCHUSTER SA, 1979, IEEE T COMPUT, V28, P446 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sedgewick R, 1988, ALGORITHMS *SEYB, 1994, IBM UNL QBIC IM CONT, P34 SHANNON CE, 1951, AT&T TECH J, V30, P50 SINGHAL A, 1995, TR951529 CORN U Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 SMIT GD, 1982, SOFTWARE PRACTICE EX, V12, P57 Sparck Jones K, 1993, 29D U CAMBR COMP LAB Sparck Jones K., 1981, INFORM RETRIEVAL EXP Sparck Jones K., 1975, 5266 U CAMBR COMP LA Spoerri A., 1993, Proceedings 1993 IEEE Symposium on Visual Languages (Cat. No.93TH0562-9), DOI 10.1109/VL.1993.269592 STIRLING KH, 1977, THESIS U CALIFORNIA SUNDHEIM B, 1992, P 4 MESS UND C MUC 4, P3, DOI 10.3115/1072064.1072066 THORELLI LE, 1962, BIT, V2, P45, DOI 10.1007/BF02024781 THORELLI LG, 1990, USING COMPUTER COLOR TONG R, 1993, NIST PUBLICATIONS, P253 TURNER F, 1995, 3 UDT NAT LIB CAN van Rijsbergen C. J., 1979, INFORM RETRIEVAL Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANDAM A, 1988, COMMUN ACM, V31, P887, DOI 10.1145/48511.48519 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 VEERASAMY A, 1996, P 19 INT ACM SIGIR C, P85, DOI 10.1145/243199.243218 VICKERY BC, 1970, TECHNIQUES INFORM RE *VIS CORP, FAC IT FAC DET FAC R VOOHREES EM, 1994, P 7 SIGIR ACM, P61 VOOHREES EM, 1993, P 16 SIGIR ACM, P171 Voorhees E., 2002, NIST SPECIAL PUBLICA VOORHEES EM, 1986, THESIS CORN U Wactlar HD, 2000, COMMUN ACM, V43, P42, DOI 10.1145/328236.328144 WADE SJ, 1989, J INFORM SCI, V15, P249, DOI 10.1177/016555158901500407 Waibel A., 1990, READINGS SPEECH RECO WALTZ DL, 1985, COGNITIVE SCI, V9, P51, DOI 10.1207/s15516709cog0901_4 WANG CHC, 1977, IEEE P 14 INT COMP S, P124 Ward J. H., 1963, J AM STAT ASSOC, V58, P235 WAYNE C, 1998, DARPA BROADC NEWS TR WEINER ML, 1995, J AGSI JUL Whittaker S., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312639 WIEDERHOLD G, 1995, COMMUN ACM, V38, P85, DOI 10.1145/205323.205347 WILKINSON R, 1994, P 7 ANN INT ACM SIGI WILKINSON R, 1995, NIST SPECIAL PUBLICA WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Wise J. A., 1995, Proceedings. Information Visualization (Cat. No.95TB100000), DOI 10.1109/INFVIS.1995.528686 Wold E, 1996, IEEE MULTIMEDIA, V3, P27, DOI 10.1109/93.556537 WOODS WA, TR9761 WU S, 1992, COMMUN ACM, V35, P83, DOI 10.1145/135239.135244 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 YOCHUM J, 1985, IEEE P NEW DIR COMP, P114 YOCHUM J, 1995, NIST SPECIAL PUBLICA, P289 YU K, 1986, QUEST TECHNOLOGY TRW, V9, P4 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X ZAMORA EM, 1981, INFORM PROCESS MANAG, V17, P305, DOI 10.1016/0306-4573(81)90044-3 ZIPH, 1949, HUMAN BEHAV PRINCIPL ZOBEL J, 1995, INFORM PROCESS MANAG, V31, P361, DOI 10.1016/0306-4573(94)00052-5 1975, P NCC 44 AN CA AFIPS 1996, D LIB MAGAZINE FEB NR 369 TC 6 Z9 6 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES BN 978-1-4419-7715-1 PY 2011 BP 1 EP 300 DI 10.1007/978-1-4419-7716-8 PG 300 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BSW30 UT WOS:000285945600001 ER PT J AU Chou, SC Cheng, CY Huang, SJ AF Chou, Shihchieh Cheng, Chinyi Huang, Szujui TI A ranking algorithm for query expansion based on the term's appearing probability in the single document SO ONLINE INFORMATION REVIEW LA English DT Article DE Query languages; Information retrieval; Information management ID INFORMATION-RETRIEVAL; RELEVANCE FEEDBACK; MODELS; LOGS AB Purpose - The purpose of this paper is to establish a new approach for solving the expansion term problem. Design/methodology/approach - This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions. Findings - The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions. Research limitations/implications - The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted. Originality/value - Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents. C1 [Chou, Shihchieh; Cheng, Chinyi] Natl Cent Univ, Dept Informat Management, Chungli 32054, Taiwan. [Huang, Szujui] Inventec Corp, Chungli, Taiwan. RP Chou, SC (reprint author), Natl Cent Univ, Dept Informat Management, Chungli 32054, Taiwan. EM scchou@mgt.ncu.edu.tw FU National Science Council, Taiwan [NSC98-2410-H-008-028-MY2] FX This work was supported in part by the National Science Council, Taiwan, under Grant No. NSC98-2410-H-008-028-MY2. CR ALSHAAR R, 2008, THESIS U WATERLOO Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 AZIMISADJADI M, 2004, P IEEE INT JOINT C N, P309 Balabanovic M., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267744 Buckley C., 1994, P 3 TEXT RETR C TREC, P69 Carpineto C, 2000, J AM SOC INFORM SCI, V51, P587 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Carpineto C, 2002, ACM T INFORM SYST, V20, P259, DOI 10.1145/568727.568728 Chau M, 2005, J AM SOC INF SCI TEC, V56, P1363, DOI 10.1002/asi.20210 Chirita P.-A., 2007, P 30 ANN INT ACM SIG, P7, DOI DOI 10.1145/1277741.1277746 Cover TM, 1991, ELEMENTS INFORM THEO CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 EFTHIMIADIS E, 1994, P 2 TEXT RETR C TREC, P279 EFTHIMIADIS E, 1995, INFORM PROCESSING MA, V32, P605 EFTHIMIADIS E. N., 1993, P 16 ANN INT ACM SIG, P146, DOI 10.1145/160688.160710 EVANS D, 1994, P 2 TEXT RETR C TREC, P137 Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 Hariri N, 2008, ONLINE INFORM REV, V32, P370, DOI 10.1108/14684520810889673 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Jing Y., 1994, P RIAO 94, P146 Jones K., 1971, AUTOMATIC KEYWORD CL KIM BM, 2001, P IFSA WORLD C 20 NA, P715 Koster CHA, 2007, LECT NOTES COMPUT SC, V4378, P270 Losee R. M., 1990, SCI INFORM MEASUREME Manning C. D., 2008, INTRO INFORM RETRIEV Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 MOSCHITTI A, 2003, LECT NOTES COMPUTER, V5075, P546 Nick ZZ, 2001, IEEE INTERNET COMPUT, V5, P18, DOI 10.1109/4236.914644 Ounis I, 2005, LECT NOTES COMPUT SC, V3408, P517 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Robertson SE, 1998, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 SHANFENG Z, 2001, P 2 C WEB INF SYST E, P262 Singhal A., 1999, P TREC 7 NIST GAITH, P239 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 NR 48 TC 1 Z9 1 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 1468-4527 J9 ONLINE INFORM REV JI Online Inf. Rev. PY 2011 VL 35 IS 2 BP 217 EP 236 DI 10.1108/14684521111128014 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 771QY UT WOS:000291177000003 ER PT J AU Wei, SK Zhao, Y Zhu, C Xu, CS Zhu, ZF AF Wei, Shikui Zhao, Yao Zhu, Ce Xu, Changsheng Zhu, Zhenfeng TI Frame Fusion for Video Copy Detection SO IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY LA English DT Article DE Frame fusion; HMM; video copy detection; viterbi algorithm ID SEARCH; SCALE AB Content-based video copy detection is very important for copyright protection in view of the growing popularity of video sharing websites, which deals with not only whether a copy occurs in a query video stream but also where the copy is located and where the copy is originated from. While a lot of work has addressed the problem with good performance, less effort has been made to consider the copy detection problem in the case of a continuous query stream, for which precise temporal localization and some complex video transformations like frame insertion and video editing need to be handled. We attempt to attack the problem by presenting a frame fusion based copy detection approach, which converts video copy detection to frame similarity search and frame fusion under a temporal consistency assumption. Our work focuses mainly on the frame fusion stage due to its critical role in copy detection performance. The proposed frame fusion scheme is based on a Viterbi-like algorithm, comprising an online back-tracking strategy with three relaxed constraints. The experimental results show that the proposed approach achieves high localization accuracy in both the query stream and the reference database even when a query video stream undergoes some complex transformations, while achieving comparable performance compared with state-of-the-art copy detection methods. C1 [Wei, Shikui; Zhao, Yao; Zhu, Zhenfeng] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. [Wei, Shikui; Zhu, Ce] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore. [Wei, Shikui] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore. [Xu, Changsheng] Chinese Acad Sci, Inst Automat, Beijing 100029, Peoples R China. RP Wei, SK (reprint author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. EM shkwei@google.com; yzhao@bjtu.edu.cn; eczhu@ntu.edu.sg; csxu@nlpt.ia.ac.cn; zhfzhu@bjtu.edu.cn FU National Natural Science Foundation of China [61025013, 60970092]; Sino-Singapore JRP [2010DFA11010]; 973 Program [2011CB302204]; Open Foundation of National Laboratory of Pattern Recognition [2009JBZ006-3]; State Scholarship for Study Aboard [2008709012] FX This work was supported in part by the National Natural Science Foundation of China (61025013, 60970092), Sino-Singapore JRP (2010DFA11010), 973 Program (2011CB302204), the Open Foundation of National Laboratory of Pattern Recognition (2009JBZ006-3), and the State Scholarship for Study Aboard (2008709012). This paper was recommended by Associate Editor T. Fujii. CR CAN T, 2007, P INT WORKSH MULT IN, P207, DOI 10.1145/1290082.1290112 Chen L, 2008, PATTERN RECOGN LETT, V29, P1824, DOI 10.1016/j.patrec.2008.05.015 Chiu CY, 2008, IEEE T CIRC SYST VID, V18, P412, DOI 10.1109/TCSVT.2008.918447 Douze M, 2010, IEEE T MULTIMEDIA, V12, P257, DOI 10.1109/TMM.2010.2046265 DOUZE M, 2008, P TRECVID Gengembre N., 2008, P ACM INT C CONT BAS, P211, DOI 10.1145/1386352.1386384 Hampapur A., 2002, VIDEOGREP VIDEO COPY Hua X. S., 2004, P INT C IM PROC SING, V1, P685 Jegou H., 2008, P ECCV, P304 Jiang Y. G., 2007, P 6 ACM INT C IM VID, P494, DOI DOI 10.1145/1282280.1282352 Joly A, 2007, IEEE T MULTIMEDIA, V9, P293, DOI 10.1109/TMM.2006.886278 JOLY A, 2008, P TRECVID Joly Alexis, 2005, P 7 ACM SIGMM INT WO, P201, DOI 10.1145/1101826.1101859 Ke Y., 2004, P 12 ACM INT C MULT, P869, DOI 10.1145/1027527.1027729 Kim C, 2005, IEEE T CIRC SYST VID, V15, P127 Kim H., 2008, P ACM INT C CONT BAS, P397, DOI 10.1145/1386352.1386404 Law-To J., 2006, P ACM INT C MULT, P835, DOI DOI 10.1145/1180639.1180826 Lee S, 2008, IEEE T CIRC SYST VID, V18, P983, DOI 10.1109/TCSVT.2008.920739 *LEM, LEM TOOLK LANG MOD I Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 MIKOLAJCZYK K, BINARIES AFFINE COVA Mikolajczyk K, 2004, INT J COMPUT VISION, V60, P63, DOI 10.1023/B:VISI.0000027790.02288.f2 Nister D., 2006, P IEEE C COMP VIS PA, P2161 Oostveen J., 2002, VISUAL LECT NOTES CO, V2, P117 Petersohn C., 2004, P TRECVID RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SATOH S., 2007, P INT WORKSH MULT IN, P237, DOI 10.1145/1290082.1290115 *TRECVID, 2008, TREC VID RETR EV Uchida S, 2007, LECT NOTES COMPUT SC, V4843, P628 VAIAPURY K, 2006, P INT C VIS INF ENG, P537 Vedaldi A., 2008, VLFEAT OPEN PORTABLE Wu X, 2009, IEEE T MULTIMEDIA, V11, P196, DOI 10.1109/TMM.2008.2009673 Yang X., 2003, P INT C INF TECHN RE, P50 Zhou XM, 2009, IEEE T MULTIMEDIA, V11, P879, DOI 10.1109/TMM.2009.2021794 Zhu J., 2008, P ACM MULT, P41, DOI 10.1145/1459359.1459366 NR 37 TC 21 Z9 21 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1051-8215 J9 IEEE T CIRC SYST VID JI IEEE Trans. Circuits Syst. Video Technol. PD JAN PY 2011 VL 21 IS 1 BP 15 EP 28 DI 10.1109/TCSVT.2011.2105554 PG 14 WC Engineering, Electrical & Electronic SC Engineering GA 725TU UT WOS:000287669800002 ER PT J AU Yin, XS Huang, JXJ Li, ZJ AF Yin, Xiaoshi Huang, Jimmy Xiangji Li, Zhoujun TI Mining and modeling linkage information from citation context for improving biomedical literature retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Probabilistic model; Citation analysis; Ranking; Biomedical information retrieval AB Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved. C1 [Yin, Xiaoshi; Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Toronto, ON M3J 2R7, Canada. [Yin, Xiaoshi; Li, Zhoujun] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China. RP Huang, JXJ (reprint author), York Univ, Sch Informat Technol, Toronto, ON M3J 2R7, Canada. EM xiaoshiyin@cse.buaa.edu.cn; jhuang@yorku.ca; lizj@buaa.edu.cn FU Natural Science and Engineering Research Council of Canada; Ontario Ministry of Research and Innovation; China Scholarship Council FX This research is supported in part by the research grant from the Natural Science and Engineering Research Council of Canada, the Early Research Award/Premier's Research Excellence Award from Ontario Ministry of Research and Innovation and the award from the China Scholarship Council. We would like to sincerely thank the anonymous reviewers for their valuable feedback and advice that helped improve the quality of this paper. CR An Y, 2004, KNOWL INF SYST, V6, P664, DOI 10.1007/s10115-003-0128-3 BEAULIEU M, 1997, P 5 TEXT RETR C TREC BERNSTAM E, 2005, J AM MED INFORM ASSN, V13, P96, DOI 10.1197/jamia.M1909 Bolelli L., 2006, PKDD 2006, P30 Borodin A., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052942 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BUCKLEY C, 2005, RETRIEVAL SYSTEM EVA Craswell N, 2005, P 28 ANN INT ACM SIG, P416, DOI 10.1145/1076034.1076106 Dorogovtsev S. N., 2003, EVOLUTION NETWORKS Hersh W., 2004, P 13 TEXT RETR C TRE HERSH W, 2005, P 14 TEXT RETR C TRE Hersh W., 2006, P 15 TEXT RETR C TRE HERSH WR, 2007, P 16 TEXT RETR C TRE HERSKOVIC J, 2005, AM MED INFORM ASS 20 HUANG X, 2005, P 14 TEXT RETR C TRE Huang XJ, 2006, IEEE DATA MINING, P295 Jin R., 2001, P 24 ANN INT ACM SIG, P402, DOI 10.1145/383952.384039 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 KRAAIJ W, 2002, P 25 ANN INT ACM SIG, P2734 Lin J, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-270 Lin YJ, 2007, J AM MED INFORM ASSN, V14, P651, DOI 10.1197/jamia.M2215 LIU H, 2009, P BIONLP 2009 NAACL NANBA H, 2000, 11 SIG CLASS RES WOR, P117 Redner S, 1998, EUR PHYS J B, V4, P131, DOI 10.1007/s100510050359 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Smalheiser NR, 2009, COMPUT METH PROG BIO, V94, P190, DOI 10.1016/j.cmpb.2008.12.006 TAGUE J, 1981, J AM SOC INFORM SCI, P32 Tunon J., 2005, ANN M INT FED LIB AS YIN X, 2009, P 2009 IEEE INT C BI YIN X, 2009, P 13 PAC AS C KNOWL Zhao DZ, 2006, INFORM PROCESS MANAG, V42, P1578, DOI 10.1016/j.ipm.2006.03.022 ZHOU W, 2007, P 30 ANN INT ACM SIG NR 33 TC 4 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2011 VL 47 IS 1 BP 53 EP 67 DI 10.1016/j.ipm.2010.03.010 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 683XG UT WOS:000284511000004 ER PT J AU Sun, HL Huang, JB Feng, BQ AF Sun, Heli Huang, Jianbin Feng, Boqin TI QoRank: A Query-Dependent Ranking Model Using LSE-Based Weighted Multiple Hyperplanes Aggregation for Information Retrieval SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS LA English DT Article AB Ranking is a core problem for information retrieval since the performance of the search system is directly impacted by the accuracy of ranking results. Ranking model construction has been the focus of both the fields of information retrieval and machine learning, and learning to rank in particular has attracted much interest. Many ranking models have been proposed, for example, RankSVM is a state-of-the-art method for learning to rank and has been empirically demonstrated to be effective. However, most of the proposed methods do not consider about the significant differences between queries, only resort to a single function in ranking. In this paper, we present a novel ranking model named QoRank, which performs the learning task dependent on queries. We also propose a LSE (least-squares estimation) -based weighted method to aggregate the ranking lists produced by base decision functions as the final ranking. Comparison of QoRank with other ranking techniques is conducted, and several evaluation criteria are employed to evaluate its performance. Experimental results on the LETOR OHSUMED data set show that QoRank strikes a good balance of accuracy and complexity, and outperforms the baseline methods. C (C) 2010 Wiley Periodicals, Inc. C1 [Sun, Heli; Feng, Boqin] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China. [Huang, Jianbin] Xidian Univ, Sch Software, Xian, Peoples R China. RP Sun, HL (reprint author), Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China. EM helisun.sunny@gmail.com; jbhuang@xidian.edu.cn FU Nation 863 High Technology Program of China [2008AA01Z131]; Natural Science Basic Research Plan in Shaanxi Province of China [SJ08-ZT14] FX We would like to thank Prof. Jiawei Han for his comments on our paper. This work was supported by the Nation 863 High Technology Program of China (2008AA01Z131) and Natural Science Basic Research Plan in Shaanxi Province of China (No. SJ08-ZT14). CR Aslam J.A., 2001, P 24 ANN INT ACM SIG, P276, DOI DOI 10.1145/383952.384007 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363 BURGES C, 2007, LEARNING RANK NONSMO, P193 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Cao Y., 2006, P 29 ANN INT ACM SIG, P186, DOI 10.1145/1148170.1148205 Cao Zhe, 2007, P 24 INT C MACH LEAR, P129, DOI 10.1145/1273496.1273513 Chang C. C., 2001, LIBSVM LIB SUPPORT V CHIRITA PA, 2005, P 14 ACM INT C INF K, P373, DOI 10.1145/1099554.1099671 COLLINS M, 2002, P 40TH ANN M ASS COM, P7 Crammer K., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Dave K., 2003, P 12 INT C WORLD WID, P519, DOI DOI 10.1145/775152.775226 Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Fagin R., 1997, P 6 INT C DAT THEOR, P247 FOX JA, 1994, ACM SIGIR 94 Freund Y., 2003, J MACHINE LEARNING R, V6, P933, DOI 10.1162/jmlr.2003.4.6.933 Friedman JH, 2002, COMPUT STAT DATA AN, V38, P367, DOI 10.1016/S0167-9473(01)00065-2 Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451 Geng X, 2008, P 31 ANN INT ACM SIG, P115, DOI 10.1145/1390334.1390356 Gyongi Z., 2004, P 30 INT C VER LARG, P576, DOI 10.1016/B978-012088469-8/50052-8 Harman D., 1992, INFORMATION RETRIEVA, P363 HARRINGTON EF, 2003, MACH LEARN INT WORKS, V20, P250 HERBRICH R, 2002, KDD 02 P 8 ACM SIGKD, P133 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lee J. H., 1997, SIGIR FORUM, V31, P267 Liu T., 2007, P SIGIR 2007 WORKSH, P3 Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005 Nallapati R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009006 Page L., 1998, PAGERANK CITATION RA PANG B, 2005, ACL 2005 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Qin T., 2008, P 17 INT C WORLD WID, P407, DOI 10.1145/1367497.1367553 Qin T, 2007, P 30 ANN INT ACM SIG, P279, DOI 10.1145/1277741.1277791 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 Robertson S., 2001, NIST SPECIAL PUBLICA, P25 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tsai M., 2007, P 30 ANN INT ACM SIG, P383, DOI 10.1145/1277741.1277808 Veloso A, 2008, P 31 ANN INT ACM SIG, P267, DOI 10.1145/1390334.1390381 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 VOORHEES EM, 1995, OV 3 TEXT RRET C TRE, P95 Witten I. H., 1999, MANAGING GIGABYTES C Xia F., 2008, P 25 INT C MACH LEAR, P1192, DOI 10.1145/1390156.1390306 Xu J., 2007, P 30 ANN INT ACM SIG, P391, DOI 10.1145/1277741.1277809 Yager RR, 1998, J AM SOC INFORM SCI, V49, P1177, DOI 10.1002/(SICI)1097-4571(1998)49:13<1177::AID-ASI5>3.0.CO;2-E YUE Y, 2007, P 30 ANN INT ACM SIG, P278 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zheng Z., 2008, P 21 ANN C NEUR INF, P1697 ZOBEL J, 1998, ACM SIGIR FORUM, V32, P34 NR 55 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0884-8173 J9 INT J INTELL SYST JI Int. J. Intell. Syst. PD JAN PY 2011 VL 26 IS 1 BP 73 EP 97 DI 10.1002/int.20455 PG 25 WC Computer Science, Artificial Intelligence SC Computer Science GA 693ZB UT WOS:000285262800005 ER PT J AU Dang, EKF Luk, RWP Allan, J Ho, KS Chan, SCF Chung, KFL Lee, DL AF Dang, E. K. F. Luk, R. W. P. Allan, J. Ho, K. S. Chan, S. C. F. Chung, K. F. L. Lee, D. L. TI A New Context-Dependent Term Weight Computed by Boost and Discount Using Relevance Information SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID RETRIEVAL; FEEDBACK AB We studied the effectiveness of a new class of context-dependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the context-dependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the context-independent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections. C1 [Dang, E. K. F.; Luk, R. W. P.; Ho, K. S.; Chan, S. C. F.; Chung, K. F. L.] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. [Allan, J.] Univ Massachusetts, Dept Comp Sci, Ctr Intelligent Informat Retrieval, Amherst, MA 01003 USA. [Lee, D. L.] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. RP Dang, EKF (reprint author), Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. EM cskfdang@comp.polyu.edu.hk; csrluk@comp.polyu.edu.hk; allan@cs.umass.edu; csksho@comp.polyu.edu.hk; csschan@comp.polyu.edu.hk; cskchung@comp.polyu.edu.hk; dlee@cse.ust.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 FU HKPU [G-U578] FX Robert Luk thanks the Center for Intelligent Information Retrieval, University of Massachusetts, for facilitating his development of the basic IR system when he was on leave there. This work was supported by HKPU Project G-U578. CR Buckley C., 1994, P 17 ANN INT ACM SIG, P292 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Dang EKF, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1629096.1629098 DASSARMA A, 2008, P ASS COMP MACH SPEC, P177, DOI 10.1145/1401890.1401916 Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Ide E., 1971, SMART RETRIEVAL SYST, P337 Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 Kleinbaum DG, 2002, LOGISTIC REGRESSION KONG YK, 2004, ASS COMP MACH SPEC I Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 LEASE M, 2008, 17 TEXT RETR C GAITH LUHN HP, 1958, IBM J RES DEV, V2, P159 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 SPARCKJONES K, 2007, ACM SIGIR FORUM, V41, P8, DOI 10.1145/1328964.1328965 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P809, DOI 10.1016/S0306-4573(00)00016-9 SPARCKJONES K, 1972, J DOC, V28, P11 Spink A, 2002, COMPUTER, V35, P107, DOI 10.1109/2.989940 TROY AD, 2007, P 30 ANN INT ACM SIG, P599, DOI 10.1145/1277741.1277844 Voorhees E. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291017 Wong WS, 2008, INFORM PROCESS MANAG, V44, P1086, DOI 10.1016/j.ipm.2007.12.002 Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 37 TC 2 Z9 2 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD DEC PY 2010 VL 61 IS 12 BP 2514 EP 2530 DI 10.1002/asi.21425 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 680KP UT WOS:000284231100011 ER PT J AU Thet, TT Na, JC Khoo, CSG AF Thet, Tun Thura Na, Jin-Cheon Khoo, Christopher S. G. TI Aspect-based sentiment analysis of movie reviews on discussion boards SO JOURNAL OF INFORMATION SCIENCE LA English DT Review DE discussion board; opinion mining; sentiment analysis ID WEB; TEXT AB In this article, a method for automatic sentiment analysis of movie reviews is proposed, implemented and evaluated. In contrast to most studies that focus on determining only sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to determine both the sentiment orientation and sentiment strength of the reviewer towards various aspects of a movie. Sentences in review documents contain independent clauses that express different sentiments toward different aspects of a movie. The method adopts a linguistic approach of computing the sentiment of a clause from the prior sentiment scores assigned to individual words, taking into consideration the grammatical dependency structure of the clause. The prior sentiment scores of about 32,000 individual words are derived from SentiWordNet with the help of a subjectivity lexicon. Negation is delicately handled. The output sentiment scores can be used to identify the most positive and negative clauses or sentences with respect to particular movie aspects. C1 [Na, Jin-Cheon] Nanyang Technol Univ, Div Informat Studies, Wee Kim Wee Sch Commun & Informat, Singapore 637718, Singapore. RP Na, JC (reprint author), Nanyang Technol Univ, Div Informat Studies, Wee Kim Wee Sch Commun & Informat, Singapore 637718, Singapore. EM tjcna@ntu.edu.sg CR Berger AL, 1996, COMPUT LINGUIST, V22, P39 Byrt T, 1996, EPIDEMIOLOGY, V7, P561 CHAOVALIT P, 2005, P 38 ANN HAW INT C S Chiu CM, 2004, J INF SCI, V30, P60, DOI 10.1177/0165551504041679 Cohen W. W., 1996, P 13 NAT C ART INT 8, V1, P709 Dave K., 2003, P 12 INT C WORLD WID, P519, DOI DOI 10.1145/775152.775226 de Marneffe M.C., 2010, STANFORD TYPED DEPEN de Marneffe M.-C., 2006, P 5 INT C LANG RES E Ding X., 2008, P INT C WEB SEARCH W, P231, DOI 10.1145/1341531.1341561 Esuli A., 2006, P 11 C EUR CHAPT ASS, P193 Ganapathibhotla M., 2008, P 22 INT C COMP LING, P241 Hatzivassiloglou Vasileios, 1997, P 35 ANN M ASS COMP, P174, DOI DOI 10.3115/976909.979640 Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Jindal N., 2008, P INT C WEB SEARCH W, P219, DOI DOI 10.1145/1341531.1341560 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Kim S.-M., 2005, P INT JOINT C NAT LA, P61 Ku LW, 2007, J AM SOC INF SCI TEC, V58, P1838, DOI 10.1002/asi.20630 Martin James R., 2005, LANGUAGE EVALUATION Mitchell T.M., 1997, MACHINE LEARNING Miyoshi T., 2007, P INT C SYST MAN CYB, P2028 Mullen T., 2004, P C EMP METH NAT LAN, P412 Na JC, 2009, J INF SCI, V35, P709, DOI 10.1177/0165551509104233 Na JC, 2010, ONLINE INFORM REV, V34, P317, DOI 10.1108/19684521011037016 Neter J., 1992, APPL STAT Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 Pang B., 2005, P 43 ANN M ASS COMP, P115, DOI DOI 10.3115/1219840.1219855 QIU G, 2009, P 21 INT JOINT C ART, P1199 Quirk Randolph, 1985, COMPREHENSIVE GRAMMA Reynar Jeff, 2008, P WWW 2008 WORKSH NL Riloff E., 2003, P 7 C NAT LANG LEARN, P25 Riloff E., 2005, P INT C INT TEXT PRO, P475 Riloff E, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P105 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Shaikh MAM, 2008, APPL ARTIF INTELL, V22, P558, DOI 10.1080/08839510802226801 Stone P. J., 1966, GEN INQUIRER COMPUTE Toutanova K, 2000, PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA, P63 Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 Vechtomova O, 2010, INFORM PROCESS MANAG, V46, P71, DOI 10.1016/j.ipm.2009.06.005 Wang J., 2008, ENCY DATA WAREHOUSIN Whitelaw C., 2005, P 14 ACM INT C INF K, P625, DOI DOI 10.1145/1099554.1099714 Wiebe J. M., 1994, Computational Linguistics, V20 Wiebe J. M., 2000, Proceedings Seventeenth National Conference on Artificial Intelligence (AAAI-2000). Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000) Wilson T, 2006, COMPUT INTELL-US, V22, P73, DOI 10.1111/j.1467-8640.2006.00275.x Wilson TS, 2005, PHIL EDUC, P347, DOI 10.3115/1220575.1220619 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yi J, 2003, P 3 IEEE INT C DAT M, P427 Zhang CL, 2009, J AM SOC INF SCI TEC, V60, P2474, DOI 10.1002/asi.21206 Zhou L, 2008, J AM SOC INF SCI TEC, V59, P98, DOI 10.1002/asi.20735 NR 49 TC 26 Z9 29 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD DEC PY 2010 VL 36 IS 6 BP 823 EP 848 DI 10.1177/0165551510388123 PG 26 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 691NE UT WOS:000285087200011 ER PT J AU Foley, C Smeaton, AF AF Foley, Colum Smeaton, Alan F. TI Division of labour and sharing of knowledge for synchronous collaborative information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Information retrieval; Collaborative information retrieval; Collaborative search ID WORK; SYSTEM; WEB AB Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR Finally we conclude with a discussion on some possible future trends for these two coordination techniques. (C) 2009 Elsevier Ltd. All rights reserved C1 [Foley, Colum; Smeaton, Alan F.] Dublin City Univ, Clar Ctr Sensor Web Technol, Dublin 9, Ireland. RP Foley, C (reprint author), Dublin City Univ, Clar Ctr Sensor Web Technol, Dublin 9, Ireland. CR ADCOCK J, 2007, TRECVID2007 Ahn J., 2005, P WORKSH NEW TECHN P, P1 Bellotti Victoria, 1996, C COMP SUPP COOP WOR, P209 Benyon D., 2004, DESIGNING INTERACTIV BLACKWELL AF, 2004, CHI 04 HUM FACT COMP, P1473 Cabri G., 1999, 7 EUR WORKSH PAR DIS, P138 COCKBURN A, 1993, MAKING CONTACT GETTI, P31 Diamadis ET, 2004, INT J HUM-COMPUT ST, V61, P699, DOI 10.1016/j.ijhcs.2004.06.003 Dietz P., 2001, UIST 01, P219, DOI DOI 10.1145/502348.502389 DILLEY KJ, 2008, IRFS2008 INF RETR FD Dourish P., 1992, CHI '92 Conference Proceedings. ACM Conference on Human Factors in Computing Systems. Striking a Balance Ellis C. A., 1991, Communications of the ACM, V34, DOI 10.1145/99977.99987 FOLEY C, 2008, COLLABORATIVE SOCIAL FOLEY C, 2006, COLLABORATECOM 2006, P1 FOLEY C, 2009, ECIR 2009, P42 Foster J, 2006, ANNU REV INFORM SCI, V40, P329, DOI 10.1002/aris.1440400115 GIANOUTSOS S, 1996, P OZ CSCW 96 DSTC TE, P96 Greenberg S., 1996, CHI 96, P271, DOI 10.1145/257089.257317 Gutwin C, 2008, J UNIVERS COMPUT SCI, V14, P1411 Han R., 2000, CSCW 2000. ACM 2000 Conference on Computer Supported Cooperative Work Hansen P, 2005, INFORM PROCESS MANAG, V41, P1101, DOI 10.1016/j.ipm.2004.04.016 HARMAN D, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1 Ingwersen P., 1992, INFORM RETRIEVAL INT Krishnappa R., 2005, THESIS U MISSOURI RO Kuutti Kari, 1991, ECSCW 91, P249 LAI KY, 1988, ACM T INFORM SYST, V6, P332, DOI 10.1145/58566.59298 Maekawa T, 2006, PerCom 2006: Fourth Annual IEEE International Conference on Pervasive Computing and Communications, Proceedings, P22 Malone T. W., 1990, WHAT IS COORDINATION, P357 MORRIS M, 2006, TABLETOP 06 P 1 IEEE, P97 MORRIS MR, 2008, CHI 2008 WORKSH SENS MORRIS MR, 2007, COLLABORATING ALONE Morris MR, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P1657 Morris SG, 2007, AM J BIOETHICS, V7, P20, DOI 10.1080/15265160701290298 Muller M., 2004, CHI 05 P SIGCHI C HU, P375 POLTROCK S, 2003, GROUP 03, P239 Poltrock S., 2009, HAW INT C SYST SCI, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROMANO NC, 1999, HICSS 99, V1 ROOT RW, 1988, CSCW 88 2 C COMP SUP, P25 SHARPLES M, 1993, CSCW PRACTISE INTRO Smeaton AF, 2006, MULTIMEDIA SYSTEMS J, V12, P375 SMEATON AF, 2008, CIVR 2008 SMEATON AF, 2006, TABLETOP2006, P149 Smyth B, 2004, USER MODEL USER-ADAP, V14, P383, DOI 10.1007/s11257-004-5270-4 Talja S., 2002, New Review of Information Behaviour Research, V3 TANG JC, 1991, INT J MAN MACH STUD, V34, P143, DOI 10.1016/0020-7373(91)90039-A TRIGG RH, 1986, COMPUTER SUPPORT COO, P153 Twidale MB, 1997, INFORM PROCESS MANAG, V33, P761, DOI 10.1016/S0306-4573(97)00040-X WANT R, 1992, ACM T INFORM SYST, V10, P91, DOI 10.1145/128756.128759 WHITTAKER S, 1993, INT J MAN MACH STUD, V39, P813, DOI 10.1006/imms.1993.1085 WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702 YAO KT, 1999, ICPP 99, P74 NR 52 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2010 VL 46 IS 6 SI SI BP 762 EP 772 DI 10.1016/j.ipm.2009.10.010 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 669ZX UT WOS:000283390000011 ER PT J AU Arif, SM Holliday, JD Willett, P AF Arif, Shereena M. Holliday, John D. Willett, Peter TI Inverse Frequency Weighting of Fragments for Similarity-Based Virtual Screening SO JOURNAL OF CHEMICAL INFORMATION AND MODELING LA English DT Article ID CLASSIFICATION; PERFORMANCE; SELECTION AB This paper discusses the weighting of two-dimensional fingerprints for similarity-based virtual screening, specifically the use of weights that assign greatest importance to the substructural fragments that occur least frequently in the database that is being screened. Virtual screening experiments using the MDL Drug Data Report and World of Molecular Bioactivity databases show that the use of such inverse frequency weighting schemes can result, in some circumstances, in marked increases in screening effectiveness when compared with the use of conventional, unweighted fingerprints. Analysis of the characteristics of the various schemes demonstrates that such weights are best used to weight the fingerprint of the reference structure in a similarity search, with the database structures' fingerprints unweighted. However, the increases in performance resulting from such weights are only observed with structurally homogeneous sets of active molecules; when the actives arc diverse, the best results arc obtained using conventional, unweightecl fingerprints for both the reference structure and the database structures. C1 [Arif, Shereena M.; Holliday, John D.; Willett, Peter] Univ Sheffield, Informat Sch, Sheffield S10 2TN, S Yorkshire, England. [Arif, Shereena M.] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ukm Bangi 43600, Malaysia. RP Willett, P (reprint author), Univ Sheffield, Informat Sch, Sheffield S10 2TN, S Yorkshire, England. EM p.willett@sheffield.ac.uk FU Government of Malaysia FX We thank a referee for valuable comments on an initial draft of this manuscript, Accelrys Inc., Digital Chemistry Limited, Sunset Molecular Discovery LLC, Symyx Technologies Inc., and Tripos Inc. for software and data, the Royal Society and the Wolfson Foundation for laboratory support, and the Government of Malaysia for funding. CR Abdo A, 2009, CHEMMEDCHEM, V4, P210, DOI 10.1002/cmdc.200800290 ADAMSON GW, 1975, J CHEM INF COMP SCI, V15, P55, DOI 10.1021/ci60001a016 Arif SM, 2009, J COMPUT AID MOL DES, V23, P655, DOI 10.1007/s10822-009-9285-0 Bender A, 2004, J CHEM INF COMP SCI, V44, P170, DOI 10.1021/ci034207y Brown N, 2006, MINI-REV MED CHEM, V6, P1217, DOI 10.2174/138955706778742768 Chen BN, 2009, J CHEMINFORMATICS, V1, DOI 10.1186/1758-2946-1-5 Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X DOWNS GM, 1993, CHEM STRUCTURES, V2, P409 Eckert H, 2007, DRUG DISCOV TODAY, V12, P225, DOI 10.1016/j.drudis.2007.01.011 Gardiner E.J., 2009, STAT ANAL DATA MININ, V2, P103 Gasteiger J., 2003, HDB CHEMOINFORMATICS Geppert H, 2010, J CHEM INF MODEL, V50, P205, DOI 10.1021/ci900419k Goldman BB, 2006, ANN REP COMP CHEM, V2, P127, DOI 10.1016/S1574-1400(06)02008-1 HARMAN D, 2005, CHARTING NEW COURSE, P69 HARRISON PJ, 1968, APPL STAT, V17, P226, DOI 10.2307/2985640 Hert J, 2008, J CHEM INF MODEL, V48, P755, DOI 10.1021/ci8000259 Hert J, 2006, J CHEM INF MODEL, V46, P462, DOI 10.1021/ci050348j Hert J, 2004, ORG BIOMOL CHEM, V2, P3256, DOI 10.1039/b409865j Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Leach AR, 2007, INTRO CHEMOINFORMATI Martin YC, 2009, QSAR COMB SCI, V28, P797, DOI 10.1002/qsar.200810176 MOOCK TE, 1988, TETRAHEDRON COMPUT M, V1, P117, DOI 10.1016/0898-5529(88)90016-4 ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sheridan RP, 2007, EXPERT OPIN DRUG DIS, V2, P423, DOI 10.1517/17460441.2.4.423 Siegel S, 1988, NONPARAMETRIC STAT B Snarey M, 1997, J MOL GRAPH MODEL, V15, P372, DOI 10.1016/S1093-3263(98)00008-4 Wang Y, 2009, J CHEM INF MODEL, V49, P1369, DOI 10.1021/ci900108d Willett P, 2006, DRUG DISCOV TODAY, V11, P1046, DOI 10.1016/j.drudis.2006.10.005 WILLETT P, 1986, QUANT STRUCT-ACT REL, V5, P18, DOI 10.1002/qsar.19860050105 Willett P, 2009, ANNU REV INFORM SCI, V43, P3 Xia XY, 2004, J MED CHEM, V47, P4463, DOI 10.1021/jm0303195 NR 33 TC 2 Z9 2 PU AMER CHEMICAL SOC PI WASHINGTON PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA SN 1549-9596 J9 J CHEM INF MODEL JI J. Chem Inf. Model. PD AUG PY 2010 VL 50 IS 8 BP 1340 EP 1349 DI 10.1021/ci1001235 PG 10 WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Pharmacology & Pharmacy; Chemistry; Computer Science GA 640BN UT WOS:000281022300003 PM 20672867 ER PT J AU Tamine-Lechani, L Boughanem, M Daoud, M AF Tamine-Lechani, Lynda Boughanem, Mohand Daoud, Mariam TI Evaluation of contextual information retrieval effectiveness: overview of issues and research SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Context; Profile; Relevance; Empirical evaluation ID PERSONALIZED WEB SEARCH; EMPIRICAL-EVALUATION; CENTERED EVALUATION; IMPLICIT FEEDBACK; SYSTEMS; RELEVANCE; USERS; IR; BEHAVIOR; SEEKING AB The increasing prominence of information arising from a wide range of sources delivered over electronic media has made traditional information retrieval systems less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval which relies on various sources of evidence issued from the user's search background and environment like interests, preferences, time and location, in order to improve the retrieval accuracy. Contextual information retrieval systems are based on different definitions of the core concept of user's context, various user's context modeling approaches and several techniques of document relevance measurement, but all share the goal of providing the most useful information to the users in accordance with their context. However, the evaluation methodologies conceived in the past several years for traditional information retrieval and widely used in the evaluation campaigns have been challenged by the consideration of user's context in the information retrieval process. Thus, we recognize that a critical review of existing evaluation methodologies in contextual information retrieval area is needed in order to design and develop standard evaluation frameworks. We present in this paper a comprehensive survey of contextual information retrieval evaluation methodologies and provide insights into how and why they are appropriate to measure the retrieval effectiveness. We also highlight some of the research challenges ahead that would constitute substantive research area for future research. C1 [Tamine-Lechani, Lynda; Boughanem, Mohand; Daoud, Mariam] Univ Paul Sabatier IRIT SIG, F-31062 Toulouse, France. RP Tamine-Lechani, L (reprint author), Univ Paul Sabatier IRIT SIG, 118 Route Narbonne, F-31062 Toulouse, France. EM lechani@irit.fr; bougha@irit.fr; daoud@irit.fr CR Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 Allan J, 2002, WORKSH HELD CTR INT Allan J., 2003, P TEXT RETR C, P24 Anand SS, 2007, ACM T INTERNET TECHN, V7, DOI 10.1145/1278366.1278367 Anderson C.R., 2001, P 10 INT C WORLD WID, P565, DOI 10.1145/371920.380716 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Beard K., 1997, International Journal on Digital Libraries, V1, DOI 10.1007/s007990050011 BELEW RK, 1996, AAAI SPRING S MACH L Bierig R., 2006, P 1 INT C INF INT CO, P79, DOI 10.1145/1164820.1164838 Bilal D, 2000, J AM SOC INFORM SCI, V51, P646, DOI 10.1002/(SICI)1097-4571(2000)51:7<646::AID-ASI7>3.0.CO;2-A Borlund P, 2003, INFORM RES, V8 Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286 Borlund P., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291019 Boughanem M, 2008, J DIGIT INF IN PRESS Bouvin N. O., 2003, New Review of Hypermedia and Multimedia, V9, DOI 10.1080/13614560410001725310 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009000 Budzik J., 2000, P 5 INT C INT US INT, P44, DOI 10.1145/325737.325776 Bylund M, 2002, COMMUN ACM, V45, P46 BYSTROM K, 1995, INFORM PROCESS MANAG, V31, P191, DOI 10.1016/0306-4573(94)00041-Z Chandrasekaran P., 2002, Proceedings Third International Conference on Mobile Data Management MDM 2002, DOI 10.1109/MDM.2002.994359 Cheverst K., 2000, P SIGCHI C HUM FACT, P17, DOI 10.1145/332040.332047 Chin DN, 2001, USER MODEL USER-ADAP, V11, P181, DOI 10.1023/A:1011127315884 Chittaro L., 2003, LECT NOTES COMPUTER, V2795 CLARCKE C, 2004, TEXT RETR C NIST CLEVERDON C, 1967, ASLIB P, P173 CRESTANI F, 2007, INF RETR, V10, P829 Daoud M., 2008, P 2 INT S INF INT CO, P57, DOI 10.1145/1414694.1414708 Davies N., 1998, 1 WORKSH HUM COMP IN Dervin B., 1986, ANN REV INFORMATION, P3 Dey AK, 2001, PERS UBIQUIT COMPUT, V5, P4, DOI 10.1007/s007790170019 Diaz A, 2008, INFORM PROCESS MANAG, V44, P1293, DOI 10.1016/j.ipm.2007.08.001 Ding C, 2007, J AM SOC INF SCI TEC, V58, P494, DOI 10.1002/asi.20497 Dumais S., 2003, P 26 ANN INT ACM SIG, P72 Ellis D, 1996, J AM SOC INFORM SCI, V47, P23, DOI 10.1002/(SICI)1097-4571(199601)47:1<23::AID-ASI3>3.0.CO;2-4 FRIASMARTINEZ E, 2007, USER MODEL USER ADAP, V17, P1391 Goker A, 2008, INFORM PROCESS MANAG, V44, P39, DOI 10.1016/j.ipm.2007.03.011 GOKER A, 2002, ECCBR WORKSH CAS BAS Goker A., 2004, P 2 EUR UN S AMB INT, P19, DOI 10.1145/1031419.1031424 Gowan J., 2003, THESIS U COLL DUBLIN Gwizdka J., 1999, INFORM RETRIEV UNPUB Harman D., 1995, P 3 TEXT RETR C TREC, P1 Harter SP, 1997, ANNU REV INFORM SCI, V32, P3 HATTORI S, 2007, P 2007 INT S APPL IN, P15 HAVELIWALA T, 2002, INT ACM WORLD WID WE, P727 Held A, 2002, P 6 WORLD MULT SYST Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 HERSH WR, 1995, P 18 ANN INT ACM SIG, P164, DOI 10.1145/215206.215355 Hupfer ME, 2006, J AM SOC INF SCI TEC, V57, P1105, DOI 10.1002/asi.20379 Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 Ingwersen P., 2005, TURN INTEGRATION INF Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 Iqbal R., 2005, P 23 ANN INT C DES C, P138, DOI 10.1145/1085313.1085346 Jansen BJ, 2008, INFORM PROCESS MANAG, V44, P1251, DOI 10.1016/j.ipm.2007.07.015 Jansen B.J., 2007, P 16 INT C WORLD WID, P1149, DOI 10.1145/1242572.1242739 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Joachims T, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1229179.1229181 John R. I., 2001, Knowledge and Information Systems, V3, DOI 10.1007/PL00011660 KEKALAINEN J, 2004, P 4 COLIS C, P253 Kelly D, 2007, INFORM PROCESS MANAG, V43, P30, DOI 10.1016/j.ipm.2006.03.006 KEPLER J, 2005, CONTEXT AWARNESS MOB Kim KS, 2002, J AM SOC INF SCI TEC, V53, P109, DOI 10.1002/asi.10014 Kim KS, 2008, INFORM PROCESS MANAG, V44, P373, DOI 10.1016/j.ipm.2006.11.008 Kjeldskov J, 2003, LECT NOTES COMPUT SC, V2795, P317 KRAFT R, 2005, CIKM 05 P 14 ACM INT, P816 Lang K., 1995, P 12 INT C MACH LEAR, P331 LAW E, 2006, EC TEL, P257 Lee J., 2005, P 28 INT SIGIR C RES, P33 Leung CWK, 2006, KNOWL INF SYST, V10, P357, DOI 10.1007/s10115-006-0002-1 Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28 MARCHIONINI G, 1995, CAMBRIDGE SERIES HUM Micarelli A, 2004, USER MODEL USER-ADAP, V14, P159, DOI 10.1023/B:USER.0000028981.43614.94 Mitchell TJF, 2005, J COMPUT ASSIST LEAR, V21, P53, DOI 10.1111/j.1365-2729.2005.00113.x Mizzaro S, 1998, INTERACT COMPUT, V10, P303, DOI 10.1016/S0953-5438(98)00012-5 Mobasher B., 2007, LECT NOTES COMPUTER Mostafa J, 2003, INFORM RETRIEVAL, V6, P199, DOI 10.1023/A:1023932221048 NAVARROPRIETO R, 2006, 5 C HUM FACT WEB Petrelli D, 2008, INFORM PROCESS MANAG, V44, P22, DOI 10.1016/j.ipm.2007.01.024 Ramesh V, 2004, J SYST SOFTWARE, V70, P165, DOI 10.1016/S0164-1212(03)00014-1 RIGAUX P, 2002, SPATIAL DATABASE APP Robertson S, 2002, INFORM RETRIEVAL, V5, P257, DOI 10.1023/A:1015754113585 ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ryan N, 1997, COMPUTER APPL ARCHEO SALTON G, 1971, SMART INFORM RETRIEV Schilit B., 1994, P WORKSH MOB COMP SY, P85, DOI DOI 10.1109/WMCSA.1994.16 SCHILIT BN, 2003, WMASHE 03 SHAMBER L, 1994, ARIST, P3 Shen C.X, 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Sieg A., 2007, P 16 ACM C INF KNOWL, P525, DOI 10.1145/1321440.1321515 SIEG A, 2004, P 2004 M INT FED CLA Smyth B, 2006, INFORM RETRIEVAL, V9, P165, DOI 10.1007/s10791-006-7148-z SOLOMON P, 1993, J AM SOC INFORM SCI, V44, P245, DOI 10.1002/(SICI)1097-4571(199306)44:5<245::AID-ASI1>3.0.CO;2-# SONNENWALD D, 1994, FRAMEWORK SUPPORT IN, P161 Sormunen E., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SPARCK-JONES K, 1976, J DOC, V32, P59 Speretta M., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence SU LT, 1992, INFORM PROCESS MANAG, V28, P503, DOI 10.1016/0306-4573(92)90007-M Tamine L, 2003, INFORM PROCESS MANAG, V39, P215, DOI 10.1016/S0306-4573(02)00048-1 TAMINE L, 2001, 23 EUR C INF RETR EC, P15 Tan AH, 2004, KNOWL INF SYST, V6, P595, DOI 10.1007/s10115-003-0130-9 Tan B, 2006, P 12 ACM SIGKDD INT, P718, DOI DOI 10.1145/1150402.1150493 TAO Y, 2003, SSTD Teevan J., 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111 Tombros A, 2005, J AM SOC INF SCI TEC, V56, P327, DOI 10.1002/asi.20106 TRAJKOVA J, 2004, P RIAO 2004 VAUCL FR TURPIN H, 2001, ACM C RES DEV INF RE, P225 UZUNER O, 1999, AAAI IAAI, P985 Vakkari P, 2004, J AM SOC INF SCI TEC, V55, P963, DOI 10.1002/asi.20046 VIEIRA V, 2007, CONTEXT, P493 Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P697, DOI 10.1016/S0306-4573(00)00010-8 Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 White RW, 2005, ACM T INFORM SYST, V23, P325, DOI 10.1145/1080343.1080347 WOODRUFF AG, 1994, J AM SOC INFORM SCI, V45, P645, DOI 10.1002/(SICI)1097-4571(199410)45:9<645::AID-ASI2>3.0.CO;2-8 Wu X., 2007, KNOWL INF SYST, V14, P1, DOI DOI 10.1007/S10115-007-0114-2 Xie HI, 2008, INFORM PROCESS MANAG, V44, P1346, DOI 10.1016/j.ipm.2007.10.003 YAU S, 2003, P 27 ANN INT COMP SO Yilmaz E, 2008, KNOWL INF SYST, V16, P173, DOI 10.1007/s10115-007-0101-7 Zhou Y, 2008, KNOWL INF SYST, V16, P155, DOI 10.1007/s10115-007-0100-8 ZOBEL J, 1998, ACM SIGIR 98, P307 NR 121 TC 20 Z9 21 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD JUL PY 2010 VL 24 IS 1 BP 1 EP 34 DI 10.1007/s10115-009-0231-1 PG 34 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 612VE UT WOS:000278931800001 ER PT J AU Tseng, SP Chiang, MC Yang, CS Tsai, CW AF Tseng, Shih-Pang Chiang, Ming-Chao Yang, Chu-Sing Tsai, Chun-Wei TI An Ontology-based Framework for Itembank Integration and Knowledge Sharing SO INTERNATIONAL JOURNAL OF FUZZY SYSTEMS LA English DT Article DE Information Retrieval; Ontology; Itembank ID SPACE AB The Internet is abounding with information, but there is simply no easy way to find all the information we need in a single place, well organized and easily accessible, let alone share. For this reason, we present a novel framework for resolving these issues. The proposed framework incorporates and unifies techniques from a variety of disciplines such as information extraction, information retrieval, fuzzy clustering, and ontology. To evaluate the performance of the proposed framework, we use it to solve the itembank integration and knowledge sharing problem faced by many e-Learning systems. Our experimental results show that the proposed framework can not only effectively integrate information available in the Internet, but it can also properly organize the information into a unified itembank of an e-Learning system, thus making it possible for the users of such a system to not just find but share all the information. C1 [Chiang, Ming-Chao; Yang, Chu-Sing] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan. [Tseng, Shih-Pang] Tajen Univ, Dept Comp Sci & Informat Engn, Pingtung, Taiwan. [Yang, Chu-Sing] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 80424, Taiwan. [Yang, Chu-Sing] Natl Sun Yat Sen Univ, Ctr Comp, Kaohsiung 80424, Taiwan. RP Chiang, MC (reprint author), Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, 70 Lienhai Rd, Kaohsiung 80424, Taiwan. EM mcchiang@cse.nsysu.edu.tw FU Chinese Knowledge and Information Processing (CKIP) group, Institute of Information Science, Academia Sinica for providing their Chinese Word Segmentation System; National Science Council, Taiwan, ROC [95-2221-E-006-509-MY2, 98-2218-E-006-003] FX The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions on the paper. The authors would also like to thank Kang Hsuan Publishing and Han Lin Publishing for providing their itembanks and the Chinese Knowledge and Information Processing (CKIP) group, Institute of Information Science, Academia Sinica for providing their Chinese Word Segmentation System to support this research. This work was supported in part by National Science Council, Taiwan, ROC, under Contract Nos. 95-2221-E-006-509-MY2 and 98-2218-E-006-003. CR [Anonymous], 2003, INT REV RES OPEN DIS, V4 BERNERSLEE T, 1999, WHY RDF MODEL IS DIF Bezdek J. C., 1981, PATTERN RECOGNITION Borst W., 1997, THESIS U TWENTE ENSC Clara N, 2007, INT J FUZZY SYST, V9, P220 Friesen N., 2004, ONLINE ED USING LEAR, P59 Graham S, 1998, PROG HUM GEOG, V22, P165, DOI 10.1191/030913298671334137 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Ismail J., 2002, INTERNET HIGH EDUC, V4, P329 JENG WJ, 2009, P NAT COMP S NCS 200 *K CONS INC, 2005, EL DISC COST MAN SYS, P1 Lassila O, 1999, RESOURCE DESCRIPTION LEHTI P, 2004, INT S APPL INT, P160 Li CH, 2008, INT J FUZZY SYST, V10, P168 Liu Bing, 2007, WEB DATA MINING EXPL MENDES M, 2002, P LCS 2002 Noy NF, 2004, SIGMOD RECORD, V33, P65 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 *PROT TEAM, PROT ONT ED KNOWL AC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RONALD HS, 1983, ITEM RESPONSE THEORY SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STOJANOVIC L, 2001, WORLD C WWW INT Tanenbaum AS, 2002, COMPUTER NETWORKS THOMPSON M, 1996, J CONTINUING HIGHER, V44, P29 TSAI CW, 2009, P INT C INT SYST DES, P919 *W3C, 2003, OWL WEB ONT LANG Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 CHINESE DOCUMENT SEG NR 30 TC 1 Z9 1 PU TAIWAN FUZZY SYSTEMS ASSOC PI TAIPEI PA NATL TAIPEI UNIV TECHNOL, 1 SEC 3 CHUNG-HSIAO E RD, TAIPEI, ROC 00000, TAIWAN SN 1562-2479 J9 INT J FUZZY SYST JI Int. J. Fuzzy Syst. PD JUN PY 2010 VL 12 IS 2 SI SI BP 116 EP 124 PG 9 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 634WL UT WOS:000280616500004 ER PT J AU Gao, W Niu, C Nie, JY Zhou, M Wong, KF Hon, HW AF Gao, Wei Niu, Cheng Nie, Jian-Yun Zhou, Ming Wong, Kam-Fai Hon, Hsiao-Wuen TI Exploiting Query Logs for Cross-Lingual Query Suggestion SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Algorithms; Performance; Experimentation; Theory; Cross-language information retrieval; query expansion; query log; query suggestion; query translation ID LANGUAGE INFORMATION-RETRIEVAL; USER LOGS; EXPANSION AB Query suggestion aims to suggest relevant queries for a given query, which helps users better specify their information needs. Previous work on query suggestion has been limited to the same language. In this article, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to the scenarios of cross-language information retrieval (CLIR) and other related cross-lingual applications. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log. Important monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, and so on, are used to estimate the cross-lingual query similarity with a discriminative model. Benchmarks show that the resulting CLQS system significantly outperforms a baseline system that uses dictionary-based query translation. Besides, we evaluate CLQS with French-English and Chinese-English CLIR tasks on TREC-6 and NTCIR-4 collections, respectively. The CLIR experiments using typical retrieval models demonstrate that the CLQS-based approach has significantly higher effectiveness than several traditional query translation methods. We find that when combined with pseudo-relevance feedback, the effectiveness of CLIR using CLQS is enhanced for different pairs of languages. C1 [Gao, Wei; Wong, Kam-Fai] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. [Niu, Cheng; Zhou, Ming; Hon, Hsiao-Wuen] Microsoft Res Asia, Beijing 100190, Peoples R China. [Nie, Jian-Yun] Univ Montreal, Montreal, PQ, Canada. RP Gao, W (reprint author), Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. EM wgao@se.cuhk.edu.hk; chengniu@microsoft.com; nie@iro.umontreal.ca; mingzhou@microsoft.com; kfwong@se.cuhk.edu.hk; hon@microsoft.com FU Hong Kong Innovation and Technology Fund [ITS/182/09]; CUHK [2050443] FX This research is sponsored in part by the Hong Kong Innovation and Technology Fund (ITS/182/09) and the CUHK direct grant (2050443), and is partially affiliated with the Microsoft-CUHK Joint laboratory for Human-Centric Computing and Interface Technologies. CR AMBATI V, 2006, P ACM SIGIR WORKSH N Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 Brown P. F., 1993, Computational Linguistics, V19 Chang C. C., 2001, LIBSVM LIB SUPPORT V Chen H-H, 2006, P 21 INT C COMP LING, P1009, DOI 10.3115/1220175.1220302 Cheng P.-J., 2004, P 27 ANN INT ACM SIG, P146, DOI 10.1145/1008992.1009020 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 FUJII A, 2000, P 4 C ASS MACH TRANS, P13 Gao J., 2001, P 24 ANN INT ACM SIG, P96, DOI 10.1145/383952.383966 Gao J., 2002, P 25 ANN INT ACM SIG, P183 Gao JF, 2005, COMPUT LINGUIST, V31, P531, DOI 10.1162/089120105775299177 Gao W., 2007, P 30 ACM C RES DEV I, P463, DOI 10.1145/1277741.1277821 GLEICH D, 2004, SVD SUBSPACE PROJECT Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Jeon J., 2005, P 14 ACM INT C INF K, P84, DOI DOI 10.1145/1099554.1099572.ISBN:1-59593-140-6 JIANG MG, 1999, P 37 ANN M ASS COMP, P223 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 KISHIDA K, 2004, P 4 NTCIR WORKSH M E, P1 Koehn P., 2007, P 45 ANN M ASS COMP, P177, DOI 10.3115/1557769.1557821 Koehn P., 2003, P 2003 C N AM CHAPT, P48 Koehn Philipp, 2005, P 10 MACH TRANSL SUM, P79 Kraaij W, 2003, COMPUT LINGUIST, V29, P381, DOI 10.1162/089120103322711587 KWOK KL, 2005, ACM T ASIAN LANGUAGE, V4, P136, DOI 10.1145/1105696.1105700 Lavrenko V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Li C., 2007, P 45 ANN M ASS COMP, P720 Lopez-Ostenero F, 2005, INFORM PROCESS MANAG, V41, P549, DOI 10.1016/j.ipm.2004.06.014 LU WH, 2001, P 24 ANN INT ACM SIG, P388, DOI 10.1145/383952.384031 McNamee P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Monz C., 2005, P 28 ANN INT ACM SIG, P520, DOI 10.1145/1076034.1076123 Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 OCH FJ, 2002, THESIS RWTH AACHEN G Och F. J., 2003, Computational Linguistics, V29, DOI 10.1162/089120103321337421 Pirkola A, 2001, INFORM RETRIEVAL, V4, P209, DOI 10.1023/A:1011994105352 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SCHAUBLE P, 2000, P 6 TEXT RETRIEVAL C, P31 Smola AJ, 2004, STAT COMPUT, V14, P199, DOI 10.1023/B:STCO.0000035301.49549.88 Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 White R.W., 2007, P 30 ANN INT ACM SIG, P831, DOI DOI 10.1145/1277741.1277931 Zhai C., 2001, P 24 ANN INT ACM SIG ZHAI CX, 2001, P 10 INT C INF KNOWL ZHANG D, 2008, P 46 ANN M ASS COMP, P89 Zhang Y., 2004, P 27 ANN INT ACM SIG, P162, DOI 10.1145/1008992.1009022 NR 48 TC 2 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 EI 1558-2868 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD MAY PY 2010 VL 28 IS 2 AR 6 DI 10.1145/1740592.1740594 PG 33 WC Computer Science, Information Systems SC Computer Science GA 612NO UT WOS:000278906300002 ER PT J AU Mu, XM Lu, K AF Mu, Xiangming Lu, Kun TI Towards effective genomic information retrieval: The impact of query complexity and expansion strategies SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE health information retrieval; query complexity; query expansion; Unified Medical Language System Metathesaurus ID SUBJECT-HEADINGS MESH; TERM SPECIFICITY; MEDLINE; PERFORMANCE; THESAURUS; SEEKING AB The goal of this study is to examine the influence of query complexity and different query expansion strategies on the effectiveness of genomic information retrieval. Query complexity is defined as the average number of terms in a query. The query expansion strategies are based on the Unified Medical Language System (UMLS) Metathesaurus. The combination of string/word indexing on concept/term level provides four different automatic UMLS expansion strategies. The test collection for the study is the TREC Genomic Track 2006 data set (24 topics). We use the Mean Average Precision (MAP), 11-point precision/recall, and average precision/recall at maximum recall point for the retrieval performance evaluation. In general, we found that applying query expansion did not improve the retrieval effectiveness as compared to the baseline. Our results also indicated that string index expansions are more effective than word index expansions. Queries with a smaller number of terms outperform queries with a larger number of terms and the difference is statistically significant. String index on term level is the best automatic UMLS expansion strategy for queries with a smaller number of terms. The worst case scenario is applying word index expansions on queries with a larger number of terms. Based on these findings, we recommend that genomic information retrieval systems should support flexible query expansion strategies to best accommodate queries with different levels of complexity. C1 [Mu, Xiangming; Lu, Kun] Univ Wisconsin, Sch Informat Studies, Milwaukee, WI 53211 USA. RP Mu, XM (reprint author), Univ Wisconsin, Sch Informat Studies, Milwaukee, WI 53211 USA. EM mux@uwm.edu RI Lu, Kun/G-2416-2015 OI Lu, Kun/0000-0001-5614-7042 CR Alemayehu N, 2003, J AM SOC INF SCI TEC, V54, P379, DOI 10.1002/asi.10217 Aronson A R, 1997, Proc AMIA Annu Fall Symp, P485 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Callan J., 2001, P 10 TEXT RETR C Cleverdon C.W., 1970, EFFECT VARIATIONS RE Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Fox S., 2006, ONLINE HLTH SEARCH 2 Gault LV, 2002, J MED LIBR ASSOC, V90, P173 Greenberg J, 2001, J AM SOC INF SCI TEC, V52, P402, DOI 10.1002/1532-2890(2001)9999:9999<::AID-ASI1089>3.3.CO;2-B Harman DK, 2006, ANNU REV INFORM SCI, V40, P113, DOI 10.1002/aris.1440400111 HEINE MH, 1995, J INFORM SCI, V21, P173, DOI 10.1177/016555159502100303 HERSH W, 2006, P 15 TEXT RETR C Hersh W. H., 2000, P AMIA ANN S Hersh W, 2009, HEALTH INFORM SER, P3 HERSH WR, 1994, J AM MED INFORM ASSN, V1, P51 Kekalainen J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290978 Keselman A, 2008, J AM MED INFORM ASSN, V15, P484, DOI 10.1197/jamia.M2449 KRISTENSEN J, 1993, INFORM PROCESS MANAG, V29, P733, DOI 10.1016/0306-4573(93)90102-J Lancaster W., 1993, INFORM RETRIEVAL TOD Lewis D., 1998, P 10 EUR C MACH LEAR Lu ZY, 2009, INFORM RETRIEVAL, V12, P69, DOI 10.1007/s10791-008-9074-8 Manning C. D., 2008, INTRO INFORM RETRIEV Poikonen T, 2009, J INF SCI, V35, P232, DOI 10.1177/0165551508098602 PRICE S, 2006, P INT WORKSH HEALTHC, P9, DOI 10.1145/1183568.1183571 PRICE S, 2007, IEEE TECHNICAL COMMI, V3 QIU Y, 1993, P 16 ANN INT ACM SIG Rekapalli H. K., 2007, P AMIA ANN S, P620 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 Shultz M, 2006, J MED LIBR ASSOC, V94, P410 SOERGEL D, 2004, MEDINFO, V93, P1 SPARCKJONES K, 1972, J DOC, V28, P11 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 Stokes N, 2009, INFORM RETRIEVAL, V12, P17, DOI 10.1007/s10791-008-9073-9 Voorhees E. M., 1994, P 17 ANN INT ACM SIG, P61 Wolfram D, 2008, INFORM PROCESS MANAG, V44, P1279, DOI 10.1016/j.ipm.2007.07.010 YANG K, P 14 TEXT RETRIEVAL, P15 Yeganova L, 2009, J AM SOC INF SCI TEC, V60, P264, DOI 10.1002/asi.20979 Zeng Q, 2002, METHOD INFORM MED, V41, P289 Zeng QT, 2006, J AM MED INFORM ASSN, V13, P80, DOI 10.1197/jamia.M1820 ZHAO FC, P 15 TEXT RETRIEVAL NR 43 TC 5 Z9 5 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD APR PY 2010 VL 36 IS 2 BP 194 EP 208 DI 10.1177/0165551509357856 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 574PO UT WOS:000276004300005 ER PT J AU Dong, H Hussain, FK Chang, E AF Dong, Hai Hussain, Farookh Khadeer Chang, Elizabeth TI A Human-Centered Semantic Service Platform for the Digital Ecosystems Environment SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE digital ecosystems (abbreviated as DEST); human-centered computing; human-centered systems; ontology revolution; QoS evaluation; service platform ID INTERMEDIARIES; RELEVANCE AB Digital Ecosystems (DEST) have emerged with the purpose of enhancing communications among small and medium enterprises (SMEs) within the worldwide Business Ecosystem. However, because of the diversity and heterogeneity of the services in the DEST environment, existing commercial products or research outputs cannot be directly applied to this field so as to fulfill the requirements of SMEs. Human-centered computing has been applied to many areas, such as social classification, community-based ontology evolution, and more importantly, human-centered systems. In this paper, we propose a framework for a human-centered semantic service platform, in order to address the issue in the DEST environment. This framework incorporates the features of human-centered metadata publishing, maintenance and clustering, community-based ontology revolution and human-centered service retrieval, evaluation and ranking. To thoroughly validate the framework, we implement a prototype in the transport service domain, and conduct a series of evaluation experiments on the basis of this prototype. C1 [Dong, Hai; Hussain, Farookh Khadeer; Chang, Elizabeth] Curtin Univ Technol, Digital Ecosyst & Business Intelligence Inst, Bentley, WA 6102, Australia. RP Dong, H (reprint author), Curtin Univ Technol, Digital Ecosyst & Business Intelligence Inst, Bentley, WA 6102, Australia. EM hai.dong@cbs.curtin.edu.au; farookh.hussain@cbs.curtin.edu.au; elizabeth.chang@cbs.curtin.edu.au RI Dong, Hai/E-9101-2010 OI Dong, Hai/0000-0002-7033-5688 CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barrett R, 1998, COMPUT NETWORKS ISDN, V30, P509, DOI 10.1016/S0169-7552(98)00084-1 Barrett R, 1999, IBM SYST J, V38, P629 Berners-Lee T., 2001, SCI AM MAGAZINE Bianchini D, 2008, WORLD WIDE WEB, V11, P227, DOI 10.1007/s11280-007-0040-y BOGERS T, 2006, 17 ASIS T SIG CR CLA, V17 Chang E., 2005 CHANG E, 2006, DIGITAL ECOSYSTEM NE CHANG E, 2006, DIGITAL ECOSYSTEM BU Chang Elizabeth, 2005, TRUST REPUTATION SER Chen YF, 2003, WIREL NETW, V9, P283, DOI 10.1023/A:1023687025164 Cibran MA, 2007, WORLD WIDE WEB, V10, P211, DOI 10.1007/s11280-006-0017-2 CONRADI R, 1994, SOFTWARE PROCESS MOD Cugola G., 1996, ACM Transactions on Software Engineering and Methodology, V5, DOI 10.1145/234426.234427 DONG H, 2008, LECT NOTES COMPUTER, V5333, P1084 Dong H., 2008, P405 DONG H, 2008, TRANSPORT SERVICE ON, P48 DONG H, 2008, 2008 IEEE INT C SERV, P820 Furnas G. W., 1988, 11th International Conference on Research and Development in Information Retrieval Gendarmi D, 2006, LECT NOTES COMPUT SC, V4277, P181 Grieco R, 2006, WORLD WIDE WEB, V9, P317, DOI 10.1007/s11280-006-8559-x Hayes P., 2004, RDF SEMANTICS Hevner AR, 2004, MIS QUART, V28, P75 HOFFMAN R, 2004, HUMAN CTR COMPUTING Hussain FK, 2004, COMPUT SYST SCI ENG, V19, P173 *IBM, WEBSPHERE APPL SERV JAIMES A, 2006, HUMAN CTR COMPUTING KARP AH, 2004, LESSONS E SPEAK Liu DR, 2003, COMP STAND INTER, V25, P103, DOI 10.1016/S0920-5489(03)00012-6 Martin D, 2007, WORLD WIDE WEB, V10, P243, DOI 10.1007/s11280-007-0033-x Mathes Adam, 2004, FOLKSONOMIES COOPERA MOORE JF, 1993, HARVARD BUS REV, V71, P75 Mukherjee S, 2008, WORLD WIDE WEB, V11, P427, DOI 10.1007/s11280-008-0046-0 Nachira F., 2007, DIGITAL BUSINESS ECO NING X, 2007, INFORM PROCESS MANAG, V44, P893 O'Reilly T., 2005, WHAT IS WEB 2 0 PATELSCHNEIDER P, 2006, MAPPING RDF GRAPH OW ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAHA D, 2003, SERVICE PLATFORM ONL, P108 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Salton G., 1971, SMART RETRIEVAL SYST SHAIKH A, 2004, IMPLEMENTATION SERVI, P106 Shaw WM, 1997, INFORM PROCESS MANAG, V33, P1, DOI 10.1016/S0306-4573(96)00043-X SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 VONRIJSBERGAN CJ, 1976, INFORM RETRIEVAL W3C, 2004, OWL WEB ONT LANG OV NR 46 TC 8 Z9 8 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD MAR PY 2010 VL 13 IS 1-2 SI SI BP 75 EP 103 DI 10.1007/s11280-009-0081-5 PG 29 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 576HC UT WOS:000276134100005 ER PT J AU Pang, H Shen, J Krishnan, R AF Pang, Hweehwa Shen, Jialie Krishnan, Ramayya TI Privacy-Preserving Similarity-Based Text Retrieval SO ACM TRANSACTIONS ON INTERNET TECHNOLOGY LA English DT Article DE Security; Privacy of search queries; security in text retrieval; singular value decomposition ID INFORMATION-RETRIEVAL; DISCLOSURE; SEARCH; SYSTEM; COMPRESSION; DATABASES; QUERIES; MATRIX AB Users of online services are increasingly wary that their activities could disclose confidential information on their business or personal activities. It would be desirable for an online document service to perform text retrieval for users, while protecting the privacy of their activities. In this article, we introduce a privacy-preserving, similarity-based text retrieval scheme that (a) prevents the server from accurately reconstructing the term composition of queries and documents, and (b) anonymizes the search results from unauthorized observers. At the same time, our scheme preserves the relevance-ranking of the search server, and enables accounting of the number of documents that each user opens. The effectiveness of the scheme is verified empirically with two real text corpora. C1 [Pang, Hweehwa; Shen, Jialie] Singapore Management Univ, Sch Informat Syst, Singapore, Singapore. [Krishnan, Ramayya] Carnegie Mellon Univ, Sch Informat Syst & Management, Pittsburgh, PA 15213 USA. RP Pang, H (reprint author), Singapore Management Univ, Sch Informat Syst, Singapore, Singapore. EM hhpang@smu.edu.sg RI PANG, Hwee Hwa/E-8570-2012; SHEN, Jialie/E-8573-2012 FU Singapore Management University [C220/MSS6C002] FX H. H. Pang is supported by a research grant (C220/MSS6C002) from the Singapore Management University. CR *AES, 2001, 197 FIPS Agrawal R., 2004, P ACM SIGMOD INT C M, P563, DOI DOI 10.1145/1007568.1007632 AGRAWAL R, 2004, P ACM INT C MAN DAT, P251 Agrawal R., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases Anderson R., 1998, P INT WORKSH INF HID Wang SX, 2007, PROCEEDINGS OF THE ASME/JSME THERMAL ENGINEERING SUMMER HEAT TRANSFER CONFERENCE 2007, VOL 2, P49 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BAO F, 2000, P 3 INT C INF SEC CR, V2015, P162 Barbaro M., 2006, NY TIMES BAWA M, 2003, P INT C VER LARG DAT BERCHTOLD S, 1998, P INT C EXT DAT TECH, P216 Bethencourt J, 2006, P IEEE S SECUR PRIV, P132, DOI 10.1109/SP.2006.27 Bloom B. H., 1970, COMMUN ACM, V13, P422 BOOKSTEIN A, 1992, INFORM PROCESS MANAG, V28, P795, DOI 10.1016/0306-4573(92)90069-C Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chaum D., 1981, COMMUN ACM, V24, P84 Chor B., 1995, Proceedings. 36th Annual Symposium on Foundations of Computer Science (Cat. No.95CB35834), DOI 10.1109/SFCS.1995.492461 Damiani E., 2003, P 10 ACM C COMP COMM, P93 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dingledine R., 2004, Proceedings of the 13th USENIX Security Symposium Domingo-Ferrer J, 2004, ANN NY ACAD SCI, V3050, P149 Domingo-Ferrer J, 2008, COMPUT NETW, V52, P3007, DOI 10.1016/j.comnet.2008.06.017 Drineas P, 2006, SIAM J COMPUT, V36, P158, DOI 10.1137/S0097539704442696 Dumais S., 1994, NATL I STANDARDS TEC, V500-215, P105 DUMAIS S., 1995, NATL I STANDARDS TEC, P219 Duncan G.T., 2001, CONFIDENTIALITY DISC, P135 FIERRO R, 2002, TOTAL LEAST SQUARES, P349 FREEDMAN MJ, 2005, P 2 THEOR CRYPT C FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Gamal Taher El, 1984, P CRYPTO 84, P10 Goh E.-J., 2003, 2003216 CRYPT Goldschlag D, 1999, COMMUN ACM, V42, P39, DOI 10.1145/293411.293443 Gopal R, 2002, OPER RES, V50, P501, DOI 10.1287/opre.50.3.501.7745 Gruteser M, 2003, PROCEEDINGS OF MOBISYS 2003, P31, DOI 10.1145/1066116.1189037 Guttman A, 1984, P ACM SIGMOD INT C M, P47 Hacigumus H., 2002, P ACM SIGMOD INT C M, P216, DOI DOI 10.1145/564691.564717 Hansell S., 2006, NY TIMES He X, 2004, P 27 ANN INT ACM SIG, P96, DOI 10.1145/1008992.1009012 Hjaltason GR, 1999, ACM T DATABASE SYST, V24, P265, DOI 10.1145/320248.320255 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 HUSBANDS P, 2001, P SIAM C COMP INF RE, P145 Jajodia S, 2001, ACM T DATABASE SYST, V26, P214, DOI 10.1145/383891.383894 Jiang W, 2008, PROC INT CONF DATA, P735, DOI 10.1109/ICDE.2008.4497482 Kadane JB, 2006, MANAGE SCI, V52, P1610, DOI 10.1287/mnsc.1060.0562 Kalnis P, 2007, IEEE T KNOWL DATA EN, V19, P1719, DOI 10.1109/TKDE.2007.190662 Kim J., 1986, SECTION SURVEY RES M, P303 KLEIN ST, 1989, ACM T INFORM SYST, V7, P230, DOI 10.1145/65943.65946 Kleinberg J.M., 1999, JACM, V46, P5 Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 Lewis DD, 2004, J MACH LEARN RES, V5, P361 LONG D, 2002, P 1 EURASIAN C INF C Machanavajjhala A., 2006, P 22 INT C DAT ENG I, P24, DOI DOI 10.1109/ICDE.2006.1 Menon S, 2007, MANAGE SCI, V53, P101, DOI 10.1287/mnsc.1060.0603 Meyerson A., 2004, P 23 ACM SIGMOD SIGA, P223, DOI 10.1145/1055558.1055591 Ogata W, 2004, J COMPLEXITY, V20, P356, DOI 10.1016/j.jco.2003.08.023 Ostrovsky R, 2007, J CRYPTOL, V20, P397, DOI 10.1007/s00145-007-0565-3 PANG H, 2008, P VERY LARG DAT BAS, P126 Pang HH, 2004, IEEE T KNOWL DATA EN, V16, P701, DOI 10.1109/TKDE.2004.15 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robinson J. J., 1981, Paper, 32nd Annual Meeting of the European Association for Animal Production ROSARIO B, 2000, 240 INFOSYS Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 Samarati P, 2001, IEEE T KNOWL DATA EN, V13, P1010, DOI 10.1109/69.971193 Sandhu R. S., 1996, IEEE COMPUT, V2, P38 Sheng CD, 2007, AEROSOL SCI TECH, V41, P1, DOI 10.1080/02786820601009704 Song DXD, 2000, P IEEE S SECUR PRIV, P44 Sweeney L, 2002, INT J UNCERTAIN FUZZ, V10, P557, DOI 10.1142/S0218488502001648 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Wang LY, 2006, COMPUT COMMUN, V29, P3812, DOI 10.1016/j.comcom.2006.06.018 Xiao X., 2006, P 2006 ACM SIGMOD IN, P229, DOI 10.1145/1142473.1142500 Yan H, 2008, DATA KNOWL ENG, V65, P108, DOI 10.1016/j.datak.2007.10.005 Zhong S., 2005, P 24 ACM SIGMOD SIGA, P139, DOI DOI 10.1197/J.JAMIA.M2441 ZOBEL J, 1995, SOFTWARE PRACT EXPER, V25, P891, DOI 10.1002/spe.4380250804 Zobel J., 2006, ACM COMPUT SURV, V38, P6, DOI DOI 10.1145/1132956.1132959 NR 76 TC 3 Z9 3 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1533-5399 J9 ACM T INTERNET TECHN JI ACM Trans. Internet. Technol. PD FEB PY 2010 VL 10 IS 1 AR 4 DI 10.1145/1667067.1667071 PG 39 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 553QA UT WOS:000274380700004 ER PT J AU Blanco, R Barreiro, A AF Blanco, Roi Barreiro, Alvaro TI Probabilistic Static Pruning of Inverted Files SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Algorithms; Experimentation; Performance; Pruning; inverted files; probabilistic models; compression; efficiency ID INFORMATION-RETRIEVAL; MODELS; TERMS AB Information retrieval (IR) systems typically compress their indexes in order to increase their efficiency. Static pruning is a form of lossy data compression: it removes from the index, data that is estimated to be the least important to retrieval performance, according to some criterion. Generally, pruning criteria are derived from term weighting functions, which assign weights to terms according to their contribution to a document's contents. Usually, document-term occurrences that are assigned a low weight are ruled out from the index. The main assumption is that those entries contribute little to the document content. We present a novel pruning technique that is based on a probabilistic model of IR. We employ the Probability Ranking Principle as a decision criterion over which posting list entries are to be pruned. The proposed approach requires the estimation of three probabilities, combining them in such a way that we gather all the necessary information to apply the aforementioned criterion. We evaluate our proposed pruning technique on five TREC collections and various retrieval tasks, and show that in almost every situation it outperforms the state of the art in index pruning. The main contribution of this work is proposing a pruning technique that stems directly from the same source as probabilistic retrieval models, and hence is independent of the final model used for retrieval. C1 [Blanco, Roi; Barreiro, Alvaro] Univ A Coruna, Dept Comp Sci, IRLab, La Coruna, Spain. RP Blanco, R (reprint author), Univ A Coruna, Dept Comp Sci, IRLab, La Coruna, Spain. EM rblanco@udc.es; barreiro@udc.es CR Amati G, 2006, LECT NOTES COMPUT SC, V3936, P13 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Anh V. N., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148235 Anh V.N., 2002, P 25 ANN INT ACM SIG, P3 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baeza-Yates Ricardo, 2007, P 30 ANN INT ACM SIG, P183, DOI DOI 10.1145/1277741.1277775 Blanco R, 2007, LECT NOTES COMPUT SC, V4425, P64 Blanco R., 2007, P 30 ANN INT ACM SIG, P777, DOI 10.1145/1277741.1277904 BRANDOW R, 1995, INFORM PROCESS MANAG, V31, P675, DOI 10.1016/0306-4573(95)00052-I Brin S., 1998, P 7 INT WORLD WID WE, P107 Buckley C., 1995, P 4 TEXT RETR C TREC, P25 Buttcher Stefan, 2006, P 15 ACM INT C INF K, P182, DOI 10.1145/1183614.1183644 Carmel D., 2001, P 24 ANN INT ACM SIG, P43, DOI 10.1145/383952.383958 CARMEL D, 2001, P 10 TEXT RETR C, P228 Chowdhury A., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CRASWELL N, 2004, P 13 TEXT RETRIEVAL CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Fagin R, 2003, SIAM J DISCRETE MATH, V17, P134, DOI 10.1137/S0895480102412856 He Ben, 2003, P 12 INT C INF KNOWL, P10 Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.2307/2332226 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval LAFFERTY J, 2003, LANGUAGE MODELING IN, P11 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Long X., 2003, P 29 INT C VER LARG, P129, DOI 10.1016/B978-012722442-8/50020-3 Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005 Ntoulas A., 2007, P 30 ANN INT ACM SIG, P191, DOI 10.1145/1277741.1277776 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x Robertson S., 1981, P 3 ANN ACM C RES DE, P35 Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sakai T., 2001, P 24 ANN INT ACM SIG, P190, DOI 10.1145/383952.383987 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2005, P 28 ANN INT ACM SIG, P162, DOI 10.1145/1076034.1076064 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 Strohman T., 2005, P 28 ANN INT ACM SIG, P219, DOI 10.1145/1076034.1076074 Theobald M., 2004, P 30 INT C VER LARG, P648, DOI 10.1016/B978-012088469-8/50058-9 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H Upstill T, 2003, ACM T INFORM SYST, V21, P286, DOI 10.1145/858476.858479 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Voorhees Ellen M., 2005, TREC EXPT EVALUATION WESTERVELD T, 2002, P 10 TEXT RETR C TRE, P663 Witten I. H., 1999, MANAGING GIGABYTES C Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zobel J., 2006, ACM COMPUT SURV, V38, P1 NR 46 TC 1 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN 10 PY 2010 VL 28 IS 1 AR 1 DI 10.1145/1658377.1658378 PG 33 WC Computer Science, Information Systems SC Computer Science GA 549EN UT WOS:000274028700001 ER PT J AU Chia, TK Sim, KC Li, HZ Ng, HT AF Chia, Tee Kiah Sim, Khe Chai Li, Haizhou Ng, Hwee Tou TI Statistical Lattice-Based Spoken Document Retrieval SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Algorithms; Experimentation; Performance; Theory; Lattice-based spoken document retrieval; probabilistic retrieval approach; retrieval of conversational speech ID SPEECH RECOGNITION; ALGORITHM; SEARCH; MODELS AB Recent research efforts on spoken document retrieval have tried to overcome the low quality of 1-best automatic speech recognition transcripts, especially in the case of conversational speech, by using statistics derived from speech lattices containing multiple transcription hypotheses as output by a speech recognizer. We present a method for lattice-based spoken document retrieval based on a statistical n-gram modeling approach to information retrieval. In this statistical lattice-based retrieval (SLBR) method, a smoothed statistical model is estimated for each document from the expected counts of words given the information in a lattice, and the relevance of each document to a query is measured as a probability under such a model. We investigate the efficacy of our method under various parameter settings of the speech recognition and lattice processing engines, using the Fisher English Corpus of conversational telephone speech. Experimental results show that our method consistently achieves better retrieval performance than using only the 1-best transcripts in statistical retrieval, outperforms a recently proposed lattice-based vector space retrieval method, and also compares favorably with a lattice-based retrieval method based on the Okapi BM25 model. C1 [Chia, Tee Kiah; Ng, Hwee Tou] Natl Univ Singapore, Dept Comp Sci, Singapore 117417, Singapore. [Sim, Khe Chai; Li, Haizhou] Inst Infocomm Res, Singapore 138632, Singapore. RP Chia, TK (reprint author), Natl Univ Singapore, Dept Comp Sci, 13 Comp Dr, Singapore 117417, Singapore. EM chiateek@comp.nus.edu.sg CR ABBERLEY D, 1998, P TREC 7 GAITH MD, P181 Allauzen C., 2004, P WORKSH INT APPR SP, P33, DOI 10.3115/1626307.1626314 ALLAUZEN C, 2004, P 9 INT C IMPL APPL, P23 Allauzen Cyril, 2003, P 41 ANN M ASS COMP, P40 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Brin S, 1998, P 7 INT WORLD WID WE BUCKLEY C, 1985, TR85686 CORN U CARMEL D, 2001, P 10 TEXT RETR C, P228 Chelba C., 2005, P 43 ANN M ASS COMP, P443, DOI http://dx.doi.org/10.3115/1219840.1219895 Chelba C, 2007, COMPUT SPEECH LANG, V21, P458, DOI 10.1016/j.csl.2006.09.001 Chen B, 2004, ACM T ASIAN LANGUAGE, V3, P128, DOI 10.1145/1034780.1034784 Chia T.K, 2007, P EMNLP CONLL PRAG, P810 CHURCH KW, 2003, P 8 EUR C SPEECH COM, P1 EVERMANN G, 2004, P FALL DARPA RICH TR EVERYMANN G, 2004, P INT C AC SPEECH SI, V1, P249 FORNEY GD, 1973, P IEEE, V61, P268, DOI 10.1109/PROC.1973.9030 GAUVAIN JL, 2000, P 9 TEXT RETR C TREC, P335 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HATCH A, 2005, P ICASSP, P169 Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 HIEMSTRA D, 1998, P 7 TEXT RETR C TREC, P174 JAMES DA, 1995, THESIS U CAMBRIDGE U JAMES DA, 1994, P INT C AC SPEECH SI, V1, P377 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Jones GJF, 1996, P 19 ANN INT ACM SIG, P30, DOI 10.1145/243199.243208 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 MacKay David J.C., 1994, NAT LANG ENG, V1, P1 Mamou J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148183 Mangu L, 2000, COMPUT SPEECH LANG, V14, P373, DOI 10.1006/csla.2000.0152 Mohri M, 2000, THEOR COMPUT SCI, V231, P17, DOI 10.1016/S0304-3975(99)00014-6 *NIST, 2000, TREC 9 SDR TRACK WEB Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Robertson S. E., 1980, P 3 ANN ACM C RES DE, P35 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 Robertson S.E., 1998, P TEXT RETRIEVAL C T, P199 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Saraclar M, 2004, HLT-NAACL 2004: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE, P129 Shafran Izhak, 2003, P IEEE INT C AC SPEE, V1, P432 SIEGLER M, 1998, P TREC 7 7 TEST RETR, P319 Siegler M. A., 1999, THESIS CARNEGIE MELL Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Stolcke A., 2002, P INT C SPOK LANG PR, V2, P901 Turunen V.T., 2007, P ACM SIGIR C RES DE, P631, DOI 10.1145/1277741.1277849 WENG F, 1998, P INT C SPOK LANG PR, V6, P2531 YOUNG S, 1989, FINFENGTR38 CAMBR U Young S, 2006, HTK BOOK HTK VERSION YU P, 2005, P HLT EMNLP ASS COMP, P947, DOI 10.3115/1220575.1220694 YU P, 2005, P IEEE INT C AC SPEE, V1, P481 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 52 TC 3 Z9 4 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 EI 1558-2868 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN 10 PY 2010 VL 28 IS 1 AR 2 DI 10.1145/1658377.1658379 PG 30 WC Computer Science, Information Systems SC Computer Science GA 549EN UT WOS:000274028700002 ER PT S AU Saldarriaga, SP Morin, E Viard-Gandin, C AF Saldarriaga, Sebastian Pena Morin, Emmanuel Viard-Gandin, Christian BE Gurrin, C He, Y Kazai, G Kruschwitz, U Little, S Roelleke, T Ruger, S VanRijsbergen, K TI Ranking Fusion Methods Applied to On-Line Handwriting Information Retrieval SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 32nd European Conference on Information Retrieval Research CY MAR 28-31, 2010 CL Milton Keynes, ENGLAND SP Open Univ, Dublin City Univ, Univ Essex, British Comp Soc, Informat Retrieval Specialist Grp, ACM SIGIR, Apriorie, FLAX, Informat Retrieval Fac, AWS, Funnelback, Microsoft Res, GCHQ, Exalead, Google, YAHOO ID RECOGNITION AB This paper presents an empirical study on the application of ranking fusion methods in the context of handwriting information retrieval. Several works in the electronic text-domain suggest that significant improvements in retrieval performance can be achieved by combining different approaches to IR. In the handwritten-domain, two quite different fat allies of retrieval approaches are encountered. first family is based on standard approaches carried out on texts obtained through handwriting recognition, therefore regarded as noisy texts, while the second one is recognition-free using word spotting algorithms. Given the large differences that exist between these two families of approaches (document and query representations, matching methods, etc.), we hypothesize that fusion methods applied to the handwritten-domain can also bring significant effectiveness improvements. Results show that for texts having a word error rate (WER) lower than 23%, the performances achieved with the combined system are close to the performances obtained with clean digital texts, i.e. without transcription errors. In addition, for poorly recognized texts (WER > 52%), improvements can also be obtained with standard fusion methods. Furthermore, we present a detailed analysis of the fusion performances, and show that existing C1 [Saldarriaga, Sebastian Pena; Morin, Emmanuel] Univ Nantes, LINA, UMR 6241, CNRS, F-44035 Nantes, France. RP Saldarriaga, SP (reprint author), Univ Nantes, LINA, UMR 6241, CNRS, F-44035 Nantes, France. CR Aslam J. A., 2001, SIGIR Forum Beitzel SM, 2004, J AM SOC INF SCI TEC, V55, P859, DOI 10.1002/asi.20012 Belkin N. J., 1993, SIGIR Forum Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 CHENG C, 2009, ICDAR 2009, P863 Farah M., 2007, SIGIR 07, P591 Hull D. A., 1996, SIGIR Forum Ide E., 1971, SMART RETRIEVAL SYST, P337 Jawahar CV, 2009, PATTERN RECOGN, V42, P1445, DOI 10.1016/j.patcog.2008.08.017 LEE JH, 1997, SIGIR 97 P 20 ANN IN, P267 Manmatha R., 2001, SIGIR Forum Montague M., 2002, CIKM 02, P538 Namboodiri A. M., 2003, INT C DOC AN REC, P655 Perraud F, 2005, IEICE T INF SYST, VE88D, P1807, DOI 10.1093/ietisy/e88-d.8.1807 Plamondon R, 2000, IEEE T PATTERN ANAL, V22, P63, DOI 10.1109/34.824821 RATH TM, 2003, CVPR, P521 Rath T.M., 2004, SIGIR, P369 RENDA ME, 2003, SAC 03 P 2003 ACM S, P841 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Russell G., 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, DOI 10.1109/IWFHR.2002.1030915 Sanderson M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval SAVOY J, 1997, TREC 5 P 5 TEXT RETR, P489 Shaw J.A., 1994, TEXT RETRIEVAL C TRE, P243 Terasawa K., 2009, ICDAR 2009, P116 Vinciarelli A, 2005, PATTERN RECOGN LETT, V26, P2262, DOI 10.1016/j.patrec.2005.03.036 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 Wu SL, 2005, LECT NOTES COMPUT SC, V3408, P275 NR 27 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-12274-3 J9 LECT NOTES COMPUT SC PY 2010 VL 5993 BP 253 EP 264 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPN36 UT WOS:000279393900024 ER PT S AU Costa, A Melucci, M AF Costa, Alberto Melucci, Massimo BE Cunningham, H Hanbury, A Ruger, S TI An Information Retrieval Model Based on Discrete Fourier Transform SO ADVANCES IN MULTIDISCIPLINARY RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 1st Information Retrieval Facility Conference (IRFC 2010) CY MAY 31, 2010 CL Vienna, AUSTRIA SP Matrixware Informat Serv, ESTeam, Univ Sheffield, STI Int, Yahoo Labs, BCS DE Discrete Fourier Trasform; Digital Filters; Information Retrieval Models ID PROBABILITY RANKING PRINCIPLE; RELEVANCE AB Information Retrieval (IR) systems combine a variety of techniques stemming from logical, vector-space and probabilistic models. This variety of combinations has produced a significant increase in retrieval effectiveness since early 1990s. Nevertheless, the quest for new frameworks has not been less intense than the research in the optimization and experimentation of the most common retrieval models. This paper presents a new framework based on Discrete Fourier Transform (DFT) for IR. Basically, this model represents a query term as a sine curve and a query is the sum of sine curves, thus it acquires an elegant and sound mathematical form. The sinusoidal representation of the query is transformed from the time domain to the frequency domain through DFT. The result of the DFT is a spectrum. Each document of the collection corresponds to a set of filters and the retrieval operation corresponds to filtering the spectrum for each document the spectrum is filtered and the result is a power. Hence, the documents are ranked by the power of the spectrum such that the more the document decreases the power of the spectrum, the higher the rank of the document. This paper is mainly theoretical and the retrieval algorithm is reported to suggest the feasibility of the proposed model. Some small-scale experiments carried out for testing the effectiveness of the algorithm indicate a performance comparable to the state-of-the-art. C1 [Costa, Alberto] Ecole Polytech, LIX, F-91128 Palaiseau, France. RP Costa, A (reprint author), Ecole Polytech, LIX, F-91128 Palaiseau, France. EM costa@lix.polytechnique.fr; melo@dei.unipd.it CR COOPER WS, 1988, INFORM PROCESS MANAG, V24, P243, DOI 10.1016/0306-4573(88)90091-X Cormen T. H., 2000, INTRO ALGORITHMS Croft W. B., 2003, LANGUAGE MODELING IN DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fine T., 1973, THEORIES PROBABILITY Fuhr N, 2008, INFORM RETRIEVAL, V11, P251, DOI 10.1007/s10791-008-9045-0 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mitra S. K., 2006, DIGITAL SIGNAL PROCE, V3rd Oppenheim A. V., 1996, SIGNALS SYSTEMS Park LAF, 2004, IEEE T KNOWL DATA EN, V16, P529, DOI 10.1109/TKDE.2004.1277815 Park LAF, 2005, ACM T INFORM SYST, V23, P267, DOI 10.1145/1080343.1080345 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Press W. H., 1992, NUMERICAL RECIPES C ROBERTS BR, 2000, DRUG DISCOVERY TOD S, V1, P10 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 Salton G., 1968, AUTOMATIC INFORM ORG Strohman T., 2009, SEARCH ENGINES INFOR TURTLE H, 1990, P ACM INT C RES DEV van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Voorhees Ellen M., 2005, TREC EXPT EVALUATION NR 24 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13083-0 J9 LECT NOTES COMPUT SC PY 2010 VL 6107 BP 84 EP 99 PG 16 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPM69 UT WOS:000279327000008 ER PT S AU Culpepper, JS Navarro, G Puglisi, SJ Turpin, A AF Culpepper, J. Shane Navarro, Gonzalo Puglisi, Simon J. Turpin, Andrew BE DeBerg, M Meyer, U TI Top-k Ranked Document Search in General Text Databases SO ALGORITHMS-ESA 2010, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 16th Annual European Symposium on Algorithms (ESA 2010) CY SEP 06-08, 2010 CL Liverpool, ENGLAND SP European Assoc Theoret Comp Sci, Int Soc Comp Geometry, London Math Soc, Springer, Univ Liverpool ID EFFICIENT ALGORITHMS; RETRIEVAL PROBLEMS; SUFFIX ARRAYS; INDEXES; TREES AB Text search engines return a set of k documents ranked by similarity to a query. Typically, documents and queries are drawn from natural language text, which can readily be partitioned into words, allowing optimizations of data structures and algorithms for ranking. However, in many new search domains (DNA, multimedia, OCR texts, Far East languages) there is often no obvious definition of words and traditional indexing approaches are not so easily adapted, or break down entirely. We present two new algorithms for ranking documents against a query without making any assumptions on the structure of the underlying text. We build on existing theoretical techniques, which we have implemented and compared empirically with new approaches introduced in this paper. Our best approach is significantly faster than existing methods in RAM, and is even three times faster than a state-of-the-art inverted file implementation for English text when word queries are issued. C1 [Culpepper, J. Shane; Puglisi, Simon J.; Turpin, Andrew] RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia. RP Culpepper, JS (reprint author), RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia. EM shane.culpepper@rmit.edu.au; gnavarro@dcc.uchile.cl; simon.puglisi@rmit.edu.au; andrew.turpin@rmit.edu.au CR Anh V. N., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148235 Apostolico A., 1985, COMBINATORIAL ALGORI, P85 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bender MA, 2000, LECT NOTES COMPUT SC, V1776, P88, DOI 10.1007/10719839_9 Ferragina P., 2007, ACM T ALGORITHMS, V3, P20, DOI 10.1145/1240233.1240243 Fischer J, 2007, LECT NOTES COMPUT SC, V4614, P459 Gagie T, 2009, LECT NOTES COMPUT SC, V5721, P1, DOI 10.1007/978-3-642-03784-9_1 Grossi R, 2003, SIAM PROC S, P841 Hon WK, 2009, ANN IEEE SYMP FOUND, P713, DOI 10.1109/FOCS.2009.19 MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 Manzini G, 2001, J ACM, V48, P407, DOI 10.1145/382780.382782 Muthukrishnan S, 2002, SIAM PROC S, P657 Navarro G, 2007, ACM COMPUT SURV, V39, DOI 10.1145/1216370.1216372 Persin M, 1996, J AM SOC INFORM SCI, V47, P749, DOI 10.1002/(SICI)1097-4571(199610)47:10<749::AID-ASI3>3.3.CO;2-U Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Puglisi SJ, 2006, LECT NOTES COMPUT SC, V4209, P122 Raman R, 2002, SIAM PROC S, P233 ROBERTSON SE, 1994, P 3 TREC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sadakane K., 2007, J DISCRETE ALGORITHM, V5, P12, DOI 10.1016/j.jda.2006.03.011 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Valimaki N, 2007, LECT NOTES COMPUT SC, V4580, P205 Witten Ian H., 1999, MANAGING GIGABYTES, V2nd ZOBEL J, 2006, ACM COMPUT SURV, V38, pNI236, DOI DOI 10.1145/1132956.1132959 NR 24 TC 16 Z9 16 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15780-6 J9 LECT NOTES COMPUT SC PY 2010 VL 6347 BP 194 EP 205 PG 12 WC Computer Science, Theory & Methods; Mathematics SC Computer Science; Mathematics GA BSZ41 UT WOS:000286165200017 ER PT B AU Hmeidi, I Al-Badarneh, A Al-Qtaish, AA AF Hmeidi, Ismail Al-Badarneh, Amer Al-Qtaish, Ahmad A. BE Soliman, KS TI A New Approach for Automatic Query Expansion SO BUSINESS TRANSFORMATION THROUGH INNOVATION AND KNOWLEDGE MANAGEMENT: AN ACADEMIC PERSPECTIVE, VOLS 3 AND 4 LA English DT Proceedings Paper CT 14th International-Business-Information-Management-Association Conference CY JUN 23-24, 2010 CL Istanbul, TURKEY SP Int Business Informat Management Assoc DE Automatic query expansion; information retrieval; correlation measure ID INFORMATION-RETRIEVAL; CONTEXT; TERMS AB This study investigates the effectiveness of a new automatic query expansion technique for the purpose of Information Retrieval (IR). Automatic query expansion have been studied in information retrieval research to solve the problem of short queries and word mismatch between user's queries and author's documents. The proposed approach uses a metric type correlation measure instead of an association type correlation measure. This type is applied on one of statistical methods called local context analysis. The idea of metric cluster is to analyze the distance between two terms in the computation of their correlation factor. The local context analysis approach and the proposed approach have been implemented and evaluated on a collection from Text Retrieval Conference (TREC). The experiments show that the performance of the proposed approach is better than the local context analysis approach. C1 [Hmeidi, Ismail; Al-Badarneh, Amer; Al-Qtaish, Ahmad A.] Jordan Univ Sci & Technol, Irbid, Jordan. EM hmeidi@just.edu.jo; amerb@just.edu.jo; qtaishah@just.edu.jo CR Akrivas G, 2002, LECT NOTES ARTIF INT, V2522, P1 Akrivas G., 2002, Proceedings 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002), DOI 10.1109/ICAIS.2002.1048064 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Brants T., 2000, P 6 C APPL NAT LANG, P224, DOI 10.3115/974147.974178 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Grootjen FA, 2006, DATA KNOWL ENG, V56, P174, DOI 10.1016/j.datak.2005.03.006 Harper J., 1978, J DOC, V34, P189 Hatcher E., 2004, ACTION SERIES Ide E., 1971, SMART RETRIEVAL SYST, P337 Jing Y., 1994, P RIAO 94, P146 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 Lu A., 1997, P TEXT RETR C TREC 5, P229 Mandala R, 2000, INFORM PROCESS MANAG, V36, P361, DOI 10.1016/S0306-4573(99)00068-0 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sakamoto T, 2000, PROCEEDINGS OF THE 4TH WORLD CONGRESS OF ECHOCARDIOGRAPHY AND VASCULAR ULTRASOUND, P95, DOI 10.1145/355214.355228 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Tombros A, 2002, THESIS U GLASGOW Vechtomova O, 2003, INFORM RETRIEVAL, V6, P251, DOI 10.1023/A:1023936321956 Xu J., 1994, IR52 U MASS CIS DEP Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Xu YJ, 2005, INFORM RETRIEVAL, V8, P41, DOI 10.1023/B:INRT.0000048496.31867.62 NR 30 TC 0 Z9 0 PU INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA PI NORRISTOWN PA 34 E GERMANTOWN PIKE, NO. 327, NORRISTOWN, PA 19401 USA BN 978-0-9821489-3-8 PY 2010 BP 1975 EP 1989 PG 15 WC Management SC Business & Economics GA BJV05 UT WOS:000330684100043 ER PT S AU Gery, M Largeron, C AF Gery, Mathias Largeron, Christine BE Geva, S Kamps, J Trotman, A TI UJM at INEX 2009 Ad Hoc Track SO FOCUSED RETRIEVAL AND EVALUATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 07-09, 2009 CL Brisbane, AUSTRALIA SP Australian Res Council Res Network Human Comm Sci ID XML RETRIEVAL AB This paper(1) presents our participation to the INEX 2009 Ad-Hoc track We have experimented the tuning of various parameters using a "training" collection (i e INEX 2008) quite different than the "testing" collection used for 2009 INEX Ad-Hoc track Several parameters have been studied for article retrieval as well as for element retrieval, especially the two main BM25 weighting function parameters b and k(1) C1 [Gery, Mathias] Univ Lyon, F-42023 St Etienne, France. RP Gery, M (reprint author), Univ Lyon, F-42023 St Etienne, France. CR Denoyer L., 2006, SIGIR Forum, V40 Gery M, 2009, LECT NOTES COMPUT SC, V5631, P46 Gery Mathias, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, DOI 10.1109/WIIAT.2008.346 GEVA S, 2010, LNCS, V6203, P88 Kamps J, 2008, LECT NOTES COMPUT SC, V4862, P24, DOI 10.1007/978-3-540-85902-4_2 Kamps J, 2009, LECT NOTES COMPUT SC, V5631, P1 Kamps J, 2005, INFORM RETRIEVAL, V8, P631, DOI 10.1007/s10791-005-0750-7 Lu W, 2006, LECT NOTES COMPUT SC, V3977, P161 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schenkel R., 2007, LNI, V103, P277 TAYLOR M, 2006, 15 ACM C INF KNOWL M, P585 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-14555-1 J9 LECT NOTES COMPUT SC PY 2010 VL 6203 BP 88 EP 94 PG 7 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BSA64 UT WOS:000284034800010 ER PT S AU Stein, B Eissen, SMZ Lipka, N AF Stein, Benno Eissen, Sven Meyer Zu Lipka, Nedim BE Mehler, A Sharoff, S Santini, M TI Web Genre Analysis: Use Cases, Retrieval Models, and Implementation Issues SO GENRES ON THE WEB: COMPUTATIONAL MODELS AND EMPIRICAL STUDIES SE Text Speech and Language Technology LA English DT Article; Book Chapter C1 [Stein, Benno; Eissen, Sven Meyer Zu; Lipka, Nedim] Bauhaus Univ Weimar, Fac Media Media Syst, D-99423 Weimar, Germany. RP Stein, B (reprint author), Bauhaus Univ Weimar, Fac Media Media Syst, D-99423 Weimar, Germany. EM benno.stein@uni-weimar.de; sven@meyer-zu-eissen.de; nedim.lipka@uni-weimar.de CR Antunes P., 2001, Proceedings Seventh International Workshop on Groupware. CRIWG 2001, DOI 10.1109/CRIWG.2001.951773 Bay S. D., 2000, KDD 00, P315 BOESE ES, 2005, P CIKM 05 NOV 2005 BRETAN I, 1998, P WEBN WORLD C WWW I Broder A., 2002, SIGIR Forum, V36 Crowston K, 2000, INFORM SOC, V16, P201 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEWDNEY N, 2001, P ACL WORKSH HUMANLA DIMITROVA M, 2002, P C HUM FACT COMP SY Dougherty J., 1995, P 12 INT C MACH LEAR, P194 Fayyad U., 1993, P 13 INT JOINT C ART, P1022 FINN A, 2003, IJCAI 03 WORKSH COMP Freund L., 2006, P 1 INT C INF INT CO, P30, DOI 10.1145/1164820.1164829 KARLGREN J, 1994, P 15 INT C COMP LING, V2, P1071, DOI 10.3115/991250.991324 KENNEDY Alistair, 2005, P 38 ANN HAW INT C S Kessler B., 1997, P 35 ANN M ASS COMP, P32 Lawrie D., 2001, SIGIR Forum LAWRIE DJ, 2003, SIGIR 03 P 26 ANN IN, P457 Lee Y.B., 2002, SIGIR 2002, P145 LIM CS, 2005, P NAT LANG PROC IJCN, P310 Meyer zu Eissen S., 2004, LECT NOTES ARTIF INT, V3228, P256 Mitchell T.M., 1997, MACHINE LEARNING Popescul A., 2000, AUTOMATIC LABELING D Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RAUBER A, 2001, ACM IEEE JOINT C DIG, P1 Rehm G., 2002, P 35 HAW INT C SYST Rendell L., 1986, Machine Learning, V1, DOI 10.1007/BF00114117 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roussinov D., 2001, P 34 HAW INT C SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini M., 2007, THESIS U BRIGHTON SANTINI M, 2006, P ECAI WORKSH TIR 06 SELLENTIN J, 1999, THESIS U STUTTGART S Stamatatos E., 2000, P 18 INT C COMP LING STEIN B, 2006, J UNIVERS COMPUT SCI, P449 Stein B, 2005, P 2 INT WORKSH TEXT, P45 Stein B., 2008, SCANDINAVIAN J INFOR, V20, P91 STEIN B, 2004, P I KNOW 04, P353 TURNEY P, 1995, MACH LEARN, V20, P23, DOI 10.1007/BF00993473 Utgoff P., 1986, MACHINE LEARNING ART, VII, P107 YOSHIOKA T, 2000, 214 CCS WP MIT SLOAN NR 41 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1386-291X BN 978-90-481-9177-2 J9 TEXT SPEECH LANG TEC PY 2010 VL 42 BP 167 EP 189 DI 10.1007/978-90-481-9178-9_8 PG 23 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BRQ01 UT WOS:000283391600008 ER PT J AU Savoy, J Gaussier, E AF Savoy, Jacques Gaussier, Eric BE Indurkhya, N Damerau, FJ TI Information Retrieval SO HANDBOOK OF NATURAL LANGUAGE PROCESSING, SECOND EDITION SE Chapman & Hall-CRC Machine Learning & Pattern Recognition Series LA English DT Article; Book Chapter ID TEXT RETRIEVAL; DOCUMENT-RETRIEVAL; QUERY EXPANSION; LANGUAGE; COOCCURRENCE; CONSISTENCY; STRATEGIES; ALGORITHM; INFERENCE; SYSTEMS C1 [Savoy, Jacques] Univ Neuchatel, Dept Comp Sci, CH-2000 Neuchatel, Switzerland. [Gaussier, Eric] Univ Grenoble 1, Lab Informat Grenoble, Grenoble, France. RP Savoy, J (reprint author), Univ Neuchatel, Dept Comp Sci, CH-2000 Neuchatel, Switzerland. CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Anderson JD, 2001, INFORM PROCESS MANAG, V37, P231, DOI 10.1016/S0306-4573(00)00026-1 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Borodin A., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052942 Braschler M, 2004, INFORM RETRIEVAL, V7, P291, DOI 10.1023/B:INRT.0000011208.60754.a1 Brin S., 1998, P 7 INT WORLD WID WE, P107 Buckley C., 2005, TREC EXPT EVALUATION, P53 Buckley C., 1996, NIST PUBLICATION, P25 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009132 Can F, 2008, J AM SOC INF SCI TEC, V59, P407, DOI 10.1002/asi.20750 Cao Y., 2006, P 29 ANN INT ACM SIG, P186, DOI 10.1145/1148170.1148205 Cao Zhe, 2007, P 24 INT C MACH LEAR, P129, DOI 10.1145/1273496.1273513 Cleverdon C., 1984, Information Services & Use, V4 CLINCHANT S, 2008, P ECIR 2008 GLASG UK, P150 CONOVER W. J., 1999, PRACTICAL NONPARAMET, V3rd COOPER WS, 1969, AM DOC, V20, P268, DOI 10.1002/asi.4630200314 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dumais Susan T., 2005, TREC EXPT EVALUATION, P123 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Fox C, 1989, ACM SIGIR FORUM, V24, P19 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GAL A, 1991, PROLOG NATURAL LANGU Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Garfield E., 1983, CITATION INDEXING IT GAUSSIER E, 2000, TRAITEMENT AUTOMATIQ, V41, P473 Gonzalo J, 2003, LECT NOTES COMPUT SC, V2785, P372 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Hawking D, 2003, INFORM RETRIEVAL, V6, P99, DOI 10.1023/A:1022904715765 HAWKING D, 2001, NIST SPECIAL PUBLICA, P87 HIEMSTRA D, 2000, THESIS U TWENTE NETH HULL DA, 1997, NIST SPECIAL PUBLICA, P167 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Joachims Thorsten, 2007, ACM Transactions on Information Systems, V25, DOI 10.1145/1229179.1229181 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 KWOK KL, 2005, NIST PUBLICATION Lacoste C, 2007, LECT NOTES COMPUT SC, V4730, P694 Lee C, 2006, ETRI J, V28, P337, DOI 10.4218/etrij.06.0105.0020 LOVINS JB, 1968, MECH TRANSL, V11, P22 Maisonnasse L, 2008, LECT NOTES COMPUT SC, V5152, P585, DOI 10.1007/978-3-540-85760-0_73 Maisonnasse L., 2007, P ACM SIGIR 2007 AMS, P695, DOI DOI 10.1145/1277741.1277863 Manning C. D., 2008, INTRO INFORM RETRIEV McNamee P, 2004, INFORM RETRIEVAL, V7, P73, DOI 10.1023/B:INRT.0000009441.78971.be Nallapati R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009006 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PETERS C, 2008, LNCS, V5152 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1971, SMART RETRIEVAL SYST Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 Savoy J, 2008, INFORM PROCESS MANAG, V44, P310, DOI 10.1016/j.ipm.2007.01.022 SAVOY J, 1993, J AM SOC INFORM SCI, V44, P1, DOI 10.1002/(SICI)1097-4571(199301)44:1<1::AID-ASI1>3.0.CO;2-1 Savoy J, 1997, INFORM PROCESS MANAG, V33, P495, DOI 10.1016/S0306-4573(97)00027-7 Savoy J, 1997, J AM SOC INFORM SCI, V48, P235, DOI 10.1002/(SICI)1097-4571(199703)48:3<235::AID-ASI5>3.0.CO;2-Y Savoy J, 2004, INFORM RETRIEVAL, V7, P121, DOI 10.1023/B:INRT.0000009443.51912.e7 Savoy J., 2005, ACM T ASIAN LANGUAGE, V4, P163, DOI 10.1145/1105696.1105701 SCHOLLER F, 2008, P ECIR 2008 GLASG UK, P28 Schtitze H., 1995, P 4 ANN S DOC AN INF, P161 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Sproat Richard W., 1992, MORPHOLOGY COMPUTATI SRIKANTH M, 2000, P ACM SIGIR 2002 TEM, P425 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VINTAR S, 2003, P ECML PKDD WORKSH A Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Voorhees Ellen M., 2006, ACM SIGIR FORUM, V40, P41, DOI 10.1145/1147197.1147205 Voorhees E.M., 2005, TREC EXPT EVALUATION, P183 Wolfram D, 2001, J AM SOC INF SCI TEC, V52, P1073, DOI 10.1002/asi.1157 WONG SKM, 1987, ACM T DATABASE SYST, V12, P723 Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957 YU H, 2003, BIOINFORMATICS S1, V19, P340 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhou WN, 2007, PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P655, DOI 10.1145/1277741.1277853 Zobel J., 2006, ACM COMPUT SURV, V38, P1 ZUNDE P, 1969, AM DOC, V20, P259, DOI 10.1002/asi.4630200313 NR 91 TC 0 Z9 0 PU CRC PRESS-TAYLOR & FRANCIS GROUP PI BOCA RATON PA 6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA BN 978-1-4200-8593-8 J9 CH CRC MACH LEARN PA PY 2010 BP 455 EP 484 PG 30 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics SC Computer Science; Linguistics GA BTC24 UT WOS:000286426700020 ER PT S AU Ma, YL Lin, HF Jin, S AF Ma, Yunlong Lin, Hongfei Jin, Song BE Cheng, PJ Kan, MY Lam, W Nakov, P TI A Revised SimRank Approach for Query Expansion SO INFORMATION RETRIEVAL TECHNOLOGY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th Asia Information Retrieval Societies Conference CY DEC 01-03, 2010 CL Taipei, TAIWAN SP Natl Taiwan Univ, Natl Sci Council, Republ China, Minist Educ, Republ China DE Search Engine; Query Expansion; Query Logs; SimRank AB Query expansion technologies based on pseudo-relevance documents have been proven to be effective in many information retrieval tasks. One problem with these methods is that some of the expansion terms extracted from feedback documents are irrelevant to the query, which may hurt the retrieval performance. In this paper, we proposed a normalized weight SimRank (NWS) approach for query expansion, with query logs collected by a practical search engine. Analyzing the relationship between queries and URLs, we create a query-click graph, and a term-relationship graph is constructed by several transformations. In order to reduce the computational complexity of NWS, strategies of pruning and radius limit were used to optimize the algorithm. Experimental results on two TREC test collections show that our approach can discover the qualified terms effectively and improve queries' accuracy. C1 [Ma, Yunlong; Lin, Hongfei; Jin, Song] Dalian Univ Technol, Sch Comp Sci & Engn, Informat Retrieval Lab, Dalian 116023, Peoples R China. RP Ma, YL (reprint author), Dalian Univ Technol, Sch Comp Sci & Engn, Informat Retrieval Lab, Dalian 116023, Peoples R China. EM kevinma@mail.dlut.edu.cn; hflin@dlut.edu.cn; jinsong@mail.dlut.edu.cn CR Antonellis I., 2008, P VLDB 2008 AUCKL NE, P408 Baeza-Yates R, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P76 Baeza-Yates R, 2007, LECT NOTES COMPUT SC, V4362, P1 Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 BOLDI P, 2009, P WSCD 2009 BARC SPA, P51 Collins-Thompson Kevyn, 2005, P 14 ACM INT C INF K, P704, DOI DOI 10.1145/1099554.1099727 CROFT WB, 1996, P SIGIR 1996 ZUR SWI, P4 Dang V., 2010, P ACM INT C WEB SEAR, P41, DOI 10.1145/1718487.1718493 Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200 Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, EXPT AUTOMATIC DOCUM, V21, P313 Silverstein C., 1999, SIGIR Forum, V33 SPARCK JK, 1971, AUTOMATIC KEYWORD CL Wang XL, 2008, Proceedings of the 27th Chinese Control Conference, Vol 7, P479, DOI 10.1145/1458082.1458147 Xu Y, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P59, DOI 10.1145/1571941.1571954 NR 17 TC 0 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-17186-4 J9 LECT NOTES COMPUT SC PY 2010 VL 6458 BP 564 EP 575 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BUF65 UT WOS:000289174800053 ER PT J AU MacFarlane, A Secker, A May, P Timmis, J AF MacFarlane, A. Secker, A. May, P. Timmis, J. TI An experimental comparison of a genetic algorithm and a hill-climber for term selection SO JOURNAL OF DOCUMENTATION LA English DT Article DE Information searches; Programming and algorithm theory; Retrieval performance evaluation; Indexing ID INFORMATION-RETRIEVAL; RELEVANCE FEEDBACK; QUERY; OKAPI AB Purpose - The term selection problem for selecting query terms in information filtering and routing has been investigated using hill-climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches, which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill-climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, this paper aims to apply a genetic algorithm (GA) to the same problem. Design/methodology/approach - A standard TREC test collection is used from the TREC-8 filtering track, recording mean average precision and recall measures to allow comparison between the hill-climber and GAs. It also varies elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as elitist and non-elitist methods are used, as well as roulette wheel and rank selection GAs. Findings - The results of tests suggest that both techniques are, on average, better than the baseline, but, the implemented GA does not match the overall performance of a hill-climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes. Research limitations/implications - The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill-climber. The evidence from term rank order would appear to provide extra useful evidence, which hill climbers can use efficiently, and effectively, to narrow the search space. Originality/value - The paper represents the first attempt to compare hill-climbers with GAs on a problem of this type. C1 [MacFarlane, A.] City Univ London, Dept Informat Sci, Ctr Interact Syst Res, London, England. [Secker, A.; May, P.] Univ Kent, Comp Lab, Canterbury, Kent, England. [Timmis, J.] Univ York, Dept Elect, York YO10 5DD, N Yorkshire, England. [Timmis, J.] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England. RP MacFarlane, A (reprint author), City Univ London, Dept Informat Sci, Ctr Interact Syst Res, London, England. EM andym@soi.city.ac.uk CR Beaulieu M., 1997, NIST SPECIAL PUBLICA, P143 Boughanem M, 2002, J AM SOC INF SCI TEC, V53, P934, DOI 10.1002/asi.10119 Chang YC, 2006, IEEE T EVOLUT COMPUT, V10, P617, DOI 10.1109/TEVC.2005.863130 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S Chen HC, 1998, J AM SOC INFORM SCI, V49, P693 Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 Goldberg D. E., 1989, GENETIC ALGORITHMS S Harman D., 1992, INFORMATION RETRIEVA, P241 HONIG J, 2000, INFORM PROCESSING MA, V36, P737 HULL DA, 2000, NIST SP, P35 Lopez-Pujalte C, 2003, INFORM PROCESS MANAG, V39, P669, DOI 10.1016/S0306-4573(02)00044-4 Lopez-Pujalte C, 2002, INFORM PROCESS MANAG, V38, P793, DOI 10.1016/S0306-4573(01)00061-9 Lopez-Pujalte C, 2003, J AM SOC INF SCI TEC, V54, P152, DOI 10.1002/asi.10179 MACFARLANE A, 2008, INFORM PROCESSING MA, V45, P159 MacFarlane A, 2003, LECT NOTES COMPUT SC, V2633, P537 MACFARLANE A, 2000, THESIS CITY U LONDON Martin JLFV, 2003, FUTURE GENER COMP SY, V19, P55 Martin-Bautista MJ, 1999, J AM SOC INFORM SCI, V50, P760, DOI 10.1002/(SICI)1097-4571(1999)50:9<760::AID-ASI4>3.3.CO;2-F Mitchell M., 1999, INTRO GENETIC ALGORI Robertson AM, 1996, J DOC, V52, P405, DOI 10.1108/eb026973 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1996, NIST SPECIAL PUBLICA, P73 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROZSYPAL A, 2001, 18 INT C MACH LEARN Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 TAMINE L, 2003, INFORM PROCESSING MA, V39, P21 TUSON A, 1998, 10 YOUNG OR C OP RES Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 Walker S., 1998, NIST SPECIAL PUBLICA, P125 YANG JJ, 1994, INT J EXPERT SYST, V7, P165 NR 33 TC 2 Z9 2 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PY 2010 VL 66 IS 4 BP 513 EP 531 DI 10.1108/00220411011052939 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 652TQ UT WOS:000282033200004 ER PT S AU Ferro, N Harman, D AF Ferro, Nicola Harman, Donna BE Peters, C DiNunzio, GM Kurimo, M Mandl, T Mostefa, D Penas, A Roda, G TI CLEF 2009: Grid@CLEF Pilot Track Overview SO MULTILINGUAL INFORMATION ACCESS EVALUATION I: TEXT RETRIEVAL EXPERIMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th Workshop of the Cross-Language Evaluation Forum CY SEP 30-OCT 02, 2009 CL Corfu, GREECE AB The Grid@CLEF track is a long term activity with the aim of running a series of systematic experiments in order to improve the comprehension of MLIA systems and gain an exhaustive picture of their behaviour with respect to languages. In particular, Grid@CLEF 2009 is a pilot track that has started to move the first steps in this direction by giving the participants the possibility of getting experienced with the new way of carrying out experimentation that is needed in Grid@CLEF to test all the different combinations of IR components and languages. Grid@CLEF 2009 offered traditional monolingual ad-hoc tasks in 5 different languages (Dutch, English, French, German, and Italian) which make use of consolidated and very well known collections from CLEF 2001 and 2002 and used a set of 84 topics. Participants had to conduct experiments according to the CIRCO framework, an XML-based protocol which allows for a distributed, loosely-coupled, and asynchronous experimental evaluation of IR systems. We provided a Java library which can be exploited to implement CIRCO and an example implementation with the Lucene IR system. The participation has been especially challenging also for the size of the XML files generated by CIRCO, which can become 50-60 times the size of the collection. Of the 9 initially subscribed participants, only 2 were able to submit runs in time and we received a total of 18 runs in 3 languages (English, French, and German) out of the 5 offered. The two participants used different IR systems or combination of them, namely Lucene, Terrier, and Cheshire II. C1 [Ferro, Nicola; Harman, Donna] Univ Padua, Dept Informat Engn, I-35100 Padua, Italy. RP Ferro, N (reprint author), Univ Padua, Dept Informat Engn, I-35100 Padua, Italy. EM ferro@dei.unipd.it; donna.harman@nist.gov CR Agirre E, 2009, LECT NOTES COMPUT SC, V5706, P15 Braschler M, 2002, LECT NOTES COMPUT SC, V2406, P9 Braschler M, 2003, LECT NOTES COMPUT SC, V2785, P9 BRASCHLER M, 2004, LNCS, V3237 Cleverdon C. W., 1997, READINGS INFORMATION, P47 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 DINUZIO GM, 2009, CLEF 2009 WORKSH EIBL M, 2009, LNCS, V1 FERRO N, 2008, P 7 NTCIR WORKSH M E, P577 FERRO N, 2009, CIRCO010 IMS U PAD D FERRO N, 2009, POSTPR 5 IT RES C DI, P72 FERRO N, 2008, POSTPR 4 IT RES C DI, P29 FERRO N, 2009, LNCS, V1 LARSON RR, 2010, LNCS, V1 PETERS C, 2010, LNCS, V1 Robertson S.E., 1981, INFORMATION RETRIEVA, P9 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J, 2002, LECT NOTES COMPUT SC, V2406, P27 Savoy J, 1999, J AM SOC INFORM SCI, V50, P944, DOI 10.1002/(SICI)1097-4571(1999)50:10<944::AID-ASI9>3.3.CO;2-H W3C, 2004, XML SCHEM 1 W3C, 2008, EXT MARK LANG XML 1 World Wide Web Consortium (W3C), 2004, XML SCHEM 2 NR 25 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15753-0 J9 LECT NOTES COMPUT SC PY 2010 VL 6241 BP 552 EP 565 PN I PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BTF32 UT WOS:000286783100068 ER PT S AU Navarro, S Munoz, R Llopis, F AF Navarro, Sergio Munoz, Rafael Llopis, Fernando BE Peters, C Caputo, B Gonzalez, J Jones, GJF KalpathyCramer, J Muller, H Tsikrika, T TI Diversity Promotion: Is Reordering Top-Ranked Documents Sufficient? SO MULTILINGUAL INFORMATION ACCESS EVALUATION II: MULTIMEDIA EXPERIMENTS, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th Workshop of the Cross-Language Evaluation Forum CY SEP 30-OCT 02, 2009 CL Corfu, GREECE ID INFORMATION-RETRIEVAL; MODELS AB In our participation in the ImageCLEF 2009 Photo Retrieval task we pursued two objectives: Firstly, to re-evaluate Multi Modal Local Context Analysis (MMLCA), our multimodal fusion technique. Secondly, to evaluate a new subquery generation technique based on clustering. From the experiments conducted: Firstly, we confirmed MMLCA performs better for generic domain collections than the other local expansion techniques evaluated. Secondly, our proposal of subquery generation based on clustering, obtained good results (5th best textual run of the Photo Retrieval task). Besides these results in this paper we try to reflect on the extent to which reordering techniques are appropriate to promote diversity. Results suggest that while reordering strategies limit the margin of improvement due to their use of a limited number of documents, the use of approaches based on subqueries generation can overcome this limitation. C1 [Navarro, Sergio; Munoz, Rafael; Llopis, Fernando] Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. RP Navarro, S (reprint author), Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. EM snavarro@dlsi.ua.es; rafael@dlsi.ua.es; llopis@dlsi.ua.es CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Gass T, 2008, LECT NOTES COMPUT SC, V5152, P492, DOI 10.1007/978-3-540-85760-0_60 Llopis F, 2003, LECT NOTES COMPUT SC, V2785, P291 Navarro S, 2010, LECT NOTES COMPUT SC, V5723, P24 PARAMITA ML, 2009, DIVERSITY PHOTORETRI ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 7 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15750-9 J9 LECT NOTES COMPUT SC PY 2010 VL 6242 BP 120 EP 123 PN II PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BTC06 UT WOS:000286416400012 ER PT S AU Navarro, S Llopis, F Munoz, R AF Navarro, Sergio Llopis, Fernando Munoz, Rafael BE Horacek, H Metais, E Munoz, R Wolska, M TI Using Evidences Based on Natural Language to Drive the Process of Fusing Multimodal Sources SO NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 14th International Conference on Applications of Natural Language to Information Systems CY JUN 24-26, 2009 CL Saarbrucken, GERMANY ID PHOTOGRAPHIC RETRIEVAL; INFORMATION-RETRIEVAL; TASK AB This paper focuses on the proposal and evaluation of two multimodal fusion techniques in the field of Visual Information Retrieval (VIR). These proposals are based on two widely used fusion strategies in the VIR area, the multimodal blind relevance feedback and the multimodal re-ranking strategy. Unlike the existent techniques, our alternative proposals are guided by the evidence found in the natural language annotations related to the images. The results achieved by our runs in two different ImageCLEF tasks, 3rd place in the Wikipedia task [1] and 4th place within all the automatic runs in the photo task [2], jointly with the results obtained in later experiments presented in this paper show us that the use of conceptual information associated with an image can improve significantly the performance of the original multimodal fusion techniques used. C1 [Navarro, Sergio; Llopis, Fernando; Munoz, Rafael] Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. RP Navarro, S (reprint author), Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. EM snavarro@dlsi.ua.es; llopis@dlsi.ua.es; rafael@dlsi.ua.es; rafael@dlsi.ua.es CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Clough P, 2007, LECT NOTES COMPUT SC, V4730, P579 DEMERDASH OE, 2008, ONLINE WORKING NOTES Deselaers T, 2008, INFORM RETRIEVAL, V11, P77, DOI 10.1007/s10791-007-9039-3 DIAZGALIANO MC, 2007, 2007 CLEF WORKSH BUD GAO S, 2007, 2007 CLEF WORKSH BUD GRUBINGER M, 2007, THESIS ENG SCI VICTO Grubinger M, 2008, LECT NOTES COMPUT SC, V5152, P433, DOI 10.1007/978-3-540-85760-0_57 LIU H, 2007, 2007 INT C CONV INF Llopis F, 2003, LECT NOTES COMPUT SC, V2785, P291 NAVARRO S, 2008, ONLINE WORKING NOTES Reid N.H., 1999, SCOTTISH ARCH, V5, P83 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Tsikrika T, 2009, LECT NOTES COMPUT SC, V5706, P539 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-12549-2 J9 LECT NOTES COMPUT SC PY 2010 VL 5723 BP 24 EP 35 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPN95 UT WOS:000279484800003 ER PT J AU Na, JC Thet, TT Khoo, CSG AF Na, Jin-Cheon Thet, Tun Thura Khoo, Christopher S. G. TI Comparing sentiment expression in movie reviews from four online genres SO ONLINE INFORMATION REVIEW LA English DT Article DE Internet; Film; Attitudes AB Purpose - This paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres - blog postings, discussion board threads, user reviews, and critic reviews. Design/methodology/approach - A collection of movie review documents was harvested from the four types of web sources, and a sample of 520 movie reviews were analysed to compare the content and textual characteristics across the four genres. The analysis focused on document and sentence length, part-of-speech distribution, vocabulary, aspects of movies discussed, star ratings used and multimedia content in the reviews. The study also identified frequently occurring positive and negative terms in the different genres, as well as the pattern of responses in discussion threads. Findings - Critic reviews and blog postings are longer than user reviews and discussion threads, and contain longer sentences. Critic reviews and blogs contain more nouns and prepositions, whereas discussion board and user reviews have more verbs and adverbs. Critic reviews have the largest vocabulary and also the highest proportion of unique terms not found in the other genres. The most informative sentiment words in each genre are provided in the paper. With regard to content, critic reviews are more comprehensive in coverage, and discuss the movie director much more often than the other genres. User reviews discuss the scene aspects (including action and visual effects) more often than the other genres, while blogs tend to talk about the cast, and discuss the music and sound slightly more often. Research limitations/implications - The study only analysed movie review documents. Similar content and text analysis studies can be carried out in other domains, such as commercial product reviews, celebrity reviews, company reviews and political opinions to compare the results. Originality/value - The main contribution of the study is the sentiment content analysis results across genres, which show the similarities and differences in content and textual characteristics in the four online opinion genres. The insights will be useful in designing automatic sentiment summarisation methods for multiple online genres. C1 [Na, Jin-Cheon; Khoo, Christopher S. G.] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Div Informat Studies, Singapore, Singapore. RP Na, JC (reprint author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Div Informat Studies, Singapore, Singapore. EM tjcna@ntu.edu.sg CR Blair-Goldensohn S., 2008, WWW 2008 WORKSH NLP Boese E.S., 2005, P 14 ACM INT C INF K, P632, DOI 10.1145/1099554.1099715 Byrt T, 1996, EPIDEMIOLOGY, V7, P561 CHOI B, 2005, STUDIES FUZZINESS SO, P221 DAS SR, 2001, EUR FIN ASS 2001 BAR FELDMAN R, 2007, P 7 IEEE INT C DAT M, P469 FINN A, 2002, P 24 EUR C INF RETR, P353 Gu B., 2006, MCCOMBS RES PAPER SE, VIROM-11-06 Herring S. C., 2005, Information Technology & People, V18, DOI 10.1108/09593840510601513 HERRING SC, 2009, INT HDB INTERNET RES Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Kwasnik B. H., 2005, Information Technology & People, V18, DOI 10.1108/09593840510601487 Montesi M, 2008, J DOC, V64, P143, DOI 10.1108/00220410810844196 Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 Quinlan R., 1993, C4 5 PROGRAMS MACHIN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 THET TT, 2008, P ICADL INT C AS DIG, P184 Toutanova K., 2003, P 2003 C N AM CHAPT, P173, DOI DOI 10.3115/1073445.1073478 Yi J, 2003, P 3 IEEE INT C DAT M, P427 Zhuang L., 2006, P 15 ACM INT C INF K, P43, DOI 10.1145/1183614.1183625 NR 20 TC 5 Z9 5 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 1468-4527 J9 ONLINE INFORM REV JI Online Inf. Rev. PY 2010 VL 34 IS 2 BP 317 EP 338 DI 10.1108/19684521011037016 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 608JW UT WOS:000278581000007 ER PT B AU Wang, J Zhu, JH AF Wang, Jun Zhu, Jianhan BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI On Statistical Analysis and Optimization of Information Retrieval Effectiveness Metrics SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn AB This paper presents a new way of thinking for IR metric optimization. It is argued that the optimal ranking problem should be factorized into two distinct yet interrelated stages: the relevance prediction stage and ranking decision stage. During retrieval the relevance of documents is not known a priori, and the joint probability of relevance is used to measure the uncertainty of documents' relevance in the collection as a whole. The resulting optimization objective function in the latter stage is, thus, the expected value of the IR metric with respect to this probability measure of relevance. Through statistically analyzing the expected values of IR metrics under such uncertainty, we discover and explain some interesting properties of IR metrics that have not been known before. Our analysis and optimization framework do not assume a particular (relevance) retrieval model and metric, making it applicable to many existing IR models and metrics. The experiments on one of resulting applications have demonstrated its significance in adapting to various IR metrics. C1 [Wang, Jun; Zhu, Jianhan] UCL, Dept Comp Sci, London WC1E 6BT, England. RP Wang, J (reprint author), UCL, Dept Comp Sci, London WC1E 6BT, England. EM wang.jun@acm.org; j.zhu@cs.ucl.ac.uk CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Arrow K. J., 1965, ASPECTS THEORY RISK ASLAM JA, 2005, SIGIR ASLAM JA, 2006, SIGIR Bishop C. H., 2006, PATTERN RECOGNITION BROWN PF, 1993, COMPUT LINGUIST BUCKLEY C, 2000, SIGIR BURGES C, 2005, ICML 05 Carbonell J., 1998, SIGIR Chen H., 2006, SIGIR CORMACK GV, 2006, SIGIR CROFT WB, 1988, DOCUMENT RETRIEVAL S HARMAN D, 1994, HLT 94 Jarvelin K., 2002, ACM T INF SYST LAFFERTY J, 2001, SIGIR Manning C. D., 2008, INTRO INFORM RETRIEV MARON ME, 1960, J ACM MIZZARO S, 1997, J AM SOC INFORM SCI ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1994, SIGIR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Singhal A., 1996, SIGIR Forum TAYLOR M, 2008, WSDM TOMLINSON S, 2006, SIGIR Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VOLKOVS MN, 2009, ICML 09 VOORHEES E, 1998, INFORM PROCESSING MA, P315 Voorhees E., 1999, TREC 8, P77 WANG J, 2009, SIGIR WANG Y, 1997, EACL YILMAZ E, 2009, INFORM RETRIEVAL YILMAZ E, 2008, SIGIR Yue Y., 2007, SIGIR Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 NR 35 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 226 EP 233 PG 8 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100030 ER PT B AU Karimzadehgan, M Zhai, CX AF Karimzadehgan, Maryam Zhai, ChengXiang BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI Estimation of Statistical Translation Models Based on Mutual Information for Ad Hoc Information Retrieval SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn DE Statistical Machine Translation; Language Models; Estimation; Smoothing; Feedback ID QUERY EXPANSION AB As a principled approach to capturing semantic relations of words in information retrieval, statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. Existing work has relied on training on synthetic queries generated based on a document collection. However, this method is computationally expensive and does not have a good coverage of query words. In this paper, we propose an alternative way to estimate a translation model based on normalized mutual information between words, which is less computationally expensive and has better coverage of query words than the synthetic query method of estimation. We also propose to regularize estimated translation probabilities to ensure sufficient probability mass for self-translation. Experiment results show that the proposed mutual information-based estimation method is not only more efficient, but also more effective than the synthetic query-based method, and it can be combined with pseudo-relevance feedback to further improve retrieval accuracy. The results also show that the proposed regularization strategy is effective and can improve retrieval accuracy for both synthetic query-based estimation and mutual information-based estimation. C1 [Karimzadehgan, Maryam; Zhai, ChengXiang] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Karimzadehgan, M (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. EM mkarimz2@illinois.edu; czhai@cs.illinois.edu CR Bai J, 2005, ACM CIKM, P688 Berger A., 1999, ACM SIGIR, P222 Brown P. F., 1993, Computational Linguistics, V19 Cao G., 2005, ACM SIGIR, p298 Dempster A. P., 1997, ACM SIGKDD B, V39, P1 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Jelinek F., 1997, STAT METHODS SPEECH JIN R, 2002, ACM SIGIR, P42 JING Y, 1994, RIAO, P141 KURLAND, 2004, ACM SIGIR, P194 Laffetry J., 2001, ACM SIGIR, P111 Lavrenko V., 2001, ACM SIGIR, P120 LAVRENKO V, 2002, ACM SIGIR, P175 LESK M, 1969, AM DOC, V20, P20 LIU S, 2004, ACM SIGIR C, P266 LIU X, 2004, ACM SIGIR, P186 MANDALA R, 1998, TREC 7, P475 METZLER D, 2005, ACM CIKM, P517 MURDOCK V, 2004, ACM SIGIR, P31 NIE JY, 1999, ACM SIGIR 99 AUG, P74 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 Ponte J. M., 1998, ACM SIGIR 98, P275 Porter M.F., 1980, PROGRAM, V14 QIU Y, 1993, ACM SIGIR, P160 RIJBERGEN CJV, 1977, J DOC, P106 Rijsbergen C. J. V., 1979, INFORM RETRIEVAL, V2nd ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1983, INTRO MODERN INFORM Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Tao T., 2006, HLT NAACL 2006, P407 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Voorhees E.M., 1994, ACM SIGIR, P61 Wei X, 2006, ACM SIGIR 2006, P178 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 XU J, 1996, ACM SIGIR C R D INF, P4 XU J, 2001, ACM SIGIR, P105 XUE X, 2008, ACM SIGIR, P475 Zhai C., 2001, ACM CIKM, P403 Zhai C., 2001, ACM INT SIGIR C SIGI, P334 NR 42 TC 9 Z9 9 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 323 EP 330 PG 8 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100043 ER PT B AU Blanco, R Zaragoza, H AF Blanco, Roi Zaragoza, Hugo BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI Finding Support Sentences for Entities SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn DE Sentence Retrieval; Entity Ranking AB We study the problem. of finding sentences that explain the relationship between a named entity and an ad-hoc query, which we refer to as entity support sentences. This is an important sub-problem of entity ranking which, to the best of our knowledge, has not been addressed before. In this paper we give the first formalization of the problem, how it can be evaluated, and present a full evaluation dataset. We propose several methods to rank these sentences, namely retrieval-based, entity-ranking based and position-based. We found that traditional bag-of-words models perform relatively well when there is a match between an entity and a query in a given sentence, but they fail to find a support sentence for a substantial portion of entities. This can be improved by incorporating small windows of context sentences and ranking them appropriately. C1 [Blanco, Roi; Zaragoza, Hugo] Yahoo Res, Barcelona, Spain. RP Blanco, R (reprint author), Yahoo Res, Barcelona, Spain. EM roi@yahoo-inc.com; hugoz@yahoo-inc.com CR ATSERIAS J, 2008, LREC 08 BAILEY P, 2007, P TREC 2007 16 TEXT CARDIE C, 2000, EXAMINING ROLE STAT, P180 Chakrabarti S., 2006, WWW 2006, P717 GIANLUCA D, 2008, 7 INT WORKSH IN EV X HARMAN D, 2002, P TREC 2002 11 TEXT Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KANUNGO T, 2009, WSDM 09, P202 Li X., 2006, CIKM 06, P238 LOSADA D, 2007, P 14 STRING PROC INF, P217 MALIK S, 2006, 5 INT WORKSH IN EV X, P1 MCSHERRY F, 2008, ECIR 2008, P414 MURDOCK V, 2008, EXPLORING SENTENCE R Murdock V., 2005, HLT 05, P684 OKAMOTO T, 2009, TSA 09, P73 ROBERTSON S, 2009, PROBABILISTIC RELEVA, V3, P333 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RODE H, 2008, THESIS U TWENTE SOBOROFF I, 2004, P TREC 2004 13 TEXT SOBOROFF I, 2003, P TREC 2003 12 TEXT SOBOROFF I, 2005, P TREC 2005 14 TEXT SOBOROFF I, 2006, P TREC 2006 15 TEXT STOKES N, 2001, P HTL01 HUM LANG TEC Sweeney S, 2008, INFORM PROCESS MANAG, V44, P663, DOI 10.1016/j.ipm.2007.03.012 Vallet D., 2008, SIGIR 08, P857 Voorhees Ellen M., 2005, TREC EXPT EVALUATION VRIES AP, 2008, 6 INT WORKSH IN EV X, P245 ZARAGOZA H, 2007, CIKM 2007 NR 29 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 339 EP 346 PG 8 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100045 ER PT B AU Lv, YH Zhai, CX AF Lv, Yuanhua Zhai, ChengXiang BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI Positional Relevance Model for Pseudo-Relevance Feedback SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn DE Positional relevance model; pseudo-relevance feedback; positional language model; proximity; passage-based feedback; query expansion ID PROXIMITY AB Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for query expansion, which is not optimal as a document may cover several different topics and thus contain much irrelevant information. In this paper, we study how to effectively select from feedback documents those words that are focused on the query topic based on positions of terms in feedback documents. We propose a positional relevance model (PRM) to address this problem in a unified probabilistic way. The proposed PRM is an extension of the relevance model to exploit terra positions and proximity so as to assign more weights to words closer to query words based on the intuition that words closer to query words are more likely to be related to the query topic. We develop two methods to estimate PRM based on different sampling processes. Experiment results on two large retrieval datasets show that the proposed PRM is effective and robust for pseudo-relevance feedback, significantly outperforming the relevance model in both document-based feedback and passage-based feedback. C1 [Lv, Yuanhua; Zhai, ChengXiang] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Lv, YH (reprint author), Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. EM ylv2@uiuc.edu; czhai@cs.uiuc.edu CR Abdul-Jaleel N., 2004, TREC 04 Allan J., 1995, SIGIR Forum BUCKLEY C, 1994, TREC 94, P69 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buscher Georg, 2008, SIGIR 08, P387 BUTTCHER S, 2005, TREC 05 Buttcher S., 2006, SIGIR, P621 Cao G., 2008, SIGIR 2008, P243 Carterette B., 2006, SIGIR 06, P268 CLARKE LA, 1995, TREC 95, P295 Cummins R, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P251, DOI 10.1145/1571941.1571986 HAWKING D, 1995, TREC 95, P500 Kaszkiel M, 2001, J AM SOC INF SCI TEC, V52, P344, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1075>3.3.CO;2-R KEEN EM, 1992, J INFORM SCI, V18, P89, DOI 10.1177/016555159201800202 KEEN EM, 1991, J DOC, V47, P1, DOI 10.1108/eb026869 Lafferty J., 2001, SIGIR Forum Lavrenko V., 2001, SIGIR Forum Liu X., 2002, CIKM 2002, P375 Lv Y., 2009, CIKM, P1895 LV YH, 2009, P 32 ANN INT ACM, P299 Metzler D., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Metzler D., 2007, SIGIR 07, P311 MONZ C, 2004, SIGIR WORKSH INF RET, P23 RASOLOFO Y, 2003, ECIR 2003 P, P207 Robertson S.E., 1994, TREC 3, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 Tao T., 2007, SIGIR 2007, P295 Vechtomova O, 2006, J INF SCI, V32, P324, DOI 10.1177/0165551506065787 Xu J., 1996, SIGIR 96, P4 Yu S., 2003, WWW, P11 Zhai C., 2001, CIKM 2001, P403, DOI 10.1145/502585.502654 Zhai C., 2001, SIGIR 2001, P334 Zhao JL, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P291, DOI 10.1145/1571941.1571993 NR 34 TC 17 Z9 17 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 579 EP 586 PG 8 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100075 ER PT B AU Fang, Y Si, L Mathur, AP AF Fang, Yi Si, Luo Mathur, Aditya P. BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI Discriminative Models of Integrating Document Evidence and Document-Candidate Associations for Expert Search SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn DE Expert search; enterprise search; discriminative models ID INFORMATION-RETRIEVAL AB Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indicated in supporting documents. On the other hand, discriminative models have received little attention in expert search research, although they have been shown to outperform generative models in many other information retrieval and machine learning applications. In this paper, we propose a principled relevance-based discriminative learning framework for expert search and derive specific discriminative models from the framework. Compared with the state-of-the-art language models for expert search, the proposed research can naturally integrate various document evidence and document-candidate associations into a single model without extra modeling assumptions or effort. An extensive set of experiments have been conducted on two TREC Enterprise track corpora (i.e., W3C and CERC) to demonstrate the effectiveness and robustness of the proposed framework. C1 [Fang, Yi; Si, Luo; Mathur, Aditya P.] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. RP Fang, Y (reprint author), Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. EM fangy@cs.purdue.edu; lsi@cs.purdue.edu; apm@cs.purdue.edu CR BAILEY P, 2007, TREC 15 Balog K, 2009, INFORM PROCESS MANAG, V45, P1, DOI 10.1016/j.ipm.2008.06.003 BALOG K, 2008, ECIR BALOG K, 2008, TREC 16 BALOG K, 2006, WWW, P1036 Balog K., 2006, SIGIR BALOG K, 2008, CIKM CAO Y, 2005, TREC 13 Carlile P. R., 1998, HUMAN RESOURCE PLANN, V21, P58 CHEN H, 2006, TREC 14 COOPER W, JASIST, V34, P31 CRASWELL N, 2005, TREC 13 Dennis J.E., 1996, NUMERICAL METHODS UN FANG H, 2007, ECIR Fang Y., 2009, SIGIR WORKSH LEARN R FU Y, 2006, TREC 14 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Liu T., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] LIU TY, 2007, SIGIR WORKSH LEARN R Macdonald C., 2006, CIKM MACDONALD C, 2008, ECIR Metzler D, 2007, INFORM RETRIEVAL, V10, P257, DOI 10.1007/s10791-006-9019-z NALLAPATI R, 2004, SIGIR Ng A. Y., 2002, NIPS PETKOVA D, 2007, CIKM ROBERTSON S, 1996, TREC 4 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SERDYUKOV P, 2008, ECIR SERDYUKOV P, 2008, SIGIR WORKSH FUT CHA SERDYUKOV P, 2008, CIKM SOBOROFF I, 2006, TREC 14 Strohman TM D, 2004, INT C INT AN TANG J, 2008, SIGKDD Tax DMJ, 2000, PATTERN RECOGN, V33, P1475, DOI 10.1016/S0031-3203(99)00138-7 Yimam-Seid D, 2003, SHARING EXPERTISE: BEYOND KNOWLEDGE MANAGEMENT, P327 ZHAI C, 2004, TOIS, V22, P214 ZHU J, KNOWLEDGE INFORM SYS, P1 NR 38 TC 5 Z9 5 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 683 EP 690 PG 8 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100088 ER PT S AU Imran, H Sharan, A AF Imran, Hazra Sharan, Aditi BE GarciaPedrajas, N Herrera, F Fyfe, C Benitez, JM Ali, M TI Improving Effectiveness of Query Expansion Using Information Theoretic ApproachE SO TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT II, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 01-04, 2010 CL Cordoba, SPAIN DE Automatic Query Expansion; Candidate Terms; Term Co-occurrence; Kullback-Leibler divergence; Relevance Feedback ID DOCUMENT-RETRIEVAL SYSTEMS; COOCCURRENCE DATA; SEARCH TERMS; RELEVANCE; FEEDBACK AB Automatic Query expansion is a well-known method to Improve the performance of in retrieval systems In this paper we have suggested information theoretic measures to improve efficiency of co-occurence based automatic query expansion We have used pseudo relevance feedback based local approach The expansion terms were selected from the top N documents using co-occurence based approach They were then ranked using two different information theoretic approaches First one is standard Kullback-Leibler divergence (KLD) As a second measure we have suggested use of a variant KLD Experiments were performed on TREC-1 damsel The result suggests that there is a scope of improving co-occurrence based query expansion by using information theoretic measures Extensive experiments were done to select two important parameters number of top N documents to be used and number of terms to be used tor expansion C1 [Imran, Hazra] Jamia Hamdard, Dept Comp Sci, New Delhi, India. RP Imran, H (reprint author), Jamia Hamdard, Dept Comp Sci, New Delhi, India. EM himran@jamiahamdard.ac.in CR Cao Guihong, 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377 CARMEL D, 2005, P ACM SIGIR 2005 WOR, P25 CARPINETO C, 1999, TREC 8 AUTOMATIC AD CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Imran Hazra, 2009, International Journal of Computer Science & Information Technology, V1 JING Y, 1994, 4 INT C RECH INF ASS, P146 Jones K., 1971, AUTOMATIC KEYWORD CL LEE CJ, 2009, P 5 AS INF RETR S LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 LIU S, 2004, P 2004 ACM SIGIR C R MANDALA R, 1999, P 7 TEXT RETRIEVAL C MANDALA R, 1999, P 1999 ACM SIGIR C R MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Qiu Y., 1993, SIGIR, P160 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruch P, 2006, P COLING ACL 2006 MA, P675, DOI 10.3115/1273073.1273160 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 SMEATON AF, 1982, THESIS U COLL DUBLIN SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 STAIRMAND MA, 1997, P 1997 ACM SIGIR C R VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Voorhees E. M., 2006, SIGIR Forum, V40 Voorhees E. M., 2005, SIGIR Forum, V39 Voorhees E.M., 2003, TREC, P69 VOORHEES FM, 1994, P 1994 ACM SIGIR C R Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 32 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13024-3 J9 LECT NOTES ARTIF INT PY 2010 VL 6097 BP 1 EP 11 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BQR32 UT WOS:000281604400001 ER PT S AU Lamirel, JC Boulila, Z Ghribi, M Cuxac, P AF Lamirel, Jean-Charles Boulila, Zied Ghribi, Maha Cuxac, Pascal BE GarciaPedrajas, N Herrera, F Fyfe, C Benitez, JM Ali, M TI A New Incremental Growing Neural Gas Algorithm Based on Clusters Labeling Maximization: Application to Clustering of Heterogeneous Textual Data SO TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT III, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 01-04, 2010 CL Cordoba, SPAIN AB Neural clustering algorithms show high performance in the usual context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental neural gas algorithm (IGNG). Nevertheless, this paper highlights clearly the drastic decrease of performance of these algorithms, as well as the one of more classical algorithms. when a heterogeneous textual dataset is considered as an input. A new incremental growing neural gas algorithm exploiting knowledge issued from clusters current labeling in an incremental way is proposed as an alternative to the original distance based algorithm. This solution leads to obtain very significant increase of performance for the clustering of heterogeneous textual data. Moreover, it provides a real incremental character to the proposed algorithm. C1 [Lamirel, Jean-Charles; Boulila, Zied] INRIA Team TALARIS LORIA, Vandoeuvre Les Nancy, France. RP Lamirel, JC (reprint author), INRIA Team TALARIS LORIA, Vandoeuvre Les Nancy, France. EM jean-charles.lamirel@loria.fr RI Cuxac, Pascal/ OI Cuxac, Pascal/0000-0002-6809-5654 CR Attik M., 2006, P IASTED INT C DAT A Calinski T., 1974, COMMUN STAT, V3, P1, DOI DOI 10.1080/03610927408827101 DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 Dempster A. P., 1979, J ROYAL STAT SOC B, V39, P1 Francois Claire, 2003, IST199920350 EICSTES Frizke B., 1995, ADV NEURAL INFORMATI, V7, P625 HAMZA H, 2008, P 19 INT C PATT REC KASSAB R, 2008, P IASTED INT C ART I Kohonen T., 2001, SELF ORG MAPS Lamirel J.-C., 2004, SCIENTOMETRICS, V60 LAMIREL JC, 2008, P IASTED INT C ART I Merkl D., 2003, P 4 WORKSH SELF ORG, P293 PONS P, 2006, J GRAPH ALGORITHMS A Prudent Y., 2005, P ESANN 2005 13 EUR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 MARTINETZ T, 1991, ARTIFICIAL NEURAL NETWORKS, VOLS 1 AND 2, P397 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13032-8 J9 LECT NOTES ARTIF INT PY 2010 VL 6098 BP 139 EP 148 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BQR33 UT WOS:000281604700015 ER PT J AU Harman, D Buckley, C AF Harman, Donna Buckley, Chris TI Overview of the Reliable Information Access Workshop SO INFORMATION RETRIEVAL LA English DT Editorial Material DE Information retrieval; Relevance feedback; Failure analysis AB The Reliable Information Access (RIA) Workshop was held in the summer of 2003, with a goal of improved understanding of information retrieval systems, in particular with regard to the variability of retrieval performance across topics. The workshop ran massive cross-system failure analysis on 45 of the TREC topics and also performed cross-system experiments on pseudo-relevance feedback. This paper presents an overview of that workshop, along with some preliminary conclusions from these experiments. Even if this workshop was held 6 years ago, the issues of improving system performance across all topics is still critical to the field and this paper, along with the others in this issue, are the first widely published full papers for the workshop. C1 [Harman, Donna] NIST, Gaithersburg, MD 20899 USA. [Buckley, Chris] Sabir Res Inc, Gaithersburg, MD 20878 USA. RP Harman, D (reprint author), NIST, Gaithersburg, MD 20899 USA. EM donna.harman@nist.gov; chrisb@sabir.com CR Buckley C., 1985, 85686 CORN U COMP SC Buckley C., 1996, Fourth Text REtrieval Conference (TREC-4) (NIST SP 500-236) Buckley C, 2009, INFORM RETRIEVAL, V12, P652, DOI 10.1007/s10791-009-9103-2 Clarke C. L., 2001, P 24 ANN INT ACM SIG, P358, DOI 10.1145/383952.384024 Clarke CLA, 2009, INFORM RETRIEVAL, V12, P680, DOI 10.1007/s10791-009-9105-0 Collins-Thompson K., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009127 Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Evans D. A., 1994, Second Text REtrieval Conference (TREC-2) (NIST-SP 500-215) EVANS DA, 1995, INFORM PROCESS MANAG, V31, P385, DOI 10.1016/0306-4573(94)00054-7 GU Z, 2004, P 27 ANN INT ACM SIG, P482, DOI 10.1145/1008992.1009081 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Milic-Frayling N., 1998, Sixth Text REtrieval Conference (TREC-6) (NIST SP 500-240) Montgomery J., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009078 Ogilvie P, 2009, INFORM RETRIEVAL, V12, P666, DOI 10.1007/s10791-009-9104-1 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMALL S, 2004, NEW DIRECTIONS QUEST, P129 Soboroff I, 2009, INFORM RETRIEVAL, V12, P642, DOI 10.1007/s10791-009-9102-3 STRZALKOWSKI T, 2008, ADV OPEN DOMAIN QUES, P149 VOORHEES E, 1997, 5 TEXT RETR C TREC 5, P1 Warren R. H., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009125 WILLIAMS DH, 1971, SPEKTROSKOPISCHE MET, P12 WILLIAMSON D, 1969, 16 ISR NSF, P1 YEUNG DL, 2004, 12 RETRIEVAL C TREC Yom-Tov Elad, 2005, P 28 ANN INT ACM SIG, P512, DOI 10.1145/1076034.1076121 ZHAI C, 2001, 10 INT C INF KNOWL M, P1 NR 28 TC 9 Z9 9 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2009 VL 12 IS 6 BP 615 EP 641 DI 10.1007/s10791-009-9101-4 PG 27 WC Computer Science, Information Systems SC Computer Science GA 511VY UT WOS:000271200600001 ER PT J AU Buckley, C AF Buckley, Chris TI Why current IR engines fail SO INFORMATION RETRIEVAL LA English DT Article DE Information retrieval evaluation; Failure analysis; Failure categorization; Comparative system analysis AB Observations from a unique investigation of failure analysis of Information Retrieval research engines held in 2003 are presented. The Reliable Information Access Workshop invited seven leading IR research groups to supply both their systems and their experts to an effort to analyze why their systems fail on some topics and whether the failures are due to system flaws, approach flaws, or the topic itself. There were surprising results from this cross-system failure analysis. One is that despite systems retrieving very different documents, the major cause of failure for any particular topic was almost always the same across all systems. Another is that relationships between aspects of a topic are not especially important for state-of-the-art systems; the systems are failing at a much more basic level where the top-retrieved documents are not reflecting some aspect at all. The investigatory framework and the lessons learned can serve as a model for needed future research in this area. C1 Sabir Res Inc, Gaithersburg, MD USA. RP Buckley, C (reprint author), Sabir Res Inc, Gaithersburg, MD USA. EM chrisb@sabir.com CR Buckley C., 1985, 85686 CORN U COMP SC Buckley C., 1996, NIST PUBLICATION, P25 Clarke C. L., 2001, P 24 ANN INT ACM SIG, P358, DOI 10.1145/383952.384024 Evans D., 1994, NIST SPECIAL PUBLICA, P137 EVANS DA, 1995, INFORM PROCESS MANAG, V31, P385, DOI 10.1016/0306-4573(94)00054-7 *LEM, 2009, LEM TOOLK LANG MOD I MILICFRAYLING N, 1998, NIST SPECIAL PUBLICA, P415 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 YEUNG DL, 2004, 12 TEXT RETR C TREC ZHAI C, 2001, 10 INT C INF KNOWL M NR 12 TC 4 Z9 4 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2009 VL 12 IS 6 BP 652 EP 665 DI 10.1007/s10791-009-9103-2 PG 14 WC Computer Science, Information Systems SC Computer Science GA 511VY UT WOS:000271200600003 ER PT J AU Ogilvie, P Voorhees, E Callan, J AF Ogilvie, Paul Voorhees, Ellen Callan, Jamie TI On the number of terms used in automatic query expansion SO INFORMATION RETRIEVAL LA English DT Article DE Automatic query expansion; Blind feedback; Pseudo relevance feedback AB This paper investigates the number of expansion terms to use in automatic query expansion by examining the behavior of eight retrieval systems participating in the NRRC Reliable Information Access Workshop. The results demonstrate that current systems are able to obtain nearly all of the benefit of using a fixed number of expansion terms per topic, but significant additional improvement is possible if systems were able to accurately select the best number of expansion terms on a per topic basis. When optimizing average effectiveness as measured by mean average precision, using a fixed number of terms increases the score a large amount for a small number of topics but has little effect for most topics. The analysis further suggests that when a topic is helped by automatic feedback, the increase is from a set of terms that reinforce each other rather than from the system finding a single excellent term. C1 [Ogilvie, Paul; Callan, Jamie] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA. [Voorhees, Ellen] NIST, Gaithersburg, MD 20899 USA. RP Ogilvie, P (reprint author), mSpoke Inc, Pittsburgh, PA USA. EM pogilvie@mspoke.com; callan@cmu.edu RI Voorhees, Ellen/D-1755-2013 CR Alemayehu N, 2003, J AM SOC INF SCI TEC, V54, P379, DOI 10.1002/asi.10217 BILLERBECK B, 2004, 15 AUSTR DAT C DUN N, P69 Buckley C., 1985, 85686 CORN U COMP SC Buckley C., 1994, P 17 ANN INT ACM SIG, P292 Carmel D., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/564376.564427 Clarke C. L., 2001, P 24 ANN INT ACM SIG, P358, DOI 10.1145/383952.384024 Efron B, 1993, INTRO BOOTSTRAP Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Evans D., 1994, NIST SPECIAL PUBLICA, P137 EVANS DA, 1995, INFORM PROCESS MANAG, V31, P385, DOI 10.1016/0306-4573(94)00054-7 HARMAN D, 2009, OVERVIEW RELIABLE IN Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 MILICFRAYLING N, 1998, NIST SPECIAL PUBLICA, P415 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sakai T., 2001, P 24 ANN INT ACM SIG, P396, DOI 10.1145/383952.384035 SMALL S, 2004, NEW DIRECTIONS QUEST, P129 Strzalkowski T., 2006, ADV OPEN DOMAIN QUES Wasserman L., 2004, ALL STAT CONCISE COU WILLIAMSON D, 1971, SMART RETRIEVAL SYST, P43 YEUNG DL, 2003, 11 TEXT RETR C TREC Zhai, 2002, THESIS CARNEGIE MELL ZHAI C, 2001, P 10 INT C INF KNOWL, P395 NR 24 TC 8 Z9 9 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2009 VL 12 IS 6 BP 666 EP 679 DI 10.1007/s10791-009-9104-1 PG 14 WC Computer Science, Information Systems SC Computer Science GA 511VY UT WOS:000271200600004 ER PT J AU Clarke, CLA Cormack, GV Lynam, TR Buckley, C Harman, D AF Clarke, Charles L. A. Cormack, Gordon V. Lynam, Thomas R. Buckley, Chris Harman, Donna TI Swapping documents and terms SO INFORMATION RETRIEVAL LA English DT Article DE Pseudo-relevance feedback; Blink feedback; Fusion AB Experiments were conducted to explore the impact of combining various components of eight leading information retrieval systems. Each system demonstrated improved effectiveness through the use of blind feedback, also known as pseudo-relevance feedback, a form of query expansion. Blind feedback uses the results of a preliminary retrieval step to augment the efficacy of a secondary retrieval step. The hybrid combination of primary and secondary retrieval steps from different systems in a number of cases yielded better effectiveness than either of the constituent systems alone. This positive combining effect was observed when entire documents were passed between the two retrieval steps, but not when only the expansion terms were passed. Several combinations of primary and secondary retrieval steps were fused using the CombMNZ algorithm; all yielded significant effectiveness improvement over the individual systems, with the best yielding an improvement of 13% (p = 10(-6)) over the best individual system and an improvement of 4% (p = 10(-5)) over a simple fusion of the eight systems. C1 [Clarke, Charles L. A.; Cormack, Gordon V.; Lynam, Thomas R.] Univ Waterloo, Waterloo, ON N2L 3G1, Canada. [Buckley, Chris] Sabir Res Inc, Gaithersburg, MA USA. [Harman, Donna] NIST, Gaithersburg, MA USA. RP Clarke, CLA (reprint author), Univ Waterloo, Waterloo, ON N2L 3G1, Canada. EM claclark@plg.uwaterloo.ca; gvcormac@uwaterloo.ca; trlynam@plg.uwaterloo.ca; chrisb@sabir.com; donna.harman@nist.gov CR Buckley C., 1985, 85686 CORN U COMP SC CLARKE CLA, 2001, SIGIR 2001 Croft W. B., 2000, ADV INFORM RETRIEVAL EVANS DA, 1994, 2 TEXT RETR C GAITH EVANS DA, 1995, 3 TEXT RETR C GAITH FOX EA, 1994, 2 TEXT RETR C Harman D, 2009, INFORM RETRIEVAL, V12, P615, DOI 10.1007/s10791-009-9101-4 *LEM, LEM TOOLK LANG MOD I MONTAGUE M, 2002, 11 INT C INF KNOWL M, P538 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1995, 3 TEXT RETRIEVAL C G ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMALL S, 2004, DATA DRIVEN AP UNPUB STRZALKOWSKI T, 2004, ADV OPEN DOMAI UNPUB VOORHEES E, 1998, 7 TEXT RETRIEVAL C G VOORHEES E, 1999, 8 TEXT RETRIEVAL C G WILLIAMSON D, 1971, SMART RETRIEVAL SYST, P43 YEUNG DL, 2003, TEXT RETRIEVAL C 200 NR 18 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2009 VL 12 IS 6 BP 680 EP 694 DI 10.1007/s10791-009-9105-0 PG 15 WC Computer Science, Information Systems SC Computer Science GA 511VY UT WOS:000271200600005 ER PT J AU Na, JC Thet, TT AF Na, Jin-Cheon Thet, Tun Thura TI Effectiveness of web search results for genre and sentiment classification SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE genre classification; product review documents; sentiment classification; snippets; web search results ID TERMS AB The motivation of this study is to enhance general topical search with a sentiment-based one where the search results (snippets) returned by the web search engine are clustered by sentiment categories. Firstly we developed an automatic method to identify product review documents using the snippets (summary information that includes the URL, title, and summary text), which is genre classification. Then the identified snippets were automatically classified into positive (recommended) and negative (non-recommended) documents, which is sentiment classification. Thereafter the user may directly decide to access the positive or negative review documents. In this study we used only the snippets rather than their original full-text documents, and applied a common machine learning technique, SVM (support vector machine), and heuristic approaches to investigate how effectively the snippets can be used for genre and sentiment classification. The results show that the web search engine should improve the quality of the snippets especially for opinionated documents (i.e. review documents). C1 [Na, Jin-Cheon] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Div Informat Studies, Singapore 637718, Singapore. RP Na, JC (reprint author), Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Div Informat Studies, Singapore 637718, Singapore. EM tjcna@ntu.edu.sg CR Boese E.S., 2005, P 14 ACM INT C INF K, P632, DOI 10.1145/1099554.1099715 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 Choi B, 2005, STUD FUZZ SOFT COMP, V180, P221 Drori O, 2003, J INFORM SCI, V29, P97, DOI 10.1177/016555103763031581 FINN A, 2002, P 24 EUR C INF RETR, P353 Jindal N., 2008, P INT C WEB SEARCH W, P219, DOI DOI 10.1145/1341531.1341560 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Jones K. S., 1997, READINGS INFORM RETR Kessler B., 1997, P 35 ANN M ASS COMP, P32 Lim CS, 2005, INFORM PROCESS MANAG, V41, P1263, DOI 10.1016/j.ipm.2004.06.004 Montesi M, 2008, INFORM PROCESS MANAG, V44, P1410, DOI 10.1016/j.ipm.2008.02.001 Mordechai Vicki, 2008, Proceedings Tenth International Conference on Enterprise Information Systems. ICEIS 2008. Human-Computer Interaction Na JC, 2005, LIBR COLLECT ACQUIS, V29, P180, DOI 10.1016/j.lcats.2005.04.007 Na JC, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P143, DOI 10.1145/1065385.1065416 Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Quinlan R., 1993, C4 5 PROGRAMS MACHIN Ravid G, 2007, J INF SCI, V33, P567, DOI 10.1177/0165551506076326 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 Stone P. J., 1966, GEN INQUIRER COMPUTE THET TT, 2007, P ICADL INT C AS DIG, P65 Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 NR 25 TC 4 Z9 4 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD DEC PY 2009 VL 35 IS 6 BP 709 EP 726 DI 10.1177/0165551509104233 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 523LK UT WOS:000272074600005 ER PT J AU Fan, WG Pathak, P Zhou, M AF Fan, Weiguo Pathak, Praveen Zhou, Mi TI Genetic-based approaches in ranking function discovery and optimization in information retrieval - A framework SO DECISION SUPPORT SYSTEMS LA English DT Article DE Information retrieval; Artificial intelligence; Evolutionary computations; Data fusion; Genetic algorithms ID RELEVANCE FEEDBACK; WEB SEARCH; ALGORITHMS; QUERY; MODEL AB An Information Retrieval (IR) system consists of document collection, queries issued by users. and the matching/ranking functions used to rank documents in the predicted order of relevance for a given query. A variety of ranking functions have been used in the literature. But studies show that these functions do not perform consistently well across different contexts. In this paper we propose a two-stage integrated framework for discovering and optimizing ranking functions used in IR. The first stage, discovery process, is accomplished by intelligently leveraging the structural and statistical information available in HTML documents by using Genetic Programming techniques to yield novel ranking functions. in the second stage, the optimization process, document retrieval scores of various well-known ranking functions are combined using Genetic Algorithms. The overall discovery and optimization framework is tested oil the well-known TREC collection of web documents for both the ad-hoc retrieval task and the routing task. Utilizing our framework we observe a significant increase in retrieval performance compared to some of the well-known stand alone ranking functions. (C) 2009 Elsevier B.V. All rights reserved. C1 [Pathak, Praveen] Univ Florida, Worrington Coll Business Adm, Gainesville, FL 32611 USA. [Fan, Weiguo] Virginia Tech, Blacksburg, VA 24061 USA. [Zhou, Mi] Xi An Jiao Tong Univ, Sch Management, Xian 710049, Shaanxi, Peoples R China. RP Pathak, P (reprint author), Univ Florida, Worrington Coll Business Adm, 339 STZ, Gainesville, FL 32611 USA. EM wfan@vt.edu; praveen@ufl.edu; zhoumi@mail.xjtu.edu.cn RI Fan, Weiguo/E-6343-2012 OI Fan, Weiguo/0000-0003-1272-5538 CR Bartell B. T., 1994, P 17 ANN INT ACM SIG, P173 Bartell BT, 1998, J AM SOC INFORM SCI, V49, P742 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Billhardt H, 2003, INT J UNCERTAIN FUZZ, V11, P87, DOI 10.1142/S0218488503001965 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chen HC, 1998, J AM SOC INFORM SCI, V49, P604 Chen HC, 1998, J AM SOC INFORM SCI, V49, P693 Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Cordon O., 2002, Soft Computing, V6, DOI 10.1007/s00500-002-0184-8 Fan W, 2006, DECIS SUPPORT SYST, V42, P362, DOI 10.1016/j.dss.2005.01.007 Fan WG, 2004, J AM SOC INF SCI TEC, V55, P628, DOI 10.1002/asi.20009 Fan WG, 2005, DECIS SUPPORT SYST, V40, P213, DOI 10.1016/j.dss.2004.02.003 Fan WG, 2006, DECIS SUPPORT SYST, V42, P975, DOI 10.1016/j.dss.2004.11.014 Fox EA, 1994, P 2 TEXT RETR C TREC, P243 GAO J, 2002, NIST SPECIAL PUBLICA, P384 Goldberg D. E., 1989, GENETIC ALGORITHMS S Gordon M., 1988, COMMUN ACM, V31, P152 Fan WG, 2004, IEEE T KNOWL DATA EN, V16, P523, DOI 10.1109/TKDE.2004.1269663 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P311, DOI 10.1002/(SICI)1097-4571(199106)42:5<311::AID-ASI1>3.0.CO;2-J Guntzer U, 2007, DECIS SUPPORT SYST, V43, P1117, DOI 10.1016/j.dss.2005.07.004 HAWKING D, 2001, P 10 TEXT RETR C TRE, P61 HAWKING D, 2000, NIST SPECIAL PUBLICA, P86 HOLLAND JH, 1992, ADAPTATION NATURAL A Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X JARVELIN K, 2001, P 23 ANN INT ACM SIG, P41 KIRKPATRICK S, 1983, SCIENCE, V220, P671, DOI 10.1126/science.220.4598.671 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Koza J, 1992, GENETIC PROGRAMMING Kraft D.H., 1997, GENETIC ALGORITHMS F, P155 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lopez-Pujalte C, 2002, INFORM PROCESS MANAG, V38, P793, DOI 10.1016/S0306-4573(01)00061-9 Martin-Bautista MJ, 1999, J AM SOC INFORM SCI, V50, P760, DOI 10.1002/(SICI)1097-4571(1999)50:9<760::AID-ASI4>3.3.CO;2-F Pathak P., 2000, P 33 HAW INT C SYST ROBERTSON SE, 1996, NIST SPECIAL PUBLICA, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SAVOY J, 1996, P 4 TEXT TRETR C TRE, P537 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 Zobel J., 1998, SIGIR Forum, V32 NR 44 TC 6 Z9 6 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD NOV PY 2009 VL 47 IS 4 BP 398 EP 407 DI 10.1016/j.dss.2009.04.005 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA 516NQ UT WOS:000271549600013 ER PT J AU Takano, K Chen, X Masuda, K AF Takano, Kosuke Chen, Xing Masuda, Keisuke TI A framework for a feedback process to analyze and personalize a document vector space in a feature extraction model SO INFORMATION TECHNOLOGY & MANAGEMENT LA English DT Article; Proceedings Paper CT 17th Workshop on Information Technology and Systems CY DEC, 2007 CL Montreal, CANADA DE Information retrieval; Semantic search; Vector space model; Feedback method; Feature extraction ID RETRIEVAL; RELEVANCE; SELECTION AB In this paper, we present a framework for a feedback process to implement a highly accurate document retrieval system. In the system, a document vector space is created dynamically to implement retrieval processing. The retrieval accuracy of the system depends on the vector space. When the vector space is created based on a specific purpose and interest of a user, highly accurate retrieval results can be obtained. In this paper, we present a method for analyzing and personalizing the vector space according to the purposes and interests of users. In order to optimize the document vector space, we defined and implemented functions for the operations of adding, deleting and weighting the terms that were used to create the vector space. By exploiting effectively and dynamically the classified-document information related to the queries, our methods allow users to retrieve relevant documents for their interests and purposes. Even if the search results of the initial retrieval space are not appropriate, by applying the proposed feedback operations, our proposed method effectively improves the search results. We also implemented an experimental search system for semantic document retrieval. Several experimental results including comparisons of our method with the traditional relevance feedback method is presented to clarify how retrieval accuracy was improved by the feedback process and how accurately documents that satisfied the purpose and interests of users were extracted. C1 [Takano, Kosuke; Chen, Xing] Kanagawa Inst Technol, Fac Informat Technol, Dept Informat & Comp Sci, Kanagawa 2430292, Japan. [Masuda, Keisuke] Kanagawa Inst Technol, Grad Sch Informat & Comp Sci, Kanagawa 2430292, Japan. RP Takano, K (reprint author), Kanagawa Inst Technol, Fac Informat Technol, Dept Informat & Comp Sci, 1030 Shimo Ogino, Kanagawa 2430292, Japan. EM takano@ic.kanagawa-it.ac.jp; chen@ic.kanagawa-it.ac.jp; s065819@cce.kanagawa-it.ac.jp CR BAEZAYATES R, 1999, MODERN INFORM RETRIE, P2768 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bilenko M., 2008, P 17 INT WORLD WID W, P51, DOI 10.1145/1367497.1367505 BUCKLEY C, 1994, P 3 TEXT RETR C, P2768 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHEN X, 2007, P 17 EUR JAP C INF M, P40 CHEN X, 2005, INFORM MODELLING KNO, V16, P46 Chirita P.-A., 2007, P 30 ANN INT ACM SIG, P7, DOI DOI 10.1145/1277741.1277746 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dumais S., 2003, P 26 ANN INT ACM SIG, P72 Dupret G. E., 2008, P 31 ANN INT ACM SIG, P331, DOI 10.1145/1390334.1390392 Harman D, 1988, P 11 ANN INT ACM SIG, P321, DOI 10.1145/62437.62469 HARMAN D, 1992, INFORM RETRIEVAL DAT, P231 IDE E, 1971, NEW EXPT RELEVANCE F, pCH16 KELLY D, 2006, P 29 ANN INT ACM SIG, P2768 Khan L, 2004, VLDB J, V13, P71, DOI 10.1007/s00778-003-0105-1 LAST M, 2001, LECT NOTES COMPUTER, P532 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 LEWIS DD, 1996, TRAINING ALGORITHMS, P298 Limbu D.K., 2006, P 1 INT C INF INT CO, P138, DOI 10.1145/1164820.1164848 MARCIALIS I, 2008, P 3 INT C INT WEB AP, P512 NEJDL W, 2005, P IRIX WORKSH ANN IN, P2768 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 SALTON G, 1971, SMART RETRIEVAL SYST, P2768 Seo Y.-W., 2000, P ACM 4 INT C AUT AG, P381, DOI 10.1145/336595.337546 Teevan J, 2006, COMMUN ACM, V49, P40, DOI 10.1145/1107458.1107488 VASSILVITSKII S, 2006, P 29 ANN INT ACM SIG, P2768 Wong S.K.M, 1985, GENERALIZED VECTOR P, P18 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Yu L, 2004, J MACH LEARN RES, V5, P1205 Yu S., 2003, P 12 INT C WORLD WID, P11 NR 32 TC 1 Z9 1 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1385-951X J9 INFORM TECHNOL MANAG JI Inf. Technol. Manag. PD SEP PY 2009 VL 10 IS 2-3 BP 151 EP 176 DI 10.1007/s10799-009-0055-4 PG 26 WC Information Science & Library Science; Management SC Information Science & Library Science; Business & Economics GA 477UN UT WOS:000268544500006 ER PT J AU Di Nunzio, GM AF Di Nunzio, Giorgio Maria TI Using scatterplots to understand and improve probabilistic models for text categorization and retrieval SO INTERNATIONAL JOURNAL OF APPROXIMATE REASONING LA English DT Article; Proceedings Paper CT Workshop on Information Retrieval and Graphical Models held at the 30th Annual International ACM SIGIR Conference CY JUL 23-27, 2007 CL Amsterdam, NETHERLANDS SP ACM DE Naive Bayes models; Text categorization; Information Retrieval ID VISUALIZATION AB The two-dimensional representation of documents which allows documents to be represented in a two-dimensional Cartesian plane has proved to be a valid visualization tool for Automated Text Categorization (ATC) for understanding the relationships between categories of textual documents, and to help users to visually audit the classifier and identify suspicious training data. This paper analyzes a specific use of this visualization approach in the case of the Naive Bayes (NB) model for text classification and the Binary Independence Model (BIM) for text retrieval. For text categorization, a reformulation of the equation for the decision of classification has to be written in such a way that each coordinate of a document is the sum of two addends: a variable component P(d|c(i)), and a constant component P(c(i)), the prior of the category. When plotted in the Cartesian plane according to this formulation, the documents that are constantly shifted along the x-axis and the y-axis can be seen, This effect of shifting is more or less evident according to which NB model, Bernoulli or multinomial, is chosen. For text retrieval, the same reformulation can be applied in the case of the BIM model. The visualization helps to understand the decisions that are taken to order the documents, in particular in the case of relevance feedback. (C) 2009 Elsevier Inc. All rights reserved. C1 Univ Padua, Dept Informat Engn, I-35131 Padua, Italy. RP Di Nunzio, GM (reprint author), Univ Padua, Dept Informat Engn, Via Gradenigo 6-A, I-35131 Padua, Italy. EM dinunzio@dei.unipd.it CR Becker Barry, 2001, INFORM VISUALIZATION, p237 Cooper W., 1991, P 14 ANN INT ACM SIG, P57, DOI 10.1145/122860.122866 Damerau FJ, 2004, INFORM PROCESS MANAG, V40, P209, DOI 10.1016/S0306-4573(03)00006-2 DINUNZIO GM, B IEEE TECHNICAL COM, V2 DINUNZIO GM, 2005, THESIS U PADUA PADOV Di Nunzio GM, 2008, LECT NOTES COMPUT SC, V5152, P13, DOI 10.1007/978-3-540-85760-0_2 DINUNZIO GM, 2004, P 16 EUR C ART INT E, P465 EYHERAMENDY S, 2005, P INT WORKSH FEAT SE, P1 Eyheramendy S., 2003, P 9 INT WORKSH ART I de Oliveira MCF, 2003, IEEE T VIS COMPUT GR, V9, P378, DOI 10.1109/TVCG.2003.1207445 FUHR N, 2000, LECT NOTES COMPUTER, V1980, P21 Hearst M., 1999, MODERN INFORM RETRIE, P257 Joachims T., 1998, LNCS, V1398, P137 Keim DA, 1996, IEEE T KNOWL DATA EN, V8, P923, DOI 10.1109/69.553159 Keim DA, 2002, IEEE T VIS COMPUT GR, V8, P1, DOI 10.1109/2945.981847 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 KROWNE A, 2005, ACM IEEE JOINT C DIG, P246 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 Mladenic D., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009034 Mozina M, 2004, LECT NOTES ARTIF INT, V3202, P337 NEDELLEC C, 1998, LECT NOTES COMPUTER, V1398 Nigam K., 1998, AAAI 98, P792 Rifkin R, 2004, J MACH LEARN RES, V5, P101 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Walter Jorg, 2003, P 3 IEEE INT C DAT M, P355 ZANG T, 2001, INFORMATION RETRIEVA, V4, P5 NR 30 TC 2 Z9 2 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0888-613X J9 INT J APPROX REASON JI Int. J. Approx. Reasoning PD JUL PY 2009 VL 50 IS 7 BP 945 EP 956 DI 10.1016/j.ijar.2009.01.002 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA 472RC UT WOS:000268148700003 ER PT J AU Boughanem, M Brini, A Dubois, D AF Boughanem, M. Brini, A. Dubois, D. TI Possibilistic networks for information retrieval SO INTERNATIONAL JOURNAL OF APPROXIMATE REASONING LA English DT Article; Proceedings Paper CT Workshop on Information Retrieval and Graphical Models held at the 30th Annual International ACM SIGIR Conference CY JUL 23-27, 2007 CL Amsterdam, NETHERLANDS SP ACM DE Information retrieval; Possibilistic networks; Bayesian networks; Relevance; Entropy ID PROBABILISTIC MODELS; TERMS; LOGIC AB This paper proposes an information retrieval (IR) model based on possibilistic directed networks. The relevance of a document w.r.t a query is interpreted by two degrees: the necessity and the possibility. The necessity degree evaluates the extent to which a given document is relevant to a query. whereas the possibility degree evaluates the reasons of eliminating irrelevant documents. This new interpretation of relevance led us to revisit the term weighting scheme by explicitly distinguishing between informative and non-informative terms in a document. Experiments carried out on three standard TREC collections show the effectiveness of the model. (C) 2008 Elsevier Inc. All rights reserved. C1 [Boughanem, M.; Brini, A.; Dubois, D.] Univ Toulouse, IRIT, F-31062 Toulouse 09, France. RP Boughanem, M (reprint author), Univ Toulouse, IRIT, 118 Route Narbonne, F-31062 Toulouse 09, France. EM bougha@irit.fr; dubois@irit.fr CR Benferhat S, 2002, INT J APPROX REASON, V29, P135, DOI 10.1016/S0888-613X(01)00061-5 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 Borgelt C, 2000, CISM COUR L, P51 Boughanem M., 2005, Adaptive Multimedia Retrieval: User, Context, and Feedback. Third International Workshop, AMR 2005. Revised Selected Papers (Lecture Notes in Computer Science Vol. 3877) BRINI A, 2005, P S STRING PROC INF, P271 BRUZA PD, 1994, INT J EXPERT SYST, V7, P107 Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 CRESTANI F, 2003, P 7 EUR C SYMB QUANT, P74 Crestani F, 2003, INFORM PROCESS MANAG, V39, P161, DOI 10.1016/S0306-4573(02)00086-9 DECAMPOS L, 2002, P BCSIRSG EUR C INF, P169 De Campos LM, 2003, INT J UNCERTAIN FUZZ, V11, P101, DOI 10.1142/S0218488503002296 DENOYER L, 2003, P INT C MACH LEARN D, P328 Dominich S., 2001, MATH FDN INFORM RETR DUBOIS D, 1998, POSSIBILITY THEORY Q, P169 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUNG R, 1995, COMMUN ACM, V38, P42, DOI 10.1145/203330.203340 HARTER S, 1975, J AM SOC INFORM SCI, V35, P280 JENSEN FV, 2000, BAYESIAN NETWORKS DE KARBASI S, 2006, LNCS, P72 Pearl J., 1988, PROBABILISTIC REASON Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Prade H, 1988, POSSIBILITY THEORY A Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 ROBERTSON S, 1981, INFORM RETRIEVAL RES, P36 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 2001, TEXT RETRIEVAL C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM Sanderson M., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SILVA I, 2000, P 23 ANN INT ACM SIG, P96, DOI 10.1145/345508.345554 SINGHAL A, 1996, P INT ACM SIGIR C, V32, P21 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Turtle H. R., 1991, THESIS U MASSACHUSET TURTLE HR, 1991, ACM T INFORM SYSTEMS, V9, P7187 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Zadeh L. A., 1978, Fuzzy Sets and Systems, V1, DOI 10.1016/0165-0114(78)90029-5 NR 39 TC 4 Z9 5 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0888-613X J9 INT J APPROX REASON JI Int. J. Approx. Reasoning PD JUL PY 2009 VL 50 IS 7 BP 957 EP 968 DI 10.1016/j.ijar.2008.10.005 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA 472RC UT WOS:000268148700004 ER PT J AU Robichaud, CO Uyar, AE Darby, MR Zucker, LG Wiesner, MR AF Robichaud, Christine Ogilvie Uyar, Ali Emre Darby, Michael R. Zucker, Lynne G. Wiesner, Mark R. TI Estimates of Upper Bounds and Trends in Nano-TiO2 Production As a Basis for Exposure Assessment SO ENVIRONMENTAL SCIENCE & TECHNOLOGY LA English DT Article ID NANOPARTICLES; REVOLUTION; FIRM AB An upper bound is estimated for the magnitude of potential exposure to nano-TiO2 with the purpose of enabling exposure assessment and, ultimately, risk assessment. Knowledge of the existing bulk TiO2 market is combined with available nano-TiO2 production data to estimate current nano-TiO2 sources as a baseline. The evolution of nano-TiO2 production as a percentage of the total TiO2 market is then projected based on material and market information along with a method that combines observations from scientific articles and patents as predictive indicators of the rate of innovative transformation. C1 [Robichaud, Christine Ogilvie; Uyar, Ali Emre; Darby, Michael R.; Zucker, Lynne G.; Wiesner, Mark R.] Duke Univ, Dept Civil & Environm Engn, Durham, NC 27708 USA. RP Wiesner, MR (reprint author), Duke Univ, Dept Civil & Environm Engn, POB 90287, Durham, NC 27708 USA. EM wiesner@duke.edu RI Zucker, Lynne/C-1467-2008; Darby, Michael/C-1457-2008 OI Zucker, Lynne/0000-0002-1852-6612; Darby, Michael/0000-0003-4565-2670 FU National Science Foundation [SES-0531146, SBE-0830983, SES-0304727]; Center for the Environmental Implications of Nano-Technology (CEINT) [EF-0830093]; University of California's Toxic Substances Research and Teaching Program (TSRTP) FX We thank Joseph Gambogi of the USGS and Michael Molnar of Altairnano for their helpful insight. This material is based upon work, Supported in part by the National Science Foundation under grants SES-0531146 and SBE-0830983 and the Center for the Environmental Implications of Nano-Technology (CEINT) under Cooperative Agreement EF-0830093 with the National Science Foundation and the Environmental Protection Agency. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Environmental Protection Agency. This work has not been subjected to EPA review and no official endorsement should be inferred. This research also has been supported in part by the National Science Foundation under grant SES-0304727 and by the UC Lead Campus for Nanotoxicology Training and Research grant from the University of California's Toxic Substances Research and Teaching Program (TSR&TP). Certain data included herein are derived from the Science Citation Index Expanded, Social Sciences Citation Index, Arts and Humanities Citation Index of the Institute for Scientific Information, Inc. (ISI), Philadelphia, PA: Copyright Institute for Scientific Information, Inc., 2005. All rights reserved. Certain data included herein are derived from the Zucker-Darby Knowledge, Innovation, and Growth Project's Science & Technology Agents of Revolution (STAR) database: copyright Lynne G. Zucker and Michael R. Darby. All rights reserved. CR Adams LK, 2006, WATER RES, V40, P3527, DOI 10.1016/j.watres.2006.08.004 Aitken RJ, 2006, OCCUP MED-OXFORD, V56, P300, DOI 10.1093/occmed/kq1051 Boxall A., 2007, CURRENT FUTURE PREDI Brunner TJ, 2006, ENVIRON SCI TECHNOL, V40, P4374, DOI 10.1021/es052069i DARBY MR, 2007, NANOBANK DATA DESCRI DARBY MR, 2005, ANN EC STAT, V78, P1 Gambogi J., 2006, MINERALS YB TITANIUM GHEMAWAT P, 1989, HARVARD BUSINESS SCH, P1 Kaida Takahiro, 2004, J Cosmet Sci, V55, P219 Li BR, 2003, MATER CHEM PHYS, V78, P184, DOI 10.1016/S0254-0584(02)00226-2 Mueller NC, 2008, ENVIRON SCI TECHNOL, V42, P4447, DOI 10.1021/es7029637 Raja PMV, 2007, TOXICOL LETT, V169, P51, DOI 10.1016/j.toxlet.2006.12.003 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rothaermel FT, 2007, RES POLICY, V36, P832, DOI 10.1016/j.respol.2007.02.008 Verhulst D., 2003, NEW PROCESS PRODUCTI Zhang J, 2007, J SHANGHAI U ENGLISH, V11, P432 ZUCKER LG, 2004, NANOSCIENCE NANOTECH ZUCKER LG, 2003, W9825 NBER ZUCKER LG, 2001, COMP STUDIES TECHNOL, P85 Zucker LG, 1997, RES POLICY, V26, P429, DOI 10.1016/S0048-7333(97)00017-6 NR 20 TC 155 Z9 160 PU AMER CHEMICAL SOC PI WASHINGTON PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA SN 0013-936X J9 ENVIRON SCI TECHNOL JI Environ. Sci. Technol. PD JUN 15 PY 2009 VL 43 IS 12 BP 4227 EP 4233 DI 10.1021/es8032549 PG 7 WC Engineering, Environmental; Environmental Sciences SC Engineering; Environmental Sciences & Ecology GA 457XF UT WOS:000266968500004 PM 19603627 ER PT J AU Murata, M Toda, H Matsuura, Y Kataoka, R AF Murata, Masaya Toda, Hiroyuki Matsuura, Yumiko Kataoka, Ryoji TI Access concentration detection in click logs to improve mobile Web-IR SO INFORMATION SCIENCES LA English DT Article DE Information retrieval; Mobile Web-IR; Click log analysis; Access concentration site ID QUERY EXPANSION; RETRIEVAL SYSTEMS; TEXT RETRIEVAL AB Effective ranking algorithms for mobile Web searches are being actively pursued. Due to the peculiar and troublesome properties of mobile contents such as scant text, few outward links, and few input keywords, conventional Web search techniques using bag-of-words ranking functions or link-based algorithms are not good enough for mobile Web searches. Our solution is to use click logs to clarify access-concentrated search results for each query and to utilize the titles and snippets to expand the queries. Many previous works regard the absolute click numbers as the degree of access concentration, but they are strongly biased such that higher-ranked search results are more easily clicked than lower-ranked ones. Therefore, it is considered that only higher-ranked search results are access-concentrated ones and that only terms extracted from them can be used to expand a query. In this paper, we introduce a new measure that is capable of estimating the degree of access concentration. This measure is used to precisely extract access concentration sites from many search results and to expand queries with terms extracted from them. We conducted an experiment using the click logs and data from an actual mobile Web search site. Results obtained show that our proposed method is a more effective way to boost the search precision than using other query expansion methods such as the top K search results or the most-often-clicked search results. (c) 2009 Elsevier Inc. All rights reserved. C1 [Murata, Masaya; Toda, Hiroyuki; Matsuura, Yumiko; Kataoka, Ryoji] NTT Corp, NTT Cyber Solut Labs, Yokosuka, Kanagawa 2390847, Japan. RP Murata, M (reprint author), NTT Corp, NTT Cyber Solut Labs, 1-1 Hikari No Oka, Yokosuka, Kanagawa 2390847, Japan. EM murata.masaya@lab.ntt.co.jp CR Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baeza Yates R., 1999, MODERN INFORM RETRIE BOYAN J, 1996, AAAI WORKSH INT BAS, P1 Brin S., 1998, P 7 INT WORLD WID WE, P107 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Cohen WW, 1999, J ARTIF INTELL RES, V10, P243 Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 DING C, 2007, P 22 INT C ACM S APP, P829, DOI 10.1145/1244002.1244186 Dmitriev P. A., 2006, P 15 INT C WORLD WID, P811, DOI 10.1145/1135777.1135900 EKMEKCIOGLU FC, 1992, J INFORM SCI, V18, P139 Goldberg J. H., 2002, Proceedings ETRA 2002. Eye Tracking Research and Applications Symposium Jansen B. J., 2003, P 4 INT C INT COMP L, P65 Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 KAWASHIGE T, 2006, P APWEB, P486 KLOECKNER K, 2004, P INT C HUM FACT COM, P1539 LUXENBURGER J, 2004, P 5 INT C WEB INF SY, P90 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Page L., 1998, PAGERANK CITATION RA Parikh J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148319 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sakai T., 2001, P 24 ANN INT ACM SIG, P396, DOI 10.1145/383952.384035 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Tao Tao, 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148201 Vechtomova O, 2006, J INF SCI, V32, P324, DOI 10.1177/0165551506065787 WALKER S, 1987, IMPROVING SUBJECT RE Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 YATES RB, 2005, ANTHONY SCIME, P307 YATES RB, 2005, P 27 INT C EUR C INF, P7 Zhuang Z., 2006, P CIKM 2006, P860, DOI 10.1145/1183614.1183767 NR 34 TC 2 Z9 2 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD MAY 30 PY 2009 VL 179 IS 12 BP 1859 EP 1869 DI 10.1016/j.ins.2009.01.046 PG 11 WC Computer Science, Information Systems SC Computer Science GA 444GG UT WOS:000265968100006 ER PT J AU Perez, JM Berlanga, R Aramburu, MJ AF Manuel Perez, Juan Berlanga, Rafael Jose Aramburu, Maria TI A relevance model for a data warehouse contextualized with documents SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Relevance-based language model; Data warehouse; Text-rich document collection AB This paper presents a relevance model to rank the facts of a data warehouse that are described in a set of documents retrieved with an information retrieval (IR) query. The model is based in language modeling and relevance modeling techniques. We estimate the relevance of the facts by the probability of finding their dimensions values and the query keywords in the documents that are relevant to the query. The model is the core of the so-called contextualized warehouse, which is a new kind of decision support system that combines structured data sources and document collections. The paper evaluates the relevance model with the Wall Street Journal (WSJ) TREC test subcollection and a self-constructed fact database. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Manuel Perez, Juan; Berlanga, Rafael; Jose Aramburu, Maria] Univ Jaume 1, E-12071 Castellon de La Plana, Spain. RP Perez, JM (reprint author), Univ Jaume 1, Campus Riu Sec, E-12071 Castellon de La Plana, Spain. EM juanma.perez@uji.es; berlanga@uji.es; aramburu@uji.es RI Berlanga, Rafael/K-9590-2014 OI Berlanga, Rafael/0000-0002-9155-269X CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Codd E. F., 1993, PROVIDING OLAP USER DANGER R, 2004, P DEXA, P243 Eguchi K, 2006, P 2006 C EMP METH NA, P345, DOI 10.3115/1610075.1610124 HARMAN D, 1995, NIST SPECIAL PUBLICA, V500, P1 Inmon W.H., 1991, BUILDING DATA WAREHO LAVRENKO V, 2003, P IEEE INT C AC SPEE, P17 Lavrenko V., 2002, P 2 INT C HUM LANG T, P115, DOI 10.3115/1289189.1289268 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Liu B., 2005, P 14 INT C WORLD WID, P342, DOI DOI 10.1145/1060745.1060797 LLIDO D, 2001, P DAT EXP SYST APPL, P62 Losada DE, 2008, INFORM RETRIEVAL, V11, P109, DOI 10.1007/s10791-007-9040-x PEDERSEN TB, 2005, IND INFORM TECHNOLOG, P1 Perez-Martinez JM, 2008, DECIS SUPPORT SYST, V45, P77, DOI 10.1016/j.dss.2006.12.005 PEREZ JM, 2007, THESIS U JAUME 1 CAS Perez J.M., 2007, P ICDE, P1477 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S. E., 1997, READINGS INFORM RETR, P281 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 NR 20 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2009 VL 45 IS 3 BP 356 EP 367 DI 10.1016/j.ipm.2008.11.001 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 449BO UT WOS:000266306000004 ER PT J AU Lan, M Tan, CL Su, J Lu, Y AF Lan, Man Tan, Chew Lim Su, Jian Lu, Yue TI Supervised and Traditional Term Weighting Methods for Automatic Text Categorization SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE LA English DT Article DE Text categorization; text representation; term weighting; SVM; kNN ID RELEVANCE AB In vector space model (VSM), text representation is the task of transforming the content of a textual document into a vector in the term space so that the document could be recognized and classified by a computer or a classifier. Different terms (i.e., words, phrases, or any other indexing units used to identify the contents of a text) have different importance in a text. The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization. In this study, we investigate several widely used unsupervised (traditional) and supervised term weighting methods on benchmark data collections in combination with SVM and kNN algorithms. In consideration of the distribution of relevant documents in the collection, we propose a new simple supervised term weighting method, i.e., tf.rf, to improve the terms' discriminating power for text categorization task. From the controlled experimental results, these supervised term weighting methods have mixed performance. Specifically, our proposed supervised term weighting method, tf.rf, has a consistently a better performance than other term weighting methods while most supervised term weighting methods based on information theory or statistical metric perform the worst in all experiments. On the other hand, the popularly used tf.idf method has not shown a uniformly good performance in terms of different data sets. C1 [Lan, Man; Lu, Yue] E China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China. [Lan, Man; Tan, Chew Lim] Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore 117590, Singapore. [Su, Jian] Inst Infocomm Res, Singapore 119613, Singapore. RP Lan, M (reprint author), E China Normal Univ, Dept Comp Sci & Technol, 500,Dongchuan Rd, Shanghai 200241, Peoples R China. EM mlan@cs.ecnu.edu.cn; tancl@comp.nus.edu.sg; sujian@i2r.a-star.edu.sg; ylu@cs.ecnu.edu.cn FU NUS-12R Project [R252-000-323-592] FX This project is supported in part by the NUS-12R Project Grant R252-000-323-592. CR Buckley C., 1994, P 3 TEXT RETR C TREC, P69 Chang C. C., 2001, LIBSVM LIB SUPPORT V Debole F., 2003, P SAC 03 18 ACM S AP, P784 Deng ZH, 2004, LECT NOTES COMPUT SC, V3007, P588 DIETTERICH TG, 1998, NEURAL COMPUT, V10, P7 Dong Y.-S., 2005, P 2005 ACM S APPL CO, P1044, DOI 10.1145/1066677.1066916 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Han E.H., 2001, P 5 PAC AS C KNOWL D, P53 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lan M., 2006, P 21 NAT C ART INT A, P763 Lan M., 2005, SPECIAL INTEREST TRA, P1032, DOI 10.1145/1062745.1062854 Lan M, 2005, IEEE IJCNN, P546 Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 Lewis DD, 2004, J MACH LEARN RES, V5, P361 McCallum A., 1998, P AAAI WORKSH LEARN Mori T, 2002, INFORM GAIN RATIO TE, P1 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SOUCY P, 2005, P 19 INT JOINT C ART, P1130 Wu H., 1981, P 4 ANN INT ACM SIGI, P30, DOI 10.1145/511754.511759 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 28 TC 69 Z9 76 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 0162-8828 J9 IEEE T PATTERN ANAL JI IEEE Trans. Pattern Anal. Mach. Intell. PD APR PY 2009 VL 31 IS 4 BP 721 EP 735 DI 10.1109/TPAMI.2008.110 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 407WX UT WOS:000263396100011 PM 19229086 ER PT J AU Cohen, T Widdows, D AF Cohen, Trevor Widdows, Dominic TI Empirical distributional semantics: Methods and biomedical applications SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Review DE Distributional semantics; Methodological review; Latent semantic analysis; Natural language processing; Semantic similarity; Random indexing; Context vectors ID MEDLINE ABSTRACTS; INFORMATION; RETRIEVAL; KNOWLEDGE; PROTEIN; REPRESENTATION; VISUALIZATION; COOCCURRENCE; SIMILARITY; DOMAIN AB Over the past 15 years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a Corpus Of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness front free text, as well as their evaluation in a variety of biomedical and Other applications. Recent methodological developments, and their applicability to several existing applications are also discussed. (C) 2009 Elsevier Inc. All rights reserved. C1 [Cohen, Trevor] Arizona State Univ, Sch Comp & Informat, Dept Biomed Informat, Ctr Decis Making & Cognit, Phoenix, AZ 85004 USA. RP Cohen, T (reprint author), Arizona State Univ, Sch Comp & Informat, Dept Biomed Informat, Ctr Decis Making & Cognit, 425 N 5th St, Phoenix, AZ 85004 USA. EM trevor.cohen@asu.edu FU US National Library of Medicine [R01 LM07894]; Google, Inc. FX The writing of this manuscript was supported in part by the US National Library of Medicine Grant (R01 LM07894). The authors would like to acknowledge Google, Inc. for their support of author DW's ongoing research on the subject. We would also like to thank Tom Landauer for providing the TASA corpus and his comments on the validity of the TOEFL test, as well as two anonymous referees for their recommendations and thorough reviews. CR *ARB, ARB PROJ KNOWC Aronson A R, 2000, Proc AMIA Symp, P17 ARONSON AR, 2001, P AMIA S, P1717 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BERRY M, 1993, CS93194 U TENN COMP Berry MW, 2003, HDB PARALLEL COMPUTI, P117 Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BRUZA P, 2006, OPERATIONAL ABDUCTIO BUDANITSK A, 2001, WORKSH WORDNET OTH L Burgess C, 2000, COGNITIVE DYNAMICS, P117 CEDERBERG S, 2008, INFORMAP NLP SOFTWAR Cederberg S., 2003, P 7 C NAT LANG LEARN, V4, P111, DOI 10.3115/1119176.1119191 Charniak E., 1993, STAT LANGUAGE LEARNI CHEN G, 2008, P 2 AUSTR WORKSH HLT, P9 CHUTE CG, 1991, P ANN S COMP APPL ME, P1859 CHUTE CG, 1992, P ANN S COMP APPL ME, V639, P43 Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671 Cohen AM, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-103 Cohen T, 2008, J BIOMED INFORM, V41, P1070, DOI 10.1016/j.jbi.2008.03.008 Cohen Trevor, 2005, AMIA Annu Symp Proc, P151 Cohen Trevor, 2008, AMIA Annu Symp Proc, P126 Cole RJ, 2005, LECT NOTES COMPUT SC, V3735, P84 COOPER EI, 1988, MATER LETT, V7, P5, DOI 10.1016/0167-577X(88)90069-9 Curran J., 2005, P 43 ANN M ASS COMP, P26, DOI 10.3115/1219840.1219844 CURRAN JR, 2001, P 38 ANN M ASS COMP, P231, DOI 10.3115/1073083.1073123 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dennis Simon, 2007, HDB LATENT SEMANTIC, P449 Domingos P, 2007, DATA MIN KNOWL DISC, V15, P21, DOI 10.1007/s10618-007-0069-7 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Elvevag B, 2007, SCHIZOPHR RES, V93, P304, DOI 10.1016/j.schres.2007.03.001 Fan JW, 2007, J AM MED INFORM ASSN, V14, P467, DOI 10.1197/jamia.M2314 Friedman C, 2001, Bioinformatics, V17 Suppl 1, pS74 FRIEDMAN C, 2000, P AMIA S, P19270 Ganapathiraju MK, 2004, IEEE SIGNAL PROC MAG, V21, P78, DOI 10.1109/MSP.2004.1296545 GILES JT, 2001, GTP GEN TEXT PARSER Glenisson P, 2003, Pac Symp Biocomput, P391 Gordon MD, 1998, J AM SOC INFORM SCI, V49, P674 Gorman J., 2006, P C EMP METH NAT LAN, P457, DOI 10.3115/1610075.1610139 Grefenstette Gregory, 1994, P 6 C EUR ASS LEX EU, P279 Griffiths TL, 2002, P 24 ANN C COGN SCI, P381 Griffiths TL, 2005, ADV NEURAL INFORM PR, V17, P537 Harris Z., 1968, MATH STRUCTURES LANG Harris ZS, 2002, J BIOMED INFORM, V35, P215, DOI 10.1016/S1532-0464(03)00011-X Hearst M. A., 1992, P 14 INT C COMP LING, P539 Heer J., 2005, CHI 05, P421 Heinrich G, 2005, PARAMETER ESTIMATION Hersh W., 2003, INFORM RETRIEVAL HLT, Vsecond Hersh William, 1994, P 17 ANN INT ACM SIG, P192 HOFMANN T, 1999, P 15 C UNC ART INT U, P289 Homayouni R, 2005, BIOINFORMATICS, V21, P104, DOI 10.1093/bioinformatics/bth464 Johnson W. B., 1984, CONT MATH, V1, P189 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Kanejiya D., 2003, P HLT NAACL 03 WORKS, V2, P53, DOI DOI 10.3115/1118894.1118902 Kanerva P., 2000, P 22 ANN C COGN SCI, P10 KARIGREN J, 2001, ROUND REAL WORLD INT, P294 KLEINSEETHARAMA.J, 2005, NSF REPORT SOC IMPLI, P128 Koehn Philipp, 2005, EUROPARL PARALLEL CO LANDAUER T, 1997, P 19 ANN C COGN SCI Landauer TK, 2000, IEEE INTELL SYST APP, V15, P27 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Landauer TK, 2004, P NATL ACAD SCI USA, V101, P5214, DOI 10.1073/pnas.0400341101 LANDAUER TK, COMMUNICATION LANDAUER TK, 2007, LAWRENCE HDB LATENT Lesk M, 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Lin D., 1998, P 17 INT C COMP LING, P768 LIN J, 2007, BMC BIOINFORMATICS, V8, P1 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 MCARTHUR R, 2006, SYST SCI 2006 HICSS, pC91 McDonald J., 1990, PATHFINDER ASS NETWO, P149 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004 Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] Pustejovsky J., 1991, Computational Linguistics, V17 QUILLIAN MR, 2004, SEMANTIC INFORM PROC, P227 RAPP R, 2003, 9 MACHINE TRANSLATIO, P315 Rijsbergen CJ, 2004, GEOMETRY INFORM RETR Riloff E., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 SAGER N, 1994, J AM MED INFORM ASSN, V1, P142 Sahlgren M., 2006, THESIS STOCKHOLM U Sahlgren M., 2004, P 20 INT C COMP LING Sahlgren M, P 30 ANN M COGN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895 Schvaneveldt R. W., 1990, PATHFINDER ASS NETWO SHANNON CE, 1951, AT&T TECH J, V30, P50 Sharda P, 2006, INT J MED INFORM, V75, P346, DOI 10.1016/j.ijmedinf.2005.07.027 Steyvers M., 2007, HDB LATENT SEMANTIC Strang G., 2003, INTRO LINEAR ALGEBRA Stuart GW, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-204 STUART GW, 2003, J BIOINFORM COMPUT B, P1475 Swayne DF, 2003, COMPUT STAT DATA AN, V43, P423, DOI 10.1016/S0167-9473(02)00286-4 Vanteru BC, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-S1-S10 Volk M, 2002, INT J MED INFORM, V67, P97, DOI 10.1016/S1386-5056(02)00058-8 WIDDOWS D, 2003, P 2003 C N AM CHAPT, V1, P197, DOI 10.3115/1073445.1073481 WIDDOWS D, 2003, NAT LANG PROC BIOM A, P9 Widdows D, 2003, MATH LANGUAGE, V8 Widdows D., 2008, P 2 AAAI S QUANT INT WIDDOWS D, 2003, P C N AM CHAP ASS CO, V4, P31, DOI 10.3115/1073427.1073443 WIDDOWS D, 2008, 6 INT C LAN IN PRESS WIDDOWS D, 2009, P 3 QUANT INT S MARC Widdows Dominic, 2004, GEOMETRY AND MEANING Yang Y, 1996, Proc AMIA Annu Fall Symp, P358 Yang Y, 1994, Proc Annu Symp Comput Appl Med Care, P157 YANG Y, 1992, P 14 INT C COMP LING, V2, P447, DOI 10.3115/992133.992139 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 Yarowsky D, 1995, P ACL, P189 Zambrano N, 2004, J BIOL CHEM, V279, P16161, DOI 10.1074/jbc.M311027200 ZHANG JJ, 2002, J BIOMED INFORM, P3517 Zipitria I., 2001, P 23 ANN C COGN SCI, P1112 TOPIC MODELING TOOLB VOCABSYN FRAMESET 1 LATENT DIRICHLET ALL NR 119 TC 31 Z9 33 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 J9 J BIOMED INFORM JI J. Biomed. Inform. PD APR PY 2009 VL 42 IS 2 BP 390 EP 405 DI 10.1016/j.jbi.2009.02.002 PG 16 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 429ZO UT WOS:000264958800020 PM 19232399 ER PT J AU Orasan, C AF Orasan, Constantin TI Comparative Evaluation of Term-Weighting Methods for Automatic Summarization SO JOURNAL OF QUANTITATIVE LINGUISTICS LA English DT Article ID RELEVANCE AB Term-based summarization assumes that it is possible to determine the importance of a sentence on the basis of the words it contains. To achieve this, words are weighted using term-weighting measures which in turn are used to weight the sentences. This article presents a comparative evaluation of summaries produced using different term-weighting measures and different combinations of parameters which are used to calculate these measures. Comparative evaluation of summaries produced reveals that in many cases simple methods such as term frequency can produce informative summaries. C1 Wolverhampton Univ, Res Grp Computat Linguist, Wolverhampton WV1 1SB, England. RP Orasan, C (reprint author), Wolverhampton Univ, Res Grp Computat Linguist, Stafford St, Wolverhampton WV1 1SB, England. EM C.Orasan@wlv.ac.uk RI Orasan, Constantin/C-2677-2012 CR BAXENDALE PB, 1958, IBM J RES DEV, V2, P354 Black W. J., 1988, Expert Systems for Information Management, V1 BRANDOW R, 1995, INFORM PROCESS MANAG, V31, P675, DOI 10.1016/0306-4573(95)00052-I Buckley Christopher, 1985, 85686 CORN U BURNARD L, 1992, TOPICS ENGLISH LINGU, P97 Church K.W., 1995, P 18 ANN INT ACM SIG, P310, DOI 10.1145/215206.215376 Donaway R., 2000, P WORKSH AUT SUMM AN, P69 EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 Frakes W. B., 1992, INFORM RETRIEVAL DAT HA LA, 2005, P RANLP2005 BOR BULG, P219 HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P HOVY E, 2003, OXFORD HDB COMPUTATI, P583 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333 Lin Chin-Yew, 1997, P 5 C APPL NAT LANG, P283, DOI 10.3115/974557.974599 LIN CY, 1998, AAAI SPRING S INT TE, P44 LUHN HP, 1958, IBM J RES DEV, V2, P159 Lyman P., 2003, MUCH INFORM 2003 Mani I., 2001, AUTOMATIC SUMMARIZAT Manning C. D., 1999, FDN STAT NATURAL LAN MLADENIC D, 1999, P C AUT LEARN DISC C, P1 NANAS N, 2003, KMI034 OP U Noreault T, 1981, PERFORM EVALUATION, P57 Orasan Constantin, 2004, P 4 INT C LANG RES E, P1037 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rose T., 2002, P 3 INT C LANG RES E, P827 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM Tapanainen P., 1997, P 5 C APPL NAT LANG, P64, DOI 10.3115/974557.974568 Teufel S., 1997, P ACL 97 EACL 97 WOR, P58 Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936 Teufel Simone, 1999, THESIS U EDINBURGH Yamamoto M, 2001, COMPUT LINGUIST, V27, P1, DOI 10.1162/089120101300346787 ZECHNER K, 1996, COLING, P986 NR 38 TC 5 Z9 5 PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND SN 0929-6174 J9 J QUANT LINGUIST JI J. Quant. Linguist. PD FEB PY 2009 VL 16 IS 1 BP 67 EP 95 DI 10.1080/09296170802514187 PG 29 WC Linguistics; Language & Linguistics SC Linguistics GA 409GH UT WOS:000263493700003 ER PT S AU Yin, XS Huang, XJ Li, ZJ AF Yin, Xiaoshi Huang, Xiangji Li, Zhoujun GP IEEE TI BioCLink: A Probabilistic Approach for Improving Genomics Search with Citation Links SO 2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE SE IEEE International Conference on Bioinformatics and Biomedicine-BIBM LA English DT Proceedings Paper CT IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009) CY NOV 01-04, 2009 CL Washington, DC SP IEEE, IEEE Comp Soc, Natl Sci Fdn DE genomics literature retrieval; probabilistic model; citation link analysis AB Combination of multiple evidences has been shown to be effective in genomics literature retrieval. Citation information is an intuitive evidence for facilitating literature retrieval. Previous research on citation analysis has demonstrated that useful linkage information can be extracted from the citation graph. However, the question of how the combination of citation evidence and content evidence should be done to maximize retrieval accuracy still remains largely unanswered. In this paper, we propose BioCLink, a new probabilistic approach that integrates citation evidence into content-based weighting function for improving genomics literature retrieval performance. Based on findings of our previous study, a strategy for modeling citation evidence is proposed. BioCLink provides the combination of content and citation evidences with a theoretical support. Moreover, exhaustive parameter tuning can be avoided using BioCLink. Extensive experiments on TREC 2006 and 2007 Genomics collections demonstrate the advantages and effectiveness of our proposed methods. C1 [Yin, Xiaoshi; Li, Zhoujun] Beihang Univ, Coll Comp Sci & Engn, Beijing, Peoples R China. RP Yin, XS (reprint author), Beihang Univ, Coll Comp Sci & Engn, Beijing, Peoples R China. EM xiaoshiyin@cse.buaa.edu.cn; jhuang@yorku.ca; lizj@buaa.edu.cn CR An Y, 2004, KNOWL INF SYST, V6, P664, DOI 10.1007/s10115-003-0128-3 Beaulieu M, 1997, P TREC 5 BERNSTAM E, 2005, J AM MED INFORM ASSN, V13, P96, DOI 10.1197/jamia.M1909 Borodin A., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052942 Dorogovtsev S. N., 2003, EVOLUTION NETWORKS HERSH W, P TREC 2006 HERSH W, P TREC 2007 HERSKOVIC J, AMIA 2005 S P HUANG X, 2005, P TREC Jensen LJ, 2006, NAT REV GENET, V7, P119, DOI 10.1038/nrg1768 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lin J., 2008, BMC BIOINFORMATICS, V9 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Tunon J., 2005, ANN M INT FED LIB AS YIN X, 2009, P PAKDD NR 16 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 2156-1125 BN 978-0-7695-3885-3 J9 IEEE INT C BIOINFORM PY 2009 BP 375 EP 378 DI 10.1109/BIBM.2009.83 PG 4 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Engineering GA BNY71 UT WOS:000275900200068 ER PT S AU Guo, HZ Chen, QC Wang, XL Wang, ZY Wu, YH AF Guo, Hongzhi Chen, Qingcai Wang, Xiaolong Wang, Zhiyong Wu, Yonghui GP IEEE TI STRank: A SiteRank Algorithm using Semantic Relevance and Time Frequency SO 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9 SE IEEE International Conference on Systems Man and Cybernetics Conference Proceedings LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics CY OCT 11-14, 2009 CL San Antonio, TX SP IEEE DE STRank; semantic relevance; time frequency; updating frequency; site ranking AB Most of the researches on web information processing are concentrated on the web pages and the hyperlinks among them. One of the important facts that a web page is just one building block of the whole website had been ignored. But the situation is gradually changed in recent years for the needs of website reputation calculation, the high level website structure mining etc. It causes the website ranking become one of the hot research topics and various site ranking algorithms, such as SiteRank, AggregateRank etc., had been proposed. But most of existing website ranking algorithm just take use of website link graphs and the content of websites are usually not put into consideration. It is obviously not enough for a reliable ranking of websites. To address this issue, this paper introduces two content based features, i.e., semantic relevance and time frequency and proposes a new STRank algorithm based on these two features. We firstly conduct a series of experiments to verify the feasibility of these two factors in site ranking task. Then the semantic relevance is applied in the calculation of transition probability, and the updating frequency of sites is combined into the ranking task. Since traditional Kendall's tau distance and Spearman's Footrule distance is not appropriate for the evaluation of site ranking, we make some modifications accordingly to evaluate website ranking algorithms. Finally, our experiments show that the STRank algorithm outperforms existing approaches on both effectiveness and efficiency. C1 [Guo, Hongzhi; Chen, Qingcai; Wang, Xiaolong; Wang, Zhiyong; Wu, Yonghui] Harbin Inst Technol Shenzhen Grad Sch, Dept Comp Sci & Technol, Shenzhen, Peoples R China. RP Guo, HZ (reprint author), Harbin Inst Technol Shenzhen Grad Sch, Dept Comp Sci & Technol, Shenzhen, Peoples R China. EM hongzhi.guo@gmail.com; qingcai.chen@gmail.com; wangxl@insun.hit.edu.cn CR Aberer K, 2003, LECT NOTES COMPUT SC, V2642, P213 Bharat K, 2001, IEEE INT C DAT MIN I Fagin R, 2003, SIAM PROC S, P28 Feng G., 2006, P 29 ANN INT ACM SIG, P75, DOI 10.1145/1148170.1148187 GREENGRASS ED, 2000, INFORM RETRIEVAL LAB, V1, P112 JIANG QC, 2007, 7 INT C COMP INF TEC, P259, DOI 10.1109/CIT.2007.35 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LIU N, 2007, EEE WSE 07, P73 Page L., 1998, PAGERANK CITATION RA Qin T., 2008, P 17 INT C WORLD WID, P407, DOI 10.1145/1367497.1367553 ROBERTSON SE, 1999, P 4 TEXT RETR C TREC, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WU J, 2004, IC200431 SWISS FED I WU L, 2004, LECT NOTES COMPUTER, P265 Yang L., 2007, P 16 ACM C C INF KNO, P1011, DOI 10.1145/1321440.1321598 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 978-1-4244-2793-2 J9 IEEE SYS MAN CYBERN PY 2009 BP 4876 EP 4881 DI 10.1109/ICSMC.2009.5346321 PG 6 WC Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BPP02 UT WOS:000279574602234 ER PT B AU Wang, LS AF Wang, Louis S. GP IEEE TI Relevance Weighting of Multi-Term Queries for Vector Space Model SO 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING LA English DT Proceedings Paper CT IEEE Symposium on Computational Intelligence and Data Mining CY MAR 30-APR 02, 2009 CL Nashville, TN SP IEEE AB The Vector Space Model is one of the most common information retrieval (IR) methods for text document search. The cosine of the angle or the Euclidean distance between the query vector and each document vector is commonly used to measure similarity for query matching. Even though the vector space model starts with a term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords based on the adjacency between the terms in the documents. Thus, when a document contains the adjacency terms, its vector will typically move closer to the query vector to show stronger relevancy between query and the document. C1 Int Sch Minnesota, Eden Prairie, MN 55344 USA. RP Wang, LS (reprint author), Int Sch Minnesota, Eden Prairie, MN 55344 USA. EM louis_s_wang@yahoo.com CR Berry MW, 2005, SOFTW ENVIRON TOOLS, V17, P1, DOI 10.1137/1.9780898718164 BERRY MW, 1995, SIAM REV, V37, P537 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gyongyi Z., 2004, P 30 INT C VER LARG, V30, P576 KLEINBERG JM, 2000, Patent No. 6112202 Manning C. D., 2008, INTRO INFORM RETRIEV Marchiori Massimo, 1997, 6 INT C WORLD WID WE, P1225 MERCIER A, 2006, P 15 TEXT RETRIEVAL RAO R, 1992, P UIST 92, P23, DOI 10.1145/142621.142625 Rasolofo Yves, 2003, P 25 EUR C IR RES, P207 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUNGSAWANG A, 1997, THESIS ENST PARIS PA SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WANG LS, 2007, Patent No. 20070179930 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2765-9 PY 2009 BP 396 EP 402 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BLZ20 UT WOS:000271487700056 ER PT B AU Zheng, L Cox, IJ AF Zheng, Lei Cox, Ingemar J. GP IEEE TI Document-Oriented Pruning of the Inverted Index in Information Retrieval Systems SO 2009 INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS: WAINA, VOLS 1 AND 2 LA English DT Proceedings Paper CT 23rd International Conference on Advanced Information Networking and Applications Workshops CY MAY 26-29, 2009 CL Bradford, ENGLAND SP IEEE AB Searching very large collections can be costly in both computation and storage. To reduce this cost, recent research has,focused on reducing the size (pruning) of the inverted index. The inverted index represents a table, the rows and columns of which are terms in the lexicon and documents in the collection, respectively A non-zero entry in the table, known as a posting, indicates that the corresponding document contains the term. Previous researches on static index pruning was either (i) posting-oriented, in which less important postings are removed from the table, or (ii) term-oriented, in which less important terms are removed from the table. In this paper we investigate a new, document-oriented pruning strategy that removes entire columns of the table, i.e. removes less important documents from the collection. Three methods for estimating the importance of a document are proposed. Methods I and 2 are dependent on the score function of the retrieval system (e.g. Okapi BM25), while Method 3 is independent of the retrieval System. Experimental results compare the three proposed methods with Carmel et al.'s posting-oriented approach, using both the FT and LA Times collections and using both ordinary and difficult queries. Based on mean average precision and precision at 10, experimental results show that Method 3 generally performs best on the FT collection for pruned indexes down to 35% of the original size. However for more severe pruning, Carmel et al.'s algorithm is better For the LA Times collection, the performance of Method 3 and that of Carmel et al. are reversed. This variation in performance across collections has not been previously reported. C1 [Zheng, Lei; Cox, Ingemar J.] UCL, London WC1E 6BT, England. RP Zheng, L (reprint author), UCL, Gower St, London WC1E 6BT, England. EM lei.zheng@ucl.ac.uk; ingemar@ieee.org CR AZZOPARDI L, 2008, P EUR C INF RETR, P482 Azzopardi L., 2008, P CIKM 2008 NAP VALL, P561, DOI 10.1145/1458082.1458157 BLANCO R., 2007, P EUR C INF RETR ECI, P64 Carmel D., 2001, P 24 ANN INT ACM SIG, P43, DOI 10.1145/383952.383958 Fox C, 1989, ACM SIGIR FORUM, V24, P19 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Manning C. D., 2008, INTRO INFORM RETRIEV Ntoulas A., 2007, P 30 ANN INT ACM SIG, P191, DOI 10.1145/1277741.1277776 Ogilvie P, 2001, P 10 TEXT RETR C TRE ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST SPARCK-JONES K, 1973, INFORM STORAGE RET, V9, P619 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P779, DOI 10.1016/S0306-4573(00)00015-7 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H NR 14 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-3999-7 PY 2009 BP 697 EP 702 DI 10.1109/WAINA.2009.147 PG 6 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BLX92 UT WOS:000271364200116 ER PT B AU Kim, JW Candan, KS AF Kim, Jong Wook Candan, K. Selcuk GP ACM TI Skip-and-Prune: Cosine-based Top-K Query Processing for Efficient Context-Sensitive Document Retrieval SO ACM SIGMOD/PODS 2009 CONFERENCE LA English DT Proceedings Paper CT 35th ACM SIGMOD Conference CY JUN 29-JUL 02, 2009 CL Providence, RI SP ACM SIGMOD, ACM, Microsoft, ORACLE, SAP, SYBASE DE Top-K; Ranking ID INFORMATION; SYSTEMS; SEARCH AB Keyword search and ranked retrieval together emerged as popular data access paradigms for various kinds of data, from web pages to XML and relational databases. A user can submit keywords without knowing much (sometimes nothing) about the complex structure underlying a data collection, yet the system can identify, rank, and return a set of relevant matches by exploiting statistics about the distribution and structure of the data. Keyword-based data models are also suitable for capturing user's search context in terms of weights associated to the keywords in the query. Given a search context, the data in the database can also be re-interpreted for semantically correct retrieval. This option; however, is often ignored as the cost of re-assessing the content in the database naively tends to be prohibitive. In this paper, we first argue that top-k query processing can help tackle this challenge by re-assessing only the relevant parts of the database, efficiently. A road-block in this process, however, is that most efficient implementations of top-k query processing assume that the scoring function is monotonic, whereas the cosine-based scoring function needed for re-interpretation of content based on user context is not. In this paper, we develop an efficient top-k query processing algorithm, skip-and-prune (SnP), which is able to process top-k queries under cosine-based non-monotonic scoring functions. We compare the use of proposed algorithm against the alternative implementations of the context-aware retrieval, including naive top-k, accumulator-based inverted files, and full-scan. The experiment results show that while being fast, naive top-k is not an effective solution due to the non-monotonicity of underlying scoring function. The proposed technique, SnP, however, matches the precision of accumulator-based inverted files and full-scan, yet it is orders of magnitude faster than these. C1 [Kim, Jong Wook; Candan, K. Selcuk] Arizona State Univ, Comp Sci & Eng Dept, Tempe, AZ 85287 USA. RP Kim, JW (reprint author), Arizona State Univ, Comp Sci & Eng Dept, Tempe, AZ 85287 USA. EM jong@asu.edu; candan@asu.edu CR Agrawal S., 2002, ICDE Arai B., 2007, VLDB Bansal N., 2008, SIGMOD Borzsonyi S., 2001, ICDE BRODER AJ, 1990, PATTERN RECOGN, V23, P171, DOI 10.1016/0031-3203(90)90057-R Chakrabarti K., 2006, SIGMOD Cheng Tao, 2007, VLDB Cohen S., 2003, VLDB Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Fagin R, 1999, J COMPUT SYST SCI, V58, P83, DOI 10.1006/jcss.1998.1600 Fukunaga K, 1975, IEEE T COMPUTERS Harman D, 1992, INFORMATION RETRIEVA, P28 Hjaltason GR, 1999, ACM T DATABASE SYST, V24, P265, DOI 10.1145/320248.320255 Ilyas Ihab F., 2003, VLDB Nepal S., 1999, ICDE Papadias D, 2005, ACM T DATABASE SYST, V30, P41, DOI 10.1145/1061318.1061320 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sun J.-T., 2005, WWW Tan K. Lee, 2001, VLDB Theobald M., 2004, VLDB, P648 Tsaparas P., 2003, ICDE untzer U. G, 2000, VLDB Zobel J, 1998, ACM T DATABASE SYST, V23, P453, DOI 10.1145/296854.277632 NR 27 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-554-3 PY 2009 BP 115 EP 126 PG 12 WC Computer Science, Information Systems SC Computer Science GA BZE10 UT WOS:000301205700011 ER PT J AU Dang, EKF Wu, HC Luk, RWP Wong, KF AF Dang, Edward Kai Fung Wu, Ho Chung Luk, Robert Wing Pong Wong, Kam Fai TI Building a Framework for the Probability Ranking Principle by a Family of Expected Weighted Rank SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Measurement; Performance; Experimentation; Probability ranking principle; optimization ID INFORMATION-RETRIEVAL SYSTEMS; RELEVANCE JUDGMENTS; PARAMETRIC DESCRIPTION; MATHEMATICAL-THEORY; MODELS; PERFORMANCE; COMMUNICATION; FOUNDATION; SPACE; TESTS AB A new principles framework is presented for retrieval evaluation of ranked outputs. It applies decision theory to model relevance decision preferences and shows that the Probability Ranking Principle (PRP) specifies optimal ranking. It has two new components, namely a probabilistic evaluation model and a general measure of retrieval effectiveness. Its probabilities may be interpreted as subjective or objective ones. Its performance measure is the expected weighted rank which is the weighted average rank of a retrieval list. Starting from this measure, the expected forward rank and some existing retrieval effectiveness measures ( e. g., top n precision and discounted cumulative gain) are instantiated using suitable weighting schemes after making certain assumptions. The significance of these instantiations is that the ranking prescribed by PRP is shown to be optimal simultaneously for all these existing performance measures. In addition, the optimal expected weighted rank may be used to normalize the expected weighted rank of retrieval systems for ( summary) performance comparison ( across different topics) between systems. The framework also extends PRP and our evaluation model to handle graded relevance, thereby generalizing the discussed, existing measures ( e. g., top n precision) and probabilistic retrieval models for graded relevance. C1 [Dang, Edward Kai Fung; Wu, Ho Chung; Luk, Robert Wing Pong] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. [Wong, Kam Fai] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. RP Luk, RWP (reprint author), Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. EM csrluk@comp.polyu.edu.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 FU CERG [PolyU 5226/05E] FX This research was supported by CERG project no. PolyU 5226/05E. CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2 Bhamidipati NL, 2009, IEEE T KNOWL DATA EN, V21, P21, DOI 10.1109/TKDE.2008.111 Bollmann P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42023 BOLLMANN P, 1983, ACM SIGIR FORUM, V17, P122, DOI 10.1145/1013230.511811 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009000 BURGIN R, 1992, INFORM PROCESS MANAG, V28, P619, DOI 10.1016/0306-4573(92)90031-T Buttcher S., 2007, P SIGIR 2007, P63, DOI 10.1145/1277741.1277755 Carterette B., 2008, P 31 ANN ACM C RES D, P685, DOI 10.1145/1390334.1390451 COOK KH, 1975, INFORM PROCESS MANAG, V11, P125, DOI 10.1016/0306-4573(75)90001-1 Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 COOPER WS, 1968, AM DOC, V41, P19 Cormack G. V., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148262 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 De Beer J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148293 Denning PJ, 2007, COMMUN ACM, V50, P13, DOI 10.1145/1272516.1272529 EISENBERG MB, 1988, INFORM PROCESS MANAG, V24, P373, DOI 10.1016/0306-4573(88)90042-8 Fagin R, 2003, SIAM J DISCRETE MATH, V17, P134, DOI 10.1137/S0895480102412856 French S., 1982, SEQUENCING SCHEDULIN French S., 1986, DECISION THEORY INTR FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Fuhr N, 2008, INFORM RETRIEVAL, V11, P251, DOI 10.1007/s10791-008-9045-0 GOFFMAN W, 1966, INFORM STORAGE RET, V3, P19, DOI 10.1016/0020-0271(66)90006-4 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P703, DOI 10.1002/(SICI)1097-4571(199112)42:10<703::AID-ASI3>3.0.CO;2-1 GORDON MD, 1992, J AM SOC INFORM SCI, V43, P1, DOI 10.1002/(SICI)1097-4571(199201)43:1<1::AID-ASI1>3.0.CO;2-5 Harman D.K., 2005, TREC EXPT EVALUATION, P21 HEINE MH, 1973, INFORM STORAGE RET, V9, P181, DOI 10.1016/0020-0271(73)90066-1 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Joachims T, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1229179.1229181 KANDO N, 2001, JOHO SHORI GAKKAI KE, V74, P105 Kendall M., 1955, RANK CORRELATION MET Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lafferty J., 2003, LANGUAGE MODELING IN, P1 Lalmas M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P110, DOI 10.1145/258525.258546 Lavrenko Victor, 2003, LANGUAGE MODELING IN, P11 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P193, DOI 10.1016/0306-4573(94)90064-7 Luk RWP, 2008, INFORM RETRIEVAL, V11, P539, DOI 10.1007/s10791-008-9062-z Marczewski E, 1958, C MATH, V6, P319 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Moffat A, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1416950.1416952 Moffat A., 2007, P 30 ANN INT ACM SIG, P375, DOI DOI 10.1145/1277741.127780 OGILVIE P, 2006, P ACM CIKM, P84, DOI 10.1145/1183614.1183631 Pharo N, 2006, J AM SOC INF SCI TEC, V57, P222, DOI 10.1002/asi.20272 POLLOCK SM, 1968, AM DOC, V19, P387, DOI 10.1002/asi.5090190406 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Ramsey F., 1926, FDN MATH OTHER LOGIC, P156 Roberts F.S., 1979, MEASUREMENT THEORY Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 ROBERTSO.SE, 1969, J DOC, V25, P93, DOI 10.1108/eb026466 ROBERTSO.SE, 1969, J DOC, V25, P1, DOI 10.1108/eb026462 Robertson S, 2007, INFORM RETRIEVAL, V10, P321, DOI 10.1007/s10791-007-9025-9 Robertson S., 2008, P 31 ANN INT ACM SIG, P689, DOI DOI 10.1145/1390334.1390453 ROBERTSON SE, 1977, J DOC, V33, P4 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, SMART RETRIEVAL SYST, P57 SAGARA Y, 2002, J JAPAN SOC INFORM K, V36, P12 Sakai T, 2003, P 26 ANN INT ACM SIG, P417 Sakai T, 2007, INFORM PROCESS MANAG, V43, P531, DOI 10.1016/j.ipm.2006.07.020 Sakai T., 2007, P 30 ANN INT ACM SIG, P71, DOI DOI 10.1145/1277741.1277756 Salton G., 1975, J AM SOC INFORM SCI, V18, P613 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C Shafer G, 1976, MATH THEORY EVIDENCE SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 Smokler H.E., 1964, STUDIES SUBJECTIVE P, P93 SPARCKJONES K, 2007, ACM SIGIR FORUM, V41, P8, DOI 10.1145/1328964.1328965 Stevens S. S., 1975, PSYCHOPHYSICS INTRO STOKOLOVA NA, 1977, INFORM PROCESS MANAG, V13, P227, DOI 10.1016/0306-4573(77)90003-6 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1974, J DOC, V30, P365 Voorhees E. M., 1999, P TREC 8, P77 Voorhees Ellen, 2001, P 24 ANN INT ACM SIG, P74, DOI 10.1145/383952.383963 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X Wang J, 2008, INFORM RETRIEVAL, V11, P477, DOI 10.1007/s10791-008-9060-1 Wang YD, 2006, INFORM PROCESS MANAG, V42, P863, DOI 10.1016/j.ipm.2005.06.005 Wang Z, 1992, P 15 ANN INT ACM SIG, P152, DOI 10.1145/133160.133190 Webber W., 2008, P 31 ACM SIGIR C RES, P51, DOI DOI 10.1145/1390334.1390346 Wilbur WJ, 1996, INFORM PROCESS MANAG, V32, P515, DOI 10.1016/0306-4573(96)00028-3 Wilbur WJ, 1998, ACM T INFORM SYST, V16, P101, DOI 10.1145/279339.279340 WONG SKM, 1986, P 9 ANN INT ACM SIGI, P175, DOI 10.1145/253168.253205 WONG SKM, 1989, INFORM SYST, V14, P47, DOI 10.1016/0306-4379(89)90024-0 Wong WS, 2008, INFORM PROCESS MANAG, V44, P1086, DOI 10.1016/j.ipm.2007.12.002 WU HC, 2007, P SIGIR 99 30 ANN IN, P713, DOI 10.1145/1277741.1277872 Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686 YAO YY, 1995, J AM SOC INFORM SCI, V46, P133, DOI 10.1002/(SICI)1097-4571(199503)46:2<133::AID-ASI6>3.0.CO;2-Z YAO YY, 1991, P 14 ACM SIGIR C RES, P211, DOI 10.1145/122860.122881 Yilmaz Emine, 2006, P 15 ACM INT C INF K, P102, DOI DOI 10.1145/1183614.1183633 Zadeh L. A., 1965, FUZZY SETS INFORM CO, V3, P338, DOI DOI 10.1016/S0019-9958(65)90241-X Zhou B, 2008, LECT NOTES ARTIF INT, V4994, P424 ZWISLOCKI JJ, 1980, PERCEPT PSYCHOPHYS, V28, P28, DOI 10.3758/BF03204312 NR 96 TC 1 Z9 1 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PY 2009 VL 27 IS 4 AR 20 DI 10.1145/1629096.1629098 PG 37 WC Computer Science, Information Systems SC Computer Science GA 533AQ UT WOS:000272794700002 ER PT S AU Gery, M Largeron, C Thollard, F AF Gery, Mathias Largeron, Christine Thollard, Franck BE Geva, S Kamps, J Trotman, A TI UJM at INEX 2008: Pre-impacting of Tags Weights SO ADVANCES IN FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 15-18, 2008 CL Dagstul, GERMANY AB This paper(1) addresses the impact of structure on terms weighting function in the context of focused Information Retrieval (IR). Our model considers a certain kind of structural information: tags that represent logical structure (title, section, paragraph, etc.) and tags related to formatting (bold, italic, center; etc.). We take into account the tags influence by estimating the probability that a tag distinguishes relevant terms. This weight is integrated in the terms weighting function. Experiments on a large collection during INEX 2008 IR competition showed improvements for focused retrieval. C1 [Gery, Mathias] Univ Lyon, F-42023 St Etienne, France. RP Gery, M (reprint author), Univ Lyon, F-42023 St Etienne, France. EM mathias.gery@univ-st-etienne.fr; christine.largeron@univ-st-etienne.fr; franck.thollard@univ-st-etienne.fr CR Denoyer L., 2006, SIGIR Forum, V40 Fuller M., 1993, SIGIR Forum Gery Mathias, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, DOI 10.1109/WIIAT.2008.346 Kamps J, 2008, LECT NOTES COMPUT SC, V4862, P24, DOI 10.1007/978-3-540-85902-4_2 RAPELA J, 2001, WIDM, P61 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 2004, CIKM 2004, P42 Trotman A, 2005, INFORM PROCESS MANAG, V41, P243, DOI 10.1016/j.ipm.2003.10.003 NR 8 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-03760-3 J9 LECT NOTES COMPUT SC PY 2009 VL 5631 BP 46 EP 53 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BNY62 UT WOS:000275895800005 ER PT S AU Zhu, JH Wang, J Taylor, M Cox, IJ AF Zhu, Jianhan Wang, Jun Taylor, Michael Cox, Ingemar J. BE Boughanem, M Berrut, C Mothe, J SouleDupuy, C TI Risk-Aware Information Retrieval SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 31st European Conference on Information Research CY APR 06-09, 2009 CL Toulouse, FRANCE SP Google, Mateixware Informat Serv, Microsoft Res, Yahoo, Exalead, GDR 13, Univ Paul Sabatier, ARIA, Inforsid & Reg, Midi Pyrennees AB Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document's rank. Further, such models seldom have an explicit utility (or cost) that is optimized when ranking documents. To address these issues, we take a Bayesian perspective that explicitly considers the uncertainty associated with the estimation of the probability of relevance, and propose an asymmetric cost function for document ranking. Our cost function has the advantage of adjusting the risk in document retrieval via a single parameter for any probabilistic retrieval model. We use the logit model to transform the document posterior distribution with probability space [0.1] into a normal distribution with variable space (-infinity, +infinity). We apply our risk adjustment approach to a language modelling framework for risk adjustable document ranking. Our experimental results show that our risk-aware model can significantly improve the performance of language models, both with and without background smoothing. When our method is applied to a language model without background smoothing, it can perform as well as the Dirichlet smoothing approach. C1 [Zhu, Jianhan; Wang, Jun; Cox, Ingemar J.] Univ Coll London, Ipswich, Suffolk, England. RP Zhu, JH (reprint author), Univ Coll London, Ipswich, Suffolk, England. EM j.zhu@ucl.ac.uk; j.wang@ucl.ac.uk; mitaylor@microsoft.com; i.cox@ucl.ac.uk CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Chen H., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148245 CRAMER J, 2006, ORIGINS DEV LOGIT MO DEVRIES AP, 2005, P SIGIR Gelman A, 2003, BAYESIAN DATA ANAL Herbrich R., 2006, P NIPS, P569 Markowitz H., 1952, J FINANCE Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S. E., 1997, READINGS INFORM RETR, P281 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Varian H. R., 1975, STUDIES BAYESIAN ECO, P198 VINAY V, 2008, P C INF KNOWL MAN CI WEBBER W, 2008, SIGIR 08, P51 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 ZELLNER A, 1986, J AM STAT ASSOC, V81, P446, DOI 10.2307/2289234 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 ZHU J, 2008, RISKY BUSINESS MODEL NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00957-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5478 BP 17 EP 28 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJG81 UT WOS:000265680800002 ER PT S AU Foley, C Smeaton, AF AF Foley, Colum Smeaton, Alan F. BE Boughanem, M Berrut, C Mothe, J SouleDupuy, C TI Synchronous Collaborative Information Retrieval: Techniques and Evaluation SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 31st European Conference on Information Research CY APR 06-09, 2009 CL Toulouse, FRANCE SP Google, Mateixware Informat Serv, Microsoft Res, Yahoo, Exalead, GDR 13, Univ Paul Sabatier, ARIA, Inforsid & Reg, Midi Pyrennees AB Synchronous Collaborative Information Retrieval refers to systems that support multiple users searching together at the same time in order to satisfy a shared information need. To date most SCIR systems have focussed on providing various awareness tools in order to enable collaborating users to coordinate the search task. However, requiring users to both search and coordinate the group activity may prove too demanding on the other hand without effective coordination policies the group search may not be effective. In this paper we propose and evaluate novel system-mediated techniques for coordinating a group search. These techniques allow for an effective division of labour across the group whereby each group member can explore a subset of the search space. We also propose and evaluate techniques to support automated sharing of knowledge across searchers in SCIR, through novel collaborative and relevance feedback techniques. In order to evaluate these techniques, we propose a framework for SCIR evaluation based on simulations. To populate these simulations we extract data from TREC interactive search logs. This work represent the first simulations of SCIR to date and the first such use of this TREC data. C1 [Foley, Colum; Smeaton, Alan F.] Dublin City Univ, CLARITY Ctr Sensor Web Technol, Dublin 9, Ireland. RP Foley, C (reprint author), Dublin City Univ, CLARITY Ctr Sensor Web Technol, Dublin 9, Ireland. EM colum.foley@computing.dcu.ie CR AALBERSBERG IJ, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P11 ADCOCK J, 2007, TRECVID 2007 TEXT RE Craswell Nick, 2008, WSDM 2008, P87 FOLEY C, 2008, THESIS DUBLIN CITY U FOLEY C, 2008, COLLABORATIVE SOCIAL GIANOUTSOS S, 1996, DSTC TECHNICAL WORKS Morris MR, 2007, UIST 2007: PROCEEDINGS OF THE 20TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P3 Pickens J., 2008, SIGIR 2008, P315 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Smeaton AF, 2006, MULTIMEDIA SYSTEMS J, V12, P375 White RW, 2005, ACM T INFORM SYST, V23, P325, DOI 10.1145/1080343.1080347 NR 11 TC 3 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00957-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5478 BP 42 EP 53 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJG81 UT WOS:000265680800004 ER PT S AU Lioma, C Blanco, R AF Lioma, Christina Blanco, Roi BE Boughanem, M Berrut, C Mothe, J SouleDupuy, C TI Part of Speech Based Term Weighting for Information Retrieval SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 31st European Conference on Information Research CY APR 06-09, 2009 CL Toulouse, FRANCE SP Google, Mateixware Informat Serv, Microsoft Res, Yahoo, Exalead, GDR 13, Univ Paul Sabatier, ARIA, Inforsid & Reg, Midi Pyrennees AB Automatic language processing tools typically assign to terms so-called 'weights' corresponding to the contribution of terms to information content. Traditionally, term weights are computed from lexical statistics, e.g., term frequencies. We propose a new type of term weight that is computed from part of speech (POS) n-gram statistics. The proposed POS-based term weight represents how informative a term is in general, based on 'POS' contexts' in which it generally occurs in language. We suggest five different computations of POS-based term weights by extending existing statistical approximations of term information measures. We apply these POS-based term weights to information retrieval, by integration them into the model that matches documents to queries. Experiments with two TREC collections and 300 queries, using TF-IDF & BM25 as baselines, show that integrating our POS-based term weights to retrieval always leasds to gains (up to +33.7% from the baseline). Additional experiments with a different retrieval model as bseline (Language Model with Dirichlet priors smoothing) and our best performing POS-based term weight, show retrieval gains always and consistently across the whole smoothing range of the baseline. C1 [Lioma, Christina] Katholieke Univ Leuven, B-3000 Louvain, Belgium. RP Lioma, C (reprint author), Katholieke Univ Leuven, B-3000 Louvain, Belgium. EM christina.lioma@cs.kuleuven.be; rblanco@udc.es CR Aslam JA, 2007, LECT NOTES COMPUT SC, V4425, P198 Baayen H., 1996, Literary & Linguistic Computing, V11, DOI 10.1093/llc/11.3.121 BAS A, 2004, FUZZY GRAMMAR READER BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BROOKES BC, 1968, J DOC, V24, P41, DOI 10.1108/eb026442 Brown P. F., 1992, Computational Linguistics, V18 BUCKLEY C, 1995, TREC 4, P25 Church K. W., 1995, NAT LANG ENG, V1, P163 COOPER WS, 1993, TREC 2, P57 Corston-Oliver Simon, 2004, TEXT SUMMARIZATION B, P43 Craswell N., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CROFT B, 2003, LANGUAGE MODELING IN HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HWA R, 2002, ACL, P392 Jespersen O., 1929, PHILOS GRAMMAR Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KOPPEL M, 2003, LIT LINGUISTIC COMPU, P401 Lioma C., 2008, RFLA, V8, P9 Lioma C, 2007, LECT NOTES COMPUT SC, V4425, P88 Lyons John, 1977, SEMANTICS, V2 MARGULIS EL, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P177 MIKK J, 2001, J QUANTITATIVE LINGU, V8, P67, DOI 10.1076/jqul.8.1.67.4094 Monz C, 2007, LECT NOTES COMPUT SC, V4425, P589 Ozmutlu S, 2004, INFORM PROCESS MANAG, V40, P319, DOI 10.1016/S0306-4573(03)00044-X PAPINENI K, 2001, NAACL, P25 PASCA M., 2001, THESIS SO METHODIST RATNAPARKHI A, 1996, EMNLP, P130 RENNIE JDM, SIGIR, P353 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SANTINI M, 2006, COLING ACL, P699 SCHMID H, 1997, NEW METHODS LANGUAGE Singhal A., 1996, SIGIR Forum Tao T., 2007, SIGIR 2007, P295 Voorhees Ellen M., 2005, TREC EXPT EVALUATION WAGNER J, 2007, EMNLP CONLL, P112 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval NR 36 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00957-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5478 BP 412 EP 423 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJG81 UT WOS:000265680800034 ER PT S AU Metzler, D Zaragoza, H AF Metzler, Donald Zaragoza, Hugo BE Azzopardi, L Kazai, G Robertspm, S Ruger, S Shokouhi, M Song, D Yilmaz, E TI Semi-parametric and Non-parametric Term Weighting for Information Retrieval SO ADVANCES IN INFORMATION RETRIEVAL THEORY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Conference on Theory of Information Retrieval CY SEP 10-12, 2009 CL Cambridge, ENGLAND SP Microsoft Res, Open Univ, British Comp Soc, IRSG, True Knowledge, Yahoo Res, Univ Glasgow, Robert Gordon Univ ID MODELS AB Most of the previous research on term weighting for information retrieval has focused on developing specialized parametric term weighting functions. Examples include T F.I D F vector-space formulations, BM25, and language modeling weighting. Each of these term weighting functions takes on a specific parametric form. While these weighting functions have proven to be highly effective, they impose strict constraints on the functional form of the term weights. Such constraints may possibly degrade retrieval effectiveness. In this paper we propose two new classes of term weighting schemes that we call semi-parametric and non-parametric weighting. These weighting schemes make fewer assumptions about the underlying term weights and allow the data to speak for itself. We argue that these robust weighting schemes have the potential to be significantly more effective compared to existing parametric schemes, especially with the growing amount of training data becoming available. EM metzler@yahoo-inc.com; hugoz@yahoo-inc.com CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 ANH V., 2005, P 28 ANN INT ACM SIG, P226, DOI 10.1145/1076034.1076075 ANH VN, 2004, P AUSTR DOC COMP S, P25 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 Fang H., 2005, P 28 ANN INT ACM SIG, P480, DOI 10.1145/1076034.1076116 Gao J., 2005, P 28 ANN INT ACM SIG, P290, DOI 10.1145/1076034.1076085 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Joachims Thorsten, 2005, P 22 INT C MACH LEAR, P377, DOI 10.1145/1102351.1102399 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Metzler D, 2008, LECT NOTES COMPUT SC, V4956, P175 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SINGER AJ, 1996, INFECT UROL, V9, P21 Taylor M., 2006, P 15 ACM INT C INF K, P585, DOI DOI 10.1145/1183614.118369 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 22 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04416-8 J9 LECT NOTES COMPUT SC PY 2009 VL 5766 BP 42 EP 53 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMB97 UT WOS:000271806000004 ER PT S AU Udupa, R Bhole, A Bhattacharyya, P AF Udupa, Raghavendra Bhole, Abhijit Bhattacharyya, Pushpak BE Azzopardi, L Kazai, G Robertspm, S Ruger, S Shokouhi, M Song, D Yilmaz, E TI "A term is known by the company it keeps": On Selecting a Good Expansion Set in Pseudo-Relevance Feedback SO ADVANCES IN INFORMATION RETRIEVAL THEORY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Conference on Theory of Information Retrieval CY SEP 10-12, 2009 CL Cambridge, ENGLAND SP Microsoft Res, Open Univ, British Comp Soc, IRSG, True Knowledge, Yahoo Res, Univ Glasgow, Robert Gordon Univ DE Information Retrieval; Relevance Feedback; Pseudo-relevance Feedback; Expansion Terms; Term-Document Matrix ID QUERY EXPANSION; RETRIEVAL AB It is well known that pseudo-relevance feedback (PRF) improves the retrieval performance of Information Retrieval (IR) systems in general. However, a recent study by Cao et al [3] has shown that a non-negligible fraction of expansion terms used by PRF algorithms are harmful to the retrieval. In other words, a PRF algorithm would be better off if it were to use only a subset of the feedback terms. The challenge then is to find a good expansion set from the set of all candidate expansion terms. A natural approach to solve the problem is to make term independence assumption and use one or more term selection criteria or a statistical classifier to identify good expansion terms independent of each other. In this work, we challenge this approach and show empirically that a feedback term is neither good nor bad in itself in general, the behavior of a term depends very much on other expansion terms. Our finding implies that a good expansion set can not be found by making term independence assumption in general. As a principled solution to the problem, we propose spectral partitioning of expansion terms using a specific term-term interaction matrix. We demonstrate on several test collections that expansion terms can be partitioned into two sets and the best of the two sets gives substantial improvements in retrieval performance over model-based feedback. C1 [Udupa, Raghavendra] Microsoft Res India, Bangalore 560080, Karnataka, India. RP Udupa, R (reprint author), Microsoft Res India, Bangalore 560080, Karnataka, India. EM raghavu@microsoft.com; abhijit.bhole@cse.iitb.ac.in; pb@cse.iitb.ac.in CR Buckley C., 1992, TREC, P59 Cao G., 2008, SIGIR 2008, P243 Carpineto C, 1999, LECT NOTES COMPUT SC, V1696, P126 Chung F. R. K., 1997, CBMS REGIONAL C SERI, V92 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Lafferty J., 2001, SIGIR Forum Lavrenko V., 2001, SIGIR Forum MEYER C, 2007, WORKSH NUM LIN ALG I ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Spielman D., 1996, SPECTRAL PARTITIONIN Tao T., 2006, SIGIR 2006, P162 von Luxburg U., 2006, 149 M PLANCK I BIOL Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 18 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04416-8 J9 LECT NOTES COMPUT SC PY 2009 VL 5766 BP 104 EP 115 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMB97 UT WOS:000271806000009 ER PT S AU Kanoulas, E Pavlu, V Dai, KS Aslam, JA AF Kanoulas, Evangelos Pavlu, Virgil Dai, Keshi Aslam, Javed A. BE Azzopardi, L Kazai, G Robertspm, S Ruger, S Shokouhi, M Song, D Yilmaz, E TI Modeling the Score Distributions of Relevant and Non-relevant Documents SO ADVANCES IN INFORMATION RETRIEVAL THEORY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Conference on Theory of Information Retrieval CY SEP 10-12, 2009 CL Cambridge, ENGLAND SP Microsoft Res, Open Univ, British Comp Soc, IRSG, True Knowledge, Yahoo Res, Univ Glasgow, Robert Gordon Univ AB Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications. In this work, we propose modeling the relevant documents' scores by a mixture of Gaussians and modeling the non-relevant scores by a Gamma distribution. Applying variational inference we automatically trade-off the goodness-of-fit with the complexity of the model. We test our model on traditional retrieval functions and actual search engines submitted to TREC. We demonstrate the utility of our model in inferring precision-recall curves. In all experiments our model outperforms the dominant exponential-Gaussian model. C1 [Kanoulas, Evangelos; Pavlu, Virgil; Dai, Keshi; Aslam, Javed A.] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA. RP Kanoulas, E (reprint author), Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA. EM ekanou@ccs.neu.edu; vip@ccs.neu.edu; daikeshi@ccs.neu.edu; jaa@ccs.neu.edu CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Arampatzis A., 2001, SIGIR Forum Baumgarten C, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P246 BENNETT PN, 2003, SIGIR 2003 Bishop C., 2001, P 8 INT C ART INT ST, P27 Bishop C. H., 2006, PATTERN RECOGNITION BOOKSTEIN A, 1977, INFORM PROCESS MANAG, V13, P377, DOI 10.1016/0306-4573(77)90057-7 COLLINSTHOMPSON K, 2003, P 11 TEXT RETR C Hiemstra D., 2001, THESIS U TWENTE Manmatha R., 2001, SIGIR Forum Ounis I., 2007, NOVATICA UPGRADE SPE, V8, P49 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x Robertson S, 2007, LECT NOTES COMPUT SC, V4425, P40 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SPITTERS M, 2000, P TDT WORKSH 2000 GA, P101 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 Voorhees Ellen M., 2005, TREC EXPT EVALUATION ZHANG Y, 2001, SIGIR 2001 P 24 ANN, P294, DOI DOI 10.1145/383952.384012 NR 19 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04416-8 J9 LECT NOTES COMPUT SC PY 2009 VL 5766 BP 152 EP 163 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMB97 UT WOS:000271806000013 ER PT S AU Koroutchev, K Korutcheva, E Shen, J AF Koroutchev, Kostadin Korutcheva, Elka Shen, Jian BE Azzopardi, L Kazai, G Robertspm, S Ruger, S Shokouhi, M Song, D Yilmaz, E TI Written Texts as Statistical Mechanical Problem SO ADVANCES IN INFORMATION RETRIEVAL THEORY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Conference on Theory of Information Retrieval CY SEP 10-12, 2009 CL Cambridge, ENGLAND SP Microsoft Res, Open Univ, British Comp Soc, IRSG, True Knowledge, Yahoo Res, Univ Glasgow, Robert Gordon Univ AB In this article we present a model of human written text based on statistical mechanics consideration. The empirical derivation of the potential energy for the parts of the text and the calculation of the thermodynamic parameters of the system, show that the "specific heat" corresponds to the semantic classification of the words in the text, separating keywords, function words and common words. This can give advantages when the model is used in text searching mechanisms. C1 [Koroutchev, Kostadin; Shen, Jian] Univ Autonoma Madrid, EPS, E-28049 Madrid, Spain. RP Koroutchev, K (reprint author), Univ Autonoma Madrid, EPS, E-28049 Madrid, Spain. EM k.koroutchev@uam.es; elka@fisfun.uned.es; jian.shen@estudiante.uam.es CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 [Anonymous], 2001, BRIT NAT CORP VERS 2 Baayen R. H., 2001, WORD FREQUENCY DISTR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Beck C., 1993, THERMODYNAMICS CHAOT Erdelyi A., 1956, ASYMPTOTIC EXPANSION *GUT, GUT COLL LABBE C, 2004, J QUANT LINGUIST, V11, P193, DOI 10.1080/0929617042000314958 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHEN J, 2008, MESSAGE EXCHANGE ENE Voorhees Ellen M., 2005, TREC EXPT EVALUATION NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04416-8 J9 LECT NOTES COMPUT SC PY 2009 VL 5766 BP 241 EP 248 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMB97 UT WOS:000271806000021 ER PT S AU Yin, XS Huang, XJ Hu, QM Li, ZJ AF Yin, Xiaoshi Huang, Xiangji Hu, Qinmin Li, Zhoujun BE Theeramunkong, T Kijsirikul, B Cercone, N Ho, TB TI Boosting Biomedical Information Retrieval Performance through Citation Graph: An Empirical Study SO ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 13th Pacific-Asia Conference on Knowledge and Data Mining CY APR 27-30, 2009 CL Bangkok, THAILAND SP Sirindhorn Int Inst Technol, Thammasat Univ, Chulalonkorn Univ, Asian Inst Technol, Natl Elect & Comp Technol Ctr, Thailand Convent & Exhibit Bureau, AF Off Sci Res, Asian Off Aerosp Res & Dev DE biomedical information retrieval; ranking; link analysis AB This paper presents art empirical study of the combination of content-based information retrieval results with linkage-teased document importance scores to improve retrieval performance on TREC biomedical literature datasets. In our study, content-based information conies front the stale-of-the-art; probability model based Okapi information retrieval system. Oil the other hand, linkage-based information conies front a citation graph generated from REFERENCES sections of a biomedical literature dataset. Three well-known linkage-based ranking algorithms (PageRank, HITS and InDegree) are applied oil the citation graph to calculate document importance scores. We use TREC 2007 Genomics dataset; for evaluation, which contains 162,259 biomedical literatures. Our approach achieves the best; document-based MAP among all results that; have been reported so far. Our major findings can be summarized as follows. First, without hyperlinks, linkage information extracted front REFERENCES sections can be used to improve the effectiveness of biomedical information retrieval. Second, performance of the integrated system is sensitive to linkage-based ranking algorithms, and a simpler algorithm, InDegree, is more suitable for biomedical literature retrieval. C1 [Yin, Xiaoshi; Huang, Xiangji] York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. RP Yin, XS (reprint author), York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. EM xyin@yorku.ca; jhuang@yorku.ca; vhu@cse.yorku.ca; lizj@buaa.edu.cn CR An Y, 2004, KNOWL INF SYST, V6, P664, DOI 10.1007/s10115-003-0128-3 Beaulieu M, 1997, P TREC 5, P143 Borodin A., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052942 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X CARRIERE J, 1997, P WWW6, P1257 HERSH W, 2007, P TREC Huang XJ, 2003, INFORM RETRIEVAL, V6, P333, DOI 10.1023/A:1026028229881 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LIN J, 2008, LAMPTR146HCIL200801 MARCHIORI M, 1997, P 6 INT C WORLD WID, P1225 NAJORK M. A., 2007, P 30 ANN INT ACM SIG, P471, DOI 10.1145/1277741.1277823 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-01306-5 J9 LECT NOTES ARTIF INT PY 2009 VL 5476 BP 949 EP 956 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BKN07 UT WOS:000268632000097 ER PT J AU Willett, P AF Willett, Peter TI Similarity Methods in Chemoinformatics SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review ID 3-DIMENSIONAL CHEMICAL-STRUCTURES; SUPPORT VECTOR MACHINES; BINARY KERNEL DISCRIMINATION; COMBINATORIAL LIBRARY DESIGN; MOLECULAR ELECTROSTATIC POTENTIALS; AGGLOMERATIVE CLUSTERING METHODS; BIOACTIVE REFERENCE STRUCTURES; SIMILAR BIOLOGICAL-ACTIVITIES; ULTRAFAST SHAPE-RECOGNITION; PROTEIN-LIGAND INTERACTIONS C1 Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. RP Willett, P (reprint author), Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. EM P.Willett@sheffield.ac.uk CR ADAMSON GW, 1973, INFORM STORAGE RET, V9, P561, DOI 10.1016/0020-0271(73)90059-4 ADAMSON GW, 1973, J CHEM DOC, V13, P153, DOI 10.1021/c160050a013 Agrafiotis DK, 2007, J CHEM INF MODEL, V47, P1279, DOI 10.1021/ci700059g Agrafiotis DK, 2002, J COMPUT AID MOL DES, V16, P335, DOI 10.1023/A:1020837112154 Agrafiotis DK, 1999, J CHEM INF COMP SCI, V39, P51, DOI 10.1021/ci980100c Agrafiotis DK, 1997, J CHEM INF COMP SCI, V37, P841, DOI 10.1021/ci9700337 Ajay, 1998, J MED CHEM, V41, P3314, DOI 10.1021/jm970666c Allen F. H., 2002, ACTA CRYSTALLOGR B, V58, P388 Alvarez J., 2005, VIRTUAL SCREENING DR A-RAZZAK M, 1992, Journal of Computer-Aided Molecular Design, V6, P349, DOI 10.1007/BF00125944 Ash J. E., 1991, CHEM STRUCTURE SYSTE ASH JE, 1975, CHEM INFORM SYSTEMS ATTIAS R, 1983, J CHEM INF COMP SCI, V23, P102, DOI 10.1021/ci00039a003 Baber JC, 2006, J CHEM INF MODEL, V46, P277, DOI 10.1021/ci050296y Bajorath JĆ¼rgen, 2002, Nat Rev Drug Discov, V1, P882, DOI 10.1038/nrd941 Ballester PJ, 2007, P R SOC A, V463, P1307, DOI 10.1098/rspa.2007.1823 Ballester PJ, 2007, J COMPUT CHEM, V28, P1711, DOI 10.1002/JCC.20681 Barker EJ, 2003, J CHEM INF COMP SCI, V43, P346, DOI 10.1021/ci0255937 Barker EJ, 2006, J CHEM INF MODEL, V46, P503, DOI 10.1021/ci050347r BARNARD JM, 1993, J CHEM INF COMP SCI, V33, P532, DOI 10.1021/ci00014a001 BARNARD JM, 1990, J CHEM INF COMP SCI, V30, P81, DOI 10.1021/ci00065a018 BARNARD JM, 1984, COMPUTER HANDLING GE BARTON IJ, 1974, COMMUN ACM, V17, P345, DOI 10.1145/355616.364034 Bawden O, 1993, CHEM STRUCTURES, P383 Bayley MJ, 1999, J MOL GRAPH MODEL, V17, P10, DOI 10.1016/S1093-3263(99)00016-9 Beitzel SM, 2004, J AM SOC INF SCI TEC, V55, P859, DOI 10.1002/asi.20012 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Bender A, 2004, J CHEM INF COMP SCI, V44, P170, DOI 10.1021/ci034207y Bender A, 2004, ORG BIOMOL CHEM, V2, P3204, DOI 10.1039/b409813g Ben-Dor A, 1999, J COMPUT BIOL, V6, P281, DOI 10.1089/106652799318274 Berks AH, 2001, WORLD PATENT INFORM, V23, P5, DOI 10.1016/S0172-2190(00)00108-3 Berman HM, 2002, ACTA CRYSTALLOGR D, V58, P899, DOI 10.1107/S0907444902003451 BLAIR J, 1974, TETRAHEDRON, V30, P1845, DOI 10.1016/S0040-4020(01)97318-1 Boecker A., 2005, J CHEM INF MODEL, V45, P807 Bohl M, 2002, QUANT STRUCT-ACT REL, V21, P590, DOI 10.1002/qsar.200290001 Bohl M, 2006, J CHEM INF MODEL, V46, P1882, DOI 10.1021/ci049657k Bƶhm Hans-Joachim, 2004, Drug Discov Today Technol, V1, P217, DOI 10.1016/j.ddtec.2004.10.009 Bohm H-.J., 2000, VIRTUAL SCREENING BI BOHM HJ, 1992, J COMPUT AID MOL DES, V6, P61, DOI 10.1007/BF00124387 Bostrom J, 2006, J MED CHEM, V49, P6716, DOI 10.1021/jm060167o BOTTLE RT, 1993, INFORM SOURCES CHEM BOYD D, 2006, COMPUTER APPL PHARM, P3 BRADSHAW J, 1997, SOME THOUGHTS SIGNIF BRADSHAW J, 1997, INTRO TVERSKY SIMILA BREIMAN L, 2001, MACH LEARN, V36, P5 BREMSER W, 1975, ORG MAGN RESONANCE, V7, P97, DOI 10.1002/mrc.1270070211 Briem H, 2000, PERSPECT DRUG DISCOV, V20, P231, DOI 10.1023/A:1008793325522 BRINT AT, 1987, J CHEM INF COMP SCI, V27, P152, DOI 10.1021/ci00056a002 Brown FK, 1998, ANNU REP MED CHEM, V33, P375, DOI DOI 10.1016/S0065-7743(08)61100-8 Brown N, 2003, J CHEM INF COMP SCI, V43, P288, DOI [10.1021/ci0103875, 10.1021/co0103875] Brown N, 2006, MINI-REV MED CHEM, V6, P1217, DOI 10.2174/138955706778742768 Brown RD, 2000, J MOL GRAPH MODEL, V18, P427, DOI 10.1016/S1093-3263(00)00072-3 Brown RD, 1996, J CHEM INF COMP SCI, V36, P572, DOI 10.1021/ci9501047 Brown RD, 1997, J MED CHEM, V40, P2304, DOI 10.1021/jm970033y Brown RD, 1997, J CHEM INF COMP SCI, V37, P1, DOI 10.1021/ci960373c Brown RD, 1998, SAR QSAR ENVIRON RES, V8, P23, DOI 10.1080/10629369808033260 Bunin B. A., 2007, CHEMOINFORMATICS THE Burbidge R, 2001, COMPUT CHEM, V26, P5, DOI 10.1016/S0097-8485(01)00094-8 Butina D, 1999, J CHEM INF COMP SCI, V39, P747, DOI 10.1021/ci9803381 Byvatov E, 2003, J CHEM INF COMP SCI, V43, P1882, DOI 10.1021/ci0341161 Cannon EO, 2006, J CHEM INF MODEL, V46, P2369, DOI 10.1021/ci0601160 Capelli AM, 2006, J CHEM INF MODEL, V46, P659, DOI 10.1021/ci050353n CARBO R, 1980, INT J QUANTUM CHEM, V17, P1185, DOI 10.1002/qua.560170612 CARHART RE, 1985, J CHEM INF COMP SCI, V25, P64, DOI 10.1021/ci00046a002 Charifson PS, 1999, J MED CHEM, V42, P5100, DOI 10.1021/jm990352k Cheeseright T, 2006, J CHEM INF MODEL, V46, P6650 Chen BN, 2006, J CHEM INF MODEL, V46, P478, DOI 10.1021/ci0505426 Chen LG, 2002, J CHEM INF COMP SCI, V42, P1296, DOI 10.1021/ci020023s Chen WL, 2006, J CHEM INF MODEL, V46, P2230, DOI 10.1021/ci060016u CHEN X, 2007, CHEMMEDCHEM, V2, P180 Chen X, 2002, J CHEM INF COMP SCI, V42, P1407, DOI 10.1021/ci025531g Chen X, 1998, J CHEM INF COMP SCI, V38, P1054, DOI 10.1021/ci980089g Cheng C, 1996, J CHEM INF COMP SCI, V36, P909, DOI 10.1021/ci9604605 Chose A., 2001, COMBINATORIAL LIB DE CHRISTIANINI N., 2000, INTRO SUPPORT VECTOR Clark D. E., 2005, ANN REP COMPUT CHEM, V1, P133, DOI 10.1016/S1574-1400(05)01010-8 Clark DE, 2002, CURR OPIN DRUG DI DE, V5, P382 CLARK DE, 1992, J MOL GRAPHICS, V10, P194, DOI 10.1016/0263-7855(92)80068-O Clark RD, 1998, J CHEM INF COMP SCI, V38, P1079, DOI 10.1021/ci980107u Clark RD, 1997, J CHEM INF COMP SCI, V37, P1181, DOI 10.1021/ci970282v Clark RD, 2002, J MOL GRAPH MODEL, V20, P281, DOI 10.1016/S1093-3263(01)00125-5 CLARK T, 2004, HDB COMPUTATIONAL CH Cleves AE, 2006, J MED CHEM, V49, P2921, DOI 10.1021/jm051139t Cole JC, 2005, PROTEINS, V60, P325, DOI 10.1002/prot.20497 Coles SJ, 2005, ORG BIOMOL CHEM, V3, P1832, DOI 10.1039/b502828k Congreve M, 2005, DRUG DISCOV TODAY, V10, P895, DOI 10.1016/S1359-6446(05)03484-7 COREY EJ, 1972, J AM CHEM SOC, V94, P421, DOI 10.1021/ja00757a020 COREY EJ, 1969, SCIENCE, V166, P178, DOI 10.1126/science.166.3902.178 Cornell WD, 2006, ANN REP COMP CHEM, V2, P297, DOI 10.1016/S1574-1400(06)02016-0 Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X Cramer C. J., 2004, ESSENTIALS COMPUTATI Cramer R. D., 1993, 3D QSAR DRUG DESIGN, P443 Cramer RD, 2007, J COMPUT AID MOL DES, V21, P23, DOI 10.1007/s10822-006-9100-0 Cramer RD, 2004, J MED CHEM, V47, P6777, DOI 10.1021/jm049501b CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 CRAMER RD, 1988, J AM CHEM SOC, V110, P5959, DOI 10.1021/ja00226a005 CRANDELL CW, 1983, J CHEM INF COMP SCI, V23, P186, DOI 10.1021/ci00040a009 Croft WB, 2000, ADV INFORMATION RETR, P1 Cruciani G, 2002, J MED CHEM, V45, P2685, DOI 10.1021/jm0011326 Cuissart B, 2002, J CHEM INF COMP SCI, V42, P1043, DOI 10.1021/ci020017w DALBY A, 1992, J CHEM INF COMP SCI, V32, P244, DOI 10.1021/ci00007a012 DANZIGER DJ, 1989, PROC R SOC SER B-BIO, V236, P101, DOI 10.1098/rspb.1989.0015 Davis CH, 1999, J AM SOC INFORM SCI, V50, P626, DOI 10.1002/(SICI)1097-4571(1999)50:7<626::AID-ASI7>3.0.CO;2-A Dean P.M., 1994, MOL SIMILARITY DRUG Dean P.M., 1999, MOL DIVERSITY DRUG D DESJARLAIS RL, 1990, P NATL ACAD SCI USA, V87, P6644, DOI 10.1073/pnas.87.17.6644 Diestel R, 2000, GRAPH THEORY DiMasi JA, 2003, J HEALTH ECON, V22, P151, DOI 10.1016/S0167-6296(02)00126-1 DITTMAR PG, 1983, J CHEM INF COMP SCI, V23, P93, DOI 10.1021/ci00039a002 Dixon SL, 1998, J CHEM INF COMP SCI, V38, P1192, DOI 10.1021/ci980105+ Dixon SL, 1999, J MED CHEM, V42, P2887, DOI 10.1021/jm980708c Dixon SL, 2006, J COMPUT AID MOL DES, V20, P647, DOI 10.1007/s10822-006-9087-6 Dixon SL, 2001, J MED CHEM, V44, P3795, DOI 10.1021/jm010137f Doman TN, 1996, J CHEM INF COMP SCI, V36, P1195, DOI 10.1021/ci960361r Doweyko AM, 2004, J COMPUT AID MOL DES, V18, P587, DOI 10.1007/s10822-004-4068-0 Downie JS, 2003, ANNU REV INFORM SCI, V37, P295, DOI 10.1002/aris.1440370108 Downs G. M., 1995, REV COMP CH, V7, P1 Downs G. M., 1994, ADV COMPUTER ASSISTE, P111 Downs GM, 2002, REV COMP CH, V18, P1, DOI 10.1002/0471433519.ch1 Eckert H, 2007, DRUG DISCOV TODAY, V12, P225, DOI 10.1016/j.drudis.2007.01.011 Eckert H, 2006, J MED CHEM, V49, P2284, DOI 10.1021/jm051110p Edgar SJ, 2000, J MOL GRAPH MODEL, V18, P343, DOI 10.1016/S1093-3263(00)00061-9 Egan WJ, 2002, CURR OPIN DRUG DISC, V5, P540 Ekins S., 2006, COMPUTER APPL PHARM ELHAMDOUCHI A, 1989, COMPUT J, V32, P220, DOI 10.1093/comjnl/32.3.220 Engels MFM, 2006, J CHEM INF MODEL, V46, P2651, DOI 10.1021/ci600219n Erickson JA, 2004, J MED CHEM, V47, P45, DOI 10.1021/jm030209y Estrada E, 2001, CURR MED CHEM, V8, P1573 Everitt BS, 2001, CLUSTER ANAL Ewing T, 2006, J CHEM INF MODEL, V46, P2423, DOI 10.1021/ci060155b Ewing TJA, 2001, J COMPUT AID MOL DES, V15, P411, DOI 10.1023/A:1011115820450 Feher M, 2006, DRUG DISCOV TODAY, V11, P421, DOI 10.1016/j.drudis.2006.03.009 FELDMAN A, 1975, J CHEM INF COMP SCI, V15, P147, DOI 10.1021/ci60003a004 FETCHNER U, 2005, QSAR COMB SCI, V24, P961 FISANICK W, 1990, J CHEM INF COMP SCI, V30, P145, DOI 10.1021/ci00066a008 Fligner MA, 2002, TECHNOMETRICS, V44, P110, DOI 10.1198/004017002317375064 Flower DR, 1998, J CHEM INF COMP SCI, V38, P379, DOI 10.1021/ci970437z FREE SM, 1964, J MED CHEM, V7, P395, DOI 10.1021/jm00334a001 FREELAND RG, 1979, J CHEM INF COMP SCI, V19, P94, DOI 10.1021/ci60018a012 Friesner RA, 2004, J MED CHEM, V47, P1739, DOI 10.1021/jm0306430 Frimurer TM, 2000, J CHEM INF COMP SCI, V40, P1315, DOI 10.1021/ci0003810 Gasteiger J., 1978, TOP CURR CHEM, V74, P93 Gasteiger J., 2003, HDB CHEMOINFORMATICS Gasteiger J, 2003, CHEMOINFORMATICS TXB Gedeck P, 2006, J CHEM INF MODEL, V46, P1924, DOI 10.1021/ci050413p Gillet V. J., 1990, TETRAHEDRON COMPUT M, V3, P681, DOI 10.1016/0898-5529(90)90167-7 Gillet V. J., 1998, DESIGNING BIOACTIVE, P149 Gillet Valerie J, 2004, Methods Mol Biol, V275, P335 Gillet VJ, 1998, J CHEM INF COMP SCI, V38, P165, DOI 10.1021/ci970431+ Gillet VJ, 2002, J CHEM INF COMP SCI, V42, P375, DOI 10.1021/ci010375j Gillet VJ, 1997, J CHEM INF COMP SCI, V37, P731, DOI 10.1021/ci970420g Gillet VJ, 2003, J CHEM INF COMP SCI, V43, P338, DOI 10.1021/ci025592e Gillet VJ, 2003, INTRO CHEMOINFORMATI Gillet VJ, 1999, J CHEM INF COMP SCI, V39, P169, DOI 10.1021/ci980332b Ginn CMR, 2000, PERSPECT DRUG DISCOV, V20, P1, DOI 10.1023/A:1008752200506 Ginn CMR, 1997, J CHEM INF COMP SCI, V37, P23, DOI 10.1021/ci960466u Glen RC, 2006, QSAR COMB SCI, V25, P1133, DOI 10.1002/qsar.200610097 Godden JW, 2002, J CHEM INF COMP SCI, V42, P885, DOI 10.1021/ci0203693 Godden JW, 2004, J MED CHEM, V47, P5608, DOI 10.1021/jm049505g Godden JW, 2004, J CHEM INF COMP SCI, V44, P21, DOI 10.1021/ci0302963 Godden JW, 2000, J CHEM INF COMP SCI, V40, P163, DOI 10.1021/ci990316u Goldman BB, 2006, ANN REP COMP CHEM, V2, P127, DOI 10.1016/S1574-1400(06)02008-1 GOOD AC, 1993, J CHEM INF COMP SCI, V33, P112, DOI 10.1021/ci00011a016 Good AC, 1997, J MED CHEM, V40, P3926, DOI 10.1021/jm970403i Good AC, 2004, J COMPUT AID MOL DES, V18, P529, DOI 10.1007/s10822-004-4067-1 Good AC, 2004, J COMPUT AID MOL DES, V18, P523, DOI 10.1007/s10822-004-4065-3 GOOD AC, 1992, J CHEM INF COMP SCI, V32, P188, DOI 10.1021/ci00007a002 Gorse AD, 2006, CURR TOP MED CHEM, V6, P3, DOI 10.2174/156802606775193310 GRAF W, 1979, J CHEM INF COMP SCI, V19, P51, DOI 10.1021/ci60017a013 Grant JA, 1996, J COMPUT CHEM, V17, P1653, DOI 10.1002/(SICI)1096-987X(19961115)17:14<1653::AID-JCC7>3.0.CO;2-K Gray N. A. B., 1986, COMPUTER ASSISTED ST GRECO G, 1998, DESIGNING BIOACTIVE GREEN DVS, 1998, DESIGN BIOACTIVE MOL, P47 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 Gund P., 1977, PROGR MOL SUBCELLULA, V11, P117 GUND P, 1980, SCIENCE, V208, P1425, DOI 10.1126/science.6104357 GUND P, 1974, INT C COMP CHEM RES Guner O. F., 2000, PHARMACOPHORE PERCEP Kurogi Y, 2001, CURR MED CHEM, V8, P1035 HAGADONE TR, 1992, J CHEM INF COMP SCI, V32, P515, DOI 10.1021/ci00009a019 Haigh JA, 2005, J CHEM INF MODEL, V45, P673, DOI 10.1021/ci049651v Hall D. L., 1992, MATH TECHNIQUES MULT Hann M, 1999, CURR OPIN CHEM BIOL, V3, P379, DOI 10.1016/S1367-5931(99)80057-X Hann MM, 2001, J CHEM INF COMP SCI, V41, P856, DOI 10.1021/ci000403i HANSCH C, 1962, NATURE, V194, P178, DOI 10.1038/194178b0 Hansch C, 2002, CHEM REV, V102, P783, DOI 10.1021/cr0102009 Hansch C., 1995, EXPLORING QSAR FUNDA HANSCH C, 1964, J AM CHEM SOC, V86, P1616, DOI 10.1021/ja01062a035 Harper G, 2001, J CHEM INF COMP SCI, V41, P1295, DOI 10.1021/ci000397q Harper G, 2004, J CHEM INF COMP SCI, V44, P2145, DOI 10.1021/ci049860f Hassan M, 1996, MOL DIVERS, V2, P64, DOI 10.1007/BF01718702 Hassan M, 2006, MOL DIVERS, V10, P283, DOI 10.1007/s11030-006-9041-5 Hawkins DM, 1997, QUANT STRUCT-ACT REL, V16, P296, DOI 10.1002/qsar.19970160404 Hawkins DM, 2004, J CHEM INF COMP SCI, V44, P1, DOI 10.1021/ci0342472 Hawkins PCD, 2007, J MED CHEM, V50, P74, DOI 10.1021/jm0603365 He L, 2005, J MOL GRAPH MODEL, V23, P503, DOI 10.1016/j.jmgm.2005.03.003 Hert J, 2006, J CHEM INF MODEL, V46, P462, DOI 10.1021/ci050348j Hert J, 2004, J CHEM INF COMP SCI, V44, P1177, DOI 10.1021/ci034231b Hert J, 2005, J MED CHEM, V48, P7049, DOI 10.1021/jm050316n Hert J, 2004, ORG BIOMOL CHEM, V2, P3256, DOI 10.1039/b409865j Hertzberg RP, 2000, CURR OPIN CHEM BIOL, V4, P445, DOI 10.1016/S1367-5931(00)00110-1 Hessler G, 2005, J MED CHEM, V48, P6575, DOI 10.1021/jm050078w Hiller C., 1987, SOFTWARE ENTWICKLUNG, V1, P53 HINCHCLIFFE A, 2003, MOL MODELLING BEGINN Holliday JD, 2003, J CHEM INF COMP SCI, V43, P819, DOI 10.1021/ci034001x Holliday JD, 2004, J CHEM INF COMP SCI, V44, P894, DOI 10.1021/ci0342674 Holliday JD, 2005, ACS SYM SER, V894, P77 Holliday JD, 2002, COMB CHEM HIGH T SCR, V5, P155 Hsu DF, 2005, INFORM RETRIEVAL, V8, P449, DOI 10.1007/s10791-005-6994-4 Huang N, 2006, J MED CHEM, V49, P6789, DOI 10.1021/jm0608356 Hudson BD, 1996, QUANT STRUCT-ACT REL, V15, P285, DOI 10.1002/qsar.19960150402 HURST T, 1994, J CHEM INF COMP SCI, V34, P190, DOI 10.1021/ci00017a025 Huser J., 2006, HIGH THROUGHPUT SCRE Hyland R, 2006, ENVIRON TOXICOL PHAR, V21, P179, DOI 10.1016/j.etap.2005.07.007 *INFOCHEM GMBH, 2007, CLASSIFY INFOCHEM RE *INT UN PUR APPL C, 2007, IUPAC INT CHEM ID IN Jacoby E, 2006, CHEMOGENEOMICS: KNOWLEDGE-BASED APPROACHES TO DRUG DISCOVERY, P1, DOI 10.1142/9781860948114 JAKES SE, 1986, J MOL GRAPHICS, V4, P12 JAKES SE, 1987, J MOL GRAPHICS, V5, P41 JARVIS RA, 1973, IEEE T COMPUT, VC-22, P1025, DOI 10.1109/T-C.1973.223640 Johnson M. A., 1990, CONCEPTS APPL MOL SI Jones G, 1997, J MOL BIOL, V267, P727, DOI 10.1006/jmbi.1996.0897 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 1997, READINGS INFORM RETR Jones K. S., 2000, Information Processing & Management, V36 Jorissen RN, 2005, J CHEM INF MODEL, V45, P549, DOI 10.1021/ci049641u KAUVAR LM, 1995, CHEM BIOL, V2, P107, DOI 10.1016/1074-5521(95)90283-X Kearsley SK, 1996, J CHEM INF COMP SCI, V36, P118, DOI 10.1021/ci950274j Keiser MJ, 2007, NAT BIOTECHNOL, V25, P197, DOI 10.1038/nbt1284 Kelley LA, 1996, PROTEIN ENG, V9, P1063, DOI 10.1093/protein/9.11.1063 Kier LB, 1986, MOL CONNECTIVITY STR Klebe G, 2000, VIRTUAL SCREENING AL Klein C, 2002, J COMPUT AID MOL DES, V16, P785, DOI 10.1023/A:1023828527638 Klein L. A., 1999, SENSOR DATA FUSION C, V2nd Kogej T, 2006, J CHEM INF MODEL, V46, P1201, DOI 10.1021/ci0504723 Kubinyi H, 1997, DRUG DISCOV TODAY, V2, P457, DOI 10.1016/S1359-6446(97)01079-9 Kubinyi H, 1998, PERSPECT DRUG DISCOV, V9-11, P225, DOI 10.1023/A:1027221424359 Kubinyi H, 1997, DRUG DISCOV TODAY, V2, P538, DOI 10.1016/S1359-6446(97)01084-2 Kubinyi H., 2004, CHEMOGENOMICS DRUG D Kubinyi H, 1998, 3D QSAR DRUG DESIGN KUNTZ ID, 1982, J MOL BIOL, V161, P269, DOI 10.1016/0022-2836(82)90153-X Lajiness M. S., 1990, COMPUTATIONAL CHEM G, p299 LANGRIDGE R, 1981, SCIENCE, V211, P661, DOI 10.1126/science.7455704 Leach A.R., 2001, MOL MODELLING PRINCI Leach AR, 2006, J MED CHEM, V49, P5851, DOI 10.1021/jm060999m LEE JH, 1997, P ACM SIGIR C RES DE, V20, P267 Leeson PD, 2007, NAT REV DRUG DISCOV, V6, P881, DOI 10.1038/nrd2445 LEITER DP, 1965, J CHEM DOC, V5, P238, DOI 10.1021/c160019a010 Lemmen C, 2000, J COMPUT AID MOL DES, V14, P215, DOI 10.1023/A:1008194019144 Lengauer T, 2004, DRUG DISCOV TODAY, V9, P27, DOI 10.1016/S1359-6446(04)02939-3 Lesk AM, 2005, INTRO BIOINFORMATICS Lewis RA, 2000, REV COMPUT CHEM, V16, P1, DOI 10.1002/9780470125939.ch1 Lind P, 2003, J CHEM INF COMP SCI, V43, P1855, DOI 10.1021/ci034107s Lindsay R. K., 1980, APPL ARTIFICIAL INTE Lipinski CA, 2005, ANN REPORTS COMPUTAT, V1, P155, DOI 10.1016/S1574-1400(05)01011-X Lipinski CA, 1997, ADV DRUG DELIVER REV, V23, P3, DOI 10.1016/S0169-409X(96)00423-1 LIPSCOMB KJ, 1989, ANNU REV INFORM SCI, V24, P189 LOFTUS F, 1991, CHEM STURCTURE SYSTE, P222 Lombardino JG, 2004, NAT REV DRUG DISCOV, V3, P853, DOI 10.1038/nrd1523 Longley P A, 2005, GEOGRAPHIC INFORM SY Low CMR, 2005, J MED CHEM, V48, P6790, DOI 10.1021/jm049069y Lynch MF, 1996, J CHEM INF COMP SCI, V36, P930, DOI 10.1021/ci950173l LYNCH MF, 1977, J AM SOC INFORM SCI, V28, P19, DOI 10.1002/asi.4630280104 LYNCH MF, 1987, J INFORM SCI, V13, P221, DOI 10.1177/016555158701300405 Lyne PD, 2002, DRUG DISCOV TODAY, V7, P1047, DOI 10.1016/S1359-6446(02)02483-2 MAGGIORA GM, 1997, 213 NAT M AM CHEM SO Maggiora GM, 2006, J CHEM INF MODEL, V46, P1535, DOI 10.1021/ci060117s Mahe P, 2005, J CHEM INF MODEL, V45, P939, DOI 10.1021/ci050039t MAIZEL RE, 1998, FIND CHEM INFORM Makara GM, 2001, J MED CHEM, V44, P3563, DOI 10.1021/jm010036h Maldonado AG, 2006, MOL DIVERS, V10, P39, DOI 10.1007/s11030-006-8697-1 Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005 MARSHALL T, 1979, COMPUTER ASSISTED DR, P205 MARTIN EJ, 1995, J MED CHEM, V38, P1431, DOI 10.1021/jm00009a003 Martin YC, 2002, J MED CHEM, V45, P4350, DOI 10.1021/jm020155c Martin YC, 2001, J COMB CHEM, V3, P231, DOI 10.1021/cc000073e MARTIN YC, 1993, J COMPUT AID MOL DES, V7, P83, DOI 10.1007/BF00141577 Mason JS, 1997, PERSPECT DRUG DISCOV, V7-8, P85 Mason JS, 1999, J MED CHEM, V42, P3251, DOI 10.1021/jm9806998 Matter H, 1997, J MED CHEM, V40, P1219, DOI 10.1021/jm960352+ Medina-Franco JL, 2006, CHEM BIOL DRUG DES, V67, P395, DOI 10.1111/j.1747-0285.2006.00397.x Mestres J, 1997, J COMPUT CHEM, V18, P934, DOI 10.1002/(SICI)1096-987X(199705)18:7<934::AID-JCC6>3.0.CO;2-S Mestres J, 2000, J COMPUT AID MOL DES, V14, P39, DOI 10.1023/A:1008168228728 Mestres J, 2000, PERSPECT DRUG DISCOV, V20, P191, DOI 10.1023/A:1008789224614 Monge A, 2006, MOL DIVERS, V10, P389, DOI 10.1007/s11030-006-9033-5 MOOCK TE, 1994, J CHEM INF COMP SCI, V34, P184, DOI 10.1021/ci00017a024 MOON JB, 1991, PROTEINS, V11, P314, DOI 10.1002/prot.340110409 MORGAN HL, 1965, J CHEM DOC, V5, P107, DOI 10.1021/c160017a018 Muller KR, 2005, J CHEM INF MODEL, V45, P249, DOI 10.1021/ci049737o Munk ME, 1998, J CHEM INF COMP SCI, V38, P997, DOI 10.1021/ci980083r MURRALL NW, 1990, J CHEM INF COMP SCI, V30, P312, DOI 10.1021/ci00067a016 Murtagh F., 1985, MULTIDIMENSIONAL CLU Ng KB, 2000, J AM SOC INFORM SCI, V51, P1177, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1030>3.0.CO;2-E Nikolova N., 2003, QSAR COMB SCI, V22, P1006, DOI DOI 10.1002/QSAR.200330831 NISHIBATA Y, 1991, TETRAHEDRON, V47, P8985, DOI 10.1016/S0040-4020(01)86503-0 NUBLING W, 1970, ANGEW CHEM INT EDIT, V9, P596, DOI 10.1002/anie.197005961 Oda A, 2006, J CHEM INF MODEL, V46, P380, DOI 10.1021/ci050283k Onodera N, 2001, J CHEM INF COMP SCI, V41, P878, DOI 10.1021/ci010005v Oprea TI, 2000, J COMPUT AID MOL DES, V14, P251, DOI 10.1023/A:1008130001697 Oprea TI, 2001, J CHEM INF COMP SCI, V41, P1308, DOI 10.1021/ci010366a Oprea TI, 2004, CURR OPIN CHEM BIOL, V8, P349, DOI 10.1016/j.cbpa.2004.06.008 Oprea TL, 2002, MOLECULES, V7, P51, DOI 10.3390/70100051 ORENGO CA, 2002, BIOINFORMATICS Ott M., 2004, CHEMINFORMATICS DEV, P83 Paolini GV, 2006, NAT BIOTECHNOL, V24, P805, DOI 10.1038/nbt1228 Paris CG, 1997, ANNU REV INFORM SCI, V32, P271 Patterson DE, 1996, J MED CHEM, V39, P3049, DOI 10.1021/jm960290n Pearlman R. S., 1987, CHEM DES AUTO NEWS, V2, P1 Pearlman RS, 1999, J CHEM INF COMP SCI, V39, P28, DOI 10.1021/ci980137x Pearlman RS, 1998, PERSPECT DRUG DISCOV, V9-11, P339, DOI 10.1023/A:1027232610247 Perekhodtsev GD, 2007, QSAR COMB SCI, V26, P346, DOI 10.1002/qsar.200610052 Pickett SD, 1996, J CHEM INF COMP SCI, V36, P1214, DOI 10.1021/ci960039g Prathipati P, 2007, CURR COMPUT-AID DRUG, V3, P133, DOI 10.2174/157340907780809516 Pretsch E., 2002, COMPUTER AIDED STRUC Proudfoot Jr, 2002, BIOORG MED CHEM LETT, V12, P1647, DOI 10.1016/S0960-894X(02)00244-5 Raha K, 2005, ANN REP COMPUT CHEM, V1, P113, DOI 10.1016/S1574-1400(05)01009-1 Rang HP, 2006, DRUG DISCOVERY DEV T Rarey M, 1996, J MOL BIOL, V261, P470, DOI 10.1006/jmbi.1996.0477 Rarey M, 2001, J COMPUT AID MOL DES, V15, P497, DOI 10.1023/A:1011144622059 Rarey M, 1998, J COMPUT AID MOL DES, V12, P471, DOI 10.1023/A:1008068904628 Rasmussen EM, 1997, ANNU REV INFORM SCI, V32, P169 RAY LC, 1957, SCIENCE, V126, P814, DOI 10.1126/science.126.3278.814 Raymond JW, 2002, COMPUT J, V45, P631, DOI 10.1093/comjnl/45.6.631 Raymond JW, 2004, J CHEM INF COMP SCI, V44, P601, DOI 10.1021/ci034234o Raymond JW, 2002, J CHEM INF COMP SCI, V42, P305, DOI 10.1021/ci010381f Raymond JW, 2003, J MOL GRAPH MODEL, V21, P421, DOI 10.1016/S1093-3263(02)00188-2 Raymond JW, 2002, J COMPUT AID MOL DES, V16, P59, DOI 10.1023/A:1016387816342 Raymond JW, 2003, J CHEM INF COMP SCI, V43, P908, DOI 10.1021/ci034002p ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rogers D, 2005, J BIOMOL SCREEN, V10, P682, DOI 10.1177/1087057105281365 ROSSLER S, 1970, J CHEM DOC, V10, P128, DOI 10.1021/c160037a014 RUSH JE, 1978, ANNU REV INFORM SCI, V13, P209 Rush TS, 2005, J MED CHEM, V48, P1489, DOI 10.1021/jm040163o Rusinko A, 1999, J CHEM INF COMP SCI, V39, P1017, DOI 10.1021/ci9903049 Sadowski J, 1998, J MED CHEM, V41, P3325, DOI 10.1021/jm9706776 Saeh JC, 2005, J CHEM INF MODEL, V45, P1122, DOI 10.1021/ci049732r Salim N, 2003, J CHEM INF COMP SCI, V43, P435, DOI 10.1021/ci025596j Salton G., 1989, AUTOMATIC TEXT PROCE SASAKI SI, 1968, ANAL CHEM, V40, P2220, DOI 10.1021/ac50158a061 Schneider G, 2005, NAT REV DRUG DISCOV, V4, P649, DOI 10.1038/nrd1799 Schneider G, 2006, QSAR COMB SCI, V25, P1162, DOI 10.1002/qsar.200610091 Schneider G, 1999, ANGEW CHEM INT EDIT, V38, P2894, DOI 10.1002/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F Schofield H, 2001, DRUG DISCOV TODAY, V6, P931, DOI 10.1016/S1359-6446(01)01923-7 Schreyer SK, 2004, J CHEM INF COMP SCI, V44, P470, DOI 10.1021/ci030025s Schuffenhauer A, 2000, J CHEM INF COMP SCI, V40, P295, DOI 10.1021/ci990263g Schuffenhauer A, 2003, J CHEM INF COMP SCI, V43, P391, DOI 10.1021/ci025569t Schuffenhauer A, 2007, J CHEM INF MODEL, V47, P325, DOI 10.1021/ci6004004 Schuffenhauer A, 2006, J CHEM INF MODEL, V46, P525, DOI 10.1021/ci0503558 Shanmugasundaram V, 2005, J MED CHEM, V48, P240, DOI 10.1021/jm0493515 Shelley C. A., 1978, Analytica Chimica Acta, Computer Techniques and Optimization, V103, DOI 10.1016/S0003-2670(01)84032-0 SHEMETULSKIS NE, 1995, J COMPUT AID MOL DES, V9, P407, DOI 10.1007/BF00123998 SHENTON K, 1988, CHEM STRUCTURES INT, P169 Sheridan RP, 2000, J CHEM INF COMP SCI, V40, P1456, DOI 10.1021/ci000045j Sheridan RP, 2004, J CHEM INF COMP SCI, V44, P1912, DOI 10.1021/ci049782w Sheridan RP, 2002, DRUG DISCOV TODAY, V7, P903, DOI 10.1016/S1359-6446(02)02411-X SHERIDAN RP, 1989, J CHEM INF COMP SCI, V29, P255, DOI 10.1021/ci00064a005 Sheridan RP, 1998, J CHEM INF COMP SCI, V38, P915, DOI 10.1021/ci980044f SHERIDAN RP, 1995, J CHEM INF COMP SCI, V35, P310, DOI 10.1021/ci00024a021 Sheridan RP, 1996, J CHEM INF COMP SCI, V36, P128, DOI 10.1021/ci950275b Sheridan RP, 2007, EXPERT OPIN DRUG DIS, V2, P423, DOI 10.1517/17460441.2.4.423 SHIVELY E, 2007, CHEM ENG NEWS, V84, P41 Sirois S, 2005, COMPUT BIOL CHEM, V29, P55, DOI 10.1016/j.compbiolchem.2004.11.003 Smeaton AF, 2004, ANNU REV INFORM SCI, V38, P371, DOI 10.1002/aris.1440380109 Snarey M, 1997, J MOL GRAPH MODEL, V15, P372, DOI 10.1016/S1093-3263(98)00008-4 Sneath P.H., 1973, NUMERICAL TAXONOMY Stahl M, 2001, J MED CHEM, V44, P1035, DOI 10.1021/jm0003992 Stahura FL, 2002, DRUG DISCOV TODAY, V7, pS41, DOI 10.1016/S1359-6446(02)02271-7 Steinbach M., 2000, 00034 U MINN DEP COM Steindl TM, 2006, J COMPUT AID MOL DES, V20, P703, DOI 10.1007/s10822-006-9066-y Stiefl N, 2006, J CHEM INF MODEL, V46, P587, DOI 10.1021/ci050324c Stiefl N, 2006, J CHEM INF MODEL, V46, P208, DOI 10.1021/ci050457y SULTAN M, 2002, BIOINFORMATICS, V18, P111 SUSSENGU.EH, 1965, J CHEM DOC, V5, P36, DOI 10.1021/c160016a007 Svetnik V, 2003, J CHEM INF COMP SCI, V43, P1947, DOI 10.1021/ci034160g TAKAHASHI Y, 1992, J CHEM INF COMP SCI, V32, P639, DOI 10.1021/ci00010a009 TATE FA, 1967, ANNU REV INFORM SCI, V2, P285 TAYLOR R, 1995, J CHEM INF COMP SCI, V35, P59, DOI 10.1021/ci00023a009 Teague SJ, 1999, ANGEW CHEM INT EDIT, V38, P3743, DOI 10.1002/(SICI)1521-3773(19991216)38:24<3743::AID-ANIE3743>3.3.CO;2-L Terrett N. K., 1998, COMBINATORIAL CHEM Thorner DA, 1996, J CHEM INF COMP SCI, V36, P900, DOI 10.1021/ci960002w Thorner DA, 1997, J COMPUT AID MOL DES, V11, P163, DOI 10.1023/A:1008034527445 Todeschini R., 2002, HDB MOL DESCRIPTORS Tong W, 1998, J CHEM INF COMP SCI, V38, P669, DOI 10.1021/ci980008g Tong WD, 2003, J CHEM INF COMP SCI, V43, P525, DOI 10.1021/ci020058s Triballeau N, 2005, J MED CHEM, V48, P2534, DOI 10.1021/jm049092j Truchon JF, 2007, J CHEM INF MODEL, V47, P488, DOI 10.1021/ci600426e TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 ULLMAN JR, 1976, J ACM, V16, P31 Van de Waterbeemd Han, 2005, Expert Opin Drug Metab Toxicol, V1, P1, DOI 10.1517/17425255.1.1.1 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd van de Waterbeemd H, 2003, NAT REV DRUG DISCOV, V2, P192, DOI 10.1038/nrd1032 Veber DF, 2002, J MED CHEM, V45, P2615, DOI 10.1021/jm020017n Verdonk ML, 2004, J CHEM INF COMP SCI, V44, P793, DOI 10.1021/ci034289q Verheij HJ, 2006, MOL DIVERS, V10, P377, DOI 10.1007/s11030-006-9040-6 Vieth M, 2006, J MED CHEM, V49, P3451, DOI 10.1021/jm0603825 VLEDUTS GE, 1963, INFORM STORAGE RET, V1, P117, DOI 10.1016/0020-0271(63)90013-5 Wagener M, 2000, J CHEM INF COMP SCI, V40, P280, DOI 10.1021/ci990266t Waldman M, 2000, J MOL GRAPH MODEL, V18, P412, DOI 10.1016/S1093-3263(00)00071-1 Walters WP, 1998, DRUG DISCOV TODAY, V3, P160, DOI 10.1016/S1359-6446(97)01163-X Wang RX, 2001, J CHEM INF COMP SCI, V41, P1422, DOI 10.1021/ci010025x Wang YA, 2007, CHEMMEDCHEM, V2, P1037, DOI 10.1002/cmdc.200700050 WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 Warmuth MK, 2003, J CHEM INF COMP SCI, V43, P667, DOI 10.1021/ci025620t WARR WA, 2003, IUPAC PROJ M 12 14 N WARR WA, 1999, 218 AM CHEM SOC NAT WARR WA, 1997, DESIGNING BIOACTIVE, P73 Warren GL, 2006, J MED CHEM, V49, P5912, DOI 10.1021/jm050362n WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005 Weisgerber DW, 1997, J AM SOC INFORM SCI, V48, P349, DOI 10.1002/(SICI)1097-4571(199704)48:4<349::AID-ASI8>3.0.CO;2-W Whittle M, 2004, J CHEM INF COMP SCI, V44, P1840, DOI 10.1021/ci049867x Whittle M, 2006, J CHEM INF MODEL, V46, P2193, DOI 10.1021/ci049615w Whittle M, 2006, J CHEM INF MODEL, V46, P2206, DOI 10.1021/ci0496144 Wild DJ, 2006, DRUG DISCOV TODAY, V11, P436, DOI 10.1016/j.drudis.2006.03.010 Wild DJ, 2000, J CHEM INF COMP SCI, V40, P155, DOI 10.1021/ci990086j WILLETT P, 1980, J CHEM INF COMP SCI, V20, P93, DOI 10.1021/ci60022a008 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Willett Peter, 2004, Methods Mol Biol, V275, P51 Willett P, 2008, ASLIB PROC, V60, P4, DOI 10.1108/00012530810847335 WILLETT P, 1986, QUANT STRUCT-ACT REL, V5, P18, DOI 10.1002/qsar.19860050105 Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 WILLETT P, 2008, J INFORM SC IN PRESS, V34 Willett P, 1987, SIMILARITY CLUSTERIN Willett P, 2000, INFORM RES, V5 WILLETT P, 1986, J CHEM INF COMP SCI, V26, P36, DOI 10.1021/ci00049a008 Wild DJ, 1996, J CHEM INF COMP SCI, V36, P159, DOI 10.1021/ci9500851 Willett P, 2006, QSAR COMB SCI, V25, P1143, DOI 10.1002/qsar.200610084 Willett P., 1986, MODERN APPROACHES CH WILLETT P, 1986, J CHEM INF COMP SCI, V26, P109, DOI 10.1021/ci00051a005 Willett P, 2007, J CHEM INF MODEL, V47, P1961, DOI 10.1021/ci700087v Williams C, 2006, MOL DIVERS, V10, P311, DOI 10.1007/s11030-006-9039-z Wilson R. J., 1996, INTRO GRAPH THEORY, V4th Wilton DJ, 2006, J CHEM INF MODEL, V46, P471, DOI 10.1021/ci050397w WIPKE WT, 1974, J AM CHEM SOC, V96, P4825, DOI 10.1021/ja00822a020 WIPKE WT, 1977, COMPUTER ASSISTED OR WIPKE WT, 1978, ARTIF INTELL, V11, P173, DOI 10.1016/0004-3702(78)90016-4 WORBOYS MF, 1995, GIS COMPUTER PERSPEC Xia XY, 2004, J MED CHEM, V47, P4463, DOI 10.1021/jm0303195 Xue L, 2001, J CHEM INF COMP SCI, V41, P746, DOI 10.1021/ci000311t Yang JM, 2005, J CHEM INF MODEL, V45, P1134, DOI 10.1021/ci050034w YIN PY, 1994, PATTERN RECOGN LETT, V15, P125, DOI 10.1016/0167-8655(94)90041-8 Yu HS, 2003, DRUG DISCOV TODAY, V8, P852, DOI 10.1016/S1359-6446(03)02828-9 Zernov VV, 2003, J CHEM INF COMP SCI, V43, P2048, DOI 10.1021/ci0340916 Zhang Q, 2006, J MED CHEM, V49, P1536, DOI 10.1021/jm050468i Zupan J.G.J., 1999, NEURAL NETWORKS CHEM NR 445 TC 34 Z9 34 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0066-4200 EI 1550-8382 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 2009 VL 43 BP 3 EP 71 PG 69 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 368QZ UT WOS:000260638900002 ER PT S AU Sacco, GM AF Sacco, Giovanni Maria BE Sacco, GM Tzitzikas, Y TI The Model SO DYNAMIC TAXONOMIES AND FACETED SEARCH: THEORY, PRACTICE, AND EXPERIENCE SE Information Retrieval Series LA English DT Article; Book Chapter ID FORMAL CONCEPT ANALYSIS; INFORMATION-RETRIEVAL; FACETED TAXONOMIES; DOCUMENT-RETRIEVAL; ALGORITHM; SYSTEMS; SEARCH; IMAGE; WEB; REPRESENTATION C1 Univ Turin, Dipartimento Informat, I-10149 Turin, Italy. RP Sacco, GM (reprint author), Univ Turin, Dipartimento Informat, Corso Svizzera 185, I-10149 Turin, Italy. EM sacco@di.unito.it CR ABROL M, 2001, P 27 VLDB C ADAR E, 1999, C INF KNOWL MAN Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Agichtein E., 2006, SIGIR 06 AGRAWAL R, 1989, SIGMOD REC, V18, P253, DOI 10.1145/67544.66950 Agrawal R, 1993, SIGMOD 93 Allen J. E., 1999, IEEE Intelligent Systems, V14, DOI 10.1109/5254.796083 AMATO G, 2008, DEXA WORKSH DYN TAX Analyti A, 2006, FUND INFORM, V73, P321 ANDREJKO A, 2006, TOOLS ACQUISITION OR [Anonymous], RDF RESOURCE DESCRIP Antoshenkov G., 1994, BYTE ALIGNED BITMAP GODIN R, 1993, INT J MAN MACH STUD, V38, P747, DOI 10.1006/imms.1993.1035 Ardissono L, 2002, COMMUN ACM, V45, P52 Association for Computing Machinery, 1998, ACM COMP CLASS SYST ATNONIOU G, 2004, SEMANTIC WEB PRIMER AVESANI P, 2003, P 1 INT C SERV OR CO BAADER F, 2004, LNCS, V3229 Baeza R., 1999, MODERN INFORM RETRIE Bast H., 2007, CIDR BAST H, 2006, WORKSH FAC SEARCH FI BAST H, COMPLETESEARCH DBLP BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 BECKERT B, 1995, J AUTOM REASONING, V11, P43 Bedel O., 2008, REV INT GEOMATIQUE, V17, P371 BEDEL O, 2006, INT C FREE OP SOURC Bell R. M., 2007, ACM SIGKDD EXPLORATI, V9, P75, DOI DOI 10.1145/1345448.1345465 BENYITZHAK O, 2008, P INT C WEB SEARCH W BERIO G, 2007, ENCY PORTAL TECHNOLO BERIO G, 2008, ENCY HUMAN RESOURCES Berners-Lee T., 2001, SCI AM, V284, P35, DOI DOI 10.1038/SCIENTIFICAMERICAN0501-34 Bharat K., 2000, P 9 INT WWW C BINTINE W, 2002, KD NET 2002 BIZER C, TRIG SYNTAX BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Bodner R., 1999, ACM COMPUT SURV, V31, DOI 10.1145/345966.346002 Borzsonyi Stephan, 2001, P 17 INT C DAT ENG I BOWMAN CM, 1994, INT C INT INF MAN SY Breese J.S., 1998, EMPIRICAL ANAL PREDI Brezillon P., 1999, KNOWL ENG REV, V14, P1 Brin S, 1997, SIGMOD 97 Brin S, 1998, P 7 INT WORLD WID WE Bruls M., 2000, P JOINT EUR IEEE TCV Brusilovsky P, 2004, USER MODEL USER-ADAP, V14, P147, DOI 10.1023/B:USER.0000029016.80122.dd Buchanan B.G., 1984, ADDISON WESLEY SERIE CALLAN J, 1996, P 19 ANN INT ACM SIG CALVANESE D, 1998, LOGICS DATABASES INF Calvanese D., 2001, P 2001 INT SEM WEB W CARDENAS AF, 1975, COMMUN ACM, V18, P253, DOI 10.1145/360762.360766 Carpineto C, 2004, J UNIVERS COMPUT SCI, V10, P985 Cellier P, 2008, INT J FOUND COMPUT S, V19, P319, DOI 10.1142/S012905410800570X CHAKRABARTI K, 2004, SIGMOD 04 Chan L. M., 1985, THEORY SUBJECT ANAL Chaudhuri S., 1997, SIGMOD Record, V26 Chen H., 2006, P 29 ANN INT ACM SIG Chen P. P., 1976, ACM T DATABASE SYST, P9, DOI 10.1145/320434.320440 CHERNOFF H, 1954, ANN MATH STAT, V25, P579, DOI 10.1214/aoms/1177728726 Chomicki J, 2003, ACM T DATABASE SYST, V28, P427, DOI 10.1145/958942.958946 *CITESEER, SCI LIT DIG LIB *CITT, 2008, REP MAL RAR Codd E., 1993, PROVIDING OLAP ON LI, P32 Codd E., 1990, RELATIONAL MODEL DAT Cohen W. W., 1995, P 12 INT C MACH LEAR Cole R, 2003, APPL ARTIF INTELL, V17, P257, DOI [10.1080/08839510390187170, 10.1080/08839540390187170] CONSOLE L, 1989, P 11 IJCAI Croft W. B., 2003, LANGUAGE MODELING IN CROFT WB, 2001, P 2 DELOS WORKSH PER Cutting D. R., 1992, P 15 ANN INT SIGIR C CUTTING DR, 1993, P 16 ANN INT C RES D DACHSELT R, 2008, CHI 08 DAKKA W, 2005, P 14 ACM C INF KNOWL DALAL M, 1988, C ART INT AAAI 88 ST Datta Ritendra, 2005, MIR 05 DEKLEER J, 1992, ARTIF INTELL, V56, P197, DOI 10.1016/0004-3702(92)90027-U DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DELCERRO LF, 2001, LNAI, V2083 Delgado J., 1999, ACM SIGIR 99 WORKSH DEMO B, 2007, DEXA WORKSH DEWEY M, 1997, DEWEY DECIMAL CLASSI Diederich J., 2007, JCDL DONG X, 2005, C INN DAT SYST RES Donini FM, 1997, INFORM COMPUT, V134, P1, DOI 10.1006/inco.1997.2625 Dork M, 2008, IEEE T VIS COMPUT GR, V14, P1205, DOI 10.1109/TVCG.2008.175 DUCROU J, 2006, LNCS, V4068 DUMAIS S, 2004, P 27 ANN INT ACM SIG DUMAIS ST, 1998, P 7 ACM C INF KNOWL DUNCAN EB, 1989, HYPERTEXT THEORY PRA Dunning T., 1993, Computational Linguistics, V19 Elmasri R., 1994, FUNDAMENTALS DATABAS, V4th *EUR UN, BAS PUBL SERV AGR EU Fellbaum C, 1998, WORDNET ELECT LEXICA FERRE S, 2000, LNCS, V1861 FERRE S, 2001, LNCS, V2120 FERRE S, 2006, RR5871 INRIA FERRE S, 2007, INT WORK DYN TAX FAC Ferre S, 2004, INFORM PROCESS MANAG, V40, P383, DOI 10.1016/S0306-4573(03)00018-9 FERRE S, 2000, LNCS, V1867 FILATOVA E, 2003, RECENT ADV NATURAL L, V3 Finkelstein L, 2001, P 10 INT WORLD WID W Fishburn PC, 1970, UTILITY THEORY DECIS FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 Flouris G, 2006, THESIS U CRETE GREEC Funk S., 2006, TRY THIS HOME Ganter B., 1999, FORMAL CONCEPT ANAL Gardenfors P., 1992, BELIEF REVISION, P1, DOI 10.1017/CBO9780511526664.001 Gardenfors P., 2000, CONCEPTUAL SPACES GE Gemmell J, 2006, COMMUN ACM, V49, P88, DOI 10.1145/1107458.1107460 GEORGILA K, 2006, P 9 INT C SPOK LANG GEVERS T, 1996, P 1 INT WORKSH IM DA GIFFORD DK, 1991, ACM S OP SYST PRINC GOPAL B, 1999, P 3 S OP SYST DES IM GROENBAEK K, 1994, COMMUNICATIONS ACM, V37 Gruber T.R., 1994, FORMAL ONTOLOGY CONC HALVEY MJ, 2007, WWW 07 HANSSON SO, 1996, J PHILOS LOGIC, V25, P307 HATZIVASSILOGLO.V, 2000, P 23 ANN INT ACM SIG Hearst M, 2002, COMMUN ACM, V45, P42 Hearst M. A., 2008, WORKSH COMP INT INF HEARST MA, FLAMENCO SEARCH INTE HEARST MA, 2006, P SIGIR 2006 WORKSH Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983 HEARST MA, 2007, ACM SIGIR 2007 Heer J, 2007, IEEE T VIS COMPUT GR, V13, P1240, DOI 10.1109/TVCG.2007.70539 HEER J, 2008, COMPUTER HUMAN INTER HEISING WP, 1963, IBM SYST J, V2, P111 Herlocker J. L, 1999, SIGIR 99 HILDEBRAND M, 2006, INT SEM WEB C Hirsh H, 2000, COMMUN ACM, V43, P102, DOI 10.1145/345124.345159 HOFMANN T, 1999, IJCAI 99 HOLI M, 2006, ASWC HORROCKS I, 2001, INT JOINT C ART INT HUYNH D, PARALLAX COMPA UNPUB HUYNH DF, 2007, WWW 07 HYVONEN E, 2007, ISWC ASWC Hyvonen E, 2005, J WEB SEMANT, V3, P224, DOI 10.1016/j.websem.2005.05.008 *IEEE COMP SOC, IEEE COMP SOC CS DIG *INT ORG STAND, 1986, 27881986 ISO *IT GOV, IT GOV PORT Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 JAGADISH HV, 1991, SIGMOD 91 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 JEH G, 2003, WWW 03 JIN R, 2004, SIGIR 04 KARGER D, 2006, PATHETIC FALLACY RDF KARLSON AK, 2006, CHI 06 Karvounarakis G., 2002, 11 INT WORLD WID WEB KATSUNO H, 1991, P KR 91 *KNOWL PROC, DIG CAM DEM *KNOWL PROC, SF BOOK DEM *KNOWL PROC, UN KNOWL PROC *KNOWL PROC, WIN DAT MIN DEM Kobilarov G., 2008, LINK DAT WEB WORKSH Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Kopidaki S, 2009, P 10 INT C WEB INF S KOREN J, 2008, P 17 INT C WORLD WID Kules B., 2006, THESIS U MARYLAND CO Laurini R, 1992, FUNDAMENTALS SPATIAL Lenzerini M, 2002, P 21 ACM SIGMOD SIGA Levin E, 2000, IEEE T SPEECH AUDI P, V8, P11, DOI 10.1109/89.817450 LEWIS D, 2002, P 11 TEXT RETRIEVAL LEWIS DD, 1996, P 19 ANN INT ACM SIG Ley M., DBLP COMPUTER SCI BI LI R, 2007, WWW 07 LINDIG C, 1995, IJCAI95 WORKSH FORM Lindsay P.H., 1977, HUMAN INFORM PROCESS Lloyd J. W., 1987, FDN LOGIC PROGRAMMIN, V2nd MACQUEEN D, 1988, LISP FUNCTIONAL PROG MAKELA E, 2006, LNCS, V4273 MANBER U, 1994, USENIX WINT C USENIX Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 Manning C. D., 1999, FDN STAT NATURAL LAN Marchionini G., 1997, INFORM SEEKING ELECT Marriott K., 1998, PROGRAMMING CONSTRAI MARTINEZ J, 2002, S APPL COMP MARTINEZ JM, 2002, MULTIMEDIA 02 MCKEOWN K, 2003, P HUM LANG TECHN C N MEGHINI C, 2004, P 3 INT C ONT DAT AP MEGHINI C, 2008, FUNDAMENTA INFORM, V83 MEGHINI C, 2007, 07093034 CORR Millen D. R., 2006, C HUM FACT COMP SYST MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037//0033-295X.101.2.343 MILLS J, 1970, BLISS BIBLIO CLASSIF MLADENIC D, 1998, PERSONAL WEB WATCHER MORET BME, 1982, COMPUT SURV, V14, P593 NARAYAN M, 2004, P WWW 04 Nourine L, 2002, J EXP THEOR ARTIF IN, V14, P217, DOI 10.1080/09528130210164152 OLSON MA, 1999, FREENIX TRACK ONEIL EJ, 1993, P 1993 ACM SIGMOD IN ONEIL PE, 1989, P 2 INT WORKSH HIGH OREN E, 2006, ISWC *ORPH, ONL DAT RAR DIS ORPH PADIOLEAU Y, 2006, ICSE PADIOLEAU Y, 2003, US ANN TECHN C USENI PALMER CR, 2001, JCDL 01 Papadakos P, 2008, P 12 PAN HELL C INF PAPADAKOS P, 2008, ANATOMY MITOS WEB SE Papadakos P, 2009, P 13 EUR C DIG LIB E PATEREK A, 2007, P KDD CUP WORKSH 13 Payne J.W., 1993, ADAPTIVE DECISION MA Perugini S., 2003, IT Professional, V5, DOI 10.1109/MITP.2003.1191787 Pirolli P., 1998, P WORK C ADV VIS INT, P83 Pollitt A.S., 1998, INT CATALOGUING BIBL, V27, P37 POOLE D, 1988, INT C 5 GEN COMP SYS PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PRIETODIAZ R, 1991, COMMUN ACM, V34, P88, DOI 10.1145/103167.103176 PRISS U, 2008, AXIOMATHES, V18, P242 Priss U, 2006, ANNU REV INFORM SCI, V40, P521, DOI 10.1002/aris.1440400120 Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 RANGANATHAN S, 1965, RUTGERS SERIES SYSTE, V4 Ranganathan S. R., 1967, PROLEGOMENA LIB CLAS, V3rd Ranganathan Shiyali Ramamrita, 1965, COLON CLASSIFICATION REGGIA JA, 1984, DEV EXPERT SYSTEMS, P35 Rensink RA, 1997, PSYCHOL SCI, V8, P368, DOI 10.1111/j.1467-9280.1997.tb00427.x Rice R. E., 2001, ACCESSING BROWSING I ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rockley A., 2002, MANAGING ENTERPRISE Sacco G, 2006, SIGIR 2006 WORKSH FA Sacco GM, 2000, IEEE T KNOWL DATA EN, V12, P468, DOI 10.1109/69.846296 SACCO GM, 2000, CORRES ACM DIGITAL L SACCO GM, 2003, P INT C IM SCI SYST SACCO GM, 2005, 5 INT C KNOWL MAN I SACCO GM, 2007, DGO 2007 SACCO GM, 2003, ACM CHI 2003 SACCO GM, 2005, 18 IEEE S COMP BAS M SACCO GM, 2003, IFIP INTERACT 03 SACCO GM, 2008, HDB RES PUBLIC INFOR SACCO GM, Patent No. 6763349 SACCO GM, 2005, P 4 INT C EL GOV EGO SACCO GM, 2004, IEEE 6 INT S MULT SO SACCO GM, 2006, LNCS, V4027 SACCO GM, Patent No. 20080133490 SACCO GM, 2008, DEXA FIND 08 SACCO GM, 2007, ENCY PORTAL TECHNOLO SACCO GM, 1987, P INT C BUS CD ROM SACCO GM, 2005, P 7 IEEE INT C E COM SACCO GM, 1988, INFORM SYST, V13, P1, DOI 10.1016/0306-4379(88)90023-3 SACCO GM, 2008, LNCS SACCO GM, 2008, ENCY HEALTHCARE INFO SACCO GM, Patent No. 7340451 SACCO GM, 2006, 2006 AAAI SPRING S S SACCO GM, 2005, P INT C E GOV EL DEM Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SANDERSON M, 1999, P 22 ANN INT ACM SIG Santini S, 2000, IEEE MULTIMEDIA, V7, P26, DOI 10.1109/93.879766 Sarwar B., 2001, P 10 INT C WORLD WID Sarwar B., 2002, P 5 INT C COMP INF T Sarwar B. M., 2000, P WORKSH WEB MIN E C Schafer J. B., 2001, Data Mining and Knowledge Discovery, V5, DOI 10.1023/A:1009804230409 Schreiber AT, 2001, IEEE INTELL SYST APP, V16, P66, DOI 10.1109/5254.940028 Seroussi B, 2001, ARTIF INTELL MED, V22, P43, DOI 10.1016/S0933-3657(00)00099-3 Shen Xuehua, 2005, P 28 ANN INT ACM SIG Shimazu H, 2002, ARTIF INTELL REV, V18, P223, DOI 10.1023/A:1020757023711 SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92, DOI 10.1145/102377.115768 *SIMILE, LONGW RDF BROWS SINHA V, 2005, P 2005 ACM SIGMOD IN Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 SMYTH MB, 1978, J COMPUT SYST SCI, V16, P23, DOI 10.1016/0022-0000(78)90048-X SNOW R, 2006, P 44 ANN M ASS COMP Sperner E, 1928, MATH Z, V27, P544, DOI 10.1007/BF01171114 Spiteri L, 1998, CAN J INFORM LIB SCI, V23, P1 SRIKANT R, 1995, VLDB 95 STEFANER M, 2007, DEXA 07 STEFANER M, 2007, EC TEL STEFANER M, 2008, DEXA WORKSH IEEE COM STOICA E, 2007, P HUM LANG TECHN ANN STOICA E, 2004, P HUM LANG TECHN C N Suchanek Fabian M., 2007, P 16 INT WORLD WID W TEEVAN J, 2004, CHI 04 TEEVAN J, 2008, P 31 ANN INT C RES D Tidwell J., 2005, DESIGNING INTERFACES *TOP MAPS, XTM TUNKELANG D, 2006, ACM SIGIR 06 WORKSH TURPIN A, 2006, SIGIR 06 TVAROZEK M, 2007, P INT C WEB ENG ICWE Tversky B, 2002, INT J HUM-COMPUT ST, V57, P247, DOI 10.1006/ijhc.1017 Tzitzikas Y, 2007, KNOWL INF SYST, V13, P337, DOI 10.1007/s10115-006-0048-0 TZITZIKAS Y, 2005, J DATA SEMANTICS, V2, P58 Tzitzikas Y, 2006, KNOWL INF SYST, V9, P430, DOI 10.1007/s10115-005-0205-x Tzitzikas Y, 2006, J INTELL INF SYST, V26, P75, DOI 10.1007/s10844-006-5452-0 TZITZIKAS Y, 2006, J INTELL DATA ANAL I, V10, P343 Tzitzikas Y, 2005, VLDB J, V14, P112, DOI 10.1007/s00778-003-0119-8 Tzitzikas Y, 2007, DATA KNOWL ENG, V62, P1, DOI 10.1016/j.datak.2006.07.005 TZITZIKAS YT, 2004, P 23 INT C CONC MOD TZITZIKAS YT, 2008, P FIND 2008 DEXA 08 TZITZIKAS YT, 2002, P 14 INT C ADV INF S TZITZIKAS YT, 2002, P 3 INT C WEB INF SY TZITZIKAS YT, PROVIDING EFFI UNPUB TZITZIKAS YT, 2003, P 19 INT C COOP INF TZITZIKAS YT, 2007, P 29 EUR C INF RETR TZITZIKAS YT, 2004, P 6 INT C FLEX QUER TZITZIKAS YT, 2004, INFORM MODELLING KNO, V15 TZITZIKAS YT, 2002, THESIS U CRETE TZITZIKAS YT, 2003, P 7 INT WORKSH COOP TZITZIKAS YT, 2004, P 3 INT S FDN INF KN Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Van Rijsbergen C. J., 1998, INFORM RETRIEVAL UNC VICKERY BC, 1986, J DOC, V42, P145, DOI 10.1108/eb026790 White JR, 2008, COMMUN ACM, V51, P5, DOI 10.1145/1400214.1400215 Wiederhold Gio, 1992, IEEE COMPUT, V25, P38 Willett W, 2007, IEEE T VIS COMPUT GR, V13, P1129, DOI 10.1109/TVCG.2007.70589 WOLLERSHEIM D, 2002, INT DAT ENG APPL S I World Wide Web Consortium (W3C), SPARQL QUER LANG RDF WU K, 2004, VLDB 04 XU S, 2008, P 31 ANN INT C RES D YANG K, 2004, P AS INF RETR S Yang Y., 2005, P 28 ANN INT ACM SIG YAO SB, 1977, COMMUN ACM, V20, P260, DOI 10.1145/359461.359475 Yates F., 1934, JR STATIST SOC S, V1, P217, DOI DOI 10.2307/2983604 YEE KP, 2003, CHI 03 Yu K., 2004, P 27 ANN INT ACM SIG Zadeh L. A., 1978, Fuzzy Sets and Systems, V1, DOI 10.1016/0165-0114(78)90029-5 ZAMIR O, 1998, P 19 ANN INT ACM SIG ZHAI CX, 2001, P 10 INT C INF KNOWL ZHANG J, 2004, JCDL 04 ZHANG L, 1999, TENCON 99 Zhang Y., 2007, P 30 ANN INT ACM SIG ZHOU DX, 2008, COMPUTER HUMAN INTER Zigoris P., 2006, P 15 ACM INT C INF K Zipf G. K., 1949, HUMAN BEHAV PRINCIPL ZIV J, 1977, IEEE T INFORM THEORY, V23, P337, DOI 10.1109/TIT.1977.1055714 NR 321 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1387-5264 BN 978-3-642-02358-3 J9 INFORM RETRIEVAL SER PY 2009 VL 25 BP 1 EP + DI 10.1007/978-3-642-02359-0_1 D2 10.1007/978-3-642-02359-0 PG 32 WC Computer Science, Cybernetics; Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BME34 UT WOS:000271995400001 ER PT S AU Navarro, S Llopis, F Munoz, R AF Navarro, Sergio Llopis, Fernando Munoz, Rafael BE Peters, C TI IR-n in the CLEF Robust WSD Task 2008 SO EVALUATING SYSTEMS FOR MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008) CY SEP 17-19, 2008 CL Aarhus, DENMARK AB In our approach to the Robust WSD task we have used a passage based system jointly with a WordNet and WSD based term expansion for the documents and queries. Furthermore, we have experimented with two well known relevance feedback methods - LCA and PRF -, in order to figure out which is more suitable to take profit of the WSD query expansion based on Wordnet. Our best run has obtained a 4th - 0.4008 MAP - A major finding is that LCA fits better than PRF to this task clue to it is able to take advantage of the expanded documents and queries. C1 [Navarro, Sergio; Llopis, Fernando; Munoz, Rafael] Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. RP Navarro, S (reprint author), Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. EM snavarro@dlsi.ua.es; llopis@dlsi.ua.es; rafael@dlsi.ua.es CR Agirre E, 2009, LECT NOTES COMPUT SC, V5706, P15 LLOPIS F, 2003, THESIS U ALICANTE ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 4 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04446-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5706 BP 134 EP 137 PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMQ35 UT WOS:000273344500016 ER PT S AU Petras, V Baerisch, S AF Petras, Vivien Baerisch, Stefan BE Peters, C TI The Domain-Specific Track at CLEF 2008 SO EVALUATING SYSTEMS FOR MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008) CY SEP 17-19, 2008 CL Aarhus, DENMARK DE Information Retrieval; Evaluation; Controlled Vocabularies AB The domain-specific track evaluates retrieval models for structured scientific bibliographic collections in English, German and Russian. Documents contain textual elements (title, abstracts) as well as subject keywords from controlled vocabularies, which can be used in query expansion and bilingual translation. Mappings between the different controlled vocabularies are provided. In 2008, new Russian language resources were provided, among them Russian-English and Russian-German terminology lists as well as a mapping table between the Russian and German controlled vocabularies. Six participants experimented with different retrieval systems and query expansion schemes. Compared to previous years, the queries were more discriminating, so that fewer relevant documents were found per query. The year 2008 marked the last year of the domain-specific track, a special issue of important experiments and results is planned. C1 [Petras, Vivien] Humboldt Univ, Berlin Sch Lib & Informat Sci, D-10117 Berlin, Germany. RP Petras, V (reprint author), Humboldt Univ, Berlin Sch Lib & Informat Sci, Dorotheenstr 26, D-10117 Berlin, Germany. EM vivien.petras@ibi.hu-berlin.de; stefan.baerisch@gesis.org CR Fautsch C, 2009, LECT NOTES COMPUT SC, V5706, P199 Fox EA, 1994, P 2 TEXT RETR C TREC, P243 GOBEILL J, 2009, LNCS, V5706 KLUCK M, 2001, LECT NOTES COMPUTER, V2069, P48 KLUCK M, 2004, LNCS, V3237, P376 KURSTEN J, 2008, LNCS, V5706, P215 LARSON RR, 2008, LNCS, V5706, P203 MEIJ E, 2008, LNCS, V5706 MULLER C, 2008, LNCS, V5706, P219 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Savoy J, 2005, LECT NOTES COMPUT SC, V3491, P233 Schmid H, 1994, P INT C NEW METH LAN, V12 2007, GIRT DESCRIPTION GIR NR 13 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04446-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5706 BP 186 EP 198 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMQ35 UT WOS:000273344500023 ER PT S AU Navarro, S Garcia-Cumbreras, MA Llopis, F Diaz-Galiano, MC Munoz, R Martin-Valdivia, MT Urena-Lopez, LA Montejo-Raez, A AF Navarro, Sergio Angel Garcia-Cumbreras, Miguel Llopis, Fernando Carlos Diaz-Galiano, Manuel Munoz, Rafael Teresa Martin-Valdivia, Maria Alfonso Urena-Lopez, L. Montejo-Raez, Arturo BE Peters, C TI Combining TEXT-MESS Systems at ImageCLEF 2008 SO EVALUATING SYSTEMS FOR MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008) CY SEP 17-19, 2008 CL Aarhus, DENMARK ID INFORMATION-RETRIEVAL; SEARCH AB This paper describes the joint work of two teams belonging to the TEXT-MESS project. The system presented at ImageCLEFPhoto task combines one module based on filtering and other based on clustering. The main objective was to study the behavior of these methods with a large number of configurations in order to increase our Chances of success. The system presented at ImageCLEFmed task uses the IR-n system with a negative query expansion based on the acquisition type of the image mixed with the SINAI system with a MeSH based query expansion. C1 [Navarro, Sergio; Llopis, Fernando; Munoz, Rafael] Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. RP Navarro, S (reprint author), Univ Alicante, Nat Language Proc & Informat Syst Grp, Alicante, Spain. EM snavarro@dlsi.ua.es; magc@ujaen.es; llopis@dlsi.ua.es; mcdiaz@ujaen.es; rafael@dlsi.ua.es; maite@ujaen.es; laurena@ujaen.es; amontejo@ujaen.es RI Diaz-Galiano, Manuel Carlos/H-1947-2015; Garcia Cumbreras, Miguel Angel/H-5708-2015; Martin-Valdivia, Maria-Teresa/; Montejo-Raez, Arturo/D-3387-2009; Urena Lopez, L. Alfonso/ OI Diaz-Galiano, Manuel Carlos/0000-0001-9298-1376; Garcia Cumbreras, Miguel Angel/0000-0003-1867-9587; Martin-Valdivia, Maria-Teresa/0000-0002-2874-0401; Montejo-Raez, Arturo/0000-0002-8643-2714; Urena Lopez, L. Alfonso/0000-0001-7540-4059 CR Arni T., 2008, LNCS, V5706, P500 Aronson A R, 2001, Proc AMIA Symp, P17 Deselaers T, 2008, INFORM RETRIEVAL, V11, P77, DOI 10.1007/s10791-007-9039-3 Diaz-Galiano MC, 2008, LECT NOTES COMPUT SC, V5152, P601, DOI 10.1007/978-3-540-85760-0_76 DIAZGALIANO MC, 2008, ON LINE WORKING NOTE GARCIACUMBRERAS MA, 2008, ON LINE WORKING NOTE GOMEZSORIANO JM, 2005, LNCS LNAI, V3658, P443 Kalpathy-Cramer J, 2007, ST HEAL T, V129, P1334 Muller H, 2009, LECT NOTES COMPUT SC, V5706, P512 NAVARRO S, 2008, ON LINE WORKING NOTE Perea-Ortega JM, 2008, LECT NOTES COMPUT SC, V5152, P823, DOI 10.1007/978-3-540-85760-0_104 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 13 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04446-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5706 BP 597 EP 604 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMQ35 UT WOS:000273344500074 ER PT J AU Taktak, I Tmar, M Ben Hamadou, A AF Taktak, Imen Tmar, Mohamed Ben Hamadou, Abdelmajid BE Andreasen, T Yager, RR Bulskov, H Christiansen, H Larsen, HL TI Query Reformulation Based on Relevance Feedback SO FLEXIBLE QUERY ANSWERING SYSTEMS: 8TH INTERNATIONAL CONFERENCE, FQAS 2009 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 8th International Conference on Flexible Query Answering Systems CY OCT 26-28, 2009 CL Roskilde, DENMARK SP Iona Coll, Machine Intelligence Inst HO Roskilde Univ, Dept Commun, Business & Informat Technol DE Relevance Feedback; Query optimization; learning ID EXPANSION AB In a Relevance Feedback process, the query can be reformulated basing on a matrix product of the RSV (Retrieval Status Value) vector and the documents-terms matrix. In such case, the challenge is to determine the most appropriate query that fulfils the retrieval process. In this paper, we present an automatic query reformulation approach based on a dual form of this product matrix which systematically generate as solution the reformulated query. This approach was spread to assure a learning strategy in order to rank the results of an information retrieval system. Some experiments have been undertaken into a dataset provided by TREC and the results show the effectiveness of our approach. C1 [Taktak, Imen; Tmar, Mohamed; Ben Hamadou, Abdelmajid] Univ Sfax, Multimedia Informat Syst & Adv Comp Lab, High Inst Comp Sci & Multimedia, Sfax, Tunisia. RP Taktak, I (reprint author), Univ Sfax, Multimedia Informat Syst & Adv Comp Lab, High Inst Comp Sci & Multimedia, Sfax, Tunisia. EM imen.taktak@gmail.com; mohamedtmar@yahoo.fr; abdelmajid.benhamadou@isimsf.rnu.tn CR BUCKLEY SG, 1992, 1 TEXT RETRIEVAL C T, P59 Campos L.M.D., 2001, DOCUMENT INSTANTIATI Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Efthimiadis EN, 2000, J AM SOC INFORM SCI, V51, P989, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1002>3.0.CO;2-B JOHNSON SC, 1967, PSYCHOMETRIKA, V32, P241, DOI 10.1007/BF02289588 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J. J., SMART RETRIEVAL SYST, P313 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1984, INTRO MODERN INFORM Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd NR 11 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04956-9 J9 LECT NOTES ARTIF INT PY 2009 VL 5822 BP 134 EP 144 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BQO06 UT WOS:000281409200012 ER PT S AU Zheng, L Cox, IJ AF Zheng, Lei Cox, Ingemar J. BE Andreasen, T Yager, RR Bulskov, H Christiansen, H Larsen, HL TI Re-ranking Documents Based on Query-Independent Document Specificity SO FLEXIBLE QUERY ANSWERING SYSTEMS: 8TH INTERNATIONAL CONFERENCE, FQAS 2009 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 8th International Conference on Flexible Query Answering Systems CY OCT 26-28, 2009 CL Roskilde, DENMARK SP Iona Coll, Machine Intelligence Inst HO Roskilde Univ, Dept Commun, Business & Informat Technol DE Query-independent knowledge; Specificity; Normalized inverse document frequency; Entropy; Ranking; Information retrieval AB The use of query-independent knowledge to improve the ranking of documents in information retrieval has proven very effective in the context of web search. This query-independent knowledge is derived from an analysis of the graph structure of hypertext links between documents. However, there are many cases where explicit hypertext links are absent or sparse, e. g. corporate Intranets. Previous work has sought to induce a graph link structure based on various measures of similarity between documents. After inducing these links, standard link analysis algorithms, e. g. Page Rank, can then be applied. In this paper, we propose and examine an alternative approach to derive query-independent knowledge, which is not based on link analysis. Instead, we analyze each document independently and calculate a "specificity" score, based on (i) normalized inverse document frequency, and (ii) term entropies. Two re-ranking strategies, i.e. hard cutoff and soft cutoff, are then discussed to utilize our query-independent "specificity" scores. Experiments on standard TREC test sets show that our re-ranking algorithms produce gains in mean reciprocal rank of about 4%, and 4% to 6% gains in precision at 5 and 10, respectively, when using the collection of TREC disk 4 and queries from TREC 8 ad hoc topics. Empirical tests demonstrate that the entropy-based algorithm produces stable results across (i) retrieval models, (ii) query sets, and (iii) collections. C1 [Zheng, Lei; Cox, Ingemar J.] UCL, Dept Comp Sci, London WC1E 6BT, England. RP Zheng, L (reprint author), UCL, Dept Comp Sci, Mortimer St, London WC1E 6BT, England. EM lei.zheng@ucl.ac.uk; ingemar@cs.ucl.ac.uk CR Balinski J, 2005, INFORM PROCESS MANAG, V41, P759, DOI 10.1016/j.ipm.2004.01.006 Berkhin P, 2005, INTERNET MATH, V2, P73, DOI 10.1080/15427951.2005.10129098 Borodin A., 2001, P 10 INT WORLD WID W, P415, DOI 10.1145/371920.372096 Brin S., 1998, P 7 INT WORLD WID WE, P107 Dhillon I. S., 2001, P 7 ACM SIGKDD INT C, P269, DOI DOI 10.1145/502512.502550 Diaz Fernando, 2005, P 14 INT C INF KNOWL, P672, DOI 10.1145/1099554.1099722 Fox C., 1990, SIGIR Forum, V24 Joachims T, 2003, P 20 INT C MACH LEAR, P290 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Kurland Oren, 2005, P SIGIR, P306, DOI 10.1145/1076034.1076087 Kurland O., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148188 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 MEYER C. D., 2006, GOOGLES PAGERANK SCI OGILVIE P, 2001, P TREC 10 Orwell G, 1945, ANIMAL FARM FAIRY ST ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST SPARCK-JONES K, 1973, INFORM STORAGE RET, V9, P619 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P779, DOI 10.1016/S0306-4573(00)00015-7 ZHAI CX, 2007, NOTES KL DIVERGENCE Zhang B, 2005, P 28 ANN INT ACM SIG, P504, DOI 10.1145/1076034.1076120 NR 23 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04956-9 J9 LECT NOTES ARTIF INT PY 2009 VL 5822 BP 201 EP 214 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA BQO06 UT WOS:000281409200018 ER PT J AU de Campos, LM Fernandez-Luna, JM Huete, JF Martin-Dancausa, C AF de Campos, Luis M. Fernandez-Luna, Juan M. Huete, Juan F. Martin-Dancausa, Carlos BE Andreasen, T Yager, RR Bulskov, H Christiansen, H Larsen, HL TI Content-Oriented Relevance Feedback in XML-IR Using the Garnata Information Retrieval System SO FLEXIBLE QUERY ANSWERING SYSTEMS: 8TH INTERNATIONAL CONFERENCE, FQAS 2009 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 8th International Conference on Flexible Query Answering Systems CY OCT 26-28, 2009 CL Roskilde, DENMARK SP Iona Coll, Machine Intelligence Inst HO Roskilde Univ, Dept Commun, Business & Informat Technol ID STRUCTURED DOCUMENT-RETRIEVAL; INFLUENCE DIAGRAMS; MODEL AB Relevance Feedback (RF) is a technique allowing to enrich an initial query according to the user feedback in order to get results closer to the user's information need. This paper presents a new RF method for keyword queries (content queries). It is based on the re-weighting of the original query terms plus the addition of new query terms from the content of elements jugded as relevant or non-relevant by the user. This RF method is integrated in our search engine, Garnata, and evaluated with the INEX 2007 collection. C1 [de Campos, Luis M.; Fernandez-Luna, Juan M.; Huete, Juan F.; Martin-Dancausa, Carlos] Univ Granada, Dept Ciencias Computac & Inteligencia Artificial, ETSI Informat & Telecomunicac, CITIC UGR, E-18071 Granada, Spain. RP de Campos, LM (reprint author), Univ Granada, Dept Ciencias Computac & Inteligencia Artificial, ETSI Informat & Telecomunicac, CITIC UGR, E-18071 Granada, Spain. EM lci@decsai.ugr.es; jmfluna@decsai.ugr.es; jhg@decsai.ugr.es; cmdanca@decsai.ugr.es RI de Campos, Luis/B-7436-2012; Huete, Juan/C-2428-2012; Fernandez Luna, Juan Manuel/C-6845-2012 OI de Campos, Luis/0000-0001-9125-1195; CR BALMIN A, 2003, VLDB 2003, P1069 Crouch CJ, 2005, LECT NOTES COMPUT SC, V3493, P292 de Campos L.M., 2006, P 11 INT C INF PROC, P1024 de Campos LM, 2004, INFORM PROCESS MANAG, V40, P829, DOI 10.1016/j.ipm.2004.04.014 de Campos LM, 2005, LECT NOTES COMPUT SC, V3408, P215 Denoyer L., 2006, SIGIR Forum, V40 Guo L., 2003, SIGMOD, P16 Jensen F.V., 2001, BAYESIAN NETWORKS DE Kamps J, 2008, LECT NOTES COMPUT SC, V4862, P24, DOI 10.1007/978-3-540-85902-4_2 MASS Y, 2004, INEX 2004 WORKSH, P154 Pan HL, 2004, LECT NOTES COMPUT SC, V3288, P854 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RUTHVEN I, 2003, KNOWLEDGE ENG REV, V18 SHACHTER RD, 1988, OPER RES, V36, P589, DOI 10.1287/opre.36.4.589 SIGURBJORNSSON B, 2004, INEX 2004 WORKSH, P104 Xu Y., 2005, SIGMOD C, P537 NR 17 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04956-9 J9 LECT NOTES ARTIF INT PY 2009 VL 5822 BP 617 EP 628 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BQO06 UT WOS:000281409200053 ER PT J AU Peters, I AF Peters, Isabella BA Peters, I BF Peters, I TI Basic Terms in Knowledge Representation and Information Retrieval SO FOLKSONOMIES: INDEXING AND RETRIEVAL IN WEB 2.0 SE Knowledge and Information-Studies in Information Science LA English DT Article; Book Chapter ID PROBABILISTIC MODELS; RELEVANCE; DISTRIBUTIONS; FOLKSONOMY; ALGORITHM; TAG RI Peters, Isabella/C-9891-2012 OI Peters, Isabella/0000-0001-5840-0806 CR Aitchison J., 2000, THESAURUS CONSTRUCTI ALBRECHT C, 2006, FOLKSONOMY Anderson Chris, 2006, LONG TAIL WHY FUTURE [Anonymous], 1986, 2788 ISO *ANSI NISO, 2003, Z3919 ANSINISO Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BARILAN J, 2006, P COLL WEB TAGG WORK BERTRAM J, 2005, EINFUHRUNG INHALTLIC Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Brin S, 1998, P 7 INT WORLD WID WE Crawford W., 2006, CITES INSIGHTS CRAWF, P6 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DICE LR, 1945, ECOLOGY, V26, P297, DOI 10.2307/1932409 *DIN, 1987, 32705 DIN *DIN, 1987, 14631 DIN Dye J, 2006, ECONTENT, V29, P38 Egghe L, 2005, LIBR INFORM SCI SER, P1 Egghe L., 1990, INTRO INFORM EVANS R, 1994, P 15 NAT ONL M MEDF, P121 Ferber Reginald, 2003, INFORM RETRIEVAL SUC FOX E, 1992, INFORMATION RETRIEVA, P393 Frakes W. B., 1992, INFORM RETRIEVAL DAT Galvez C, 2005, J DOC, V61, P520, DOI 10.1108/00220410510607507 Garfield E., 1979, CITATION INDEXING GIUNCHIGLIA F, 2005, P AAAI 05 WORKSH CON GRUBER T, 2005, ONTOLOGY FOLKSONOMY GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 HALPIN H, 2006, EVOLVING ONTOLOGIES Harman D., 1986, P 9 ANN INT ACM SIGI, P186, DOI 10.1145/253168.253206 Harman D., 1992, INFORMATION RETRIEVA, P363 HENRICHS N, 1970, CONCEPTUS, V4, P133 HENRICHS N, 1970, NACHR DOK, V21, P20 HUANG H, 2006, P 17 ANN ASIS T SIG Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KALBACH J, 2008, B ASIST, V34, P36, DOI 10.1002/bult.2008.1720340211 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kipp M. E. I., 2006, P 7 INF ARCH SUMM VA Kipp Margaret E.I, 2007, P N AM S KNOWL ORG T, P63 KIPP MEI, 2006, P 17 ANN M AM SOC IN KIPP MEI, 2006, CANADIAN J INFORM LI, V30 KLEINBERG J, 2009, ACM COMPUTING SURVEY, V31 Kroski E., 2005, HIVE MIND FOLKSONOMI KUHLEN R, 2004, GRUNDLAGEN PRAKTISCH KUHLEN R, 1977, EXPT MORPHOLOGIE INF Kulyukin VA, 2001, J AM SOC INF SCI TEC, V52, P1224, DOI 10.1002/asi.1190 Lancaster F. W., 2003, INDEXING ABSTRACTING Lee J.H., 1993, P 16 ANN INT ACM SIG, P291, DOI 10.1145/160688.160741 Lewandowski D., 2005, INFORM SERVICES USE, V25, P137 Lewandowski Dirk, 2005, WEB INFORM RETRIEVAL LIDDY ED, 1993, Patent No. 5873058 Lin X., 2006, P 17 ANN ASIS T SIG LOVINS JB, 1968, MECH TRANSL, V11, P22 LUHN HP, 1958, IBM J RES DEV, V2, P159 Macgregor G., 2006, Library Review, V55, DOI 10.1108/00242530610667558 Mai J. E., 2006, Bulletin of the American Society for Information Science and Technology, V33, DOI 10.1002/bult.2006.1720330107 Manning C. D., 2008, INTRO INFORM RETRIEV MARKEY K, 1984, LIBR INFORM SCI RES, V6, P155 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mathes Adam, 2004, FOLKSONOMIES COOPERA MEDEIROS N, 2008, OCLC SYSTEMS SERVICE, V24 MILLER GA, 1998, WORDNET ELECT LEXICA, P23 Newman MEJ, 2005, CONTEMP PHYS, V46, P323, DOI 10.1080/00107510500052444 Page L., 1998, PAGERANK CITATION RA PETERS I, 2007, JOINING RES PRACTICE, P1510 PETERS I, 2008, INFORM WISSENSCHAFT, V59, P100 Peterson Elaine, 2006, D LIB MAGAZINE, V12 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QUINTARELLI E, 2005, P ISKO IT UNIMIB M M Rasmussen E., 1992, INFORMATION RETRIEVA, P419 Redmond-Neal A., 2005, ASIS T THESAURUS INF Richardson M., 2006, P 15 INT C WORLD WID, P707, DOI DOI 10.1145/1135777.1135881 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMITZ J, 2007, P 29 ONL TAG DGI FRA, P31 SHIRKY C, 2005, SEMISTRUCTURED METAD Shirky C., 2005, ONTOLOGY IS OVERRATE Shirky C., 2003, EXPOSURE FRICTION FR, P77 Sinclair J, 2008, J INF SCI, V34, P15, DOI 10.1177/0165551506078083 SKUSA A, 2008, WEB 2 0 DIENSTE ERGA, P1 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SNEATH PHA, 1957, J GEN MICROBIOL, V17, P184 Sparck-Jones K., 1997, READINGS INFORM RETR Stock W. G., 2007, INFORM RETRIEVAL INF Stock W. G., 2008, WISSENSREPRASENTATIO Stock WG, 2006, J AM SOC INF SCI TEC, V57, P1126, DOI 10.1002/asi.20359 Stock Wolfgang G, 2007, Information Services & Use, V27 Taylor Arlene G., 1999, ORG INFORM Tennis J., 2006, P 17 ANN ASIS T SIG TUDHOPE D, 2006, NEW REV HYPERMEDIA M, V12, P9 Van Damme C, 2007, P ESWC 2007 WORKSH B, P71 Voss J., 2006, COLLABORATIVE THESAU VOSS J, 2007, P 10 INT S INF SCI C Wasserman S., 1994, SOCIAL NETWORK ANAL Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Weinberger D., 2006, TAXONOMIES TAGS TREE WELLER K, 2006, P 28 ONL TAG DGI FRA, P108 Weller K, 2007, APPL WEB 2 0 INNOVAT, P108 YALTAGHIAN B, 2002, P C CTR ADV STUD COL ZENG ML, 2000, TAXONOMY KNOWLEDGE O ZIMMERMANN HH, 1998, AUTOMATISCHE INDEXIE NR 106 TC 0 Z9 0 PU WALTER DE GRUYTER & CO PI BERLIN 30 PA GENTHINER STRASSE 13, D-10785 BERLIN 30, GERMANY BN 978-3-598-44185-1 J9 KNOWL INFO-STUD INFO PY 2009 BP 119 EP 151 D2 10.1515/9783598441851 PG 33 WC Information Science & Library Science SC Information Science & Library Science GA BOX12 UT WOS:000277899800003 ER PT J AU Peters, I AF Peters, Isabella BA Peters, I BF Peters, I TI Information Retrieval with Folksonomies SO FOLKSONOMIES: INDEXING AND RETRIEVAL IN WEB 2.0 SE Knowledge and Information-Studies in Information Science LA English DT Article; Book Chapter ID WORLD-WIDE-WEB; QUERY EXPANSION; PROBABILISTIC MODELS; RELEVANCE FEEDBACK; VOCABULARY PROBLEM; SOCIAL NETWORKS; SEARCH ENGINE; SYSTEMS; COMMUNITIES; TAG RI Peters, Isabella/C-9891-2012 OI Peters, Isabella/0000-0001-5840-0806 CR *AG SOC MED, 2009, ERG MEAS SUMM Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 Ahern S, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P1, DOI 10.1145/1255175.1255177 Alani H, 2003, IEEE INTELL SYST, V18, P18, DOI 10.1109/MIS.2003.1193653 ALBRECHT C, 2006, THESIS TU WIEN Al-Khalifa H S, 2007, P 3 INT C WEB INF SY ALKHALIFA HS, 2007, P 2 EUR C TECHN ENH ALKHALIFA HS, 2006, P 2 INT IEEE C INN I Ames M., 2007, P SIGCHI C HUM FACT, DOI 10.1145/1240624.1240772 Angeletou S, 2007, P 4 EUR SEM WEB C IN, P30 ANICK P, 2003, P 26 ANN INT ACM SIG, P88 [Anonymous], 2006, [No title captured], Patent No. 20060242139 Hill W. C., 1994, Information Society, V10 Auray N., 2007, COMMUNICATIONS STRAT, V65, P67 Austin John Langshaw, 1972, THEORIE SPRECHAKTE Baeza-Yates R, 2007, LECT NOTES COMPUT SC, V4362, P1 BAEZAYATES R, 2007, LECT NOTES COMPUTER, V4702, P4 Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 BARILAN J, 2006, P COLL WEB TAGG WORK Bateman S., 2008, P ACM C HYP HYP, P193, DOI 10.1145/1379092.1379130 BEAL A, 2006, WM TANNE DISCUSSES F Begelman G., 2006, P COLL WEB TAGG WORK BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BENDER G, 2008, WEB 2 0 NEUE PERSPEK, P173, DOI 10.1007/978-3-540-73701-8_11 BERTRAM J, 2005, EINFUHRUNG INHALTLIC Bertram J., 2009, INFORM WISSENSCHAFT, V60, P19 BIRKENHAKE B, 2008, GOOD TAGS BAD TAGS S, P153 Bischoff K., 2008, P 17 ACM C INF KNOWL, P193, DOI 10.1145/1458082.1458112 BLANK M, 2008, GOOD TAGS BAD TAGS S, P85 Bradford SC, 1934, ENGINEERING-LONDON, V137, P85 Braun M., 2008, P 17 INT C WORLD WID, P1031, DOI 10.1145/1367497.1367641 BRIN S, 1998, P 7 INT WORLD WID WE, V7 Broder A., 2002, ACM SIGIR FORUM, V36, P3, DOI 10.1145/792550.792552 Brooke-Rose C, 2006, CONTEMPORARY FICTION AND THE USES OF THEORY, P9 BROOKS C, 2006, P COLL WEB TAGG WORK Brusilovsky P, 2008, LECT NOTES COMPUT SC, V4910, P5 BUTTERFIELD D, 2006, Patent No. 20060242178 Candan K.S., 2008, P 2008 ACM WORKSH SE, P75, DOI DOI 10.1145/1458583.1458597 Canino C., 2008, SEMANTIC ANAL TAG SI Capocci A., 2008, J PHYS A, V41, P22 Carman M J, 2008, P 2008 ACM WORKSH SE, P27, DOI DOI 10.1145/1458583.1458591 Cattuto C., 2008, P 7 INT SEM WEB C KA, P615 Cattuto C, 2006, EUR PHYS J C, V46, P33, DOI 10.1140/epjcd/s2006-03-004-4 CHEN HC, 1994, COMPUTER, V27, P58 CHI EH, 2006, UNDERSTANDING NAVIGA CHIANG K, 2006, B ASIST, V32 Chopin K, 2008, J DOC, V64, P552, DOI 10.1108/00220410810884075 Choy S. O., 2006, P IEEE WIC ACM INT C, P352 Christiaens S, 2006, LECT NOTES COMPUT SC, V4277, P199 Cormode G., 2008, 1 MONDAY, V13 Cox A., 2008, INFORM RES, V13 Crawford W., 2006, CITES INSIGHTS CRAWF, P6 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 CULLISS GA, 1997, Patent No. 6006222 DENNIS BM, 2006, P COLL WEB TAGG WORK Dieberger A, 1997, INT J HUM-COMPUT ST, V46, P805, DOI 10.1006/ijhc.1996.0111 Dieberger A., 2000, INTERACTIONS, V7, P36, DOI DOI 10.1145/352580.352587 Diederich J., 2006, P 1 INT WORKSH BUILD *DIN, 1988, 316263 DIN Dmitriev P. A., 2006, P 15 INT C WORLD WID, P811, DOI 10.1145/1135777.1135900 DOURISH P, 1994, P HCI GLASG SCOTL Dubinko M., 2006, P 15 INT C WORLD WID, P193, DOI 10.1145/1135777.1135810 Dye J, 2006, ECONTENT, V29, P38 Edelman, 2008, ED TRUST BAR 2008 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Efthimiadis EN, 2000, J AM SOC INFORM SCI, V51, P989, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1002>3.0.CO;2-B EGOZI O, 2008, TAXONOMY SOCIAL SEAR Evans B.M, 2008, P 2008 ACM WORKSH SE, P83, DOI 10.1145/1458583.1458600 FARRELL S, 2007, P 20 ANN ACM S US IN, P91, DOI 10.1145/1294211.1294228 FEINBERG M, 2006, P 17 ANN ASIS T SIG Feldman S, 2000, ONLINE, V24, P49 FICHTER D, 2008, ONLINE LEADING M NOV, P54 Fichter D, 2006, ONLINE, V30, P43 FOKKER J, 2006, P COLL WEB TAGG WORK FORSBERG M, 1998, P 4 ERCIM WORKSH US Freyne J., 2007, P 12 INT C INT US IN, P52, DOI 10.1145/1216295.1216312 Freyne J, 2004, LECT NOTES COMPUT SC, V3137, P95 Fujimura K, 2008, P 17 INT C WORLD WID, P1087, DOI DOI 10.1145/1367497.1367669 Furnas G. W., 2006, P C HUM FACT COMP SY, P36 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 FURNER J, 2007, P WORLD LIB INF C DU Ganesan K.A., 2008, P 17 INT C WORLD WID, P1181, DOI 10.1145/1367497.1367716 Garfield E., 1979, CITATION INDEXING GIUNCHIGLIA F, 2005, P AAAI 05 WORKSH CON GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 GOLOV E, 2008, P POST DEM SESS 7 IN Golovchinsky G., 2008, 1 INT WORKSH COLL IN Goodrum A, 2001, INFORM PROCESS MANAG, V37, P295, DOI 10.1016/S0306-4573(00)00033-9 Gordon-Murnane L., 2006, Searcher, V14 GRAEFE G, 2007, ALTERNATIVE SEARCHIN GRAHAM R, 2008, P 17 INT C WORLD WID, P1165, DOI 10.1145/1367497.1367708 Grahl M., 2007, P INT C KNOWL MAN I, P356 Greenberg J, 2001, J AM SOC INF SCI TEC, V52, P402, DOI 10.1002/1532-2890(2001)9999:9999<::AID-ASI1089>3.3.CO;2-B GRUBER T, 2005, ONTOLOGY FOLKSONOMY GRUZD A, 2006, P 17 ANN ASIS T SIG Guy M., 2006, D LIB MAGAZINE, V12 GYONGYI P, 2004, P 30 INT C VER LARG, P576 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 HALPIN H, 2006, EVOLVING ONTOLOGIES Hammond T., 2005, D LIB MAGAZINE, V11 HAN P, 2006, P IEEE WIC ACM INT C, P757 Harman D., 1992, INFORMATION RETRIEVA, P363 Hassan-Montero Y., 2006, P INT C MULT INF SCI HAYES C, 2007, P IJCAI 2007 HYD IND, P2772 HAYES C, 2007, P ICWSM 2007 BOULD C, P67 HEARST M, 2008, P 41 HAW INT C SYST Hearst M. A., 2008, P 2008 ACM WORKSH SE, P95, DOI DOI 10.1145/1458583.1458599 Heymann P., 2008, P INT C WEB SEARCH W, P195, DOI 10.1145/1341531.1341558 Heymann P., 2006, COLLABORATIVE CREATI HOPEZHUERTAS MJ, 1997, J DOC, V53, P139 HOTHO A, 2008, GOOD TAGS BAD TAGS W Hotho A., 2006, P CONC STRUCT TOOL I, P87 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 HOTHO A, 2006, P FGIR 2009 WORKSH I HOTHO A, 2006, P FGIR 2006 WORKSH I HOTHO A, 2006, SOCIAL SOFTWARE WERT JACKSON DM, 1970, INFORM STORAGE RET, V6, P187, DOI 10.1016/0020-0271(70)90061-6 Jaffe A, 2006, P 8 ACM INT WORKSH M, P89, DOI 10.1145/1178677.1178692 Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506 JASCHKE R, 2006, P 18 WORKSH GRUNDL D, P80 JASCHKE R, 2008, P 2 INT C WEB SOC ME JIANG T, 2008, B ASIST, V34 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 JOHN A, 2006, P 15 INT C WORLD WID Jones L.A., 2006, SOCIAL BROWSING FLIC JORGENSEN C, 2007, P 18 WORKSH ASIS T S Jung S, 2007, INFORM PROCESS MANAG, V43, P791, DOI 10.1016/j.ipm.2006.07.021 Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 Kennedy L., 2007, P 15 INT C MULT, P631, DOI 10.1145/1291233.1291384 Kern R., 2007, P 18 INT C DAT EXP S, P283 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kipp Margaret E.I, 2007, P N AM S KNOWL ORG T, P63 KIPP MEI, 2008, P 71 ASIS T ANN M PE KIPP MEI, 2007, TAGG FIND DO TAGS HE KIPP MEI, 2006, P 17 ANN M AM SOC IN KIPP MEI, 2006, CANADIAN J INFORM LI, V30 KIPP MEI, 2006, P 17 ANN ASIS T SIG Kleinberg J. M., 1999, ACM COMPUTING SURVEY, V31 KNAUTZ K, 2008, P 30 ONL TAG DGI FRA, P269 KOLBITSCH J, 2007, P I MEDIA I SEMANTIC, P77 KOLBITSCH J, 2007, WORDFLICKR FOLKSONOM Kome S.H., 2005, THESIS U N CAROLINA Koutrika G., 2007, P 3 INT WORKSH ADV I, P57, DOI 10.1145/1244408.1244420 KRAUSE B, 2008, P 30 EUR C IR RES EC, P101 KROSKI E, 2005, HIVE MIND FOLKSNOMIE Kuo B. Y.-L., 2007, P 16 INT C WORLD WID, P1203, DOI DOI 10.1145/1242572.1242766.2 KWIATKOWSKI M, 2007, INFORM WISSENSCHAFT, V58, P265 Lambiotte R, 2006, LECT NOTES COMPUT SC, V3993, P1114 Laniado D., 2007, P 4 IT SEM WEB WORKS, P192 Laniado D., 2007, P 18 C HYP HYP MANCH, P153, DOI 10.1145/1286240.1286282 Lee C.S., 2009, J DIGITAL INFORM, V10 Lee Hae-Lim, 2007, Plant Biology (Rockville), V2007, P294 Lee SS, 2007, P 7 IEEE INT C COMP, P200 LEE SS, 2008, P 2008 ACM WORKSH SE, P59, DOI 10.1145/1458583.1458595 LEE SS, 2008, P 2 INT C WEBL SOC M LERMAN K, 2007, PERSONALIZING IMAGE LEWANDOWSKI D, 2007, P 29 ONL TAG DGI FRA, P83 Lewandowski D., 2005, INFORM SERVICES USE, V25, P137 Lewandowski Dirk, 2005, WEB INFORM RETRIEVAL LI R, 2007, 16 INT WWW C BANFF A, P943 LUHN HP, 1958, IBM J RES DEV, V2, P159 MAASS W, 2007, P INT WORKSH EM SEM, P45 MACAULAY C, 1998, THESIS MAIER R, 2007, P XML TAG BERL GERM, P75 Maier Ronald, 2008, International Journal of Technology Enhanced Learning, V1, DOI 10.1504/IJTEL.2008.020231 Maness J. M., 2006, WEBOLOGY, V3 Marlow C., 2006, P COLL WEB TAGG WORK Marlow C., 2006, P 17 C HYP HYP, P31, DOI DOI 10.1145/1149941.1149949 Mathes Adam, 2004, FOLKSONOMIES COOPERA MAYR P, 2006, BEITRAGE BIBLIOTHEKS, P151 McFedries P, 2006, IEEE SPECTRUM, V43, P80, DOI 10.1109/MSPEC.2006.1584367 MEJIAS UA, 2005, TAG LIT MERHOLZ P, 2005, C SHIRKYS VIEWPOINTS Merton RK, 2004, TRAVELS AND ADVENTURES OF SERENDIPITY: A STUDY IN SOCIOLOGICAL SEMANTICS AND THE SOCIOLOGY OF SCIENCE, P1 MERTON ROBERT K., 1949, SOCIAL THEORY SOCIAL Mika P, 2005, LECT NOTES COMPUT SC, V3729, P522 Miller DB, 2007, PALGR MAC SER INT PO, P21 Millen D.R., 2006, P SIGCHI C HUM FACT, P111, DOI 10.1145/1124772.1124792 MILLEN DR, 2006, P AH2006 WORKSH SOC Mishne G., 2006, P 15 INT C WORLD WID, P953, DOI DOI 10.1145/1135777.1135961 Mislove A., 2008, P 1 WORKSH ONL SOC N, P25, DOI DOI 10.1145/1397735.1397742 Moffat A, 2007, INFORM RETRIEVAL, V10, P205, DOI 10.1007/s10791-006-9014-4 Morrison PJ, 2008, INFORM PROCESS MANAG, V44, P1562, DOI 10.1016/j.ipm.2007.12.010 Morrison P.J., 2007, B AM SOC INFORM SCI, V34, P12, DOI 10.1002/bult.2007.1720340105 MULLER MJ, 2007, P INT ACM C SUPP GRO, P341, DOI 10.1145/1316624.1316676 MUNRO A, 1998, P VRI WORKSH VIS REP Naaman M, 2006, COMPUTER, V39, P108, DOI 10.1109/MC.2006.339 Neubauer Tanja, 2008, P 2008 ACM WORKSH SE, P3, DOI DOI 10.1145/1458583.1458589 Page L., 1998, PAGERANK CITATION RA Pang B., 2008, OPINION MINING SENTI Panke Stefanie, 2008, NUTZERPERSPEKTIVEN S PAOLILLO J, 2007, P 40 HAW INT C SYST PASSANT A, 2007, P ICWSM 2007 BOULD C PETERS I, 2009, LIB HI TECH IN PRESS PETERS I, 2008, PASSWORD, V5, P18 Peters I., 2007, P 70 ASIS T ANN M JO, P1510 Peters I., 2008, WEBOLOGY, V5, P58 Peterson Elaine, 2006, D LIB MAGAZINE, V12 PLUZHENSKAIA M, 2006, P 17 ANN ASIS T SIG QUINTARELLI E, 2005, P ISKO IT UNIMIB M M QUINTARELLI E, 2007, P IA SUMM LAS VEG NE Quintarelli E., 2007, B ASIST, V33, P10, DOI DOI 10.1002/BULT.2007.1720330506 REESPOTTER LK, 1989, INFORM PROCESS MANAG, V25, P677, DOI 10.1016/0306-4573(89)90101-5 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Richardson SL, 2006, DIAM RELAT MATER, V15, P707, DOI 10.1016/j.diamond.2005.12.043 RIVADENEIRA AW, 2007, P SIGCHI C HUM FACT, P995, DOI 10.1145/1240624.1240775 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rorissa A, 2008, INFORM PROCESS MANAG, V44, P1741, DOI 10.1016/j.ipm.2008.03.004 Ruge G, 1998, NFD INFORM-WISS PRAX, V49, P361 RUSSELL T, 2007, TAG DECAY VIEW AGING Russell T., 2006, P 6 ACM IEEE CS JOIN, P364, DOI 10.1145/1141753.1141859 SALTON G, 1987, INFORM RETRIEVAL GRU SANDUSKY R, 2006, P 17 ANN ASIS T SIG SCHILERWEIN S, 2008, GOOD TAGS BAD TAGS S, P141 Schmidt S, 2009, J AM SOC INF SCI TEC, V60, P863, DOI 10.1002/asi.21043 SCHMITZ C, 2007, P 16 INT WWW C BANFF Schmitz P., 2006, COLL WEB TAGG WORKSH SCHNEIDER JW, LECT NOTES COMPUTER, V3507, P226 Sen S., 2006, P ACM CSCW 06, P89, DOI 10.1145/1180875.1180890 Sen S, 2006, P 2006 20 ANN C COMP, P181, DOI 10.1145/1180875.1180904 SEN S, 2007, P 2007 INT ACM C SUP, P361, DOI 10.1145/1316624.1316678 SHANNON CE, 1948, AT&T TECH J, V27, P623 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 SHERMAN C, 2006, WHATS BIG DEAL SOCIA Shirky C., 2003, EXPOSURE FRICTION FR, P77 Sinclair J, 2008, J INF SCI, V34, P15, DOI 10.1177/0165551506078083 Sinha R., 2006, FINDABILITY TAGS FAC SKUSA A, 2008, WEB 2 0 DIENSTE ERGA, P1 Smith G., 2008, B ASIST, V34, P14, DOI 10.1002/bult.2008.1720340607 Smith M, 2008, P 2008 ACM WORKSH SE, P91, DOI DOI 10.1145/1458583.1458602 SPALDING T, 2007, TAGMASH BOOK TAGGING Spyns P, 2006, LECT NOTES COMPUT SC, V4275, P738 STERLING B, 2005, WIRED MAGAZINE, V13 Stock W. G., 2000, INFORMATIONSWIRTSCHA Stock W. G., 2007, INFORM RETRIEVAL INF Stock W. G., 2008, WISSENSREPRASENTATIO Stock WG, 2006, J AM SOC INF SCI TEC, V57, P1126, DOI 10.1002/asi.20359 Stock Wolfgang G, 2007, Information Services & Use, V27 STOREY MAD, 2007, P 2007 ACM S DOC ENG, P2 Strohmaier M, 2008, P 2008 ACM WORKSH SE, P35, DOI 10.1145/1458583.1458603 Sturtz D. N., 2004, COMMUNAL CATEGORIZAT Svensson M, 2000, P IUI 2000, P260, DOI 10.1145/325737.325866 SVENSSON M, 1998, EXPLORING NAVIGATION, P72 SZEKELY B, 2005, RANKING BOOKMARKS BI Szomszor M., 2007, P 4 EUR SEM WEB C IN, P71 Thorsten, 2008, GOOD TAGS BAD TAGS S, P73 Tonkin E., 2008, ARIADNE, V54 Tudhope D., 2008, ALISS, V3, P13 TUDHOPE D, 2006, NEW REV HYPERMEDIA M, V12, P3, DOI 10.1080/13614560600856433 *U SO CAL ANN KETC, 2009, MED MYTHS REAL Van Damme C, 2007, P ESWC 2007 WORKSH B, P71 Van Damme C., 2008, P 1 SEM INT C SEM SY, P118 Vander Wal T., 2008, GOOD TAGS BAD TAGS S, P7 Vander Wal T., 2005, FOLKSONOMY EXPLANATI VANDERWAL T, 2004, FEED THIS VANHOOLAND S, 2006, P CILIP CAT IND GROU Viegas FB, 2008, INTERACTIONS, V15, P49, DOI DOI 10.1145/1374489.1374501 Voss J., 2006, COLLABORATIVE THESAU Wang J., 2008, P 2008 ACM WORKSH SE, P43, DOI 10.1145/1458583.1458592 WASH R, 2007, P 70 ASIS T ANN M JO Wash R., 2006, COLLABORATIVE FILTER Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 WEINBERGER D, 2005, TAGGING WHY IT MATTE Weiss A., 2005, Networker, V9, DOI [10.1145/1086762.1086763, 10.1145/1065368.1065369] Weller K, 2008, INFORM WISSENSCHAFT, V59, P165 Wexelblat A., 1999, P SIGCHI C HUM FACT, P270, DOI 10.1145/302979.303060 WIGGINS R, 2002, SEARCHER MAGAZINE DA, V10 WINGET M, 2006, P 17 ANN ASIS T SIG Wu H., 2006, P 17 C HYP HYP OD DE, P111, DOI 10.1145/1149941.1149962 Wu X., 2006, P 15 INT C WORLD WID, P417, DOI 10.1145/1135777.1135839 XU Z, 2006, SEMANTIC WEB COLLABO Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 Yanbe Y, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P107, DOI 10.1145/1255175.1255198 Zhang L, 2006, LECT NOTES COMPUT SC, V4090, P168 Zhou D, 2008, P 17 INT C WORLD WID, P715, DOI DOI 10.1145/1367497.1367594 ZIEGLER C, 2006, IX ARCH, V10, P106 ZOLLERS A, 2007, P 16 INT WWW C BANFF NR 283 TC 0 Z9 0 PU WALTER DE GRUYTER & CO PI BERLIN 30 PA GENTHINER STRASSE 13, D-10785 BERLIN 30, GERMANY BN 978-3-598-44185-1 J9 KNOWL INFO-STUD INFO PY 2009 BP 283 EP 409 D2 10.1515/9783598441851 PG 127 WC Information Science & Library Science SC Information Science & Library Science GA BOX12 UT WOS:000277899800005 ER PT S AU Lavrenko, V AF Lavrenko, V TI Generative Theory of Relevance SO GENERATIVE THEORY OF RELEVANCE SE Information Retrieval Series LA English DT Book ID PROBABILISTIC INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; CONTINGENCY-TABLES; MAXIMUM-ENTROPY; MODEL; JUDGMENTS; CLASSIFICATION; PRINCIPLE; INFERENCE; SYSTEMS CR Allan J., 2002, Information Retrieval Techniques for Speech Applications (Lecture Notes in Computer Science Vol.2273) ALLAN J, 2000, P 9 TEXT RETR C TREC, P551 Allan J., 1998, P DARPA BROADC NEWS, P194 ALLAN J, 1999, CLSP WORKSH FIN REP ALLAN J, 2003, NIST TOP DET TRACK W Allan J., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354843 Allan James, 2001, P 24 ANN INT ACM SIG, P10, DOI 10.1145/383952.383954 Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BLEI DM, 2002, UCBCSD021194 Bodoff D, 2004, J AM SOC INF SCI TEC, V55, P471, DOI 10.1002/asi.10398 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Brown P. F., 1990, Computational Linguistics, V16 Bruza P. D., 1991, SIGIR Forum, V25 BRUZA PD, 1992, COMPUT J, V35, P208, DOI 10.1093/comjnl/35.3.208 BRUZA PD, 1993, THESIS U NIJMEGEN NI CARBONELL J, 1998, P INT ACM SIGIR C RE Carson C., 1999, P 3 INT C VIS INF SY, P509 Chen S.F., 1996, P 34 ANN M ACL CIERI C, 2002, CORPORA TOPIC DETECT, P33 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Cosijn E, 2000, INFORM PROCESS MANAG, V36, P533, DOI 10.1016/S0306-4573(99)00072-2 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 CRESTANI F, 1995, P ACM SIGIR C RES DE, P291, DOI 10.1145/215206.215373 Croft W. B., 2000, KLUWER INT SERIES IN CROFT WB, 1976, INFORM PROCESS MANAG, V12, P327, DOI 10.1016/0306-4573(76)90051-0 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 2001, P 24 ANN INT ACM SIG Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CUADRA CA, 1967, P AM DOC I, V4, P95 CUADRA CA, 1967, J DOC, V23, P291, DOI 10.1108/eb026436 DUMAIS S, 1994, TREC 3 P GAITH MD, P219 Duygulu P., 2002, P EUR C COMP VIS, P97, DOI DOI 10.1007/3-540-47979-1_7 EISENBERG M, 1986, THESIS SYRACUSE U SY FAIRTHORNE RA, 1963, INFORMATION RETRIEVA, P109 FENG S, 2004, P INT C PATT REC CVP Fiscus J.G., 2002, TOPIC DETECTION TRAC, P17 FOSKETT DJ, 1972, INFORM STORAGE RET, V8, P77, DOI 10.1016/0020-0271(72)90009-5 FOSKETT DJ, 1970, ASLIB PROC, V22, P90, DOI 10.1108/eb050231 GOFFMAN W, 1969, INFORMATION STORAGE, V4, P361 HARMAN D, 2002, P 11 TEXT RETR C TRE, P17 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE U HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 Harter SP, 1996, J AM SOC INFORM SCI, V47, P37 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HEARST M, 1999, P 22 ANN INT ACM SIG Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Hiemstra D., 2001, THESIS U TWENTE ENSC Hiemstra D, 1999, LECT NOTES COMPUT SC, V1696, P274 Hoffman Thomas, 1999, P 22 ANN INT ACM SIG, P50 Ingwersen P., 1992, INFORM RETRIEVAL INT JANES JW, 1993, P AM SOC INFORM SCI, P104 JANES JW, 1991, J AM SOC INFORM SCI, V42, P754, DOI 10.1002/(SICI)1097-4571(199112)42:10<754::AID-ASI9>3.0.CO;2-C Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 1997, READINGS INFORM RETR Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 2001, P WORKSH LANG MOD IN, P12 Jones K. S., 2000, Information Processing & Management, V36 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 KATTER RV, 1968, INFORM STORAGE RET, V4, P1, DOI 10.1016/0020-0271(68)90002-8 KULLBACK S, 1962, TECHNOMETRICS, V4, P573, DOI 10.2307/1266291 Kurland O., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009027 Lafferty J., 2003, PROBABILISTIC RELEVA, P1 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 LALMAS M, 1992, P BCS 14 INF RETR C, P1 LALMAS M, 1993, P WORKSH INC UNC INF, P62 Lancaster F. W., 1979, INFORM RETRIEVAL SYS LAVRENKO V, 2004, P INT C AC SPEECH SI LAVRENKO V, 2007, P HUM LANG TECHN C N, P89 LAVRENKO V, 2003, RELEVANCE MODELS INF, P11 Lavrenko V., 2003, P 17 ANN C NEUR INF Lavrenko V, 2002, P HUM LANG TECHN C H, P104 LAVRENKO V, 2004, P INT WORKSH DOC IM LEE JJ, 1991, J AM SOC INFORM SCI, V42, P166, DOI 10.1002/(SICI)1097-4571(199104)42:3<166::AID-ASI2>3.0.CO;2-A LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745 Leuski A., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Manmatha R., 1997, WORD SPOTTING INDEXI, P43 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Martin A, 1997, EUROSPEECH, V4, P1895 MEGHINI C, 1993, P 16 ANN INT ACM SIG, P298, DOI 10.1145/160688.160753 METZLER D, 2004, P 27 INT ACM SIGIR C Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Miller D, 1998, J INDO-EUR STUD MONO, P133 MIZARRO S, 1998, INTERACTING COMPUTER, V10, P305 MIZARRO S, 1997, J AM SOC INFORM SCI, V48, P810 Mori Y., 1999, P 1 INT WORKSH MULT Nallapati R., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 NALLAPATI R, 2003, P CIKM 2003 C, P560 NALLAPATI R, 2003, SIGIR 03 WORKSH MATH Neyman J, 1928, BIOMETRIKA, V20A, P263, DOI 10.2307/2332112 Nie J., 1988, P ACM SIGIR GREN FRA, P495, DOI 10.1145/62437.62493 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Nie JY, 1995, COMPUT J, V38, P643, DOI 10.1093/comjnl/38.8.643 NIE JY, 1992, P 15 ANN ACM SIGIR C, P140, DOI 10.1145/133160.133188 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 *NIST, 2001, P TDT 2001 WORKSH NO Papka R., 1999, THESIS U MASSACHUSET Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 RATH T, 2003, MM42 CIIR ROBERTSON S, 2002, SIGIR 2002 WORKSH MA ROBERTSON S, 1991, 5739 BRIT LIB RES DE ROBERTSON S, 2003, SIGIR 2003 WORKSH MA Robertson S., 2001, P WORKSH LANG MOD IN, P21 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson SE, 1998, P 7 TEXT RETR C TREC, P253 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1983, UNIFIED PROBABILISTI, P108 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Saracevic T., 1996, INFORM SCI INTEGRATI, P201 Sebastiani F., 1994, P 17 ANN INT ACM SIG, P122 SEBASTIANI F, 1998, INFORMATION PROCESSI, V38, P1 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Silverman B., 1986, DENSITY ESTIMATION S, P75 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 SONG F, 1999, P 8 INT C INF KNOWL Sparck Jones K, 2003, LANGUAGE MODELLING R, P57 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H. R., 1990, THESIS U MASSACHUSET *U PA, 1993, TIPST, V1 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1986, P INT ACM SIGIR C RE, P200 vanRijsbergen CJ, 1996, J AM SOC INFORM SCI, V47, P385 VICKERY BC, 1959, 1958 P INT C SCI INF, V2, P1275 VICKERY BC, 1959, 1958 P INT C SCI INF, V2, P855 VOORHEES E, 1986, THESIS CORNELL U NY Voorhees E. M., 2000, P 9 TEXT RETRIEVAL C VOORHEES EM, 1998, P 7 TEXT RETRIEVAL C WALPOLE RE, 1989, PROBABILITY STAT ENG, P108 Wilks SS, 1935, ANN MATH STAT, V6, P190, DOI 10.1214/aoms/1177732564 WILLETT P, 1988, FDN INFORM SCI, V3 WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X XU J, 1999, P 22 INT ACM SIGIR C, P15 XU J, 2000, P 9 TEXT RETRIEVAL C, P106 Xu J., 2001, P 24 ANN INT ACM SIG, P105, DOI 10.1145/383952.383968 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 YI X, 2007, P RIAO 2007 8 C LARG YU CT, 1983, INFORM TECHNOL R & D, V2, P129 Zhai, 2002, THESIS CARNEGIE MELL Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2002, P 25 ANN INT ACM SIG, P49 NR 160 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-540-89363-9 J9 INFORM RETRIEVAL SER PY 2009 VL 26 BP 1 EP 197 PG 197 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BJW81 UT WOS:000267328200007 ER PT B AU Galeas, P Kretschmer, R Freisleben, B AF Galeas, Patricio Kretschmer, Ralph Freisleben, Bernd GP ACM TI Document Relevance Assessment via Term Distribution Analysis Using Fourier Series Expansion SO JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES LA English DT Proceedings Paper CT 9th Annual International ACM/IEEE Joint Conference on Digital Libraries CY JUN 15-19, 2009 CL Austin, TX SP ACM SIGWEB, ACM Speial Intrest Grp Informat Retrieval, IEEE Comp Soc, IEEE DE Fourier series; query expansion; ranked retrieval; term distribution ID TEXT RETRIEVAL-SYSTEMS; FEEDBACK; CONTEXT; SEARCH AB In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents for a given search query. In this paper, term distribution analysis using Fourier series expansion as a novel approach for calculating an abstract representation of term positions in a document corpus is introduced. Based on this approach, two methods for improving the evaluation of document relevance are proposed: (a) a function-based ranking optimization representing a user defined document region, and (b) a query expansion technique based on overlapping the term distributions in the top-ranked documents. Experimental results demonstrate the effectiveness of the proposed approach in providing new possibilities for optimizing the retrieval process. C1 [Galeas, Patricio; Freisleben, Bernd] Univ Marburg, Dept Math & Comp Sci, D-35032 Marburg, Germany. RP Galeas, P (reprint author), Univ Marburg, Dept Math & Comp Sci, Hans Meerwein Str 3, D-35032 Marburg, Germany. EM galeas@informatik.uni-marburg.de; kretschmer@kretschmer-software.com; freisleb@informatik.uni-marburg.de CR Amati G., 2002, Inf Syst, V20, P357 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 ATTAR R, 1981, INFORM PROCESS MANAG, V17, P115 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Berry M., 1994, SIAM REV, V37, P573 Billerbeck B., 2003, CIKM 03, P2 Buckley C., 1994, 17 ANN INT ACM SIGIR, P292 Buckley C., 1995, NIST SPECIAL PUBLICA, P69 Cai D, 2004, SIGIR 2004, P456 Cover TM, 1991, ELEMENTS INFORM THEO Efthimiadis E. N., 1996, Annual review of information science and technology. Vol.31 Efthimiadis E., 1992, THESIS CITY U LONDON Efthimiadis E., 1994, NIST SPECIAL PUBLICA, p[279, 1994] Fan W., 2004, SIGIR 04, P138 Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 Folkers A., 2002, ICPR 02, V3, P30521 Foote U. N. Jonathan, 2002, 3 INT C MUS INF RETR Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Huang X., 2005, P 2005 IEEE INT C GR, P474 Kaszkiel M., 1997, SIGIR Forum Katz S.M., 1996, NAT LANG ENG, V2, P15, DOI 10.1017/S1351324996001246 Laurence M. P., 2004, T KNOWLEDGE DATA ENG, V16, P529 Lawrence S., 2000, IEEE DATA ENG B, V23, P25 Li J., 2005, MACHINE LEARNING CYB, P2302 Miller DRH, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P214 Moore D. S., 2003, BASIC PRACTICE STAT Ounis I., 2006, P ACM SIGIR 06 WORKS Razek M. A., 2003, P 12 INT WORLD WID W, P968 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Shen X., 2003, 26 ANN INT ACM SIGIR, P377 Spiegel M. R., 1974, SCHAUMS OUTLINE THEO Sun R., 2006, SIGIR 2006, P382 Tao T., 2007, SIGIR 2007, P295 Xu JX, 2000, PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA, P95 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Yu S., 2003, P 12 INT C WORLD WID, P11 NR 37 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-697-7 PY 2009 BP 277 EP 284 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science GA BWY55 UT WOS:000295296700036 ER PT S AU Zakaria, C Cure, O Salzano, G Smaili, K AF Zakaria, Chahnez Cure, Olivier Salzano, Gabriella Smaili, Kamel BE Meersman, R Dillon, T Herrero, P TI Formalized Conflicts Detection Based on the Analysis of Multiple Emails: An Approach Combining Statistics and Ontologies SO ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2009, PT 1 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT On the Move Confederated International Conference CY NOV 01-06, 2009 CL Vilamoura, PORTUGAL SP Algardata SA, Collibra, Lecture Notes Comp Sci, Univ Algarve AB In Computer Supported Cooperative Work (CSCW), it is crucial for project leaders to detect conflicting situations as early as possible. Generally, this task is performed manually by studying a set of documents exchanged between team members. In this paper, we propose a full-fledged automatic solution that identifies documents, subjects and actors involved in relational conflicts. Our approach detects conflicts in emails, probably the most popular type of documents in CSCW, but the methods used can handle other text-based documents. These methods rely on the combination of statistical and ontological operations. The proposed solution is decomposed in several steps: (i) we enrich a simple negative emotion ontology with terms occuring in the corpus of entails, (ii) we categorize each conflicting email according to the concepts of this ontology and (iii) we identify emails, subjects and team members involved in conflicting emails using possibilistic description logic and a set of proposed measures. Each of these steps are evaluated and validated on concrete examples. Moreover, this approach's framework is generic and can be easily adapted to domains other than conflicts, e.g. security issues, and extended with operations making use of our proposed set of measures. C1 [Zakaria, Chahnez; Cure, Olivier; Salzano, Gabriella] Univ Paris Est, IGM Terre Digitale, Marne La Vallee, France. RP Zakaria, C (reprint author), Univ Paris Est, IGM Terre Digitale, Marne La Vallee, France. EM chahnez.zakaria@univ-mlv.fr; olivier.cure@univ-mlv.fr; gabriella.salzano@univ-mlv.fr; smaili@loria.fr CR Baader F., 2003, DESCRIPTION LOGIC HD BIGI B, 2001, P STRING PROC INF RE BROCHES RS, 2008, THESIS U WESLEYAN CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Crammer K., 2001, J MACHINE LEARNING R, V2, P2001 DENOUAL E, 2006, THESIS U J FOURIER DIAS G, 2002, THESIS U ORLEANS Dubois D., 1994, HDB LOGIC ARTIFICIAL, V3, P439 EDEN L, 1994, MULTINATIONALES AMRI ENGUERARD C., 1995, J QUANT LINGUIST, V2, P27, DOI 10.1080/09296179508590032 Francisco V, 2007, LECT NOTES COMPUT SC, V4524, P88 Garcia-Rojas A., 2006, P 1 INT WORKSH SHAP, P63 HATON J-P, 2006, RECONNAISSANCE AUTOM LARIVERY M, 1998, LETTRE PSY, V2 LAVECCHIA C, 2007, 8 C INTERSPEECH Mathieu YY, 2005, LECT NOTES COMPUT SC, V3784, P350 McIntyre G, 2007, LECT NOTES COMPUT SC, V4552, P411 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOLTZ R, 2003, P 12 INT WORLD WID W, P139 Weston J., 1998, MULTICLASS SUPPORT V Whittaker S., 1996, C P HUM FACT COMP SY, P276 Xu LH, 2007, LECT NOTES COMPUT SC, V4738, P723 Yan J., 2008, ENG LETT, V16, P166 Yang Y., 1999, REEXAMINATION TEXT C Zacklad M., 2003, GROUP 2003, P190 Zhu B, 2008, DECIS SUPPORT SYST, V45, P778, DOI 10.1016/j.dss.2008.02.004 NR 28 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-05147-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5870 BP 94 EP 111 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPL24 UT WOS:000279119400006 ER PT B AU Li, Q Chen, YP Lin, ZX AF Li, Qing Chen, Yuanzhu Peter Lin, Zhangxi BE PazosArias, JJ DelgadoKloos, C LopezNores, M TI Filtering Techniques for Selection of Contents and Products SO PERSONALIZATION OF INTERACTIVE MULTIMEDIA SERVICES: A RESEARCH AND DEVELOPMENT PERSPECTIVE LA English DT Article; Book Chapter ID RECOMMENDER SYSTEMS; RETRIEVAL-SYSTEMS AB With the development of c-commerce and the proliferation of easily accessible information, filtering systems (also known as recommender systems) have become a popular technique to prune large information spaces so that users are directed to products that best meet their needs and preferences. In this chapter, we present an overview of the field of filtering techniques and describe the current filtering methods that are usually classified as content-based, collaborative, and hybrid filtering approaches. We also analyze various limitations of current filtering techniques and discuss the challenges for building a large-scale and practical filtering system. In particular, content-based filtering methods, which select the appropriate information for a user by matching user preferences, are first introduced. Second, we look into the memory-based and model-based collaborative filtering approaches to elaborate on the collaborative mechanism, which utilizes the opinions of other users to make recommendations for personalized information filtering. Third, hybrid filtering techniques are introduced, which combines the above filtering methods. At last, we address the challenges for building a large-scale and commercial recommender system. These challenges include scalability of filtering algorithms, data sparsity, effectiveness of recommender systems and so forth. C1 [Chen, Yuanzhu Peter] Mem Univ Newfoundland, St John, NF A1C 5S7, Canada. [Lin, Zhangxi] Texas Tech Univ, Lubbock, TX 79409 USA. EM kooliqing@gmail.com; yzchen@cs.mun.ca; kooliqing@gmail.com CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 ANICK PG, 1989, 13 ANN INT ACM SIGIR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BASU C, 1998, 15 NAT C ART INT MAD Billsus D, 2000, USER MODEL USER-ADAP, V10, P147, DOI 10.1023/A:1026501525781 Brand M., 2003, 3 SIAM INT C DAT MIN Breese John S., 1998, 14 C UNC ART INT MAD Brunato M., 2003, 1 IEEE INT C PERV CO Kwon OB, 2003, EXPERT SYST APPL, V25, P387, DOI 10.1016/S0957-4174(03)00063-0 CANNY J, 2002, IEEE S SEC PRIV OAKL Chen A., 2005, C HUM FACT COMP SYST Claypool M., 1999, ACM SIGIR WORKSH REC DELGADO J, 1998, LECT NOTES ARTIF INT, V1435, P206 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 GOOD N, 1999, 16 NAT C ART INT ORL GUPTA D, 1999, WORKSH REC SYST CONJ Han P, 2004, EXPERT SYST APPL, V27, P203, DOI 10.1016/j.eswa.2004.01.003 HAN P, 2004, 8 PAC AS C ADV KNOWL HAUVER DB, 2001, 1 INT C WEB DEL MUS Herlocker J., 1999, 22 ANN INT ACM SIGIR Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 HOFMANN T, 2003, 26 ANN INT ACM SIGIR HU R, 2006, 16 INT C ART REAL TE Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775 JIN R, 2003, 19 C UNC ART INT AC JIN R, 2003, 12 ACM INT C INF KNO Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 KIM BM, 2008, APPL ARTIFICIAL INTE, V22 KIM M, 2000, ACM SIGIR WORKSH MAT LEE JH, 1994, INFORM PROCESS MANAG, V30, P79, DOI 10.1016/0306-4573(94)90025-6 Lemire D., 2005, INT J INTERACTIVE TE, V2, P179 LI Q, 2004, 27 ANN INT ACM SIGIR Li Q, 2007, INFORM PROCESS MANAG, V43, P473, DOI 10.1016/j.ipm.2006.07.005 LI Q, 2003, IEEE WIC INT C WEB I Massa P, 2004, INT C COOP INF SYST NASRAOUI O, 2004, 3 INT WORKSH WEB DYN ODONOVAN J, 2005, 10 INT C INT US INT OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Pennock D.M., 2000, 16 C UNC ART INT STA POLAT H, 2003, INT J ELECTRON COMM, V9, P9 POPESCUL A, 2001, 17 C UNC ART INT SEA Resnick P., 1994, ACM C COMP SUPP COOP ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Sarwar B, 2002, 5 INT C COMP INF TEC Sarwar Bardul M., 2001, 10 INT WORLD WID WEB SARWAR BM, 2000, INT WORKSH WEB MIN E Shardanand U., 1995, SIGCHI C HUM FACT CO SI L, 2003, 12 INT C MACH LEARN TERRY DB, 1993, P C ORG COMP SYST MI TVEIT A, 2001, 1 INT WORKSH MOB COM VERHOEFF J, 1961, COMMUN ACM, V4, P557, DOI 10.1145/366853.366861 Wade W, 2003, NY TIMES WANG J, 2006, 29 ANN INT ACM SIGIR WASFI AMA, 1998, 4 INT C INT US INT L WILKINSON R, 2001, 14 ANN INT ACM SIGIR WITTENBURG K, 1995, 4 INT WORLD WID WEB XUE GR, 2005, 28 ANN INT ACM SIGIR YANG Y, 2001, WORKSH INF TECHN SYS YU K, 2002, 2 SIAM INT C DAT MIN Yu ZW, 2006, IEEE PERVAS COMPUT, V5, P68 Zacharia G, 2000, DECIS SUPPORT SYST, V29, P371, DOI 10.1016/S0167-9236(00)00084-1 NR 64 TC 0 Z9 0 PU NOVA SCIENCE PUBLISHERS, INC PI HAUPPAUGE PA 400 OSER AVE, STE 1600, HAUPPAUGE, NY 11788-3635 USA BN 978-1-60456-680-2 PY 2009 BP 49 EP + PG 18 WC Computer Science, Cybernetics; Telecommunications SC Computer Science; Telecommunications GA BMR93 UT WOS:000273429800003 ER PT B AU Zhu, JH Wang, J Cox, I Taylor, M AF Zhu, Jianhan Wang, Jun Cox, Ingemar Taylor, Michael BE Sanderson, M Zhai, CX Zobel, J Allan, J Aslam, JA TI Risky Business: Modeling and Exploiting Uncertainty in Information Retrieval SO PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2009 CL Boston, MA SP ACM SIGIR, Microsoft Res, Yahoo, Google, IBM Res, Sun ID RELEVANCE AB Most retrieval models estimate the relevance of each document to a query and rank the documents accordingly. However, such an approach ignores the uncertainty associated with the estimates of relevancy. If a high estimate of relevancy also has a high uncertainty, then the document may be very relevant or not relevant at all. Another document may have a slightly lower estimate of relevancy but the corresponding uncertainty may be much less. In such a circumstance, should the retrieval engine risk ranking the first document highest, or should it choose a more conservative (safer) strategy that gives preference to the second document? There is no definitive answer to this question, as it depends on the risk preferences of the user and the information retrieval system. In this paper we present a general framework for modeling uncertainty and introduce an asymmetric loss function with a single parameter that can model the level of risk the system is willing to accept. By adjusting the risk preference parameter, our approach can effectively adapt to users' different retrieval strategies. We apply this asymmetric loss function to a language modeling framework and a practical risk-aware document scoring function is obtained. Our experiments on several TREC collections show that our "risk-averse" approach significantly improves the Jelinek-Mercer smoothing language model, and a combination of our "risk-averse" approach and the Jelinek-Mercer smoothing method generally outperforms the Dirichlet smoothing method. Experimental results also show that the "risk-averse" approach, even without smoothing from the collection statistics, performs as well as three commonly-adopted retrieval models, namely, the Jelinek-Mercer and Dirichlet smoothing methods, and BM25 model. C1 [Zhu, Jianhan; Wang, Jun; Cox, Ingemar] UCL, London WC1E 6BT, England. RP Zhu, JH (reprint author), UCL, London WC1E 6BT, England. EM jianhan.zhu@ucl.ac.uk; jun_wang@acm.org; ingemar@ieee.org; mitaylor@microsoft.com CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Blei D. M., 2001, P NIPS, P601 Chen H., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148245 CHURCH K, 1995, J NATURAL LANGUAGE E, P67203 GELMAN A, 2003, BAYESIAN DATA ANAL, P67203 HIEMSTRA D, 2001, THESIS U TWENTE, P67203 Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop KENDALL M, 1969, ADV THEORY STAT, V1, P67203 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 LUKACS E, 1970, CHARACTERISTIC FUNCT, P27 Madsen R. E., 2005, P 22 INT C MACH LEAR, P545, DOI 10.1145/1102351.1102420 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Risson J, 2006, COMPUT NETW, V50, P3485, DOI 10.1016/j.comnet.2006.02.001 Robertson S. E., 1982, Information Technology: Research and Development, V1 Robertson S. E., 1997, READINGS INFORM RETR, P281 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1995, TEXT RETRIEVAL C TRE, P67203 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 THOM JA, 2007, AUSTR DOC COMP S, P100 VANRIJSBERGEN CJ, 1979, INFORM RETRIEVAL, P67203 WANG J, 2009, ACM SIGIR 2009, P67203 ZARAGOZA H, 2003, P SICIR 03, P67203 ZELLNER A, 1986, J AM STAT ASSOC, V81, P446, DOI 10.2307/2289234 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 ZHU J, 2009, P EUR C INF RETR ECI, P17 NR 28 TC 2 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-483-6 PY 2009 BP 99 EP 106 DI 10.1145/1571941.1571961 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLT26 UT WOS:000270976500014 ER PT B AU Wang, J Zhu, JH AF Wang, Jun Zhu, Jianhan BE Sanderson, M Zhai, CX Zobel, J Allan, J Aslam, JA TI Portfolio Theory of Information Retrieval SO PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2009 CL Boston, MA SP ACM SIGIR, Microsoft Res, Yahoo, Google, IBM Res, Sun DE Modern portfolio theory; Mean-variance analysis; Probability ranking principle; Ranking under uncertainty ID PROBABILITY RANKING PRINCIPLE AB This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are dependent between each other. Inspired by the Modern Portfolio Theory, an economic theory dealing with investment in financial markets, we argue that ranking under uncertainty is not just about picking individual relevant documents, but about choosing the right combination of relevant documents. This motivates us to quantify a ranked list of documents on the basis of its expected overall relevance (mean) and its variance; the latter serves as a measure of risk, which was rarely studied for document ranking in the past. Through the analysis of the mean and variance, we show that an optimal rank order is the one that balancing the overall relevance (mean) of the ranked list against its risk level (variance). Based on this principle, we then derive an efficient document ranking algorithm. It generalizes the well-known probability ranking principle (PRP) by considering both the uncertainty of relevance predictions and correlations between retrieved documents. Moreover, the benefit of diversification is mathematically quantified; we show that diversifying documents is an effective way to reduce the risk of document ranking. Experimental results in text retrieval confirm the theoretical insights with improved retrieval performance. C1 [Wang, Jun; Zhu, Jianhan] UCL, Dept Comp Sci, London WC1E 6BT, England. RP Wang, J (reprint author), UCL, Dept Comp Sci, Malet Pl, London WC1E 6BT, England. EM jun_wang@acm.org; jianhan.zhu@ucl.ac.uk CR BISHOP CM, 2006, PATTERN RECOGNITION, P67203 BOYD S, 2004, CONVEX OPTIMIZATION, P67203 CARBONELL J, 1998, SIGIR, P67203 CHEN H, 2006, SIGIR, P67203 CLARKE CLA, 2008, SIGIR, P67203 COOPER WS, 1971, INADEQUACY PROBABILI, P67203 ELTON EJ, 2006, MODERN PORTFOLIO THE, P67203 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P703, DOI 10.1002/(SICI)1097-4571(199112)42:10<703::AID-ASI3>3.0.CO;2-1 JARVELIN K, 2002, ACM T INF SYST, P67203 LAFFERTY J, 2001, SIGIR, P67203 MARKOWITZ H, 1952, J FINANCE, P67203 METZLER D, 2005, TREC, P67203 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J DOC, P93 ROBERTSON SE, 1994, SIGIR, P67203 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SARWAR B, 2001, WWW, P67203 THOM JA, 2007, 20 AUSTR DOC COMP S, P67203 TOMLINSON S, 2006, SIGIR, P67203 WANG J, 2006, SIGIR, P67203 WANG J, 2009, ECIR, P67203 ZELLNER A, 1986, J AM STAT ASSOC, V81, P446, DOI 10.2307/2289234 ZHAI C, 2001, SIGIR, P67203 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 ZHU J, 2009, SIGIR, P67203 NR 25 TC 31 Z9 32 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-483-6 PY 2009 BP 115 EP 122 DI 10.1145/1571941.1571963 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLT26 UT WOS:000270976500016 ER PT B AU Zhao, JL Yun, Y AF Zhao, Jinglei Yun, Yeogirl BE Sanderson, M Zhai, CX Zobel, J Allan, J Aslam, JA TI A Proximity Language Model for Information Retrieval SO PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2009 CL Boston, MA SP ACM SIGIR, Microsoft Res, Yahoo, Google, IBM Res, Sun DE Information Retrieval; Proximity Model; Proximity Language Model ID TERM; QUERIES AB The proximity of query terms in a document is a very important information to enable ranking models go beyond the "bag of word" assumption in information retrieval. This paper studies the integration of term proximity information into the unigram language modeling. A new proximity language model (PLM) is proposed which views query terms' proximity centrality as the Dirichlet hyper-paxameter that weights the parameters of the unigram document language model. Several forms of proximity measure are developed to be used in PLM which could compute a query term's proximate centrality in a specific document. In experiments, the proximity language model is compared with the basic language model and previous works that combine the proximity information with language model using linear score combination. The experiment results show that the proposed model performs better in both top precision and average precision. C1 [Zhao, Jinglei] iZENEsoft Inc, Shanghai, Peoples R China. RP Zhao, JL (reprint author), iZENEsoft Inc, 1 PuDong Ave, Shanghai, Peoples R China. EM zhao.jinglei@gmail.com; yeogirl@gmail.com CR BAI J, 2008, INVESTIGATION PARTIA, P67203 Beeferman D., 1997, P 35 ANN M ASS COMP, P373 BUTTCHER S, 2005, P 14 TEXT RETR C GAI, P67203 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 FAGAN J, 1987, EXPT AUTOMATIC PHRAS, P67203 FERGUSON TS, 1973, ANN STAT, V1, P209, DOI 10.1214/aos/1176342360 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Hawking D., 1995, P 4 TEXT RETR C TREC, P131 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 JONES K, 1998, PROBABILISTIC MODEL, P67203 LACLARK C, 2000, ACM T INFORM SYSTEMS, V18, P44 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Nallapati R., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 OGILVIE P, 2002, NIST SPECIAL PUBLICA, P103 PONTE J, 1998, LANGUAGE MODELING AP, P67203 Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207 Robertson S., 1999, NIST SPECIAL PUBLICA, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 29 TC 19 Z9 20 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-483-6 PY 2009 BP 291 EP 298 DI 10.1145/1571941.1571993 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLT26 UT WOS:000270976500038 ER PT J AU Sirisha, BS Jeevan, VKJ Kumar, RVR Goswami, A AF Sirisha, B. S. Jeevan, V. K. J. Kumar, R. V. Raja Goswami, A. TI A personalised information support system for searching portals and e-resources SO PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS LA English DT Article DE India; Higher education; Data collection; Modelling; Information retrieval ID WORLD-WIDE-WEB; DISCOVERY AB Purpose - The purpose of this paper is to describe the development of a personalised information Support System to help faculty members to search various portals and e-resources without typing the search terms in different interfaces and to obtain results re re-ordered without human intervention. Design/methodology/approach - After a careful survey of various tools and techniques available for computerised client-centred information services, the study selected to apply web usage mining, proxy level data collection and a vector space retrieval model to develop the personalised information Support for teaching and research in a higher education institution. Findings - There are practical constraints in the implementation stage. There is considerable difficulty ill getting real and correct User interests and mapping them effectively into the products and services offered by the library. Also the interests Of users change continuously. If multiple users share the same PC, it is difficult to identify the user as there is no one-to-one mapping between user and IP address. Research limitations/implications - The paper has not considered cases for all the faculty members due to time constraints. The results obtained from the system also need correlation with the sources actually used by the faculty to test its efficacy in a highly fluid research situation like higher academics. Practical implications - A pragmatic client-centred information support prototype described in this paper may find use in other institutions needing similar information support. Originality/value - This paper demonstrates the pragmatic application of ICT for linking users and e-resources in an academic library. C1 [Jeevan, V. K. J.] Indira Gandhi Natl Open Univ, Lib & Documentat Div, New Delhi, India. [Kumar, R. V. Raja] Indian Inst Technol, Dept Elect & Elect Commun Engn, Kharagpur 721302, W Bengal, India. [Goswami, A.] Indian Inst Technol, Dept Math, Kharagpur 721302, W Bengal, India. RP Jeevan, VKJ (reprint author), Indira Gandhi Natl Open Univ, Lib & Documentat Div, New Delhi, India. EM vkjj@rediffmail.com CR Agrawal R., 1994, P 20 INT C VER LARG, P478 Anand SS, 2005, LECT NOTES ARTIF INT, V3169, P1 ARORA J, 2003, MAPPING TECHNOLOGY L, P271 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 CATLEDGE LD, 1995, COMPUT NETWORKS ISDN, V27, P1065, DOI 10.1016/0169-7552(95)00043-7 CHEN CC, 2001, P US MOD Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Cooley R., 1999, Knowledge and Information Systems, V1 DIEBOLD B, 2001, P 2001 AS PAC S INF, V9, P159 Eirinaki M, 2003, ACM T INTERNET TECHN, V3, P1, DOI DOI 10.1145/643477.643478 Han E.-H., 1998, P 2 INT C AUT AG, P408, DOI DOI 10.1145/280765.280872 HAN JW, 1993, IEEE T KNOWL DATA EN, V5, P29, DOI 10.1109/69.204089 Jeevan V. K. J., 2006, Library Review, V55, DOI 10.1108/00242530610706761 Jeevan VKJ, 2006, ELECTRON LIBR, V24, P680, DOI 10.1108/02640470610707277 Joachims T., 1997, P 15 INT JOINT C ART Kramer J., 2000, Communications of the ACM, V43, DOI 10.1145/345124.345139 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Manber U, 2000, COMMUN ACM, V43, P35, DOI 10.1145/345124.345136 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahabi C, 2002, LECT NOTES ARTIF INT, V2356, P113 Srivastava J., 2000, ACM SIGKDD EXPLORATI, V1, P12, DOI 10.1145/846183.846188 Steinbach M., 2000, COMP DOCUMENT CLUSTE VOZALIS E, 2001, INTELLIGENT TECHNIQU ZHANG F, 2002, P 1 IEEE INT C MACH, P986 NR 26 TC 1 Z9 1 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0033-0337 J9 PROGRAM-ELECTRON LIB JI Program-Electron. Libr. Inf. Syst. PY 2009 VL 43 IS 1 BP 77 EP 93 DI 10.1108/00330330910934129 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 416ZR UT WOS:000264044600007 ER PT S AU Berendt, B Trumper, D AF Berendt, Bettina Trumper, Daniel BE Ting, IH Wu, HJ TI Semantics-Based Analysis and Navigation of Heterogeneous Text Corpora: The Porpoise News and Blogs Engine SO WEB MINING APPLICATIONS IN E-COMMERCE AND E-SERVICES SE Studies in Computational Intelligence LA English DT Article; Book Chapter DE text mining; semantic text analysis; ontology learning; news analysis; blogs analysis; visualisation AB Many information sites such as news services and search engine, offer options beyond keyword search to help people group and identify relevant sources of information. However, their search options are limited to fixed and mostly syntactic criteria (like date or source identify) or semantic criteria that are opaque ("similar documents"). Also, these interfaces do not support the systematic comparing and contrasting of different information sources that is central to information literacy. In this paper, we describe Porpoise, a system that, provides users with a toolkit for doing an in-depth, semantic analysis of a collection of archives of their choice. Users are able to analyse, compare and contrast corpora in "global" and in "local" fashion. Clustering, semi-automatic ontology learning, a new form of multidimensional nearest-neighbour search and visualisation help users to navigate a multilingual corpus of news and blogs by semantic criteria of their own design. C1 [Berendt, Bettina] Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium. Humboldt Univ, Inst Informat Syst, D-10178 Berlin, Germany. RP Berendt, B (reprint author), Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium. CR BERENDT B, 2007, ISSI NEWSLETTER, V3, P55 Berendt B, 2006, LECT NOTES COMPUT SC, V4172, P495 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cunningham H., 2002, P 40 ANN M ASS COMP DAVIDSON GS, 2002, J AM SOC INFORM SCI, V53, P764 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fortuna B., 2005, Informatica, V29 Fortuna B, 2006, LECT NOTES COMPUT SC, V4289, P121 Gibson D., 2005, WWW2005, P830 He Q, 2007, IEEE DATA MINING, P493 Herman D., 2005, ROUTLEDGE ENCY NARRA Kohonen T., 2001, SELF ORGANIZING MAPS, V3rd KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Maedche A, 2002, WEB INTELLIGENCE Osinski S, 2004, ADV SOFT COMP, P359 Osinski S, 2006, LECT NOTES COMPUT SC, V3936, P167 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 THELWALL M, 2006, P 15 INT WWW C 3 ANN Wise J., 1995, INFORMATION VISUALIZ, P51 NR 20 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-540-88080-6 J9 STUD COMPUT INTELL PY 2009 VL 172 BP 45 EP 64 D2 10.1007/978-3-540-88081-3 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BJL87 UT WOS:000266779500003 ER PT B AU Ma, HY AF Ma, Hongyan BE Filipe, J Cordeiro, J TI IMPROVING WEB SEARCH BY EXPLOITING SEARCH LOGS SO WEBIST 2009: PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES LA English DT Proceedings Paper CT 5th International Conference on Web Information Systems and Technologies CY MAR 23-26, 2009 CL Lisbon, PORTUGAL SP Workflow Management Coalit, ACM SIGWEB, ACM SIGMIS HO Inst Syst & Technol Informat, Control & Commun DE Web searching; Relevance feedback; Probabilistic model; Log mining ID INFORMATION AB With the increased use of Web search engines, acute needs evolve for more adaptive and more personalizable Information Retrieval (IR) systems. This study proposes an innovative probabilistic method exploiting search logs to gather useful data about contexts and users to support adaptive retrieval. Real users' search logs from an operational Web search engine, Infocious, were processed to obtain past queries and click-through data for adaptive indexing and unified probabilistic retrieval. An empirical experiment of retrieval effectiveness was conducted. The results demonstrate that the log-based probabilistic system yields statistically superior performance over the baseline system. C1 Florida State Univ, Coll Informat, Tallahassee, FL 32306 USA. RP Ma, HY (reprint author), Florida State Univ, Coll Informat, POB 3062100, Tallahassee, FL 32306 USA. EM hma@fsu.edu CR Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 ANICK P, 2003, P 26 ANN INT ACM SIG, P88 Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 Billerbeck Bodo, 2003, P 12 INT C INF KNOWL, P2 Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198 BURNS E, US SEARCH ENGINE RAN CUI Q, 2005, P 7 ANN ACM INT WORK, P59, DOI 10.1145/1097047.1097060 DING C, 2007, P 22 INT C ACM S APP, P829, DOI 10.1145/1244002.1244186 HOU Y, 2006, P 2006 IEEE WIC ACM, P749 Huang CK, 2003, J AM SOC INF SCI TEC, V54, P638, DOI 10.1002/asi.10256 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 1997, READINGS INFORMATION, P1 MA HY, 2008, THESIS U CALIFORNIA NUNBERG G, 2003, NY TIMES 0518 Ozmutlu HC, 2005, INFORM PROCESS MANAG, V41, P1243, DOI 10.1016/j.ipm.2004.04.018 Robertson S.E., 1997, SIMPLE PROVEN APPROA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Scholer F, 2004, J AM SOC INF SCI TEC, V55, P637, DOI 10.1002/asi.20011 Shen C.X, 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747 Shen Xuehua, 2005, P 28 ANN INT ACM SIG TAN Q, 2004, P 9 INT C DAT SYST A Turpin A.H., 2001, P 24 ANN INT ACM SIG, P225, DOI 10.1145/383952.383992 WEIRES R, 2008, 4 INT C WEB INF SYST Wen J. R., 2001, P 10 INT C WORLD WID, P162, DOI 10.1145/371920.371974 Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 XUE GR, 2002, P 3 INT C WEB INF SY, P238 Zha H., 2006, P 15 ACM INT C INF K, P307, DOI 10.1145/1183614.1183660 ZHOU J, 2006, P 2006 C CTR ADV STU, P35 NR 30 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-989-8111-81-4 PY 2009 BP 208 EP 216 PG 9 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BJR70 UT WOS:000267048800031 ER PT J AU Ko, Y An, H Seo, J AF Ko, Youngjoong An, Hongkuk Seo, Jungyun TI Pseudo-relevance feedback and statistical query expansion for web snippet generation SO INFORMATION PROCESSING LETTERS LA English DT Article DE Information retrieval; Text summarization; Pseudo-relevance feedback; Query expansion; Snipper generation AB A (page or web) snippet is a document excerpt allowing a user to understand if a document is indeed relevant without accessing it. This paper proposes an effective snippet generation method. A statistical query expansion approach with pseudo-relevance feedback and text summarization techniques are applied to salient sentence extraction for good quality snippets. In the experimental results, the proposed method showed much better performance than other methods including those of commercial Web search engines such as Google and Naver. (C) 2008 Elsevier B.V. All rights reserved. C1 [Ko, Youngjoong] Dong A Univ, Dept Comp Engn, Pusan 604714, South Korea. [An, Hongkuk; Seo, Jungyun] Sogang Univ, Dept Comp Sci, Seoul 121742, South Korea. [An, Hongkuk; Seo, Jungyun] Sogang Univ, Program Integrated Biotechnol, Seoul 121742, South Korea. RP Ko, Y (reprint author), Dong A Univ, Dept Comp Engn, 840 Hadan 2 Dong, Pusan 604714, South Korea. EM yjko@dau.ac.kr; anhg99@sogang.ac.kr; seojy@sogang.ac.kr FU Intelligent Robotics Development Program; Ministry of Knowledge Economy of Korea FX This research (paper) was performed for the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. CR Fagni T, 2006, ACM T INFORM SYST, V24, P51, DOI 10.1145/1125857.1125859 Frakes W. B., 1992, INFORM RETRIEVAL DAT Goldstein J., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312665 Harman D, 1988, P 11 ANN INT ACM SIG, P321, DOI 10.1145/62437.62469 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 McDonald DM, 2006, ACM T INFORM SYST, V24, P111, DOI 10.1145/1125857.1125861 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sanderson M., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288640 Turpin A., 2007, P 30 ANN INT ACM SIG, P127, DOI 10.1145/1277741.1277766 Wasson M., 1998, P 17 INT C COMP LING, P1364 ZECHNER K, 1997, P 16 INT C COMP LING, P986 NR 11 TC 3 Z9 3 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0020-0190 J9 INFORM PROCESS LETT JI Inf. Process. Lett. PD DEC 16 PY 2008 VL 109 IS 1 BP 18 EP 22 DI 10.1016/j.ipl.2008.08.004 PG 5 WC Computer Science, Information Systems SC Computer Science GA 378XD UT WOS:000261357200003 ER PT J AU Wang, J Robertson, S de Vries, AP Reinders, MJT AF Wang, Jun Robertson, Stephen de Vries, Arjen P. Reinders, Marcel J. T. TI Probabilistic relevance ranking for collaborative filtering SO INFORMATION RETRIEVAL LA English DT Article DE collaborative filtering; recommender systems; Probability Ranking Principle; relevance ranking; personalization ID INFORMATION-RETRIEVAL; MODELS AB Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly cast as a relevance ranking problem. We begin with the classic Probability Ranking Principle of information retrieval, proposing a probabilistic item ranking framework. In the framework, we derive two different ranking models, showing that despite their common origin, different factorizations reflect two distinctive ways to approach item ranking. For the model estimations, we limit our discussions to implicit user preference data, and adopt an approximation method introduced in the classic text retrieval model (i.e. the Okapi BM25 formula) to effectively decouple frequency counts and presence/absence counts in the preference data. Furthermore, we extend the basic formula by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance), which largely alleviates the data sparsity problem. Apart from a theoretical contribution, our experiments on real data sets demonstrate that the proposed methods perform significantly better than other strong baselines. C1 [Wang, Jun] UCL, Ipswich IP5 3RE, Suffolk, England. [Robertson, Stephen] Microsoft Res, Cambridge, England. [de Vries, Arjen P.] CWI, NL-1009 AB Amsterdam, Netherlands. [Reinders, Marcel J. T.] Delft Univ Technol, Delft, Netherlands. RP Wang, J (reprint author), UCL, Adastral Pk Campus,Ross Bldg 2, Ipswich IP5 3RE, Suffolk, England. EM j.wang@adastral.ucl.ac.uk; ser@microsoft.com; arjen@acm.org; m.j.t.reinders@tudelft.nl RI de Vries, Arjen/ OI de Vries, Arjen/0000-0002-2888-4202 CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bishop C. H., 2006, PATTERN RECOGNITION Breese J S, 1998, P 14 C UNC ART INT, P43 Canny J, 2002, SIGIR, P238 Claypool M., 2001, INTELLIGENT USER INT, V1, ppp COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 EYHERAMENDY S, 2003, P ART INT STAT Gelman A, 2003, BAYESIAN DATA ANAL HARTER S, 1975, J AM SOC INFORM SCI, V35, P280 HARTER S, 1975, J AM SOC INFORM SCI, V35, P197 Herlocker JL, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P230 Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 HULL D, 1993, SIGIR 93 P 16 ANN IN, P16 Jin R, 2006, INFORM RETRIEVAL, V9, P357, DOI 10.1007/s10791-006-4651-1 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Jordan M., 1999, LEARNING GRAPHICAL M LAFFERTY J, 2003, KLUWER INT SERIES IN, V13, P1 Marlin B., 2004, THESIS U TORONTO McLaughlin M. R, 2004, SIGIR 2004, P329 Pennock D. M., 2000, UAI, P473 Resnick P., 1994, CSCW 94, P175 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x Robertson S. E., 1982, Information Technology: Research and Development, V1 Robertson S. E., 1997, READINGS INFORM RETR, P281 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sarwar B., 2001, WORLD WIDE WEB, P285, DOI DOI 10.1145/371920.372071 TAYLOR MJ, 2003, RANKING CLASSES FIND Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd WANG J, 2008, ACM T INFOR IN PRESS Wang J., 2006, SIGIR 06, P501, DOI DOI 10.1145/1148170.1148257 WANG J, 2007, PERSONALIZED COLLABO WANG J, 2006, P EUR C INF RETR, P37 Xue G. -R., 2005, SIGIR 05, P114 Zaragoza H, 2003, SIGIR 03 P 26 ANN IN, P4 Zhai C., 2001, SIGIR 2001, P334 NR 40 TC 10 Z9 11 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2008 VL 11 IS 6 BP 477 EP 497 DI 10.1007/s10791-008-9060-1 PG 21 WC Computer Science, Information Systems SC Computer Science GA 336XW UT WOS:000258401100001 ER PT J AU Luk, RWP AF Luk, Robert W. P. TI On event space and rank equivalence between probabilistic retrieval models SO INFORMATION RETRIEVAL LA English DT Article DE probabilistic models; event space; information retrieval ID INFORMATION-RETRIEVAL; PRINCIPLE AB This paper discusses various issues about the rank equivalence of Lafferty and Zhai between the log-odds ratio and the query likelihood of probabilistic retrieval models. It highlights that Robertson's concerns about this equivalence may arise when multiple probability distributions are assumed to be uniformly distributed, after assuming that the marginal probability logically follows from Kolmogorov's probability axioms. It also clarifies that there are two types of rank equivalence relations between probabilistic models, namely strict and weak rank equivalence. This paper focuses on the strict rank equivalence which requires the event spaces of the participating probabilistic models to be identical. It is possible that two probabilistic models are strict rank equivalent when they use different probability estimation methods. This paper shows that the query likelihood, p(q vertical bar d, r), is strict rank equivalent to p(q vertical bar d) of the language model of Ponte and Croft by applying assumptions 1 and 2 of Lafferty and Zhai. In addition, some statistical component language model may be strict rank equivalent to the log-odds ratio, and that some statistical component model using the log-odds ratio may be strict rank equivalent to the query likelihood. Finally, we suggest adding a random variable for the user information need to the probabilistic retrieval models for clarification when these models deal with multiple requests. C1 Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. RP Luk, RWP (reprint author), Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. EM csrluk@comp.polyu.edu.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 CR Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 CROFT WB, 2003, LANGUAGE MODELING IN FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Fuhr N, 2008, INFORM RETRIEVAL, V11, P251, DOI 10.1007/s10791-008-9045-0 Gao MC, 2004, DESALINATION, V170, P177, DOI 10.1016/j.desal.2004.02.099 Hiemstra D., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hiemstra D, 1998, LNCS, V1513, P569 Lafferty J., 2003, LANGUAGE MODELING IN, P1 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lidstone George James, 1920, T FACULTY ACTUARIES, V8, P182 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 NEY H, 1994, INFORM TECHNOLOGY RE, V3, P33 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1982, P SIGIR 1982, P108 ROBERTSON SE, 2003, ACM SIGIR WORKSH MAT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTSO.SE, 1974, J DOC, V30, P41 Roelleke T., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148192 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 Sparck J. K., 2000, INFORMATION PROCESSI, V36, P779 SPARCK JK, 2003, LANGUAGE MODELING IN, P57 WU HC, 2007, P SIGIR 99 30 ANN IN, P713, DOI 10.1145/1277741.1277872 WU HC, ACM T INFOR IN PRESS YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 31 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2008 VL 11 IS 6 BP 539 EP 561 DI 10.1007/s10791-008-9062-z PG 23 WC Computer Science, Information Systems SC Computer Science GA 336XW UT WOS:000258401100003 ER PT J AU Zhang, J AF Zhang, Jin TI An experimental investigation on the distance angle integrated similarity measure SO CANADIAN JOURNAL OF INFORMATION AND LIBRARY SCIENCE-REVUE CANADIENNE DES SCIENCES DE L INFORMATION ET DE BIBLIOTHECONOMIE LA English DT Article ID SEARCH REQUEST FORMULATIONS; INFORMATION-RETRIEVAL; DOCUMENT SIMILARITY; RELEVANCE FEEDBACK; MODEL; TERMS AB An experimental study was conducted to examine and test retrieval performance of the distance angle integrated similarity measure introduced in an earlier paper. Retrieval performance of the new similarity measure within the angle, distance, conjunction, disjunction, and ellipse retrieval models was compared against retrieval performance of the traditional distance similarity measure and the angle similarity measure, respectively. The results demonstrate the distance angle integrated similarity measure achieved satisfactory performance. C1 Univ Wisconsin, Sch Informat Studies, Milwaukee, WI 53211 USA. RP Zhang, J (reprint author), Univ Wisconsin, Sch Informat Studies, Milwaukee, WI 53211 USA. EM jzhang@uwm.edu RI zhang, jin /A-6769-2010 CR Allan J, 2001, INFORM PROCESS MANAG, V37, P435, DOI 10.1016/S0306-4573(00)00056-X Atlam ES, 2000, INFORM PROCESS MANAG, V36, P717, DOI 10.1016/S0306-4573(00)00009-1 Atlam ES, 2003, INFORM PROCESS MANAG, V39, P809, DOI 10.1016/S0306-4573(03)00019-0 Bartell BT, 1998, J AM SOC INFORM SCI, V49, P742 Burrell QL, 2005, J AM SOC INF SCI TEC, V56, P704, DOI 10.1002/asi.20160 Calado P, 2006, J AM SOC INF SCI TEC, V57, P208, DOI 10.1002/asi.20266 Chen ZX, 2007, J AM SOC INF SCI TEC, V58, P1392, DOI 10.1002/asi.20612 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CRONIN B, 1994, J AM SOC INFORM SCI, V45, P537, DOI 10.1002/(SICI)1097-4571(199408)45:7<537::AID-ASI8>3.0.CO;2-Q Egghe L, 2003, INFORM PROCESS MANAG, V39, P771, DOI 10.1016/S0306-4573(02)00027-4 Egghe L, 2006, J AM SOC INF SCI TEC, V57, P1165, DOI 10.1002/asi.20396 Ellis D., 1993, PERSPECTIVES INFORMA, V3, P128 Falkowski BJ, 1998, J AM SOC INFORM SCI, V49, P854, DOI 10.1002/(SICI)1097-4571(199807)49:9<854::AID-ASI11>3.0.CO;2-Q Fricke M, 1997, J AM SOC INFORM SCI, V48, P882, DOI 10.1002/(SICI)1097-4571(199710)48:10<882::AID-ASI4>3.0.CO;2-Y Glass G. V., 1995, STAT METHODS ED PSYC, V3a GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 HAMERS L, 1989, INFORM PROCESS MANAG, V25, P315, DOI 10.1016/0306-4573(89)90048-4 Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 Korfhage R., 1997, INFORM STORAGE RETRI KWOK KL, 1985, J AM SOC INFORM SCI, V36, P342, DOI 10.1002/asi.4630360510 MCGILL M, 1979, EVALUATION FACTORS A Michel C, 2001, INFORM PROCESS MANAG, V37, P603, DOI 10.1016/S0306-4573(00)00040-6 Na SH, 2007, INFORM PROCESS MANAG, V43, P887, DOI 10.1016/j.ipm.2006.08.008 NUCHPRAYYOON A, 1996, THESIS U PITTSBURGH OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 Olsen K. A., 1994, Proceedings. IEEE Symposium on Visual Languages (Cat. No.94TH8010), DOI 10.1109/VL.1994.363612 Qin J, 2000, J AM SOC INFORM SCI, V51, P166, DOI 10.1002/(SICI)1097-4571(2000)51:2<166::AID-ASI8>3.3.CO;2-Q RADECKI T, 1982, J DOC, V38, P14, DOI 10.1108/eb026719 RADECKI T, 1985, INFORM PROCESS MANAG, V21, P501, DOI 10.1016/0306-4573(85)90004-4 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rousseau R, 1998, INFORM PROCESS MANAG, V34, P87, DOI 10.1016/S0306-4573(97)00067-8 Salton G., 1989, AUTOMATIC TEXT PROCE Torvik VI, 2005, J AM SOC INF SCI TEC, V56, P140, DOI [10.1002/asi.20105, 10.1002/asi/20105] TRAVISON D, 1987, INFORMATION PROCESSI, V23, P183 Tudhope D, 1997, INFORM PROCESS MANAG, V33, P233, DOI 10.1016/S0306-4573(96)00067-2 Watters C, 2000, J AM SOC INFORM SCI, V51, P793, DOI 10.1002/(SICI)1097-4571(2000)51:9<793::AID-ASI20>3.3.CO;2-H Zhang J, 1999, J AM SOC INFORM SCI, V50, P772, DOI 10.1002/(SICI)1097-4571(1999)50:9<772::AID-ASI5>3.0.CO;2-E Zhang J, 2001, INFORM PROCESS MANAG, V37, P639, DOI 10.1016/S0306-4573(00)00042-X ZHANG J, 2000, P 63 ANN M AM SOC IN, P248 Zhang J, 2001, INFORM PROCESS MANAG, V37, P279, DOI 10.1016/S0306-4573(00)00027-3 Zhang J, 1999, J AM SOC INFORM SCI, V50, P779, DOI 10.1002/(SICI)1097-4571(1999)50:9<779::AID-ASI6>3.0.CO;2-R NR 42 TC 0 Z9 0 PU CANADIAN ASSOC INFORMATION SCIENCE PI OTTAWA PA PO BOX 6174, STATION J, OTTAWA, ONTARIO K2A 1T2, CANADA SN 1195-096X J9 CAN J INFORM LIB SCI JI Can. J. Inf. Libr. Sci.-Rev. Can. Sci. Inf. Bibl. PD SEP-DEC PY 2008 VL 32 IS 3-4 BP 141 EP 168 PG 28 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 476WO UT WOS:000268477500003 ER PT J AU Robertson, S AF Robertson, Stephen TI On the history of evaluation in IR SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE cranfield; evaluation; experimentation; information retrieval; relevance; TREC ID INFORMATION-RETRIEVAL; RELEVANCE; ASK AB This paper is a personal take on the history of evaluation experiments in information retrieval. It describes some of the early experiments that were formative in our understanding, and goes on to discuss the current dominance of TREC ( the Text REtrieval Conference) and to assess its impact. C1 Microsoft Res, Cambridge CB3 0FB, England. RP Robertson, S (reprint author), Microsoft Res, 7JJ Thomson Ave, Cambridge CB3 0FB, England. EM ser@microsoft.com CR [Anonymous], 1948, ROYAL SOC SCI UNPUB BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 Blair DC, 2002, INFORM PROCESS MANAG, V38, P445, DOI 10.1016/S0306-4573(01)00026-7 Cleverdon C. W., 1966, FACTORS DETERMINING Cleverdon C. W., 1962, REPORT TESTING ANAL Croft W. B., 2000, ADV INFORM RETRIEVAL Croft W.B, 2003, LANGUAGE MODELLING I HANCOCKBEAULIEU M, 1997, J DOC, V53, P8 Jones K., 1977, REPORT DESIGN STUDY Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1971, AUTOMATIC KEYWORD CL Jones K. S., 2000, Information Processing & Management, V36 KEEN EM, 1973, J DOC, V29, P1, DOI 10.1108/eb026547 KEEN EM, 1971, SMART RETRIEVAL SYST, P74 KEEN EM, 1978, 5454 BLRDD LANCASTER FW, 1969, AM DOC, V20, P119, DOI 10.1002/asi.4630200204 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MITEV N, 1985, 39 BRIT LIB *NAT AC SCI, 1958, P INT C SCI INF WASH *NIST, TEXT RETR C ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1971, SMART RETRIEVAL SYST Sparck Jones K, 1975, REPORT NEED PROVISIO Sparck Jones K., 1981, INFORM RETRIEVAL EXP SWANSON DR, 1971, LIBR QUART, V41, P223 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Voorhees Ellen M., 2005, TREC EXPT EVALUATION WALKER S, 1989, ONLINE CATALOGUE DEV, P84 2003, INFORM PROCESSING MA, V39, P153 NR 36 TC 22 Z9 22 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD AUG PY 2008 VL 34 IS 4 BP 439 EP 456 DI 10.1177/0165551507086989 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 334XN UT WOS:000258255200006 ER PT J AU Anagnostopoulos, A Broder, A Punera, K AF Anagnostopoulos, Aris Broder, Andrei Punera, Kunal TI Effective and efficient classification on a search-engine model SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE text classification; search engine; feature selection; query efficiency; WAND; term correlations AB Traditional document classification frameworks, which apply the learned classifier to each document in a corpus one by one, are infeasible for extremely large document corpora, like the Web or large corporate intranets. We consider the classification problem on a corpus that has been processed primarily for the purpose of searching, and thus our access to documents is solely through the inverted index of a large scale search engine. Our main goal is to build the "best" short query that characterizes a document class using operators normally available within search engines. We show that surprisingly good classification accuracy can be achieved on average over multiple classes by queries with as few as 10 terms. As part of our study, we enhance some of the feature-selection techniques that are found in the literature by forcing the inclusion of terms that are negatively correlated with the target class and by making use of term correlations; we show that both of those techniques can offer significant advantages. Moreover, we show that optimizing the efficiency of query execution by careful selection of terms can further reduce the query costs. More precisely, we show that on our set-up the best 10-term query can achieve 93% of the accuracy of the best SVM classifier (14,000 terms), and if we are willing to tolerate a reduction to 89% of the best SVM, we can build a 10-term query that can be executed more than twice as fast as the best 10-term query. C1 [Punera, Kunal] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA. [Anagnostopoulos, Aris; Broder, Andrei] Yahoo Res, Sunnyvale, CA 94089 USA. RP Punera, K (reprint author), Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA. EM kunal@lans.ece.utexas.edu RI Anagnostopoulos, Aris/G-8256-2012 CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Broder Andrei Z., 2003, P 12 INT C INF KNOWL, P426, DOI DOI 10.1145/956863.956944 CARMEL D, 2001, P 10 TEXT RETRIEVAL Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Chang CH, 1999, IEEE T KNOWL DATA EN, V11, P595 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Fawcett Tom, 2003, HPL20034 FLAKE GW, 2002, P 11 INT WWW C, P317 Friedman JH, 1997, DATA MIN KNOWL DISC, V1, P55, DOI 10.1023/A:1009778005914 Glover E.J., 2001, SAINT, P23 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 HANCOCKBEAULIEU M, 1997, 5 TEXT RETRIEVAL C T Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Lewis DD, 2004, J MACH LEARN RES, V5, P361 McCallum A., 1998, AAAI 98 WORKSH LEARN Mitchell T.M., 1997, MACHINE LEARNING Porter MF, 1997, ALGORITHM SUFFIX STR, P313 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RENNIE J, 2001, AIM2001026 Rifkin R, 2004, J MACH LEARN RES, V5, P101 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1982, SIGIR 82, P151 Vapnik V, 1995, NATURE STAT LEARNING Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 26 TC 9 Z9 10 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD AUG PY 2008 VL 16 IS 2 BP 129 EP 154 DI 10.1007/s10115-007-0102-6 PG 26 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 332EE UT WOS:000258064200001 ER PT J AU Witschel, HF AF Witschel, Hans Friedrich TI Global term weights in distributed environments SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE distributed information retrieval; term weighting; language modeling ID INFORMATION-RETRIEVAL; NUMBER AB This paper examines the estimation of global term weights (such as IDF) in information retrieval scenarios where a global view on the collection is not available. In particular, the two options of either sampling documents or of using a reference corpus independent of the target retrieval collection are compared using standard IR test collections. In addition, the possibility of pruning term lists based on frequency is evaluated. The results show that very good retrieval performance can be reached when just the most frequent terms of a collection, - an "extended stop word list" - are known and all terms which ire not in that list are treated equally. However,the list cannot always be fully estimated from a general-purpose reference corpus, but some "domain-specific stop words" need to be added. A good solution for achieving this is to mix estimates from small samples of the target retrieval collection with ones derived from a reference corpus. (C) 2007 Elsevier Ltd. All rights reserved. C1 Univ Leipzig, NLP Dept, D-04009 Leipzig, Germany. RP Witschel, HF (reprint author), Univ Leipzig, NLP Dept, POB 100920, D-04009 Leipzig, Germany. EM witschel@informatik.uni-leipzig.de CR Boneh S, 1998, J AM STAT ASSOC, V93, P372, DOI 10.2307/2669633 Brants Thorsten, 2003, P 26 ANN INT ACM SIG, P330 Callan J., 2000, ADV INFORM RETRIEVAL, P127 Callan J.P., 1996, P 19 ANN INT ACM SIG, P262, DOI 10.1145/243199.243273 CAMEL D, 2001, P SIGIR 01, P43 CHOWDHURY AR, 2001, THESIS Cuenca-Acuna F. M., 2003, Proceedings 12th IEEE International Symposium on High Performance Distributed Computing EFRON B, 1976, BIOMETRIKA, V63, P435, DOI 10.1093/biomet/63.3.435 French J. C., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312684 Gale W., 1995, J QUANT LINGUIST, V2, P217, DOI 10.1080/09296179508590051 KARBHARI P, 2004, P PASS ACT MEAS WORK KLEMM F, 2005, P DBISP2P 05 Kornai A., 2002, GLOTTOMETRICS, V4, P61 KRONFOL AZ, 2002, THESIS PRINCETON U Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Manning C. D., 1999, FDN STAT NATURAL LAN PAPKA R, 1998, LINE NEW EVENT DETEC Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1994, P 2 TEXT RETR C, P21 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Tang CQ, 2003, ACM SIGCOMM COMP COM, V33, P89, DOI 10.1145/774763.774777 VILES CL, 1995, P 18 ANN INT ACM SIG, P12, DOI 10.1145/215206.215327 Yang Yiming, 1998, P 21 ANN INT ACM SIG, P28, DOI DOI 10.1145/290941.290953 ZHAI C, 1998, P TREC 7, P96 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 25 TC 3 Z9 3 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2008 VL 44 IS 3 BP 1049 EP 1061 DI 10.1016/j.ipm.2007.09.003 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 292LN UT WOS:000255267600005 ER PT J AU Wong, WS Luk, RWP Leong, HV Ho, KS Lee, DL AF Wong, W. S. Luk, R. W. P. Leong, H. V. Ho, K. S. Lee, D. L. TI Re-examining the effects of adding relevance information in a relevance feedback environment SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE relevance feedback; controlled experiment; vector space model ID AUTOMATIC QUERY EXPANSION; GENETIC ALGORITHMS; SEARCH TERMS; RETRIEVAL; OPTIMIZATION; PERFORMANCE; SELECTION; MODEL AB This paper presents an investigation about how to automatically formulate effective queries using full or partial relevance information (i.e., the terms that are in relevant documents) in the context of relevance feedback (RF). The effects of adding relevance information in the RF environment are studied via controlled experiments. The conditions of these controlled experiments are formalized into a set of assumptions that form the framework of our study. This framework is called idealized relevance feedback (IRF) framework. In our IRF settings, we confirm the previous findings of relevance feedback studies. In addition, our experiments show that better retrieval effectiveness can be obtained when (i) we normalize the term weights by their ranks, (ii) we select weighted terms in the top K retrieved documents, (iii) we include terms in the initial title queries, and (iv) we use the best query sizes for each topic instead of the average best query size where they produce at most five percentage points improvement in the mean average precision (MAP) value. We have also achieved a new level of retrieval effectiveness which is about 55-60% MAP instead of 40+% in the previous findings. This new level of retrieval effectiveness was found to be similar to a level using a TREC ad hoc test collection that is about double the number of documents in the TREC-3 test collection used in previous works. (C) 2007 Elsevier Ltd. All rights reserved. C1 [Wong, W. S.; Luk, R. W. P.; Leong, H. V.; Ho, K. S.] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. [Lee, D. L.] Hong Kong Univ Sci & Technol, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. RP Luk, RWP (reprint author), Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. EM csrluk@comp.polyu.edu.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 CR Allan J, 1996, P 19 ANN INT ACM SIG, P270, DOI 10.1145/243199.243274 BUCKLEY C, 1993, P TREC 2 GAITH MD US, P45 Buckley C., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148284 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 2003, RELIABLE INFORM ACCE, P1 BUCKLEY C, 1995, P 18 ANN INT ACM SIG, P292 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Carpineto C, 2002, ACM T INFORM SYST, V20, P259, DOI 10.1145/568727.568728 CHUNG TL, 2006, ACM T ASIAN LANGUAGE, V5, P245, DOI 10.1145/1194936.1194941 Chung YM, 2004, INFORM PROCESS MANAG, V40, P891, DOI 10.1016/j.ipm.2003.11.003 Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 EFTHIMIADIS EN, 1993, P 16 ANN INT ACM SIG, P148 Fan W., 2004, P 27 ANN INT ACM SIG, P138, DOI 10.1145/1008992.1009018 GU Z, 2004, P 27 ANN INT ACM SIG, P482, DOI 10.1145/1008992.1009081 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Ide E., 1971, SMART RETRIEVAL SYST, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Keenan S, 2001, J AM SOC INF SCI TEC, V52, P570, DOI 10.1002/asi.1096.abs Khan MS, 2004, J AM SOC INF SCI TEC, V55, P29, DOI 10.1002/asi.10341 Kuriyama K, 2002, INFORM RETRIEVAL, V5, P41, DOI 10.1023/A:1012778807438 Lopez-Pujalte C, 2003, INFORM PROCESS MANAG, V39, P669, DOI 10.1016/S0306-4573(02)00044-4 Lopez-Pujalte C, 2002, INFORM PROCESS MANAG, V38, P793, DOI 10.1016/S0306-4573(01)00061-9 MANO H, 2001, P 24 ANN INT ACM C R, P390, DOI 10.1145/383952.384032 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S, 2007, INFORM RETRIEVAL, V10, P321, DOI 10.1007/s10791-007-9025-9 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1977, J DOCUEMENTATION, V33, P106 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 SINGHAL A, 1997, P 6 TEXT RETR C TREC, P215 SPINK A, 2002, IEEE COMPUT, V35, P107 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Vines Phil, 1999, P 4 INT WORKSH INF R, P118 VOORHEES E, 1998, P 6 TEXT RETR C TREC, P1 Voorhees E. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291017 Voorhees E.M., 2000, P 9 TEXT RETRIEVAL C, P1 Voorhees E.M., 1999, P TREC, P1 VOORHEES EM, 1998, P 25 ANN INT ACM SIG, P316 WALKER S, 1997, P 6 TEXT RETR C TREC, P125 Wu HC, 2007, INFORM PROCESS MANAG, V43, P1308, DOI 10.1016/j.ipm.2006.10.009 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 Zobel J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291014 NR 51 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2008 VL 44 IS 3 BP 1086 EP 1116 DI 10.1016/j.ipm.2007.12.002 PG 31 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 292LN UT WOS:000255267600008 ER PT J AU Thompson, P AF Thompson, Paul TI Looking back: On relevance, probabilistic indexing and information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE probabilistic information retrieval ID EXPERT OPINION APPROACH; DOCUMENT-RETRIEVAL; SEEKING; MODEL; COMBINATION; SYSTEM AB Forty-eight years ago Maxon and Kuhns published their paper, "On Relevance, Probabilistic Indexing and Information Retrieval" (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the most widely cited papers in the field of information retrieval, many researchers today may not be familiar with its influence. This paper describes the Maron and Kuhns article and the influence that it has had on the field of information retrieval. (c) 2007 Elsevier Ltd. All rights reserved. C1 Dartmouth Coll, Sudikoff Lab 6211, Hanover, NH 03755 USA. RP Thompson, P (reprint author), Dartmouth Coll, Sudikoff Lab 6211, Hanover, NH 03755 USA. EM Paul.Thompson@dartmouth.edu CR BAGLEY P, 1951, THESIS MIT BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Bodoff D, 2004, J AM SOC INF SCI TEC, V55, P471, DOI 10.1002/asi.10398 Bodoff D, 1999, J AM SOC INFORM SCI, V50, P49, DOI 10.1002/(SICI)1097-4571(1999)50:1<49::AID-ASI7>3.0.CO;2-9 Brin S., 1998, P 7 WORLD WID WEB C BROWN L, 1959, P INT C SCI INF WASH, V1, P3 Bush V., 1945, ATLANTIC MONTHLY JUL Cleverdon C. W., 1962, REPORT TESTING ANAL CLEVERDON CW, 1959, P INT C SCI INF WASH, V1, P687 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Croft W. B., 2000, ADV INFORM RETRIEVAL CROFT WB, 2003, LANGUAGE MODELING IN DABNEY DP, 1986, LAW LIBR J, V78, P349 Fairthorne R. A, 1961, INFORM RETRIEVAL FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1986, 1986 ACM C RES DEV I, P249 GOOD IJ, 1958, RC78 IBM RES REP Hodge G. M., 1998, COMPUTER SUPPORT IND KLEINBERG J, 1998, P ACM SIAM S DISCR A LUHN HP, 1961, INFORMATION RETRIE 2, V3, P1021 MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 MARON ME, 1984, PROGR COMMUNICATION, V5, P145 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1959, 3 DAT SYST PROJ OFF MARON ME, 1958, SOME EXPT PROBABILIS MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 MOOERS CN, 1950, ASS COMP MACH M MAR *NAT AC SCI, 1959, P INT C SCI INF WASH ROBERTSON S, 2003, WORKSH MATH FDN INF Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1983, RES DEV INFORMATION, P108 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1966, THESIS HARVARD U SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 Salton G., 1983, INTRO MODERN INFORM SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P177, DOI 10.1002/(SICI)1097-4571(198805)39:3<177::AID-ASI3>3.0.CO;2-F SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SPARCKJONES K, 1972, J DOC, V28, P11 TAUBE M, 1958, INFORM STORAGE RETRI THOMPSON P, 1988, J DOC, V44, P119, DOI 10.1108/eb026821 THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P383, DOI 10.1016/0306-4573(90)90098-M THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P371, DOI 10.1016/0306-4573(90)90097-L TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 van Rijsbergen C. J., 1986, SIGIR, P194 VANRIJSBERGEN CJ, 2005, CHARTING NEW COURSE, P23 NR 51 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2008 VL 44 IS 2 BP 963 EP 970 DI 10.1016/j.ipm.2007.10.002 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 285YX UT WOS:000254813200032 ER PT J AU Xu, YJ Yin, HN AF Xu, Yunjie Yin, Hainan TI Novelty and topicality in interactive information retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID RELEVANCE CRITERIA; SEARCH TERMS; SCIENCE; USERS; DIMENSIONS; MODEL; IR AB The information science research community is characterized by a paradigm split, with a system-centered cluster working on information retrieval (IR) algorithms and a user-centered cluster working on user behavior. The two clusters rarely leverage each other's insight and strength. One major suggestion from user-centered studies is to treat the relevance judgment of documents as a subjective, multidimensional, and dynamic concept rather than treating it as objective and based on topicality only. This study explores the possibility to enhance users' topical ity-based relevance judgment with subjective novelty judgment in interactive IR. A set of systems is developed which differs in the way the novelty judgment is incorporated. In particular, this study compares systems which assume that users' novelty judgment is directed to a certain subtopic area and those which assume that users' novelty judgment is undirected. This study also compares systems which assume that users judge a document based on topicality first and then novelty in a stepwise, noncompensatory fashion and those which assume that users consider topicality and novelty simultaneously and as compensatory to each other. The user study shows that systems assuming directed novelty in general have higher relevance precision, but systems assuming a stepwise judgment process and systems assuming a compensatory judgment process are not significantly different. RP Xu, YJ (reprint author), 3 Sci Dr 2, Singapore 117543, Singapore. EM xuyj@comp.nus.edu.sg; Hainan.Yin@gimonda.com CR Allan J., 2003, P 26 ANN INT ACM SIG, P314 Baeza-Yates R., 1999, MODERN INFORM RETRIV BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2 BATEMAN J, 1998, P 61 AM SOC INF SCI, P23 BATES MJ, 1990, INFORM PROCESS MANAG, V26, P575, DOI 10.1016/0306-4573(90)90103-9 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286 Borlund P, 2000, J DOC, V56, P71, DOI 10.1108/EUM0000000007110 BOYCE B, 1982, INFORM PROCESS MANAG, V18, P105, DOI 10.1016/0306-4573(82)90033-4 Brants Thorsten, 2003, P 26 ANN INT ACM SIG, P330 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Cosijn E, 2000, INFORM PROCESS MANAG, V36, P533, DOI 10.1016/S0306-4573(99)00072-2 Efthimiadis EN, 2000, J AM SOC INFORM SCI, V51, P989, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1002>3.0.CO;2-B Fitzgerald MA, 2001, J AM SOC INF SCI TEC, V52, P989, DOI 10.1002/asi.1152 FROEHLICH TJ, 1994, J AM SOC INFORM SCI, V45, P124, DOI 10.1002/(SICI)1097-4571(199404)45:3<124::AID-ASI2>3.0.CO;2-8 Greisdorf H, 2003, INFORM PROCESS MANAG, V39, P403, DOI 10.1016/S0306-4573(02)00032-8 HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q Ingwersen P., 2005, TURN INTEGRATION INF Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137 Kirk R. E., 1995, EXPT DESIGN Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044 Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Nordlie R., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312618 Park H, 1997, INFORM PROCESS MANAG, V33, P339, DOI 10.1016/S0306-4573(96)00072-6 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Saracevic T, 1999, J AM SOC INFORM SCI, V50, P1051, DOI 10.1002/(SICI)1097-4571(1999)50:12<1051::AID-ASI2>3.0.CO;2-Z SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P33 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R *U CAL, 2001, WEB TERM DOC FREQ FO Vakkari P, 2003, INFORM PROCESS MANAG, V39, P445, DOI 10.1016/S03064573(02)0003l-6 Wang PL, 1998, J AM SOC INFORM SCI, V49, P115, DOI 10.1002/(SICI)1097-4571(1998)49:2<115::AID-ASI3>3.0.CO;2-1 Xu YJ, 2006, J AM SOC INF SCI TEC, V57, P961, DOI 10.1002/asi.20361 Yang Y., 2002, P 8 ACM SIGKDD INT C, P688, DOI DOI 10.1145/775047.775150 Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zhang Y., 2002, P 25 ANN INT ACM SIG, P81 NR 37 TC 11 Z9 11 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JAN 15 PY 2008 VL 59 IS 2 BP 201 EP 215 DI 10.1002/asi.20709 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 248NT UT WOS:000252161000004 ER PT B AU Dong, H Hussain, FK Chang, E AF Dong, Hai Hussain, Farookh Khadeer Chang, Elizabeth GP IEEE TI A Survey in Traditional Information Retrieval Models SO 2008 2ND IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES SE IEEE International Conference on Digital Ecosystems and Technologies LA English DT Proceedings Paper CT 2nd IEEE International Conference on Digital Ecosystems and Technologies CY FEB 26-29, 2008 CL Phitsanuloke, THAILAND SP IEEE DE index-term based information retrieval models; Boolean models; algebraic models; probabilistic models AB As a matter of fact, many so-called semantic search algorithms are derived from the traditional index-term-based search models. In this paper, we survey the traditional information retrieval models by categorizing them into three main classes and eleven subclasses, and analyse their benefits and issues of them. C1 [Dong, Hai] Curtin Univ Technol, DEBII, Perth, WA, Australia. RP Dong, H (reprint author), Curtin Univ Technol, DEBII, Perth, WA, Australia. EM hai.dong@cbs.curtin.edu.au; farookh.hussain@cbs.curtin.edu.au; elizabeth.chang@cbs.curtin.edu.au RI Dong, Hai/E-9101-2010 OI Dong, Hai/0000-0002-7033-5688 CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE CARTHY DCJ, 2003, SYST INF ENG DES S C, P13 Furnas G. W., 1988, 11th International Conference on Research and Development in Information Retrieval OGAWA Y, 1991, FUZZY SETS SYSTEMS, V39 Pearl J., 1988, PROBABILISTIC REASON RIBEIRO BA, 1996, P 19 ANN INT ACM SIG, P235 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1971, SAMRT EXPT AUTOMATIC SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 WILKINSON R, 1991, 14 ANN INT ACM SIGIR, P202 WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 Wong S.K.M., 1995, ACM T INFORM SYSTEMS, V13, P39 Zadeh L.A., 1993, READINGS FUZZY SETS NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1489-5 J9 I C DIGIT ECOSYST TE PY 2008 BP 500 EP 505 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJC56 UT WOS:000264749700079 ER PT B AU Martinez, R Cebrian, M Rodriguez, FD Camacho, D AF Martinez, Rafael Cebrian, Manuel Rodriguez, Francisco de Borja Camacho, David GP IEEE TI Contextual Information Retrieval based on Algorithmic Information Theory and statistical outlier detection SO 2008 IEEE INFORMATION THEORY WORKSHOP LA English DT Proceedings Paper CT IEEE Information Theory Workshop CY MAY 05-08, 2008 CL Porto, PORTUGAL SP IEEE ID IDENTIFICATION AB This work presents an Information Retrieval technique based on Algorithmic Information Theory (using the Normalized Compression Distance), Statistical Data Outlier Detection, and a novel database structure. The paper shows how they all can be integrated to retrieve information from generic databases using long text-based queries. Two important problems are addressed. On the one hand, we analyze and tyr to solve the detection of a particular case of false positives: when the distance among two documents is outlyingly low but there is not actual similarity. On the other hand, we propose a way to structure the database such that the similarity distance estimation scales well with the length of the size of the query. All design choices are justified with an experimental evaluation. C1 [Martinez, Rafael; Cebrian, Manuel; Rodriguez, Francisco de Borja; Camacho, David] Univ Autonoma Madrid, Dept Ingn Informat, E-28049 Madrid, Spain. RP Martinez, R (reprint author), Univ Autonoma Madrid, Dept Ingn Informat, E-28049 Madrid, Spain. EM Rafael.Martinez@estudiante.uam.es; Manuel.Cebrian@uam.es; F.Rodriguez@uam.es; David.Camacho@uam.es RI Rodriguez, Francisco de Borja/F-7812-2013; Camacho, David/B-5779-2014 OI Rodriguez, Francisco de Borja/0000-0003-4053-099X; Camacho, David/0000-0002-5051-3475 CR BAEZAYATES R, 1999, INFORM RETRIEVAL Barnett V., 1994, OUTLIERS STAT DATA Cebrian M., 2005, COMMUNICATIONS INFOR, V5, P367 Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059 DATE CJ, 1975, INTRO DATA BASE SYST DAVIES L, 1993, J AM STAT ASSOC, V88, P782, DOI 10.2307/2290763 Doyle L, 1975, INFORM RETRIEVAL PRO ELKIN N, 1978, J DOC, V34, P55 FREIRE M, 2007, AC INTEGRATED SOURCE GRANADOS A, 2007, ARXIV07114075ARXIVOR Pearson RK, 2002, IEEE T CONTR SYST T, V10, P55, DOI 10.1109/87.974338 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1975, COMMUN ACM, V18 SMITH LC, 1976, INFORM PROCESS MANAG, V12, P189, DOI 10.1016/0306-4573(76)90005-4 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Wilcox R.R., 2003, APPL CONT STAT TECHN Zhang X, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P874 1968, AUTOMATIC INFORM ORG NR 18 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2269-2 PY 2008 BP 292 EP 297 DI 10.1109/ITW.2008.4578672 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIG31 UT WOS:000259299000066 ER PT B AU Wang, XH Ju, SG Wu, SL AF Wang, Xiuhong Ju, Shiguang Wu, Shengli BE Yu, F Luo, Q TI Challenges in Chinese text similarity research SO 2008 INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING AND 2008 INTERNATIONAL PACIFIC WORKSHOP ON WEB MINING AND WEB-BASED APPLICATION LA English DT Proceedings Paper CT International Symposium on Information Processing (ISIP 2008)/International Pacific Workshop on Web Mining and Web-based Application (WMWA 2008) CY MAY 23-25, 2008 CL Moscow, RUSSIA SP Hunan Agr Univ, Peoples Friendship Univ Russia, IEEE System, Man, & Cybernet Soc, Int Assoc Engineers, Univ Amsterdam, Cent S Univ, Intelligent Informat Technol Applicat Res Inst AB There are many opportunities and challenges in Chinese text similarity research, which is one of the most important issues in the information retrieval field Quite a few models and approaches have been investigated for this. Chinese is one of the most complicated languages on morphology, syntax, semantics and pragmatics. In Chinese, there is not an explicit delimiter between words as in English. The difficulties in Chinese natural language processing, such as segmentation, knock down both effectiveness and efficiency of text similarity computation. This paper addresses some challenges in Chinese text similarity computation, which are undergoing from Chinese linguistics, models and approaches used in information retrieval. We consider Chinese text similarity computing tasks to cover broad topics of word, sentence and document similarity. Our work provides insights into the difficulties and bottleneck in the research, including tradeoffs between effectiveness and efficiency. New directions of the future work are discussed. C1 [Wang, Xiuhong] Jiangsu Univ, Inst Sci & Technol Informat, Zhenjiang, Jiangsu, Peoples R China. RP Wang, XH (reprint author), Jiangsu Univ, Inst Sci & Technol Informat, Zhenjiang, Jiangsu, Peoples R China. CR BAO J, 2003, J SOFTWARE, P1753 Beaza-Yates R., 1999, MODERN INFORM RETRIE Bo Jin, 2007, J DALIAN U TECHNOLOG, P125 CHE W, 2003, LANGUAGE COMPUTATION, P520 Chowdhury A, 2002, ACM T INFORM SYST, V20, P171, DOI 10.1145/506309.506311 CHRISTOPHER D, 2007, INTRO INFORM RETRIEV, P9 Dong Zh.D., 1999, HOWNET JAVED A, 2003, ACM SIGIR, P449 JING Y, 2004, J COMPUTER ENG APPL, P36 LANCASTER FW, 1993, WARNER INFORM RETRIE LI B, 2003, J COMPUTER APPL, P15 Lin Dekang, 1998, P 15 INT C MACH LEAR LIU Q, 2002, 3 S CHIN WORDS SEM LIU T, 2005, J HARBIN I TECHNOLOG, V12, P408 Lu S., 2001, J COMPUTER RES DEV, V38, P662 MCGILL M, 1979, EVALUATION FACTORS A Mei J.J., 1996, Chinese Synonym Thesaurus ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON, 1968, J ACM, P8 SPOERRI A, 1995, THESIS MIT *WIK, INF RETR ZHANG C, 2005, J SYSTEM SIMULATION, P138 ZHANG GQ, 2001, COMPUTER ENG, V27 NR 23 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3151-9 PY 2008 BP 297 EP 302 DI 10.1109/ISIP.2008.76 PG 6 WC Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHZ19 UT WOS:000257616800056 ER PT J AU Wang, J De Vries, AP Reinders, MJT AF Wang, Jun De Vries, Arjen P. Reinders, Marcel J. T. TI Unified relevance models for rating prediction in collaborative filtering SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE algorithms; experimentation; performance; theory; collaborative filtering; recommendation; personalization ID INFORMATION-RETRIEVAL; RECOMMENDATION; ALGORITHM AB Collaborative filtering aims at predicting a user's interest for a given item based on a collection of user profiles. This article views collaborative filtering as a problem highly related to information retrieval, drawing an analogy between the concepts of users and items in recommender systems and queries and documents in text retrieval. We present a probabilistic user-to-item relevance framework that introduces the concept of relevance into the related problem of collaborative filtering. Three different models are derived, namely, a user-based, an item-based, and a unified relevance model, and we estimate their rating predictions from three sources: the user's own ratings for different items, other users' ratings for the same item, and ratings from different but similar users for other but similar items. To reduce the data sparsity encountered when estimating the probability density function of the relevance variable, we apply the nonparametric (data-driven) density estimation technique known as the Parzen-window method (or kernel-based density estimation). Using a Gaussian window function, the similarity between users and/or items would, however, be based on Euclidean distance. Because the collaborative filtering literature has reported improved prediction accuracy when using cosine similarity, we generalize the Parzen-window method by introducing a projection kernel. Existing user-based and item-based approaches correspond to two simplified instantiations of our framework. User-based and item-based collaborative filterings represent only a partial view of the prediction problem, where the unified relevance model brings these partial views together under the same umbrella. Experimental results complement the theoretical insights with improved recommendation accuracy. The unified model is more robust to data sparsity because the different types of ratings are used in concert. C1 [Wang, Jun] UCL, Ipswich IP5 3RE, Suffolk, England. [De Vries, Arjen P.] CWI, NL-1090 GB Amsterdam, Netherlands. [Reinders, Marcel J. T.] Delft Univ Technol, Dept Mediamat, NL-2628 CD Delft, Netherlands. RP Wang, J (reprint author), UCL, Adastral Pk Campus,Ross Bldg,Adastral Pk, Ipswich IP5 3RE, Suffolk, England. EM j.wang@adastral.ucl.ac.uk; arjen@acm.org; j.t.reinders@tudelft.nl RI de Vries, Arjen/ OI de Vries, Arjen/0000-0002-2888-4202 CR Bodoff D, 2004, J AM SOC INF SCI TEC, V55, P471, DOI 10.1002/asi.10398 Bodoff D, 1999, J AM SOC INFORM SCI, V50, P49, DOI 10.1002/(SICI)1097-4571(1999)50:1<49::AID-ASI7>3.0.CO;2-9 Breese J S, 1998, P 14 C UNC ART INT, P43 Canny J., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/564376.564419 Cheung KW, 2004, INFORM RETRIEVAL, V7, P395, DOI 10.1023/B:INRT.0000011212.66249.b7 CHEUNG KW, 2004, INF RETR, V7, P3 *COMPAQ, 1997, EACHMOVIE DAT Croft W. B., 2003, LANGUAGE MODELING IN DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 DESHPANDE M, 2004, ACM T INFORM SYST, V22, P1 Duda RO, 2001, PATTERN CLASSIFICATI, V2nd DUIN RPW, 1976, IEEE T COMPUT, V25, P1175 Goldberg K, 2001, INFORM RETRIEVAL, V4, P133, DOI 10.1023/A:1011419012209 *GROUPLENS, 2003, EACHMOVIL DAT Herlocker J. L., 2000, THESIS U MINNESOTA Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hiemstra D., 2001, THESIS U TWENTE Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 Hu R, 2006, ICAT 2006: 16th International Conference on Artificial Reality and Telexistence - Worshops, Proceedings, P184 Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775 HUANG Z, 2004, ACM T INFORM SYST, V22, P1 Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Jin R, 2004, P 27 ANN INT ACM SIG, P337, DOI 10.1145/1008992.1009051 LAFFERTY J, 2003, LANGUAGE MODELING IN, V13, P1 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V., 2004, THESIS U MASSACHUSET Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu LX, 2004, MUTATION BREEDING RE, V14, P1 MARON ME, 1960, J ASSOC COMPUTER MAC, V7, P3 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 PACLIK P, 2000, PATTERN RECOGN LETT, V21, P13 Paclik P, 2000, PATTERN RECOGN LETT, V21, P1165, DOI 10.1016/S0167-8655(00)00078-7 Pennock D., 2000, P 16 C UNC ART INT S, P473 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rennie J. D. M., 2005, P 22 INT C MACH LEAR, P713, DOI 10.1145/1102351.1102441 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 ROBERTSON S, 2003, MATH FORMAL METHODS Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1977, J DOC, V33, P4 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sarwar B., 2000, P ACM WEBKDD WORKSH Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Scholkopf B., 2001, LEARNING KERNELS SUP Scholkopf B., 2000, P NEUR INF PROC SYST, P301 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 Si L., 2003, P 20 INT C MACH LEAR, P704 SKURICHINA M, 1990, STAT PROBLEMS CONTRO, V95, P216 Tomasi C., 2004, ESTIMATING GAUSSIAN Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Wang J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148257 WANG J, 2006, P EUR C INF RETR, P37 Xue G. R., 2005, P 28 ANN INT ACM SIG, P114, DOI 10.1145/1076034.1076056 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 NR 58 TC 15 Z9 18 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 EI 1558-2868 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PY 2008 VL 26 IS 3 AR 16 DI 10.1145/1361684.1361689 PG 42 WC Computer Science, Information Systems SC Computer Science GA 317OC UT WOS:000257028500005 ER PT S AU Song, RH Taylor, MJ Wen, JR Hon, HW Yu, Y AF Song, Ruihua Taylor, Michael J. Wen, Ji-Rong Hon, Hsiao-Wuen Yu, Yong BE Macdonald, C Ounis, I Plachouras, V Ruthven, I White, RW TI Viewing term proximity from a different perspective SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 30th European Conference on Information Retrieval (ECIR 2008) CY MAR 30-APR 03, 2008 CL Glasgow, SCOTLAND SP Univ Glasgow, Dept Comp Sci, Google, Microsoft Res, Matrixware, Yahoo Res, Univ Strathclyde, ACM, BCS ID CO-OCCURRENCE DATA; RETRIEVAL; QUERIES; WEB AB This paper extends the state-of-the-art probabilistic model BM25 to utilize term proximity from a new perspective. Most previous work only consider dependencies between pairs of terms, and regard phrases as additional independent evidence. It is difficult to estimate the importance of a phrase and its extra contribution to a relevance score, as the phrase actually overlaps with the component terms. This paper proposes a new approach. First, query terms are grouped locally into non-overlapping phrases that may contain one or more query terms. Second, these phrases are not scored independently but are instead treated as providing a context for the component query terms. The relevance contribution of a term occurrence is measured by how many query terms occur in the context phrase and how compact they are. Third, we replace term frequency by the accumulated relevance contribution. Consequently, term proximity is easily integrated into the probabilistic model. Experimental results on TREC-10 and TREC-11 collections show stable improvements in terms of average precision and significant improvements in terms of top precisions. C1 [Song, Ruihua; Yu, Yong] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China. RP Song, RH (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China. CR Brin S, 1998, P 7 INT WORLD WID WE Buttcher S., 2006, P 29 ANN INT ACM SIG Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 CLARKE CLA, 1995, P TREC, V4 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 *CSIRO, TREC WEB TRACKS HOM Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016 Fox C., 1990, SIGIR Forum, V24 Gao MC, 2004, DESALINATION, V170, P177, DOI 10.1016/j.desal.2004.02.099 HARMAN DK, P TREC, V4, P1 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Hawking D., 1996, TRCS9608 AUSTR NAT U HAWKING D, 1995, P TREC, V4, P131 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X Mishne G, 2005, LECT NOTES COMPUT SC, V3408, P502 Nallapati R., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PRATT EJ, 1989, COMPLETE POEMS Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1983, GEN TERM DEPENDENCE NR 28 TC 12 Z9 12 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-78645-0 J9 LECT NOTES COMPUT SC PY 2008 VL 4956 BP 346 EP 357 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BHN96 UT WOS:000254685500029 ER PT S AU Larson, RR AF Larson, Ray R. BE Peters, C Jikoun, V Mandl, T Muller, H Oard, DW Penas, A Petras, V Santos, D TI Cheshire at GeoCLEF 2007: Retesting Text Retrieval Baselines SO ADVANCES IN MULTILINGUAL AND MULTIMODAL INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 8th Workshop of the Cross-Language Evaluation Forum CY SEP 19-21, 2007 CL Budapest, HUNGARY AB In this paper we will briefly describe the approaches taken by Berkeley for the main GeoCLEF 2007 tasks (Mono and Bilingual retrieval). The approach this year was to use probabilistic text retrieval based on logistic regression and incorporating blind relevance feedback for all of the runs. Our intent was to establish a baseline result without explicit geographic processing for comparision with future geographic processing approaches. All translation for bilingual tasks was performed using the LEC Power Translator machine translation system. C1 Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. RP Larson, RR (reprint author), Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. CR Chen AT, 2003, LECT NOTES COMPUT SC, V2785, P28 *LEC, LEC POW TRANSL 11 PR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 3 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85759-4 J9 LECT NOTES COMPUT SC PY 2008 VL 5152 BP 811 EP 814 PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIK64 UT WOS:000260420000102 ER PT S AU Galeas, P Freisleben, B AF Galeas, Patricio Freisleben, Bernd BE Gelbukh, A TI Word distribution analysis for relevance ranking and query expansion SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 9th International Conference on Intelligent Text Processing and Computational Linguistics CY FEB 17-23, 2008 CL Haifa, ISRAEL SP Computat Linguist Grp, Univ Haifa, Caesarea Edmond Benjamin de Rothschild Fdn Inst Interdisciplinary Applicat Comp Sci HO Univ Haifa ID RETRIEVAL; CONTEXT; SEARCH AB Apart from the frequency of terms in a document collection, the distribution of words plays an important role in determining the relevance of documents for a given search query. In this paper, word distribution analysis as a novel approach for using descriptive statistics to calculate a compressed representation of word positions in a document corpus is introduced. Based on this statistical approximation, two methods for improving the evaluation of document relevance are proposed: (a) a relevance ranking procedure based on how query terms are distributed over initially retrieved documents, and (b) a query expansion technique based on overlapping the distributions of terms in the top-ranked documents. Experimental results obtained for the TREC-8 document collection demonstrate that the proposed approach leads to an improvement of about 6.6% over the term frequency/inverse document frequency weighting scheme without applying query reformulation or relevance feedback techniques. C1 [Galeas, Patricio; Freisleben, Bernd] Univ Marburg, Dept Math & Comp Sci, D-35032 Marburg, Germany. RP Freisleben, B (reprint author), Univ Marburg, Dept Math & Comp Sci, Hans Meerwein Str 3, D-35032 Marburg, Germany. CR BERRY M, 1994, UTCS94270 SIAM Billerbeck Bodo, 2003, P 12 INT C INF KNOWL, P2 Bookstein A, 1998, J AM SOC INFORM SCI, V49, P102 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley C., 1994, 17 ANN INT ACM SIGIR, P292 BUCKLEY C, 1981, OV 3 TEXT RETR C, P69 Cai D, 2004, SIGIR 2004, P456 Efthimiadis E. N., 1996, Annual review of information science and technology. Vol.31 EFTHIMIADIS E, 1994, P 2 TEXT RETR C TREC, P279 Efthimiadis E., 1992, THESIS CITY U LONDON Fan Weiguo, 2004, P 27 ANN INT ACM SIG FERNANDEZ M, 2007, P INT C REC ADV NAT, P220 Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 HUANG X, 2005, P 2005 IEEE INT C GR Katz S.M., 1996, NAT LANG ENG, V2, P15, DOI 10.1017/S1351324996001246 Lawrence S., 2000, IEEE DATA ENG B, V23, P25 LI J, 2005, MACHINE LEARNING CYB, P2305 Razek M. A., 2003, P 12 INT WORLD WID W, P968 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shen X., 2003, 26 ANN INT ACM SIGIR, P377 Sun R., 2006, SIGIR 2006, P382 TUKEY JW, 1977, SERIES BEHAV SCI Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Yu S., 2003, P 12 INT C WORLD WID, P11 NR 25 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-78134-9 J9 LECT NOTES COMPUT SC PY 2008 VL 4919 BP 500 EP 511 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BHJ77 UT WOS:000253658200043 ER PT S AU Gery, M Largeron, C Thollard, F AF Gery, Mathias Largeron, Christine Thollard, Franck BE Fuhr, N Kamps, J Lalmas, M Trotman, A TI UJM at INEX 2007: Document Model Integrating XML Tags SO FOCUSED ACCESS TO XML DOCUMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-19, 2007 CL Dagstuhl, GERMANY AB Different approaches have been used to represent textual documents, based on boolean model, vector space model or probabilistic models. In text mining as in information retrieval (IR), these models have shown good results about textual documents modeling. They nevertheless do not take into account documents structure. In many applications however, documents are inherently structured (e.g. XML documents). In this article(1), we propose an extended probabilistic representation of documents in order to take into account a certain kind of structural information: logical tags that represent the different parts of the document and formatting tags used to emphasized text. Our approach includes a learning step that estimates the weight of each tag. This weight is related to the probability for a given tag to distinguish the relevant terms. C1 [Gery, Mathias; Largeron, Christine; Thollard, Franck] Univ St Etienne, Hubert Curien Lab, St Etienne, France. RP Gery, M (reprint author), Univ St Etienne, Hubert Curien Lab, St Etienne, France. EM mathias.Gery@univ-st-etienne.fr; Christine.Largeron@univ-st-etienne.fr; Franck.Thollard@univ-st-etienne.fr CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE DEFUDE B, 1986, THESIS I NATL POLYTE FOUREL F, 1998, THESIS U GRENOBLE 1 KAMPS J, 2007, LNCS, V4518 KONOPNICKI D, 1995, 21 INT C VER LARG DA, P54 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sauvagnat K, 2006, LECT NOTES COMPUT SC, V3977, P88 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 WILKINSON R, 2007, 17 ACM C RES DEV INF NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85901-7 J9 LECT NOTES COMPUT SC PY 2008 VL 4862 BP 103 EP 114 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIP02 UT WOS:000261444400009 ER PT S AU Larson, RR AF Larson, Ray R. BE Fuhr, N Kamps, J Lalmas, M Trotman, A TI Logistic Regression and EVIs for XML Books and the Heterogeneous Track SO FOCUSED ACCESS TO XML DOCUMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-19, 2007 CL Dagstuhl, GERMANY ID INFORMATION-RETRIEVAL; PROBABILISTIC RETRIEVAL; ONLINE CATALOG; FUSION; FEEDBACK; ENGLISH AB For this year's INEX UC Berkeley focused on the Book track and the Heterogeneous track, For these runs we used the TREC2 logistic regression probabilistic model with blind feedback as well as Entry Vocabulary Indexes (EVIs) for the Books Collection MARC data. For the full text records of the book track we encountered a number of interesting problems in setting up the database, and ended up using page-level indexing of the full collection. As (once again) the only group to actually submit runs for the Het track, we are guaranteed both the highest, and lowest, effectiveness scores for each task. However, because it was again deemed pointless to conduct the actual relevance assessments on the submissions of a single system, we do not know the exact values of these results. C1 Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. RP Larson, RR (reprint author), Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. EM ray@ischool.berkeley.edu CR Callan J., 2000, ADV INFORM RETRIEVAL, P127 Callan J., 1998, P 21 ANN INT ACM SIG, P112, DOI 10.1145/290941.290974 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Chen A, 2004, INFORM RETRIEVAL, V7, P149, DOI 10.1023/B:INRT.0000009444.89549.90 Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Chen AT, 2003, LECT NOTES COMPUT SC, V2785, P28 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 COOPER WS, 1994, NIST SPECIAL PUBLICA, P57 Cooper W.S., 1994, TEXT RETR C TREC 2, P57 GEY FC, 2001, P 1 INT C HUM LANG T, P91 Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 GRAVANO L, 1995, INT C VER LARG DAT V, P78 Harman D., 1992, INFORMATION RETRIEVA, P241 LARSON RR, 1991, LIBR QUART, V61, P133 Larson RR, 2005, INFORM RETRIEVAL, V8, P601, DOI 10.1007/s10791-005-0749-0 LARSON RR, 1992, J AM SOC INFORM SCI, V43, P34, DOI 10.1002/(SICI)1097-4571(199201)43:1<34::AID-ASI4>3.0.CO;2-F Larson RR, 2006, LECT NOTES COMPUT SC, V3977, P225 Larson RR, 2007, LECT NOTES COMPUT SC, V4518, P318 Larson R. R., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Larson RR, 2003, LECT NOTES COMPUT SC, V2769, P487 Mass Y, 2005, LECT NOTES COMPUT SC, V3493, P73 Petras V, 2006, LECT NOTES COMPUT SC, V4022, P226 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Voorhees E., 1998, 7 TEXT RETR C TREC 7 Voorhees E. M., 1999, 8 TEXT RETR C TREC 8 NR 25 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85901-7 J9 LECT NOTES COMPUT SC PY 2008 VL 4862 BP 162 EP 174 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIP02 UT WOS:000261444400015 ER PT S AU de Campos, LM Fernandez-Luna, JM Huete, JF Romero, AE AF de Campos, Luis M. Fernandez-Luna, Juan M. Huete, Juan F. Romero, Alfonso E. BE Fuhr, N Kamps, J Lalmas, M Trotman, A TI Probabilistic Methods for Structured Document Classification at INEX'07 SO FOCUSED ACCESS TO XML DOCUMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-19, 2007 CL Dagstuhl, GERMANY ID CATEGORIZATION AB This paper exposes the results of our participation in the Document Mining track at INEX'07. We have focused on the task of classification of XML documents. Our approach to deal with structured document representations uses classification methods for plain text, applied to flattened versions of the documents, where some of their structural properties have been translated to plain text. We have explored several options to convert structured documents into flat documents, in combination with two probabilistic methods for text categorization. The main conclusion of our experiments is that taking advantage of document structure to improve classification results is a difficult task. C1 [de Campos, Luis M.; Fernandez-Luna, Juan M.; Huete, Juan F.; Romero, Alfonso E.] Univ Granada, Dept Ciencias Computac & Inteligencia Artificial, ETSI Informat & Telecomunicac, E-18071 Granada, Spain. RP de Campos, LM (reprint author), Univ Granada, Dept Ciencias Computac & Inteligencia Artificial, ETSI Informat & Telecomunicac, E-18071 Granada, Spain. EM lci@decsai.ugr.es; jmfluna@decsai.ugr.es; jhg@decsai.ugr.es; aeromero@decsai.ugr.es RI Romero, Alfonso/C-8218-2011; de Campos, Luis/B-7436-2012; Huete, Juan/C-2428-2012; Fernandez Luna, Juan Manuel/C-6845-2012; Romero, Alfonso E./D-9212-2013 OI de Campos, Luis/0000-0001-9125-1195; Romero, Alfonso E./0000-0001-8855-5569 CR Acid S, 2005, MACH LEARN, V59, P213 Bratko A, 2006, INFORM PROCESS MANAG, V42, P679, DOI 10.1016/j.ipm.2005.06.003 de Campos LM, 2000, INT J APPROX REASON, V24, P11, DOI 10.1016/S0888-613X(99)00042-0 Koller D., 1997, P 14 INT C MACH LEAR, P170 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 MCCALLUM A, 1998, AAAI ICML WORKSH LEA, P137 Pearl J., 1988, PROBABILISTIC REASON ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 NR 10 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85901-7 J9 LECT NOTES COMPUT SC PY 2008 VL 4862 BP 195 EP 206 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIP02 UT WOS:000261444400018 ER PT J AU Jones, CB Purves, RS Clough, PD Joho, H AF Jones, C. B. Purves, R. S. Clough, P. D. Joho, H. TI Modelling vague places with knowledge from the Web SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE LA English DT Article DE gazetteers; geographical information retrieval; geo-parsing; surface modelling; vagueness ID GEOGRAPHICAL INFORMATION-RETRIEVAL; SEARCH ENGINE; ONTOLOGIES; REGIONS; SPIRIT AB Place names are often used to describe and ti enquire about geographical information. It is common for users to employ vernacular names that have vague spatial extent and which do not correspond to the official and administrative place name terminology recorded within typical gazetteers. There is a need therefore to enrich gazetteers with knowledge of such vague places and hence improve the quality of place name-based information retrieval. Here we describe a method for modelling vague places using knowledge harvested from Web pages. It is found that vague place names are frequently accompanied in text by the names of more precise co-located places that lie within the extent of the target vague place. Density surface modelling of the frequency of co-occurrence of such names provides an effective method of representing the inherent uncertainty of the extent of the vague place while also enabling approximate crisp boundaries to be derived from contours if required. The method is evaluated using both precise and vague places. The use of the resulting approximate boundaries is demonstrated using an experimental geographical search engine. C1 [Jones, C. B.] Cardiff Univ, Sch Comp Sci, Cardiff, S Glam, Wales. [Purves, R. S.] Univ Zurich, Dept Geog, Zurich, Switzerland. [Clough, P. D.] Univ Sheffield, Dept Informat Studies, Sheffield, S Yorkshire, England. [Joho, H.] Univ Glasgow, Dept Comp Sci, Glasgow, Lanark, Scotland. RP Jones, CB (reprint author), Cardiff Univ, Sch Comp Sci, Cardiff, S Glam, Wales. EM c.b.jones@cs.cf.ac.uk FU NSFC [60721003, 60736026]; National 863 Project of China [2006AA04Z428] FX This work was supported by the NSFC under Grant 60721003 and 60736026 and National 863 Project of China under Grant 2006AA04Z428. CR AITKEN SC, 1990, GEOGR ANAL, V22, P301 Alani H, 2001, INT J GEOGR INF SCI, V15, P287, DOI 10.1080/13658810110038942 Arampatzis A, 2006, COMPUT ENVIRON URBAN, V30, P436, DOI 10.1016/j.compenvurbsys.2005.08.001 BRIN S, 1998, P 7 INT WORLD WID WE, P14 BRUNSDON C, 1995, COMPUT GEOSCI, V21, P877, DOI 10.1016/0098-3004(95)00020-9 BYRKIT JW, 1992, LAND SKY PEOPLE SOUT Clarke C. L., 2001, P 24 ANN INT ACM SIG, P358, DOI 10.1145/383952.384024 Cohn AG, 1996, GEOGRAPHIC OBJECTS I, P171 Cowie J, 1996, COMMUN ACM, V39, P80, DOI 10.1145/234173.234209 Cunningham H., 2002, P 40 ANN M ASS COMP Egenhofer M. J., 2002, P 10 ACM INT S ADV G, P1, DOI 10.1145/585147.585148 Fisher P, 2000, FUZZY SET SYST, V113, P7, DOI 10.1016/S0165-0114(99)00009-3 Fisher P, 2004, T I BRIT GEOGR, V29, P106, DOI 10.1111/j.0020-2754.2004.00117.x Gold CM, 1996, INT J GEOGR INF SYST, V10, P291, DOI 10.1080/02693799608902080 Harpring P., 1997, MDA INFORMATION, V2, P5 Hill L., 2006, GEOREFERENCING GEOGR HILL LL, 1999, DIGITAL LIB 5 JONES CB, 2001, LECT NOTES COMPUT SC, V2205, P336 Jones CB, 2003, LECT NOTES COMPUT SC, V2888, P934 Jones CB, 2004, LECT NOTES COMPUT SC, V3234, P125 Kilgarriff A., 2003, COMPUTATIONAL LINGUI, V29, P1 Kuhn W, 2001, INT J GEOGR INF SCI, V15, P613, DOI 10.1080/13658810110061180 Kulik L, 2001, LECT NOTES COMPUTER, V2205, P44 Kwok C., 2001, P 10 WORLD WID WEB C, P150, DOI 10.1145/371920.371973 LAM CS, 2002, BUILDING NEIGHBORHOO Larson R.R., 2003, FDN GEOGRAPHIC INFOR, P223 LI H, 2003, P HLT NAACL 2003 WOR McDonald D., 1996, CORPUS PROCESSING LE, P21 Montello D., 2003, SPAT COGN COMPUT, V2, P185, DOI 10.1207/S15427633SCC032&3_06 OSULLIVAN D, 2003, GEOGRAPHIC INFORMATI PURVES R, 2003, P GIS RES UK 13 ANN, P313 Purves RS, 2007, INT J GEOGR INF SCI, V21, P717, DOI 10.1080/13658810601169840 Radev DR, 2002, J AM SOC INF SCI TEC, V53, P359, DOI 10.1002/asi.10053 Rauch E, 2003, P HLT NAACL 2003 WOR, P50 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHLIEDER C, 2001, LNCS, V2205, P336 Smith D.A., 2003, P HLT NAACL 2003 WOR, P45 Vogele T, 2003, LECT NOTES COMPUT SC, V2825, P239 WANG FJ, 1994, INT J GEOGR INF SYST, V8, P143, DOI 10.1080/02693799408901991 WORBOYS MF, 1998, GEOINFORMATICA, V2, P57 ZHANG VW, 2006, WORKSHOP GEOGRAPHICA NR 41 TC 38 Z9 42 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 1365-8816 J9 INT J GEOGR INF SCI JI Int. J. Geogr. Inf. Sci. PY 2008 VL 22 IS 10 BP 1045 EP 1065 DI 10.1080/00207720802085245 PG 21 WC Computer Science, Information Systems; Geography; Geography, Physical; Information Science & Library Science SC Computer Science; Geography; Physical Geography; Information Science & Library Science GA 339QI UT WOS:000258591700002 ER PT S AU Dominich, S AF Dominich, Sandor BA Dominich, S BF Dominich, S TI The Modern Algebra of Information Retrieval Introduction SO MODERN ALGEBRA OF INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Editorial Material; Book Chapter ID AUTOMATIC TEXT-RETRIEVAL; WORLD-WIDE-WEB; CONCEPT HIERARCHIES; SEARCH ENGINES; LATTICE; PROBABILITY; RELEVANCE; NETWORKS; TERM; COMPLEXITY C1 Univ Pannonia, Dept Comp Sci, H-8200 Veszprem, Hungary. RP Dominich, S (reprint author), Univ Pannonia, Dept Comp Sci, Egyetem U 10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu CR Aberer K, 2003, LECT NOTES COMPUT SC, V2642, P213 Adamic L., 2000, SCIENCE, V287, p2115a, DOI 10.1126/science.287.5461.2115a ADAMIC LA, 2003, ZIPF POWER LAWS PARE ALBERT R, 2000, THESIS U NOTRE DAME GODIN R, 1993, INT J MAN MACH STUD, V38, P747, DOI 10.1006/imms.1993.1035 BaezaYates R, 2003, INT J APPROX REASON, V34, P97, DOI 10.1016/j.ijar.2003.07.002 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barabasi AL, 2000, PHYSICA A, V281, P69, DOI 10.1016/S0378-4371(00)00018-2 BELEW KK, 2000, FINDING OUT ABOUT Berry W., 1999, UNDERSTANDING SEARCH Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621 BLAIR D, 2006, BACK ROUGH GROUND WI BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B Borlund P., 2003, INFORM RES, V8, P1 Brin S., 1998, P 7 INT WORLD WID WE, P107 Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9 Burris S., 2000, COURSE UNIVERSAL ALG CALADO P, 2003, ACM T INFORM SYSTEMS, V21, P1 Carpineto C, 2005, LECT NOTES ARTIF INT, V3626, P161 Carriere SJ, 1997, COMPUT NETWORKS ISDN, V29, P1257, DOI 10.1016/S0169-7552(97)00062-7 Cheung KSK, 2005, INFORM RETRIEVAL, V8, P285, DOI 10.1007/s10791-005-5663-y Chu H.T., 1996, P 59 ANN M AM SOC IN, P127 Clarke SJ, 1997, ASLIB PROC, V49, P184, DOI 10.1108/eb051463 Cole C, 2005, NEW DIRECTIONS COGNI COOPER GF, 1990, ARTIF INTELL, V42, P393, DOI 10.1016/0004-3702(90)90060-D COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CRESTANI F, 1998, INFORM RETRIEVAL UNC Croft W. B., 1998, P ACM SIGIR, P275 Croft W.B, 2003, LANGUAGE MODELLING I Cummins R, 2006, INFORM RETRIEVAL, V9, P311, DOI 10.1007/s10791-006-1682-6 De Wilde P., 1996, NEURAL NETWORK MODEL DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dewey J., 1929, EXPERIENCE NATURE Dominich S, 2007, J AM SOC INF SCI TEC, V58, P1108, DOI 10.1002/asi.20586 DOMINICH S, 2006, SOFT COMPUTING WEB R, P161 Dominich S, 2003, INFORM PROCESS MANAG, V39, P167, DOI 10.1016/S0306-4573(02)00046-8 Dominich S., 2002, P ACM SIGIR 2002 WOR, P43 Dominich S, 2005, LECT NOTES COMPUT SC, V3408, P67 Dominich S, 2004, J AM SOC INF SCI TEC, V55, P613, DOI 10.1002/asi.20008 Dominich S., 2001, MATH FDN INFORM RETR DOMINICH S, 2005, ALKALMAZOTT NYELVTUD, V1, P5 Doob JL, 1994, MEASURE THEORY Egghe L, 2000, SCIENTOMETRICS, V47, P237, DOI 10.1023/A:1005634925734 Faloutsoss M, 1999, P ACM SIGCOMM, P251, DOI 10.1145/316188.316229 Farahat A, 2006, SIAM J SCI COMPUT, V27, P1181, DOI 10.1137/S1064827502412875 FELDMAN JA, 1982, COGNITIVE SCI, V6, P205, DOI 10.1207/s15516709cog0603_1 Feynman RP, 1964, FEYNMAN LECT PHYS FOX EA, 1994, NIST PUBLICATION, V243 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471 GARFIELD E, 1955, SCIENCE, V108 GELLER NL, 1978, INFORM PROCESS MANAG, V14, P93, DOI 10.1016/0306-4573(78)90066-3 GODIN R, 1989, SIGIR FORUM, V23, P32, DOI 10.1145/75334.75339 Grinbaum A, 2005, FOUND PHYS LETT, V18, P563, DOI 10.1007/s10702-005-1129-0 GROSSBERG S, 1976, BIOL CYBERN, V23, P121, DOI 10.1007/BF00344744 Grossman D. A., 2004, INFORM RETRIEVAL ALG Guilmi C D, 2003, EC B, V15, P1 GWIZDKA J, 1999, 9901 U TOR HAYS DD, 1966, READINGS AUTOMATIC L HEINE MH, 1999, P WORKSH LOG UNC MOD, P55 HOPFIELD JJ, 1984, P NATL ACAD SCI-BIOL, V81, P3088, DOI 10.1073/pnas.81.10.3088 Ingwersen P., 2005, TURN INTEGRATION INF JAMES W, 1988, NEUROCOMPUTING FDN R, P253 Jauch J. M., 1968, FDN QUANTUM MECH KAHNG B, 2002, PHYS REV, V66, P1 Kalogeraki V., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Kim M, 2004, INT J HUM-COMPUT ST, V60, P201, DOI 10.1016/j.ijhcs.2003.10.004 KIYOSI I, 2000, ENCY DICT MATH Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 KNEALE K, 1962, DEV LOGIC Knuth KH, 2005, NEUROCOMPUTING, V67, P245, DOI 10.1016/j.neucom.2004.11.039 Koester B., 2006, FOOCA WEB INFORM RET KOESTER B, 2005, P LWA 2005 LERN WISS, P178 Koester B, 2006, LECT NOTES ARTIF INT, V4065, P176 Kolmogorov A., 1956, FDN THEORY PROBABILI, V2nd Korfhage R., 1997, INFORM STORAGE RETRI Kowalski G., 1997, INFORM RETRIEVAL SYS KUMAR R, 1998, P WWW8 C Kurtz M., 1991, HDB APPL MATH ENG SC Lanczos K., 1970, SPACE AGES Langville A N, 2006, GOOGLES PAGERANK Langville AN, 2005, SIAM REV, V47, P135, DOI 10.1137/S0036144503424786 LEIGHTON HV, 1999, J AM SOC INFORM SCI, V50, P882 Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041 LEQUAN H, 2003, COMPUTATIONAL LINGUI, V1, P77 LESK M, 2007, 7 AGES INFORM RETRIE LUHN HP, 1966, READINGS AUTOMATED L, P159 MARON ME, 1960, J ASSOC COMPUT MACH, V7, P219 MARTIN WT, 1961, ELEMENTARY DIFFERENT Meadow C. T., 1999, TEXT INFORM RETRIEVA MESSAI N, 2006, P 4 INT C CONC LATT Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 MOOERS CN, 1959, P INT C SCI INF WASH, P1327 NG AY, 2001, P 17 INT JOINT C ART Nielson J, 1993, USABILITY ENG Over DE, 2007, COGNITIVE PSYCHOL, V54, P62, DOI 10.1016/j.cogpsych.2006.05.002 Pearl J., 1988, PROBABILISTIC REASON Pennock DM, 2002, P NATL ACAD SCI USA, V99, P5207, DOI 10.1073/pnas.032085699 PIZIAK R, 1978, MATH MAG, V51, P299 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Priss U, 2000, KNOWL ORGAN, V27, P132 Rajapakse RK, 2006, INFORM PROCESS MANAG, V42, P1260, DOI 10.1016/j.ipm.2005.12.005 Recski A., 1989, MATROID THEORY ITS A Redei M., 1996, STUD HIST PHILOS M P, V27, P493, DOI 10.1016/S1355-2198(96)00017-2 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 2002, P ACM SIGIR MF IR WO ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1974, CONTRIBUTIONS THEORY, V74, P584 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1966, READINGS AUTOMATIC L, P169 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J., 1991, Electronic Publishing: Origination, Dissemination and Design, V4 Shafi S. M., 2005, WEBOLOGY, V2 Shannon C. E., 1949, MATH THEORY COMMUNIC Shiode N, 2000, POWER LAW DISTRIBUTI Silva I. R., 2004, Proceedings. International Database Engineering and Applications Symposium, DOI 10.1109/IDEAS.2004.1319782 Silverstein C., 1998, 1998014 COMPAQ SYST Simmonds JG, 1982, BRIEF TENSOR ANAL SKOBELTSYN G., 2007, P WWW 2007 C MAY 8 1, P1185, DOI 10.1145/1242572.1242757 SMITH FJ, 1985, INFORM PROCESS MANAG, V21, P215, DOI 10.1016/0306-4573(85)90106-2 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Stalnaker R., 1968, AM PHILOS Q MONOGRAP, V2, P98 Strogatz SH, 2001, NATURE, V410, P268, DOI 10.1038/35065725 SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 SU LT, 1998, P 61 ANN M AM SOC IN, P348 TAIT JI, 2005, CHARTING NEW COURSE TANG MC, 2003, LIBRES, V13 Thelwall M, 2004, ASLIB PROC, V56, P24, DOI 10.1108/00012530410516840 Thorndike EL, 1937, PSYCHOL REC, V1, P399 Tsikrika T, 2004, INFORM PROCESS MANAG, V40, P751, DOI 10.1016/j.ipm.2004.04.008 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Ullman J.D., 1980, PRINCIPLES DATABASE Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 2004, GEOMETRY IR Vegas J, 2007, J INF SCI, V33, P77, DOI 10.1177/0165551506067123 Wang Z, 1991, FUZZY MEASURE THEORY Weiss M. A., 1995, DATA STRUCTURES ALGO WELLISH H, 1991, INDEXING A Z WIDDOWS D, 2003, P MATH LANG BLOOM JU, P141 Widdows D., 2003, P 41 ANN M ASS COMP, P136 Widdows Dominic, 2004, GEOMETRY AND MEANING Wille R, 2005, LECT NOTES ARTIF INT, V3626, P1 Wolf K., 1993, SOFTSTAT 93 ADV STAT, V4, P429 Wong S., 1984, P 7 ANN INT ACM SIGI, P167 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Yang A, 1996, DATA KNOWL ENG, V20, P211, DOI 10.1016/S0169-023X(96)00006-7 YU CT, 1989, ACM T DATABASE SYST, V14, P147, DOI 10.1145/63500.63519 YULE GU, 1924, PHILOS T, P213 Zimmerman HJ, 1996, FUZZY SET THEORY ITS Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 158 TC 7 Z9 7 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-540-77658-1 J9 INFORM RETRIEVAL SER PY 2008 VL 24 BP 1 EP + PG 40 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BJW77 UT WOS:000267327600001 ER PT S AU David, J Euzenat, J AF David, Jerome Euzenat, Jerome BE Sheth, A Staab, S Paolucci, M Maynard, D Finin, T Krishnaprasad, T TI Comparison between Ontology Distances (Preliminary Results) SO SEMANTIC WEB - ISWC 2008 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th International Semantic Web Conference (ISWC 2008) CY OCT 26-30, 2008 CL Karlsruhe, GERMANY SP Ontoprise, BBN, eyeworkers, Microsoft, NeOn, SAP Res, Vulcan, ACTIVE, ADUNA, Saltlux, SUPER, X Media, Yahoo ID METRICS AB There are many reasons for measuring a distance between ontologies. In particular, it is useful to know quickly if two ontologies are close or remote before deciding to match them. To that extent, a distance between ontologies must be quickly computable. We present constraints applying to such measures and several possible ontology distances. Then we evaluate experimentally some of them in order to assess their accuracy and speed. C1 [David, Jerome] INRIA Grenoble Rhone Alpes, Grenoble, France. RP David, J (reprint author), INRIA Grenoble Rhone Alpes, Grenoble, France. EM Jerome.David@inrialpes.fr; Jerome.Euzenat@inrialpes.fr CR Alani H., 2005, P 3 INT C KNOWL CAPT, P51, DOI 10.1145/1088622.1088633 DAQUIN, 2007, P POST SESS INT SEM Ehrig M., 2005, P 13 EUR C INF SYST Euzenat J., 2004, P 16 EUR C ART INT E, P333 Euzenat J, 2007, ONTOLOGY MATCHING GRACE III J.M., 2007, P 2 ISWC ONT MATCH W, P1 Hu B, 2006, LECT NOTES ARTIF INT, V4248, P166 Jung JJ, 2007, LECT NOTES COMPUT SC, V4519, P267 Jung JJ, 2007, LECT NOTES COMPUT SC, V4505, P622 MADCHE A, 2002, LNCS LNAI, V2473, P251 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Stuckenschmidt H, 2004, LECT NOTES COMPUT SC, V3298, P289 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 VALTCHEV P, 1999, THESIS U GRENOBLE 1 Vrandecic D, 2007, LECT NOTES COMPUT SC, V4519, P311 NR 15 TC 15 Z9 15 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-88563-4 J9 LECT NOTES COMPUT SC PY 2008 VL 5318 BP 245 EP 260 PG 16 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIY37 UT WOS:000263688400016 ER PT B AU Mothe, J Tanguy, L AF Mothe, Josiane Tanguy, Ludovic TI Linguistic Analysis of Users' Queries: towards an adaptive Information Retrieval System SO SITIS 2007: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGIES & INTERNET BASED SYSTEMS LA English DT Proceedings Paper CT IEEE International Conference on Signal Image Technology and Internet Based Systems CY DEC 16-19, 2007 CL Shanghai, PEOPLES R CHINA SP IEEE, IEEE Comp Soc, Shanghai Jiatong Univ, SIGAPP fr AB Most of Information Retrieval Systems transform natural language users' queries into bags of words that are matched to documents, also represented as bags of words. Through such process, the richness of the query is lost. In this paper we show that linguistic features of a query are good indicators to predict systems failure to answer it. The experiments described here are based on 42 systems or system variants and 50 TREC topics that consist of a descriptive part expressed in natural language. C1 [Mothe, Josiane] Univ Toulouse, UMR 5505, Inst Rech Informat Toulouse, F-31062 Toulouse 04, France. RP Mothe, J (reprint author), Univ Toulouse, UMR 5505, Inst Rech Informat Toulouse, 118 Route Narbonne, F-31062 Toulouse 04, France. CR Beitzel SM, 2004, J AM SOC INF SCI TEC, V55, P859, DOI 10.1002/asi.20012 Benzecri JP, 1973, ANAL DONNEES, VI Biber Douglas, 1988, VARIATION SPEECH WRI BUCKLEY C, 2004, REL INF ACC FIN WORK CARMEL D, 2006, 29 ANN INT ACM SIGIR, P390 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DKAKI T, 2004, COMBINING POSITIVE N, P661 Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367 FABRE C, 2001, LINGUISTIC CLUES COR Harman D., 2002, TEXT RETR C TREC 200, P46 JONES KS, 2003, DOCUMENT RETRIEVAL S, P1 KARLGREN J, 1999, STYLISTIC EXPT INFOR LEE JH, 1997, 20 ANN INT ACM SIGIR, P267 MANDL T, 2002, LINGUISTIC STAT ANAL MITRA M, 1997, ANAL STAT SYNTACTIC, P200 Mothe J., 2005, SIGIR WORKSH PRED QU, P7 Ponte J. M., 1998, 21 ACM SIGIR C RES D, P275 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd NR 20 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3122-9 PY 2008 BP 77 EP 84 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BII29 UT WOS:000259670300010 ER PT S AU Anh, VN Wan, R Moffat, A AF Anh, Vo Ngoc Wan, Raymond Moffat, Alistair BE Amir, A Turpin, A Moffat, A TI Term Impacts as Normalized Term Frequencies for BM25 Similarity Scoring SO STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th International Symposium on String Processing and Information Retrieval CY NOV 10-12, 2008 CL Melbourne, AUSTRALIA ID RETRIEVAL AB The BM25 similarity computation has been shown to provide effective document retrieval. In operational terms, the formulae which form the basis for BM25 employ both term frequency and document length normalization. This paper considers an alternative form of normalization using document-centric impacts. and shows that the new normalization simplifies BM25 and reduces the number of tuning parameters. Motivation is provided by a preliminary analysis of a document collection that shows that impacts are more likely to identify documents whose lengths resemble those of the relevant judgments. Experiments on TREC data demonstrate that impact-based BM25 is as good as or better than the original term frequency-based BM25 in terms of retrieval effectiveness. C1 [Anh, Vo Ngoc; Moffat, Alistair] Univ Melbourne, Dept Comp Sci & Software Engn, Melbourne, Vic 3010, Australia. RP Anh, VN (reprint author), Univ Melbourne, Dept Comp Sci & Software Engn, Melbourne, Vic 3010, Australia. RI Wan, Raymond/I-2885-2012; Moffat, Alistair/ OI Wan, Raymond/0000-0003-3202-7008; Moffat, Alistair/0000-0002-6638-0232 CR ANH V., 2005, P 28 ANN INT ACM SIG, P226, DOI 10.1145/1076034.1076075 BUCKLEY C, 1993, SPECIAL PUBLICATION, P59 CLARKE CLA, 2007, P 30 ANN INT ACM SIG Cleveland W. S., 1992, STAT MODELS S Fuhr N., 2001, Lectures on Information Retrieval. Third European Summer-School, ESSIR 2000. Revised Lectures (Lecture Notes in Computer Science Vol.1980) Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Metzler D, 2008, LECT NOTES COMPUT SC, V4956, P175 ROBERTSON S, 1998, SPECIAL PUBLICATION, P253 Robert S., 1994, V13, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Zobel Justin, 1998, ACM SIGIR FORUM, V32, P18, DOI 10.1145/281250.281256 NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-89096-6 J9 LECT NOTES COMPUT SC PY 2008 VL 5280 BP 51 EP 62 PG 12 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BIR40 UT WOS:000262212000005 ER PT S AU Zhang, J AF Zhang, J TI Visualization for Information Retrieval SO VISUALIZATION FOR INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Book ID AUTHOR COCITATION ANALYSIS; INDIVIDUAL-DIFFERENCES; DOMAIN VISUALIZATION; DOCUMENT COLLECTIONS; SIMILARITY MEASURE; SELF-ORGANIZATION; REPRESENTATIONS; WEB; SCIENCE; DESIGN CR ALLBRITTON DW, 1995, J EXP PSYCHOL LEARN, V21, P612, DOI 10.1037//0278-7393.21.3.612 ALLEN B, 1998, P 3 ACM C DIG LIB PI, P1, DOI 10.1145/276675.276676 Ang C. S., 1994, Proceedings. Visualization '94 (Cat. No.94CH35707), DOI 10.1109/VISUAL.1994.346342 *ANT SYST INC, 2005, VIS NET Arnheim Rudolf, 1972, VISUAL THINKING Baecker RM, 1995, READINGS HUMAN COMPU Bartell B., 1992, P 15 ANN INT ACM SIG, P161, DOI 10.1145/133160.133191 Bates M.J., 2002, P COLSIS4, P137 BEAUDOIN L, 2005, CHEOPS COMPACT EXPLO Bederson B. B., 1994, P 7 ANN ACM S US INT, P17, DOI 10.1145/192426.192435 Bederson B. B., 2000, P ACM C US INT SOFTW, P217, DOI 10.1145/354401.354782 BENFORD S, 1997, P INT ACM SIGGROUP C, P71, DOI 10.1145/266838.266866 BENFORD S, 1995, P EUR 95 MAASTR, P349 Benyon D, 1999, HUM-COMPUT INTERACT, V14, P159, DOI 10.1207/s15327051hci1401&2_5 BERTIN J, 1999, READINGS INFORM VISU, P62 BOLT RA, 1984, HUMAN INTERFACE WHER Booth P. A., 1989, INTRO HUMAN COMPUTER Borg I., 1997, MODERN MULTIDIMENSIO Boyack KW, 2002, J AM SOC INF SCI TEC, V53, P764, DOI 10.1002/asi.10066 BUJA A, 2001, XGVIS INTERACTIVE DA Burke K, 1962, GRAMMAR MOTIVES RHET BUZYDLOWSKI JW, 2001, P JOINT C DIG LIB 01, P133 Cadez I., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347151 Cai G, 2002, P VIS INT DIG LIB JC, P171 Card S. K., 1996, P SIGCHI C HUM FACT, P111, DOI 10.1145/238386.238446 Card Stuart K., 1999, READINGS INFORM VISU, P1 CAREY M, 2003, P INT C DISTR MULT S, P23 CARROLL JM, 1982, IEEE T SYST MAN CYB, V12, P107, DOI 10.1109/TSMC.1982.4308795 CENTNER D, 1997, AM PSYCHOL, V52, P45 Chalmers Matthew, 1992, P 15 ANN INT ACM SIG, P330, DOI DOI 10.1145/133160.133215 CHANG SJ, 1993, ANNU REV INFORM SCI, V28, P231 CHEN C, 1996, J VISUAL COMMUNICATI, V7, P88 CHEN C, 1996, P HCI PEOPL COMP 96, V11, P265 Chen C., 2003, P IEEE S INF VIS INF, P67 Chen CM, 2000, INT J HUM-COMPUT ST, V53, P631, DOI 10.1006/ijhc.2000.0421 Chen CM, 2005, IEEE COMPUT GRAPH, V25, P12, DOI 10.1109/MCG.2005.91 Chen CM, 1999, INFORM PROCESS MANAG, V35, P401, DOI 10.1016/S0306-4573(98)00068-5 Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100 Chen J., 2004, P 7 INT WORKSH WEB D, P13, DOI 10.1145/1017074.1017079 Cheng SL, 1997, J SHOULDER ELB SURG, V6, P11, DOI 10.1016/S1058-2746(97)90065-3 Chi E. H., 1998, P ACM C HUM FACT COM, P400, DOI 10.1145/274644.274699 Chi EH, 2002, IEEE INTERNET COMPUT, V6, P64, DOI 10.1109/4236.991445 CHRISTEL MG, 2001, ACM IT C MULT SEPT 3, P483 Christel M. G., 1999, Proceedings ACM Multimedia 99, DOI 10.1145/319463.319633 COLONNA JF, 1994, FRONTIERS SCI VISUAL, P181 Conti G., 2004, P 2004 ACM WORKSH VI, P45, DOI 10.1145/1029208.1029216 Cooke NJ, 1996, HUM-COMPUT INTERACT, V11, P29, DOI 10.1207/s15327051hci1101_2 Cooper A., 1995, FACE ESSENTIALS USER COOPER A, 1995, VISUAL BASIC PRO JUN Craik K. J. W., 1943, NATURE EXPLANATION CRESQUES A, 1978, MAPAMUNDI CATALAN AT CUGINI J, 2005, PRESENTING SEARCH RE Czerwinski M., 1999, P CHI 99, P560, DOI 10.1145/302979.303158 DEARHOLT DW, 1990, STUDIES KNOWLEDGE OR, P1 Dillon A., 2003, MACMILLAN ENCY COGNI, V4, P453 DOUGLAS SA, 1983, P CHI 83 HUMAN FACTO, P207, DOI 10.1145/800045.801612 DUBIN D, 1995, P 18 ANN INT ACM SIG, P199, DOI 10.1145/215206.215360 Duncker E., 2002, P 2 ACM IEEE CS JOIN, P223 DURAND DG, 1998, P ACM HYP 98 PITTSB, P66, DOI 10.1145/276627.276635 Eades P., 1984, C NUMERANTIUM, V42, P149 Eades P., 1994, COMPUTATIONAL GEOMET, V4, P235, DOI 10.1016/0925-7721(94)00014-X Eick SG, 2001, COMMUN ACM, V44, P45, DOI 10.1145/381641.381710 ELLIS G, 2004, P WORK C ADV VIS INT, P197, DOI 10.1145/989863.989895 ELLSBERG D, 1961, Q J ECON, V75, P643, DOI 10.2307/1884324 Erickson T., 2003, P CHI 2003 EXT ABSTR, P846 Fang XW, 2000, INT J HUM-COMPUT INT, V12, P73, DOI 10.1207/S15327590IJHC1201_3 Fauconnier Gilles, 1997, MAPPINGS THOUGHT LAN FESTINGER L, 1957, THEOTY COGNITIVE DIS Fishkin K., 1995, P HUM FACT COMP SYST, P415, DOI 10.1145/223904.223960 Fowler R.H., 1991, P 14 ANN INT ACM SIG, P142, DOI 10.1145/122860.122874 FOWLER RH, 1996, NAG9551966 U TEX DEP FOWLER RH, 1990, STUDIES KNOWLEDGE OR, P165 FREITAS CMD, 2005, EVALUATION USABILITY FRUCHTERMAN TMJ, 1991, SOFTWARE PRACT EXPER, V21, P1129, DOI 10.1002/spe.4380211102 Furnas G., 1986, P ACM CHI 86 C HUM F, P16, DOI 10.1145/22627.22342 Futrelle R. P., 1999, Proceedings 1999 IEEE Symposium on Visual Languages, DOI 10.1109/VL.1999.795889 Gaver W. W., 2003, P SIGCHI C HUM FACT, P233, DOI DOI 10.1145/642611.642653 GENTNER D., 1983, MENTAL MODELS, P99 Gentner D, 1996, COMMUN ACM, V39, P70, DOI 10.1145/232014.232032 GRINSTEIN GG, 2005, BENCHMARK DEV EVALUA GUTTMAN L, 1968, PSYCHOMETRIKA, V33, P469, DOI 10.1007/BF02290164 Hamilton A, 2000, ACM J COMPUTER DOCUM, V24, P237, DOI 10.1145/353927.353935 Hasan M. Z., 1996, SIGMOD Record, V25 Havre S, 2002, IEEE T VIS COMPUT GR, V8, P9, DOI 10.1109/2945.981848 Havre S., 2001, Proceedings IEEE Symposium on Information Visualization 2001. INFOVIS 2001 Haykin S., 1994, NEURAL NETWORKS COMP Hearst M., 1999, MODERN INFORM RETRIE, P257 Hearst Marti A., 1995, P ACM SIGCHI C HUM F, P59, DOI 10.1145/223904.223912 Hemmje M., 1994, P 17 ANN INT ACM SIG, P249 Herder E., 2005, P C HUM FACT COMP SY, P1451 Hochheiser H, 2001, J AM SOC INF SCI TEC, V52, P331, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1066>3.3.CO;2-P Hong J. I., 2001, P 10 INT WORLD WID W, P717, DOI 10.1145/371920.372188 Hutchins Edwin, 1989, STRUCTURE MULTIMODAL, P11 Indurkhya B., 1992, METAPHOR COGNITION Inselberg A., 1997, Proceedings. IEEE Symposium on Information Visualization (Cat. No.97TB100195), DOI 10.1109/INFVIS.1997.636793 JACKENDOFF R, 1992, LANGUAGES MINDS Jerding D. F., 1995, Proceedings. Information Visualization (Cat. No.95TB100000), DOI 10.1109/INFVIS.1995.528685 Johnson M., 1980, COGNITION FIGURATIVE, P47 Johnson-Laird PN, 1983, MENTAL MODELS COGNIT Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KAMADA T, 1989, INFORM PROCESS LETT, V31, P7, DOI 10.1016/0020-0190(89)90102-6 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Keim D, 2001, COMMUN ACM, V44, P38, DOI 10.1145/381641.381656 Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kohonen T., 2001, SPRINGER SERIES INFO, V30 KOIKE H, 1993, ACM T INFORM SYST, V11, P266, DOI 10.1145/159161.155370 KOMLODI A, 2004, INFORM VISUALIZATION KONCHADY M, 1998, P 1998 WORKSH NEW PA, P13, DOI 10.1145/324332.324335 Koren Y., 2003, P 9 ACM SIGKDD INT C, P589 Korfhage RR, 1991, P 14 ANN INT ACM SIG, P134, DOI 10.1145/122860.122873 KORFHAGE RR, 1988, LIS001IS88001 U PI 1 KORFHAGE RR, 1994, P 3 ANN S DOC AN INF, P199 Koshman S, 2004, J DOC, V60, P565, DOI 10.1108/00220410410560627 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565 Kuhn W., 1996, C COMP HUM FACT COMP, P346, DOI 10.1145/257089.257361 Lagus K, 1999, ARTIF INTELL REV, V13, P345, DOI 10.1023/A:1006586221250 Lagus K, 2002, NEURAL PROCESS LETT, V15, P21, DOI 10.1023/A:1013853012954 Lagus K., 1999, P 9 INT C ART NEUR N, P371 Lakoff G., 1987, WOMEN FIRE DANGEROUS Lakoff G., 1980, METAPHORS WE LIVE BY Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 Leuski A., 2000, P RIAO COLL FRANC, P665 Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 LYU MR, 2002, P 2 ACM IEEE CS JOIN, P145 Ma KL, 2004, COMPUT GRAPHICS-US, V38, P4 MACCORMAC ER, 1989, COGNITIVE THEORY MET Mackinlay J., 1990, Human-Computer Interaction, V5, DOI 10.1207/s15327051hci0502&3_2 Mackinlay J. D., 1995, P SIGCHI C HUM FACT, P67, DOI 10.1145/223904.223913 Mackinlay J.D., 1991, P CHI 91, P173, DOI 10.1145/108844.108870 MacQueen J., 1967, P 5 BERK S MATH STAT, P281, DOI DOI 10.1234/12345678 Mankoff J, 2000, COMPUT GRAPH-UK, V24, P819, DOI 10.1016/S0097-8493(00)00085-6 Marchionini G., 1988, IEEE COMPUT, V21, P70 Marcus A., 1994, Proceedings of the Workshop on Advanced Visual Interfaces AVI '94, DOI 10.1145/192309.192317 McCormick B.H., 1987, COMPUT GRAPH, V21, P1 MERKL D, 1997, P 9 IT WORKSH NEUR N, P324 Miller N. E., 1998, Proceedings Visualization '98 (Cat. No.98CB36276), DOI 10.1109/VISUAL.1998.745302 Morse E, 2002, J AM SOC INF SCI TEC, V53, P28, DOI 10.1002/asi.10006 Mostafa J, 2004, J AM SOC INF SCI TEC, V55, P869, DOI 10.1002/asi.20033 Munzner T., 2000, THESIS STANFORD U Munzner T, 2002, IEEE COMPUT GRAPH, V22, P20, DOI 10.1109/MCG.2002.974514 NATION D, 1998, P CHI 98 C SUMM HUM, P185 NELSON MJ, 2007, P ANN C CAN ASS INF Nielsen J., 2000, DESIGNING WEB USABIL Norman D. A., 1988, DESIGN EVERYDAY THIN Nuchprayoon A., 1994, Proceedings. IEEE Symposium on Visual Languages (Cat. No.94TH8010), DOI 10.1109/VL.1994.363639 Nuchprayoon A., 1997, Proceedings. 1997 IEEE Symposium on Visual Languages (Cat. No.97TB100180), DOI 10.1109/VL.1997.626581 Nurnberger A, 2002, IEEE IJCNN, P1912, DOI 10.1109/IJCNN.2002.1007811 Olsen K. A., 1993, Scandinavian Journal of Information Systems, V5 OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 Olsen K. A., 1994, Proceedings. IEEE Symposium on Visual Languages (Cat. No.94TH8010), DOI 10.1109/VL.1994.363612 ORTONY A, 1979, PSYCHOL REV, V86, P161, DOI 10.1037//0033-295X.86.3.161 Paivio A., 1990, MENTAL REPRESENTATIO PAMPALK E, 2003, P 9 ACM SIGKDD INT C, P157 PEJTERSEN AM, 1991, P 1 INT ISKO C TOOLS, V2, P175 Pitkow J., 1999, P 2 USENIX S INT TEC, P139 Plaisant C., 2004, P WORK C ADV VIS INT, VThe challenge of information visualization evaluation, P109, DOI 10.1145/989863.989880 Preece J., 1994, HUMAN COMPUTER INTER Quist M, 2004, J MACH LEARN RES, V5, P399 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 RAUBER A, 1999, P 4 ACM C DIG LIB DL, P240, DOI 10.1145/313238.313412 Rauber A., 1999, P INT JOINT C NEUR N, P3527 REESPOTTER LK, 1989, INFORM PROCESS MANAG, V25, P677, DOI 10.1016/0306-4573(89)90101-5 Robertson G., 1998, P 11 ANN ACM S US IN, P153, DOI DOI 10.1145/288392.288596 ROBERTSON GG, 1989, PROCEEDINGS OF THE ACM SIGGRAPH SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P10, DOI 10.1145/73660.73662 ROBERTSON PK, 1991, IEEE COMPUT GRAPH, V11, P56, DOI 10.1109/38.79454 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROMERO E, 2005, J LITERARY SEMANTICS, V34, P2 ROSE DE, 1993, P ACM SIGIR PITTSBUR, P260, DOI 10.1145/160688.160735 ROWE LA, 1987, SOFTWARE PRACTICE EX, V17, P67 RUBENSTEIN R, 1984, HUMAN FACTOR DESIGNI RUSHMEIER H, 1995, P VIS 95, P422 Salton G., 1989, AUTOMATIC TEXT PROCE Salton G, 1996, INFORM PROCESS MANAG, V32, P127, DOI 10.1016/S0306-4573(96)85001-1 Scaife M, 1996, INT J HUM-COMPUT ST, V45, P185, DOI 10.1006/ijhc.1996.0048 Schaffer D., 1996, ACM T COMPUT-HUM INT, V3, P162, DOI 10.1145/230562.230577 SCHNEIDER JW, 2004, P 12 NORD C INF DOC, P44 SCHNEIDER JW, 2005, P ISSI 2005 10 INT C, P406 Schvaneveldt R. W., 1989, PSYCHOL LEARN MOTIV, V24, P249 Seagull F. J., 1992, International Journal of Human-Computer Interaction, V4 Sebrechts M. M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312634 Shneiderman B., 1996, Proceedings. IEEE Symposium on Visual Languages (Cat. No.96TB100066), DOI 10.1109/VL.1996.545307 Sidiropoulos ND, 1999, IEEE T SIGNAL PROCES, V47, P771, DOI 10.1109/78.747782 SMALL H, 1985, J INFORM SCI, V11, P147, DOI 10.1177/016555158501100402 Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.3.CO;2-7 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SMALL H, 1994, SCIENTOMETRICS, V26, P5 *SPACETREE, 2005, NOV NOD LINK TREE BR Spence R., 1982, Behaviour and Information Technology, V1 Spoerri A, 1993, P 2 INT C INF KNOWL, P11, DOI 10.1145/170088.170095 Spoerri A., 1993, Proceedings Visualization '93. (Cat. No.93CH3354-8), DOI 10.1109/VISUAL.1993.398863 *STRETCH, 2005, VIS ORG MAN INF Swayne DF, 2003, COMPUT STAT DATA AN, V43, P423, DOI 10.1016/S0167-9473(02)00286-4 TAKANE Y, 1977, PSYCHOMETRIKA, V42, P7, DOI 10.1007/BF02293745 TAKEUCHI A, 1979, BIOL CYBERN, V35, P63, DOI 10.1007/BF00337432 Tatemura J., 2000, P 5 INT C INT US INT, P272, DOI 10.1145/325737.325870 Teoh ST, 2002, P IEEE VIS C 2002, P505 THOMPSON RH, 1989, INT J MAN MACH STUD, V30, P639, DOI 10.1016/S0020-7373(89)80014-8 TORGERSON WS, 1952, PSYCHOMETRIKA, V17, P401 Tory M., 2003, P IEEE VIS C, P371 TOURANGEAU R, 1982, COGNITION, V11, P203, DOI 10.1016/0010-0277(82)90016-6 Tufte ER, 1983, VISUAL DISPLAY QUANT Turner W. A, 1988, HDB QUANTITATIVE STU, P291 VEERASAMY A, 1996, P 19 INT ACM SIGIR C, P85, DOI 10.1145/243199.243218 VICENTE KJ, 1987, HUM FACTORS, V29, P349 Viegas F. B., 2004, P SIGCHI C HUM FACT, P575, DOI DOI 10.1145/985692.985765 Viegas FB, 2004, P HAW INT C SYST SCI, P105 Voorbij HJ, 1999, J AM SOC INFORM SCI, V50, P598 Wakita A, 2003, COMPUT GRAPHICS-US, V37, P29 Walter J. A., 2002, P ACM SIGKDD INT C K, P123 Waterworth J. A., 1991, Hypermedia, V3 *WEBMAP TECHN INC, 2003, WEBMAP Weherend S., 1990, P 1 C VIS 90 SAN FRA, P139 Weiner E. J., 1984, Computational Linguistics, V10 Weippl E, 2001, AUST COMP S, V23, P34, DOI 10.1109/AUIC.2001.906274 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 White HD, 2003, J AM SOC INF SCI TEC, V54, P1250, DOI 10.1002/asi.10325 Wiesman F, 2004, J AM SOC INF SCI TEC, V55, P565, DOI 10.1002/asi.10410 Williams JG, 1995, ANNU REV INFORM SCI, V30, P161 WILLSHAW DJ, 1976, PROC R SOC SER B-BIO, V194, P431, DOI 10.1098/rspb.1976.0087 WINCKLER MA, 2004, P 3 ANN C TASK MOD D, P165, DOI 10.1145/1045446.1045475 Wise JA, 1999, J AM SOC INFORM SCI, V50, P1224, DOI 10.1002/(SICI)1097-4571(1999)50:13<1224::AID-ASI8>3.0.CO;2-4 WISHART D, 2001, CLUSTAN PROFESSIONAL Wiss U., 1998, Proceedings. 1998 IEEE Conference on Information Visualization. An International Conference on Computer Visualization and Graphics (Cat. No.98TB100246), DOI 10.1109/IV.1998.694211 WRIGHT W, 1999, READINGS INFORM VISU, P83 Yang CC, 2003, DECIS SUPPORT SYST, V35, P89, DOI 10.1016/S0167-9236(02)00101-X Yates F. A., 1966, ART MEMORY York J., 1995, P S ADV INT PROC AN, P73 YOUNG D, 1993, J AM SOC INFORM SCI, V44, P327, DOI 10.1002/(SICI)1097-4571(199307)44:6<327::AID-ASI3>3.0.CO;2-J Young G, 1941, PSYCHOMETRIKA, V6, P331, DOI 10.1007/BF02288589 ZEKI S, 1992, SCI AM, V267, P69 ZHANG H, 2000, P 63 ANN M AM SOC IN, P248 Zhang HG, 2001, INT J HUM-COMPUT INT, V13, P75, DOI 10.1207/S15327590IJHC1301_5 Zhang J, 2001, INFORM PROCESS MANAG, V37, P639, DOI 10.1016/S0306-4573(00)00042-X Zhang J, 2001, INFORM PROCESS MANAG, V37, P279, DOI 10.1016/S0306-4573(00)00027-3 Zhang J, 2001, J AM SOC INF SCI TEC, V52, P615 Zhang J, 1999, J AM SOC INFORM SCI, V50, P779, DOI 10.1002/(SICI)1097-4571(1999)50:9<779::AID-ASI6>3.0.CO;2-R Zhang J, 2005, J INTELL INF SYST, V24, P61, DOI 10.1007/s10844-005-0267-y Zhang J, 2005, INFORM PROCESS MANAG, V41, P1003, DOI 10.1016/j.ipm.2004.03.005 Zhang J, 2002, INFORM PROCESS MANAG, V38, P325, DOI 10.1016/S0306-4573(01)00035-8 Zhang J, 2001, INFORM RETRIEVAL, V4, P61, DOI 10.1023/A:1011420324125 Zhao Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Zhu J H, 2004, ACM T INTERNET TECHN, V4, P185, DOI 10.1145/990301.990305 NR 247 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-540-75147-2 J9 INFORM RETRIEVAL SER PY 2008 VL 23 BP 1 EP 292 DI 10.1007/978-3-540-75148-9 PG 292 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BJW79 UT WOS:000267328000013 ER PT J AU Roelleke, T Wu, H Wang, J Azzam, H AF Roelleke, Thomas Wu, Hengzhi Wang, Jun Azzam, Hany TI Modelling retrieval models in a probabilistic relational algebra with a new operator: the relational Bayes SO VLDB JOURNAL LA English DT Article DE probabilistic relational modelling; retrieval models; probabilistic databases; DB plus IR integration ID INFORMATION-RETRIEVAL; RELEVANCE; DATABASES; INTERFACE; SYSTEM; TEXT AB This paper presents a probabilistic relational modelling (implementation) of the major probabilistic retrieval models. Such a high-level implementation is useful since it supports the ranking of any object, it allows for the reasoning across structured and unstructured data, and it gives the software (knowledge) engineer control over ranking and thus supports customisation. The contributions of this paper include the specification of probabilistic SQL (PSQL) and probabilistic relational algebra (PRA), a new relational operator for probability estimation (the relational Bayes), the probabilistic relational modelling of retrieval models, a comparison of modelling retrieval with traditional SQL versus modelling retrieval with PSQL, and a comparison of the performance of probability estimation with traditional SQL versus PSQL. The main findings are that the PSQL/PRA paradigm allows for the description of advanced retrieval models, is suitable for solving large-scale retrieval tasks, and outperforms traditional SQL in terms of abstraction and performance regarding probability estimation. C1 [Roelleke, Thomas; Wu, Hengzhi; Wang, Jun; Azzam, Hany] Univ London, London E1 4NS, England. RP Roelleke, T (reprint author), Univ London, Mile End Rd, London E1 4NS, England. EM thor@dcs.qmul.ac.uk; hzwoo@dcs.qmul.ac.uk; wangjun@dcs.qmul.ac.uk; hany@dcs.qmul.ac.uk CR ABITEBOUL S., 2003, LOWELL DATABASE RES Agrawal, 2003, CIDR AGRAWAL S, 2002, ICDE, P5 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 AZZAM H, 2006, 2 VLDB WORKSH ONT BA BARBARA D, 1990, LECT NOTES COMPUT SC, V416, P60 BARBARA D, 1992, IEEE T KNOWL DATA EN, V4, P487, DOI 10.1109/69.166990 Berger A, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P222 BOSC P, 1988, FUZZY SET SYST, V28, P333, DOI 10.1016/0165-0114(88)90039-5 BOSC P, 1994, P ACM S APPL COMP PH, P170, DOI 10.1145/326619.326690 Cavallo R., 1987, Proceedings of the Thirteenth International Conference on Very Large Data Bases: 1987 13th VLDB Chaudhuri S, 2006, ACM T DATABASE SYST, V31, P1134, DOI 10.1145/1166074.1166085 Chaudhuri S., 2005, CIDR, P1 Chaudhuri S., 2004, VLDB, P888 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Dalvi N., 2005, VLDB, P805 Dalvi N.N., 2004, VLDB, P864 DEVRIES A, 2005, ACM SIGIR Elmasri R, 2000, FUNDAMENTALS DATABAS Ercegovac V., 2005, VLDB, P313 Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 FORST JF, 2006, P TREC 2006, pR20 Frommholz I., 2006, JCDL, P55 Fuhr N., 1995, P 18 ANN INT ACM SIG, P282, DOI 10.1145/215206.215372 Fuhr N, 1997, ACM T INFORM SYST, V15, P32, DOI 10.1145/239041.239045 FUHR N, 1990, P 16 INT C VER LARG, P696 FUHR N, 1996, P 2 WORLD C INT DES, P17 Grabs T, 2004, KNOWL INF SYST, V6, P465, DOI 10.1007/s1O115-003-0120-y Grabs T., 2001, CIKM, P411 Grossman D., 2004, INFORM RETRIEVAL ALG, V15 Grossman DA, 1997, J AM SOC INFORM SCI, V48, P122, DOI 10.1002/(SICI)1097-4571(199702)48:2<122::AID-ASI3>3.0.CO;2-# GROSSMAN DA, 1998, INFORM RETRIEVAL Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hristidis V., 2002, VLDB, P670, DOI 10.1016/B978-155860869-6/50065-2 Hristidis Vagelis, 2003, VLDB, P850 LAFFERTY J, 2002, PROBABILISTIC RELEVA, pCH1 Lakshmanan LVS, 1997, ACM T DATABASE SYST, V22, P419, DOI 10.1145/261124.261131 LALMAS M, 2002, INTELLIGENT EXPLORAT LALMAS M, 2004, LNCS LEE SK, 1992, PROC INT CONF VERY L, P211 Li C., 2005, SIGMOD, P131 MACLEOD IA, 1991, J AM SOC INFORM SCI, V42, P155, DOI 10.1002/(SICI)1097-4571(199104)42:3<155::AID-ASI1>3.0.CO;2-H MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MOTRO A, 1988, ACM T INFORM SYST, V6, P187, DOI 10.1145/45945.48027 Motro A., 1990, SIGMOD Record, V19 NIEMI T, 1995, INFORM PROCESS MANAG, V31, P215, DOI 10.1016/0306-4573(94)00040-A PFEIFER U, 1995, P 18 INT ACM SIGIR C, P189, DOI 10.1145/215206.215359 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1981, SIGIR, P22 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROELLEKE T, 1999, THESIS Roelleke T, 2003, P 26 ANN INT ACM SIG, P227 ROELLEKE T, 2001, P 24 ANN INT ACM SIG ROELLEKE T, 2006, ACM SIGIR ROSS JGR, 2002, 13 INT S METH INT SY SCHEK HJ, 1982, P 8 INT C VER LARG D, P197 SILBERSCHATZ A, 2002, DATABASE SYSTEMS CON Suciu D, 2005, SIGMOD C, P963 Theobald M., 2004, VLDB, P648 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 YU CT, 1982, J ACM, V29, P152, DOI 10.1145/322290.322300 Zhai C., 2002, SIGIR, P49 NR 66 TC 4 Z9 4 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1066-8888 J9 VLDB J JI VLDB J. PD JAN PY 2008 VL 17 IS 1 BP 5 EP 37 DI 10.1007/s00778-007-0073-y PG 33 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science GA 265ZN UT WOS:000253400700002 ER PT J AU Theobald, M Bast, H Majumdar, D Schenkel, R Weikum, G AF Theobald, Martin Bast, Holger Majumdar, Debapriyo Schenkel, Ralf Weikum, Gerhard TI TopX: efficient and versatile top-k query processing for semistructured data SO VLDB JOURNAL LA English DT Review DE efficient XML full-text search; content- and structure-aware ranking; top-k query processing; cost-based index access scheduling; probabilistic candidate pruning; dynamic query expansion; DB&IR integration ID COMBINING FUZZY INFORMATION; DOCUMENT-RETRIEVAL; SELECTION QUERIES; SIMILARITY SEARCH; METRIC-SPACES; DATABASES; XML; PERFORMANCE; ALGORITHMS; RANKING AB Recent IR extensions to XML query languages such as Xpath 1.0 Full-Text or the NEXI query language of the INEX benchmark series reflect the emerging interest in IR-style ranked retrieval over semistructured data. TopX is a top-k retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the k top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dynamic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, (2) efficient and effective top-k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia. C1 [Theobald, Martin; Bast, Holger; Majumdar, Debapriyo; Schenkel, Ralf; Weikum, Gerhard] Max Planck Inst Informat, Saarbrucken, Germany. RP Schenkel, R (reprint author), Max Planck Inst Informat, Saarbrucken, Germany. EM martin.theobald@mpi-inf.mpg.de; schenkel@mpi-inf.mpg.de CR ABOULNAGA A, 2001, VLDB, P591 Agrawal, 2003, CIDR ALKHALIFA S, 2003, SIGMOD 2003, P4 Al-Khalifa S., 2002, ICDE, P141 Amato G, 2003, ACM T INFORM SYST, V21, P192, DOI 10.1145/763693.763696 Amer-Yahia S., 2004, SIGMOD, P83 AMERYAHIA S, 2006, SIGMOD, P575 Amer-Yahia S., 2004, WWW, P583 Amer-Yahia S, 2006, IBM SYST J, V45, P335 Amer-Yahia S, 2005, SIGMOD RECORD, V34, P71 AMERYAHIA S, 2006, SIGMOD REC, V36, P16 Amer-Yahia Sihem, 2005, VLDB, P361 Anh V.N., 2006, SIGIR, P372 Anh VN, 2001, SIGIR, P35 ANH VN, 2002, SIGIR, P3 [Anonymous], 2005, XQUERY 1 0 XPATH 2 0 Avnur R., 2000, SIGMOD, P261 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bast H., 2006, VLDB, P475 Bast H., 2006, SIGIR, P364 Bohm C, 2001, ACM COMPUT SURV, V33, P322, DOI 10.1145/502807.502809 Bruno N., 2002, SIGMOD, P310 Bruno N, 2002, ACM T DATABASE SYST, V27, P153, DOI 10.1145/568518.568519 Buckley C., 1985, SIGIR 85, P97 Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 Carmel D., 2003, SIGIR, P151 Chang K.C.-C., 2002, SIGMOD C, P346 Chaudhuri S, 2004, IEEE T KNOWL DATA EN, V16, P992, DOI 10.1109/TKDE.2004.30 Chinenyanga T. T., 2001, SIGIR Forum Choi B, 2003, LECT NOTES COMPUT SC, V2736, P28 Ciaccia P., 2000, ICDE, P244 Cohen S., 2003, VLDB, P45 Consens MP, 2005, LECT NOTES COMPUT SC, V3818, P22 Cormen T., 2001, INTRO ALGORITHMS CRASWELL N, 2003, TREC, P78 de Vries A.P., 2002, SIGMOD, P322 Denoyer L., 2006, WIKIPEDIA XML CORPUS Donjerkovic D., 1999, Very Large Data Bases. Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Fagin R, 2002, SIGMOD RECORD, V31, P109 Fagin R, 1999, J COMPUT SYST SCI, V58, P83, DOI 10.1006/jcss.1998.1600 Fegaras L, 2004, LECT NOTES COMPUT SC, V3186, P51 Fellbaum C, 1998, WORDNET ELECT LEXICA Fuhr N., 2001, SIGIR Forum Goldman Roy, 1997, VLDB, P436 Grabs T, 2003, LECT NOTES COMPUT SC, V2818, P193 Graupmann Jens, 2005, VLDB, P529 Grust T, 2003, VLDB, P524 GRUST T, 2002, SIGMOD, P109 Guntzer U, 2001, INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, PROCEEDINGS, P622 Guntzer U., 2000, VLDB, P419 Guo L., 2003, SIGMOD, P16 Hjaltason GR, 1999, ACM T DATABASE SYST, V24, P265, DOI 10.1145/320248.320255 Hjaltason GR, 2003, ACM T DATABASE SYST, V28, P517, DOI 10.1145/958942.958948 HRISTIDIS V, 2003, ICDE, P367 HUNG E, 2004, SIGMOD, P719 JAGADISH HV, 2001, LECT NOTES COMPUTER, V2397, P149 Jiang H., 2003, VLDB, P273 Kaushik R., 2004, SIGMOD, P779 Kaushik Raghav, 2002, SIGMOD, P133 Kazai G, 2006, LECT NOTES COMPUT SC, V3977, P16 LALMAS M, 2007, LECT NOTES COMPUTER, V4518 Li YJ, 2004, VTC2004-FALL: 2004 IEEE 60TH VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-7, P72 LIM L, 2002, VLDB, P442, DOI 10.1016/B978-155860869-6/50046-9 List J, 2005, INFORM RETRIEVAL, V8, P547, DOI 10.1007/s10791-005-0747-2 LIU S, 2005, WEBDB, P79 Marian A, 2004, ACM T DATABASE SYST, V29, P319, DOI 10.1145/1005566.1005569 Marian A, 2005, PROC INT CONF DATA, P162 MASS Y, 2004, 3 INT WORKSH IN EV X, V3493, P73 Moffat A, 1996, ACM T INFORM SYST, V14, P349, DOI 10.1145/237496.237497 Natsev A., 2001, VLDB, P281 NEPAL S, 1999, ICDE, P22 Persin M, 1996, J AM SOC INFORM SCI, V47, P749, DOI 10.1002/(SICI)1097-4571(199610)47:10<749::AID-ASI3>3.3.CO;2-U Polyzotis N, 2006, ACM T DATABASE SYST, V31, P1014, DOI 10.1145/1166074.1166082 POLYZOTIS N, 2004, SIGMOD, P263 Reid J, 2006, INFORM PROCESS MANAG, V42, P74, DOI 10.1016/j.ipm.2005.03.006 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SCHENKEL R, 2006, ECIR, P326 SCHENKEL R, 2006, EDBT, P331 Schlieder T, 2002, J AM SOC INF SCI TEC, V53, P489, DOI 10.1002/asi.10060 Soffer A., 2001, SIGIR, P43 Suchanek F. M., 2007, WWW TAO Y, 2003, CIKM, P83 THEOBALD A, 2002, EDBT 2002, P477 THEOBALD A, 2000, WEBDB, P35 THEOBALD A, 2004, VLDB, P648 THEOBALD A, 2007, SIGMOD THEOBALD A, 2005, SIGIR, P242 THEOBALD A, 2005, VLDB, P625 THEOBALD A, 2003, WEBDB, P1 TROTMAN A, 2004, 3 INT WORKSH IN EV X, V3493, P16 VAGENA Z, 2004, WEBDB, P43 VORHEES E, 2004, TREC, P69 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Williams HE, 2004, ACM T INFORM SYST, V22, P573, DOI 10.1145/1028099.1028102 WU Y, 2003, ICDE, P443 YU C, 2001, JCDL 01, P215 ZHANG C, 2001, SIGMOD, P425 Zobel J., 2006, ACM COMPUT SURV, V38 INDEX ORGANIZED TABL NR 101 TC 23 Z9 24 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1066-8888 EI 0949-877X J9 VLDB J JI VLDB J. PD JAN PY 2008 VL 17 IS 1 BP 81 EP 115 DI 10.1007/s00778-007-0072-z PG 35 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science GA 265ZN UT WOS:000253400700005 ER PT J AU Cornacchia, R Heman, S Zukowski, M de Vries, AP Boncz, P AF Cornacchia, Roberto Heman, Sandor Zukowski, Marcin de Vries, Arjen P. Boncz, Peter TI Flexible and efficient IR using array databases SO VLDB JOURNAL LA English DT Article DE information retrieval; array databases; query optimization; database compression ID QUERY OPTIMIZATION; CLUSTER; SEARCH AB The Matrix Framework is a recent proposal by Information Retrieval (IR) researchers to flexibly represent information retrieval models and concepts in a single multidimensional array framework. We provide computational support for exactly this framework with the array database system SRAM (Sparse Relational Array Mapping), that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules. To demonstrate their effect on text retrieval, we apply them in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage. C1 [Cornacchia, Roberto; Heman, Sandor; Zukowski, Marcin; de Vries, Arjen P.; Boncz, Peter] Ctr Wiskunde & Informat, NL-1098 SJ Amsterdam, Netherlands. RP Heman, S (reprint author), Ctr Wiskunde & Informat, Kruislaan 413, NL-1098 SJ Amsterdam, Netherlands. EM roberto@cwi.nl; heman@cwi.nl; marcin@cwi.nl; arjen@cwi.nl; boncz@cwi.nl RI de Vries, Arjen/ OI de Vries, Arjen/0000-0002-2888-4202 CR Ailamaki A., 2001, Proceedings of the 27th International Conference on Very Large Data Bases ANH V., 2005, P 28 ANN INT ACM SIG, P226, DOI 10.1145/1076034.1076075 Anh VN, 2005, INFORM RETRIEVAL, V8, P151, DOI 10.1023/B:INRT.0000048490.99518.5c Barroso LA, 2003, IEEE MICRO, V23, P22, DOI 10.1109/MM.2003.1196112 Baumann P., 1999, Next Generation Information Technologies and Systems. 4th International Workshop, NGITS'99. Proceedings (Lecture Notes in Computer Science Vol.1649) Boncz P, 2005, P BIENN C INN DAT SY, P225 Broder Andrei Z., 2003, P 12 INT C INF KNOWL, P426, DOI DOI 10.1145/956863.956944 Buneman P., 1994, SIGMOD Record, V23 CHAKRAVARTHY US, 1990, ACM T DATABASE SYST, V15, P162, DOI 10.1145/78922.78924 CLARKE CLA, 2005, P TEXT RETR C TREC G CLARKE CLA, 2004, P TEXT RETR C TREC G DEMMEL J, 2000, SOC IND APPL MATH Eisenberg A, 2004, SIGMOD RECORD, V33, P119 FURTADO P, 1999, P 15 INT C DAT ENG I, P408 GalindoLegaria C, 1997, ACM T DATABASE SYST, V22, P43, DOI 10.1145/244810.244812 GOLDSTEIN J, 1998, P INT C DAT ENG IEEE Golub G. H., 1983, MATRIX COMPUTATIONS Grabs T, 2004, KNOWL INF SYST, V6, P465, DOI 10.1007/s1O115-003-0120-y GRAEFE G, 1994, IEEE T KNOWL DATA EN, V6, P120, DOI 10.1109/69.273032 Grossman DA, 1997, J AM SOC INFORM SCI, V48, P122, DOI 10.1002/(SICI)1097-4571(199702)48:2<122::AID-ASI3>3.0.CO;2-# HIEMSTRA D, 1998, ECDL, P569 HOWE B, 2004, VLDB, P924 HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898 Kotlyar V., 1997, Euro-Par '97 Parallel Processing. Third International Euro-Par Conference. Proceedings Lerner A., 2003, VLDB, P345 Libkin L., 1996, P ACM SIGMOD INT C M, P228, DOI 10.1145/233269.233335 MAHESH K, 1999, P TEXT RETR C TREC G Maier D., 1993, P 12 ACM S PRINC DAT, P1, DOI 10.1145/153850.153851 PONTE J, 1998, P 21 ACM SIGIR C RES PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1998, P TEXT RETR C TREC, P143 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROELLEKE T, 2005, IP M, V42, P4 STEINBRUNN M, 1993, OPTIMIZING JOIN ORDE Trotman A, 2003, INFORM RETRIEVAL, V6, P5, DOI 10.1023/A:1022949613039 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H VANBALLEGOOIJ A, 2003, INSR0301 DBMS Witten I. H., 1999, MANAGING GIGABYTES C ZUKOWSKI M, 2006, P INT C DAT ENG IEEE NR 39 TC 4 Z9 5 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013 USA SN 1066-8888 J9 VLDB J JI VLDB J. PD JAN PY 2008 VL 17 IS 1 BP 151 EP 168 DI 10.1007/s00778-007-0071-0 PG 18 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science GA 265ZN UT WOS:000253400700007 ER PT S AU Murata, M Toda, H Matsuura, Y Kataoka, R AF Murata, Masaya Toda, Hiroyuki Matsuura, Yumiko Kataoka, Ryoji BE Bailey, J Maier, D Schewe, KD Thalheim, B Wang, XS TI Improving Mobile Web-IR Using Access Concentration Sites in Search Results SO WEB INFORMATION SYSTEMS ENGINEERING - WISE 2008, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT International Workshop on Web Information Systems Engineering (WISE 2008) CY SEP 01-04, 2008 CL Auckland, NEW ZEALAND SP WISE Soc, Auckland Univ Technol, Informat Sci Res, Inst Infocomm Res, Christia-Albrechts Univ ID QUERY EXPANSION; TEXT RETRIEVAL AB Effective ranking algorithms for mobile web search are being actively pursued. Due to the peculiar and troublesome properties of mobile contents such as scant text, few outward links, and few input keywords, conventional web search techniques using bag-of-words ranking functions or link-based algorithms are not good enough for mobile web search. Our solution is to use click logs; the aim is to extract only access concentrated search results from among the many search results. Users typically click a search result after seeing its title and snippet, so the titles and snippets of the access concentrated sites must be good relevance feedback sources that will greatly improve mobile web search performance. In this paper, we introduce a new measure that is capable of estimating the degree of access concentration and present a method that uses the measure to precisely extract the access concentration sites from many search results. Query expansion with terms extracted from the access concentration sites is then performed. The effectiveness of our proposal is verified in an experiment that uses click logs and data from a real mobile web search site. C1 [Murata, Masaya; Toda, Hiroyuki; Matsuura, Yumiko; Kataoka, Ryoji] NTT Corp, Cyber Solut Labs, Yokosuka, Kanagawa 2390847, Japan. RP Murata, M (reprint author), NTT Corp, Cyber Solut Labs, 1-1 Hikari No Oka, Yokosuka, Kanagawa 2390847, Japan. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Brin S., 1998, P 7 INT WORLD WID WE, P107 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 Jansen BJ, 2003, IC'03: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERNET COMPUTING, VOLS 1 AND 2, P65 Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Page L., 1998, PAGERANK CITATION RA Parikh J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148319 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sakai T., 2001, P 24 ANN INT ACM SIG, P396, DOI 10.1145/383952.384035 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Tao Tao, 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148201 Zhuang Z., 2006, P CIKM 2006, P860, DOI 10.1145/1183614.1183767 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85480-7 J9 LECT NOTES COMPUT SC PY 2008 VL 5175 BP 221 EP 234 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BII85 UT WOS:000259899100017 ER PT J AU Robertson, S Tait, J AF Robertson, Stephen Tait, John TI Karen Sparck Jones - In memoriam - Obituary SO INFORMATION PROCESSING & MANAGEMENT LA English DT Biographical-Item C1 Microsoft Res, Cambridge CB3 0FB, England. JohnTait Net Ltd, Thewles TS21 3JN, England. RP Robertson, S (reprint author), Microsoft Res, 7JJ Thomson Ave,Roger Needham House, Cambridge CB3 0FB, England. EM ser@microsoft.com; John@johntait.net CR Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1971, AUTOMATIC KEYWORD CL Jones KS, 2007, INFORM PROCESS MANAG, V43, P1449, DOI 10.1016/j.ipm.2007.03.009 Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 1996, LNAI, V1083 JONES KS, 2005, TREC EXPT EVALUATION, V421, P421 JONES KS, 1986, THESIS U CAMBRIDGE E JONES S, 1977, REPORT DESIGN STUDY ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sparck Jones K., 1976, J DOC, V32, P59, DOI 10.1108/eb026616 Sparck Jones K., 1981, INFORM RETRIEVAL EXP NR 11 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2007 VL 43 IS 6 BP 1441 EP 1444 DI 10.1016/j.ipm.2007.07.006 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 214MO UT WOS:000249742500001 ER PT J AU Yan, R Hauptmann, AG AF Yan, Rong Hauptmann, Alexander G. TI A review of text and image retrieval approaches for broadcast news video SO INFORMATION RETRIEVAL LA English DT Review DE video retrieval; text retrieval; image retrieval; concept-based retrieval; fusion; review ID OF-THE-ART; INFORMATION-RETRIEVAL; SEGMENTATION; FEATURES AB The effectiveness of a video retrieval system largely depends on the choice of underlying text and image retrieval components. The unique properties of video collections (e.g., multiple sources, noisy features and temporal relations) suggest we examine the performance of these retrieval methods in such a multimodal environment, and identify the relative importance of the underlying retrieval components. In this paper, we review a variety of text/image retrieval approaches as well as their individual components in the context of broadcast news video. Numerous components of text/image retrieval have been discussed in detail, including retrieval models, text sources, temporal expansion methods, query expansion methods, image features, and similarity measures. For each component, we conduct a series of retrieval experiments on TRECVID video collections to identify their advantages and disadvantages. To provide a more complete coverage of video retrieval, we briefly discuss an emerging approach called concept-based video retrieval, and review strategies for combining multiple retrieval outputs. C1 IBM TJ Watson Res, Intelligent Informat Management Dept, Hawthorne, NY 10532 USA. Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA. RP Yan, R (reprint author), IBM TJ Watson Res, Intelligent Informat Management Dept, 19 Skyline Dr, Hawthorne, NY 10532 USA. EM yanr@us.ibm.com; alex@cs.cmu.edu RI Rohlf, F/A-8710-2008 CR ADCOCK J, 2004, P NIST TREC VID RETR AMIR A, 2003, P NIST TREC VID RETR Antani S, 2002, PATTERN RECOGN, V35, P945, DOI 10.1016/S0031-3203(01)00086-3 Aslam J.A., 2001, P 24 ANN INT ACM SIG, P276, DOI DOI 10.1145/383952.384007 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 BUCKLEY C, 1999, P 8 TEXT RETRIEVAL C Burges C. J. C., 1998, DATA MIN KNOWL DISC, V2, P955 Carson C., 1997, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.97TB100175), DOI 10.1109/IVL.1997.629719 CHANG SF, 2005, P NIST TREC VID RETR CHANG SF, 2005, P IEEE INT C AC SPEE Cheng YH, 2005, EUR J PHARM SCI, V26, P9, DOI 10.1016/j.ejps.2005.03.014 CHRISTEL M, 2005, P INT C IM VID RETR Christel M, 1998, J INTELL INF SYST, V11, P235, DOI 10.1023/A:1008638024786 CHUA TS, 2005, P NIST TREC VID RETR CHUA TS, 1997, P INT C MULT COMP SY, P362 CHUA TS, 2004, P NIST TREC VID RETR Chuang C.-H., 1996, IEEE T IMAGE PROCESS, V5, P56 COOKE E, 2004, P NIST TREC VID RETR COX IJ, 1996, P INT C PATT REC, V2, P557, DOI 10.1109/ICPR.1996.546886 Deerwester S., 1990, J AM SOC INFORM SCI, V41 Del Bimbo A., 2001, VISUAL INFORM RETRIE Faloutsos C., 1994, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V3, DOI 10.1007/BF00962238 Fellbaum C, 1998, WORDNET ELECT LEXICA FOLEY C, 2005, P NIST TREC VID RETR GAUGHAN G, 2003, P 5 ACM SIGMM INT WO, P23, DOI 10.1145/973264.973269 HAUPTMANN A, 2004, P NIST TREC VID RETR Hauptmann A. G., 2004, P ACM MULT NEW YORK, P668, DOI 10.1145/1027527.1027681 HAUPTMANN AG, 2003, P NIST TREC VID RETR HAUPTMANN AG, 2003, STORAGE RETRIEVAL MU HAUPTMANN AG, 2006, ENCY LANGUAGE LINGUI HAUPTMANN AG, 2005, P NIST TREC VID RETR He J., 2004, P ACM INT C MULT, P9, DOI DOI 10.1145/1027527.1027531 He X., 2002, P 10 ACM INT C MULT, P343 Hua X- S, 2001, P ACM MULT 2001 WORK, P24 Huang J, 1997, PROC CVPR IEEE, P762 Huang X., 1993, COMPUTER SPEECH LANG, V7, P137, DOI 10.1006/csla.1993.1007 HUURNINK B, 2005, THESIS U AMSTERDAM N IYENGAR G, 2005, P ACM INT C MULT Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 JIN R, 2002, P IEEE INT C IM PROC Jing Y., 1994, P RIAO 94, P146 Kennedy L.S., 2005, P 13 ANN ACM INT C M, P882, DOI 10.1145/1101149.1101339 KRAAIJ W, 2004, VARIATIONS LANGUAGE LAFFERTY J, 2003, KLUWER INT SERIES IN, V13 Gauvain JL, 2002, SPEECH COMMUN, V37, P89, DOI 10.1016/S0167-6393(01)00061-9 Lee TS, 1996, IEEE T PATTERN ANAL, V18, P959 LEI Z, 1997, P MULT STOR ARCH SYS, V2, P232 Leroy A. M., 1987, ROBUST REGRESSION OU LEW M, 2002, P INT C IM VID RETR Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 LI BC, 1994, INT C PATT RECOG, P352 Li J., 2000, P 8 ACM INT C MULT, P147, DOI 10.1145/354384.354452 Lienhart R., 2003, VIDEO MINING Lin Ching-Yung, 2003, IEEE INT C MULT EXP LU H, 1994, P 1994 INT C APPL DA, P95 MA WY, 1995, P INT C IM PROC, V2 Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 MARR D, 1979, PROC R SOC SER B-BIO, V204, P301, DOI 10.1098/rspb.1979.0029 McDonald K, 2005, LECT NOTES COMPUT SC, V3568, P61 Mehtre BM, 1997, INFORM PROCESS MANAG, V33, P319, DOI 10.1016/S0306-4573(96)00069-6 MITICHE A, 1985, IMAGE VISION COMPUT, V3, P50, DOI 10.1016/0262-8856(85)90013-7 NAGASAKA A, 1992, P IFIP TC2 WG 2 6 2, V2, P113 Naphade M.R., 2004, P 12 ANN ACM INT C M, P660, DOI 10.1145/1027527.1027680 Naphade M.R., 1998, P IEEE INT C IM PROC, V3, P536, DOI 10.1109/ICIP.1998.999041 NATSEV A, 2003, IEEE INT C MULT EXP Neo S. Y., 2006, P C IM VID RETR CIVR, P370 Nevatia R., 1986, HDB PATTERN RECOGNIT Ngo C.W., 2001, P ACM MULT OTT ON CA, P51 OHANIAN PP, 1992, PATTERN RECOGN, V25, P819, DOI 10.1016/0031-3203(92)90036-I PAL NR, 1993, PATTERN RECOGN, V26, P1277, DOI 10.1016/0031-3203(93)90135-J Pass G, 1999, MULTIMEDIA SYST, V7, P234, DOI 10.1007/s005300050125 Perona P., 1998, LNCS, V1406, P655 Platt J. C., 1999, ADV LARGE MARGIN CLA Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Puzicha J, 1997, PROC CVPR IEEE, P267, DOI 10.1109/CVPR.1997.609331 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 RAUTIAINEN M, 2004, P NIST TREC VID RETR RAUTIAINEN M, 2004, IEEE INT C MULT EXP Rickman R, 1996, P SOC PHOTO-OPT INS, V2670, P2, DOI 10.1117/12.234772 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 1992, TEXT RETR C, P21 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROWE LA, 2004, P ACM MULT Rui Y., 1997, Proceedings. International Conference on Image Processing (Cat. No.97CB36144), DOI 10.1109/ICIP.1997.638621 Rui Y., 1996, P 1 INT WORKSH IM DA Salton G., 1989, AUTOMATIC TEXT PROCE SARKAR S, 1996, IEEE C COMP VIS PATT, P478 Sato T., 1998, IEEE INT WORKSH CONT, p52 Schmid C, 1997, IEEE T PATTERN ANAL, V19, P530, DOI 10.1109/34.589215 SHAW JA, 1994, TEXT RETRIEVAL C GAI Shi J., 1998, P INT C COMP VIS, P1154 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 Sivic J., 2003, Proceedings Ninth IEEE International Conference on Computer Vision SMEATON A, 2003, P INT C IM VID RETR, P19 Smeaton A. F., 2006, P 8 ACM INT WORKSH M, P321, DOI DOI 10.1145/1178677.1178722 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith J. R., 1996, ACM MULTIMEDIA, P87 SMITH JR, 2002, P INT WORKSH DIG COM SMITH JR, 1996, P ICASSP ATL MAY, V4, P2239 Smith JR, 1996, P SOC PHOTO-OPT INS, V2670, P426, DOI 10.1117/12.234781 SNOEK C, 2004, P NIST TREC VID RETR Snoek C. G., 2005, P 13 ANN ACM INT C M, P399, DOI 10.1145/1101149.1101236 Snoek CGM, 2005, MULTIMED TOOLS APPL, V25, P5, DOI 10.1023/B:MTAP.0000046380.27575.a5 SRIKANTH M, 2005, P NIST TREC VID RETR STRICKER M, 1995, P SOC PHOTO-OPT INS, V2410, P381 STRICKER MA, 1994, P SOC PHOTO-OPT INS, V2185, P15, DOI 10.1117/12.171784 Su Z., 2001, P 9 ACM INT C MULT O, P98 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P1 SZUMMER M, 2002, IEEE INT WORKSH CONT Thomas S.Huang, 1997, INT S MULT INF PROC THYAGARAJAN K, 1996, P INT C IM PROC ICIP, P640 Turtle H. R., 1991, THESIS U MASSACHUSET Tuytelaars T., 1999, P 3 INT C VIS INF SY, P493 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 VOLKMER T, 2006, IEEE INT C MULT EXP, P765 WACTLAR H, 1999, IEEE COMPUT, V32, P66 WESTERVELD T, 2004, THESIS CWI Westerveld T, 2004, LECT NOTES COMPUT SC, V3115, P344 WESTERVELD T, 2003, P NIST TREC VID RETR White RW, 2006, INFORM PROCESS MANAG, V42, P166, DOI 10.1016/j.ipm.2004.08.010 Wu Y., 2004, P 12 ANN ACM INT C M, P572, DOI 10.1145/1027527.1027665 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Yan R., 2006, THESIS CARNEGIE MELL Yan R, 2006, P 29 ANN INT ACM SIG, P324, DOI 10.1145/1148170.1148228 Yan R., 2004, P 12 ANN ACM INT C M, P548, DOI 10.1145/1027527.1027661 Yang H., 2003, P 11 ACM INT C MULT, P632 YANG J, 2004, P INT C IM VID RETR, P270 YUAN J, 2005, P NIST TREC VID RETR ZAHN CT, 1972, IEEE T COMPUT, VC 21, P269 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 ZHAI Y, 2005, P NIST TREC VID RETR ZHANG C, 1994, CHINESE GRASSLANDS, V1, P9 NR 136 TC 21 Z9 21 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD OCT PY 2007 VL 10 IS 4-5 BP 445 EP 484 DI 10.1007/s10791-007-9031-y PG 40 WC Computer Science, Information Systems SC Computer Science GA 213KD UT WOS:000249664800005 ER PT J AU Wu, HC Luk, RWP Wong, KF Kwok, KL AF Wu, H. C. Luk, Robert W. P. Wong, K. F. Kwok, K. L. TI A retrospective study of a hybrid document-context based retrieval model SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; model; theory; retrospective experiment ID INFORMATION-RETRIEVAL; OPERATORS; WORDS; SPACE AB This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance decisions, and it is a hybrid of various existing effective models and techniques. It estimates the relevance decision preference of a document context as the log-odds and uses smoothing techniques as found in language models to solve the problem of zero probabilities. It combines these estimated preferences of document contexts using different types of aggregation operators that comply with different relevance decision principles (e.g., aggregate relevance principle). Our model is evaluated using retrospective experiments (i.e.,, with full relevance information), because such experiments can (a) reveal the potential of our model, (b) isolate the problems of the model from those of the parameter estimation, (c) provide information about the major factors affecting the retrieval effectiveness of the model, and (d) show that whether the model obeys the probability ranking principle. Our model is promising as its mean average precision is 60-80% in our experiments using different TREC ad hoc English collections and the NTCIR-5 ad hoc Chinese collection. Our experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents. (c) 2006 Elsevier Ltd. All rights reserved. C1 Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. CUNY Queens Coll, Dept Comp Sci, Flushing, NY 11367 USA. RP Wu, HC (reprint author), Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. EM cshcwu@comp.poIyu.edu.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 CR Bai J, 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725 Bollmann P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42023 BRUZA P, 2003, P ACM SIGIR 2003, P219 BUCKLEY C, 2003, WORKSH REL INF ACC 2 Burgess C, 1997, LANG COGNITIVE PROC, V12, P177 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Chen Stanley F., 1996, P 34 ANN M ASS COMP, P310, DOI 10.3115/981863.981904 Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 DOMBI J, 1982, FUZZY SET SYST, V8, P149, DOI 10.1016/0165-0114(82)90005-7 Dominich S, 2000, J AM SOC INFORM SCI, V51, P614, DOI 10.1002/(SICI)1097-4571(2000)51:7<614::AID-ASI4>3.0.CO;2-S DYCKHOFF H, 1984, FUZZY SET SYST, V14, P143, DOI 10.1016/0165-0114(84)90097-6 FOX E, 1992, INFORMATION RETRIEVA, P393 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 HARMAN D, 2004, COMMUNICATION HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HIEMSTRA D, 2001, DELOS WORKSH PERS RE Jeffreys H., 1948, THEORY PROBABILITY, V2nd Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop JOHNSON WE, 1932, MIND, V41, P421 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 KISHIDA K, 2005, P NTCIR 5 WORKSH NII Kong Y. K., 2004, ACM SIGIR WORKSH MAT Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333 LAFFERTY J, 2003, LANGUAGE MODELING IN Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lidstone George James, 1920, T FACULTY ACTUARIES, V8, P182 Liu X., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 LUK RWP, 2002, ACM TRANSACTIONS ASI, V1, P207 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Kaszkiel M, 1999, ACM T INFORM SYST, V17, P406, DOI 10.1145/326440.326445 Miyamoto S., 1990, FUZZY SETS INFORM RE NEY H, 1994, COMPUT SPEECH LANG, V8, P1, DOI 10.1006/csla.1994.1001 PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1999, P 8 TEXT RETR C TREC, P151 ROBERTSON SE, 1977, J DOCUEMENTATION, V33, P106 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 Salton G., 1975, J AM SOC INFORM SCI, V18, P613 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Song D, 2003, J AM SOC INF SCI TEC, V54, P321, DOI 10.1002/asi.10213 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P779, DOI 10.1016/S0306-4573(00)00015-7 VECHTOMOVA O, 2000, 22 ANN C INF RETR RE VECHTOMOVA O, 2006, DOCUMENT RELEVANCE L WALKER S, 1997, P 6 TEXT RETR C TREC, P125 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 WU HC, ACM T INFORM SYSTEMS WU HC, 2005, P 28 ACM SIGIR, P663, DOI 10.1145/1076034.1076179 Xi WS, 2001, J INFORM SCI, V27, P73 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 YAGER RR, 1988, IEEE T SYST MAN CYB, V18, P183, DOI 10.1109/21.87068 YAO YY, 1991, P 14 ACM SIGIR C RES, P211, DOI 10.1145/122860.122881 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 62 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2007 VL 43 IS 5 BP 1308 EP 1331 DI 10.1016/j.ipm.2006.10.009 PG 24 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 173JR UT WOS:000246869800011 ER PT J AU Arazy, O Woo, C AF Arazy, Ofer Woo, Carson TI Enhancing information retrieval through statistical natural language processing: A study of collocation indexing SO MIS QUARTERLY LA English DT Article DE document management; information retrieval (IR); word ambiguity; natural language processing (NLP); collocations; distance; directionality; weighting; general settings ID DOCUMENT-RETRIEVAL; SYSTEMS; COOCCURRENCE AB In this paper, we provide preliminary evidence for the usefulness of statistical natural language processing (NLP) techniques, and specifically of collocation indexing, for IR in general settings. We investigate the effect of three key parameters on collocation indexing performance: directionality, distance, and weighting. We build on previous work in IR to (1) advance our knowledge of key design elements for collocation indexing, (2) demonstrate gains in retrieval precision from the use of statistical NLP for general-settings IR, and, finally, (3) provide practitioners with a useful cost benefit analysis of the methods under investigation. Although the management of information assets-specifically, of text documents that make up 80 percent of these assets an provide organizations with a competitive advantage, the ability of information retrieval (IR) systems to deliver relevant information to users is severely hampered by the difficulty of disambiguating natural language. The word ambiguity problem is addressed with moderate success in restricted settings, but continues to be the main challenge for general settings, characterized by large, heterogeneous document collections. C1 Univ Alberta, Edmonton, AB T6G 2R6, Canada. Univ British Columbia, Sauder Sch Business, Vancouver, BC V6T 1Z2, Canada. RP Arazy, O (reprint author), Univ Alberta, 3-23 Business Bldg, Edmonton, AB T6G 2R6, Canada. EM ofer.arazy@ualberta.ca; carson.woo@ubc.ca CR Alvarez C., 2004, P RIAO RECH INF ASS, P686 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BUCKLEY C, 1996, P 4 TEXT RETR C CARMEL D, 2001, P 10 TEXT RETR C CROGT WB, 1991, P 14 ANN C RES DEV I, P32 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FAGAN J, 1987, EXPT AUTOMATIC PHRAS FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Firth J. R., 1968, SELECTED PAPERS J R, p[168, 1] Church K. W., 1990, Computational Linguistics, V16 Hevner AR, 2004, MIS QUART, V28, P75 Hsinchun Chen, 2001, KNOWLEDGE MANAGEMENT JAIN H, 1998, MIS Q, V22, P77 JIANG M, 2004, 8 S AI MATH SPECIAL KHOO C, 2001, INFORMATION PROCESSI, P119 LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 Lewis DD, 1996, COMMUN ACM, V39, P92, DOI 10.1145/234173.234210 LUHN HP, 1958, IBM J RES DEV, V2, P314 MAAREK YS, 1991, IEEE T SOFTWARE ENG, V17, P800, DOI 10.1109/32.83915 Manning C. D., 2003, FDN STAT NATURAL LAN MARTIN W, 1983, LEXICOGRAPHY PRINCIP, P77 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Mittendorf E, 2000, INFORM RETRIEVAL, V3, P243, DOI 10.1023/A:1026520926673 Morita K, 2004, INFORM PROCESS MANAG, V40, P957, DOI 10.1016/j.ipm.2003.08.009 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton Gerard, 1991, P 14 ANN INT ACM SIG, P21, DOI 10.1145/122860.122863 Spangler S, 2003, J MANAGE INFORM SYST, V19, P191 SPARCKJONES KS, 1999, P 8 TEXT RETR C SPRAGUE RH, 1995, MIS QUART, V19, P29, DOI 10.2307/249710 Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Stokoe C, 2003, P 26 ANN INT ACM SIG, P159, DOI DOI 10.1145/860435.860466 STRZALKOWSKI T, 1996, P 5 TEXT RETR C VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 NR 40 TC 8 Z9 8 PU SOC INFORM MANAGE-MIS RES CENT PI MINNEAPOLIS PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455 USA SN 0276-7783 J9 MIS QUART JI MIS Q. PD SEP PY 2007 VL 31 IS 3 BP 525 EP 546 PG 22 WC Computer Science, Information Systems; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA 200XW UT WOS:000248797100006 ER PT J AU Wang, P Morgan, AA Zhang, Q Sette, A Peters, B AF Wang, Peng Morgan, Alexander A. Zhang, Qing Sette, Alessandro Peters, Bjoern TI Automating document classification for the Immune Epitope Database SO BMC BIOINFORMATICS LA English DT Article ID PROTEIN INTERACTIONS; TEXT CATEGORIZATION; RESOURCE; MACHINE; GENOME AB Background: The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results: We here report our experience in automating this process using Naive Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e. g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion: By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. C1 La Jolla Inst Allerg & Immunol, La Jolla, CA 92037 USA. Stanford Univ, Sch Med, Stanford, CA 94305 USA. RP Peters, B (reprint author), La Jolla Inst Allerg & Immunol, 9420 Athena Circle, La Jolla, CA 92037 USA. EM pwang@liai.org; alexmo@stanford.edu; qlehmann@liai.org; alex@liai.org; bpeters@liai.org CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131 Bader GD, 2003, NUCLEIC ACIDS RES, V31, P248, DOI 10.1093/nar/gkg056 Chen D, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-370 Dobrokhotov PB, 2003, BIOINFORMATICS, V19, pi91, DOI 10.1093/bioinformatics/btg1011 Donaldson I, 2003, BMC BIOINFORMATICS, V4, DOI 10.1186/1471-2105-4-11 Eppig JT, 2005, NUCLEIC ACIDS RES, V33, pD471, DOI 10.1093/nar/gki113 Han B, 2006, BIOINFORMATICS, V22, P2136, DOI 10.1093/bioinformatics/btl350 HANLEY JA, 1982, RADIOLOGY, V143, P29 HAYES P, 1990, P 6 IEEE CAIA, P321 Hersh W, 2005, BRIEF BIOINFORM, V6, P344, DOI 10.1093/bib/6.4.344 HERSH W, 2006, TREC 2006 GENOMICS T JOACHIMS T, 1998, TEXT CATEGORIZATION, P137 Kanehisa M, 2004, NUCLEIC ACIDS RES, V32, pD277, DOI 10.1093/nar/gkh063 Kohavi R., 1995, STUDY CROSS VALIDATI, P1137 Krovetz R, 1993, VIEWING MORPHOLOGY I, P191 Manning C. D., 1999, FDN STAT NATURAL LAN McCallum A., 1998, AAAI 98 WORKSH LEARN Miotto Olivo, 2005, Genome Inform, V16, P32 Mitchell T., 1997, MCGRAW HILL SERIES C Nigam K., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Peters B, 2005, IMMUNOGENETICS, V57, P326, DOI 10.1007/s00251-005-0803-5 Peters B, 2005, PLOS BIOL, V3, P379, DOI 10.1371/journal.pbio.0030091 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahami M., 1998, AAAI 98 WORKSH LEARN Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Suomela BP, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-75 Vita R, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-341 Witten I. H., 2005, DATA MINING PRACTICA Xenarios I, 2002, NUCLEIC ACIDS RES, V30, P303, DOI 10.1093/nar/30.1.303 NR 31 TC 17 Z9 20 PU BIOMED CENTRAL LTD PI LONDON PA MIDDLESEX HOUSE, 34-42 CLEVELAND ST, LONDON W1T 4LB, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD JUL 26 PY 2007 VL 8 AR 269 DI 10.1186/1471-2105-8-269 PG 10 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 207TM UT WOS:000249274000001 PM 17655769 ER PT J AU Bhogal, J Macfarlane, A Smith, P AF Bhogal, J. Macfarlane, A. Smith, P. TI A review of ontology based query expansion SO INFORMATION PROCESSING & MANAGEMENT LA English DT Review DE query expansion; context; ontologies; WordNet; relevance feedback; information retrieval ID DOCUMENT-RETRIEVAL SYSTEMS; INFORMATION-RETRIEVAL; THESAURUS; FEEDBACK; CONTEXT; SEARCH; CLASSIFICATION; SELECTION; RULES AB This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described. (c) 2006 Elsevier Ltd. All rights reserved. C1 Univ Cent England, Dept Comp, Birmingham B42 2SU, W Midlands, England. City Univ London, Sch Informat, London EC1V 0HB, England. RP Bhogal, J (reprint author), Univ Cent England, Dept Comp, Feeney Bldg,Perry Barr, Birmingham B42 2SU, W Midlands, England. EM jagdev.bhogal@uce.ac.uk CR Bao J., 2004, P INT C INF KNOWL EN, P82 Barzilay R., 1997, P INT SCAL TEXT SUMM BATEMAN J, 2005, J BATEMANS ONTOLOGY BATES M, 2002, 1 MONDAY PEER REV J BAUER T, 2001, CIKM 2001 BAZIZ M, 2005, 5 INT C CONC LIB INF, P171 Bentivogli Luisa, 2004, P 2 GLOB WORDNET C B, P47 Bharat K, 2000, COMPUT NETW, V33, P493, DOI 10.1016/S1389-1286(00)00047-5 BHAVNANI S, 2001, P ASIST, P340 BILLERBECK B, 2004, P 15 AUSTR DAT C ADC Broder A., 2002, SIGIR Forum, V36 Buckland M, 2003, TRANSLINGUAL INFORM Buckley C., 1995, NIST SPECIAL PUBLICA, P69 CALLAN J, 1992, INQUERY RETRIEVAL SY, P78 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 CHEN H, 1993, IEEE EXPERT, P25 CHENG C, 2004, CROSSRODADS ARCH, V10, P5 CHU W, 2002, TEXTUAL DOCUMENT IND CLIMENT S, 1956, EUROWORDNET COATESSTEPHENS S, 1991, ANAL ACQUISITION PRO CROFT B, 2001, DELOS WORKSH PERS RE Cui Hang, 2003, IEEE T KNOWLEDGE DAT, V15 Davies J., 2004, P 37 HAW INT C SYST DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 EFTHIMIADIS E, 1992, INTERACTIVE QUERY EX Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 EGUCHI K, 2005, P 5 NTCIR WORKSH M E ELBELTAGY S, 2001, J DIGITAL INFORM, V2 Fellbaum C., 1997, WORDNET ELECT LEXICA Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 Frei H., 1993, P 16 ANN INT ACM SIG, P160 FU G, 2005, OTM CONF INT C 4 NOV FU L, 2005, P 8 INT C AS DIG LIB, P342 Gangemi A., 2001, P INT C FORM ONT INF, P285 GONZALO J, 1998, COLING ACL 98 Grootjen FA, 2006, DATA KNOWL ENG, V56, P174, DOI 10.1016/j.datak.2005.03.006 GROVER C, 2003, P ONT INF EXTR INT W GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 HANCOCKBEAULIEU M, 1992, J DOC, V48, P406, DOI 10.1108/eb026906 Harabagiu S., 1999, P ACL SIGLEX99 STAND, P1 Harman D, 1988, P 11 ANN INT ACM SIG, P321, DOI 10.1145/62437.62469 HARMAN DK, 1992, RELEVANCE FEEDBACK R, P1 HEARST M, 1992, 14 INT C COMP LING F HERSH W, 2003, PHRASES BOOSTING QUE, P503 Hoashi K., 2001, SIGIR Forum HUANG L, 2005, P 28 ANN INT ACM SIG, P613 HUANG L, 2000, ECSL HWANG C., 1999, P 6 INT WORKSH KNOWL HYVONEN E, 2003, WWW2003, P199 INGWERSEN P, 2004, INFORM RES, V10 Jarvelin K, 2001, INFORM RETRIEVAL, V4, P231, DOI 10.1023/A:1011998222190 JONES GJF, 2003, MOB HCI WORKSH MOB U, P227 JONES S, 1993, J INFORM SCI, V19, P167, DOI 10.1177/016555159301900301 JONES S, 1995, J AM SOC INFORM SCI, V46, P52, DOI 10.1002/(SICI)1097-4571(199501)46:1<52::AID-ASI6>3.0.CO;2-1 Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 KASHYAP V, 2001, AOS WORKSH ROM NOV 2 KHAN L, 2002, 8 INT W S MULT INF S, P56 Klink S, 2004, LECT NOTES COMPUT SC, V2956, P281 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 LAME G, 2003, ICAIL 2003 W S LEG O Leger A., 2001, MKBEEM DEV MULTILING LEROY G, 2000, METHODS INFO MED LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 LIFANTSEV M, 1998, OPEN GRID PROJECT Lin D., 1999, P 37 ANN M ASS COMP, P317, DOI 10.3115/1034678.1034730 LOUKACHEVITCH NV, 2000, MACHINE TRANSLATION, P10 LOWE D, 2000, IMPROVING WEB SEARCH Magnini Bernardo, 2002, P ONT 2002 ONT LEX K, P43 MANDALA R, 1998, USE WORDNET INFORM R, P191 MANDALA R, 1999, RES DEV INFORM RETRI MENA E, 2000, INT J COOPERATIVE IN Morris J., 1991, Computational Linguistics, V17 Navigli R., 2003, 14 EUR C MACH LEARN NILSSON K, 2005, P 15 NODALIDA C OGAWA Y, 2000, TREC 2000 OSULLIVAN D, 1995, P WORKSH BAS ONT SIS PAVEL S, 2003, P 5 RUSS C DIG LIB R PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PUSTEJOVSKY J, 1995, CORE LEXICAL ENGINE RHODES BJ, 2000, IBM SYSTEMS J, V39 Robertson A. M., 1993, Literary & Linguistic Computing, V8, DOI 10.1093/llc/8.3.143 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO R, 1971, RELEVANCE FEEDBACK I RUTHVEN I, 2004, SIGIR INF RETR CONT Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 SAIAS J, 2002, C LEG KNOWL IS DEC 1, P11 Salton G., 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 SANDERSON M, 2004, LECT NOTES COMPUTER, P42 SANDERSON M, 2000, BUILDING TESTING APP SCHATZ BR, 1996, INTERACTION TERM SUG SCOTT S, 1998, USAGE WORDNET NLP SY SHOVAL P, 1985, INFORM PROCESS MANAG, V21, P475, DOI 10.1016/0306-4573(85)90002-0 Sihvonen A, 2004, J DOC, V60, P673, DOI 10.1108/00220410410568151 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Smyth B, 2005, P 10 INT C INT US IN, P167 Soergel D, 1999, J AM SOC INFORM SCI, V50, P1119, DOI 10.1002/(SICI)1097-4571(1999)50:12<1119::AID-ASI12>3.0.CO;2-I SPARCKJONES K, 1973, INFORM STORAGE RETRI, V9, P339 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P809, DOI 10.1016/S0306-4573(00)00016-9 Spink A, 1997, J AM SOC INFORM SCI, V48, P382 Stairmand M. A., 1997, SIGIR Forum SUOMELA S, 2005, ECIR 2005, P315 TOMBROS A, 2001, 23 BCS EUR ANN C IR Tombros Anastasios, 1998, 21 ANN INT ACM SIGIR, P2 TOMS E, 2004, INF RETR CONT WORKSH TORRISSEN BC, 1998, DEWEY GOES SURFING A VAKKARI P, 2003, AM SOC INFORM SCI TE, V60 VECHTOMOVA O, 2003, QUERY EXPANSION LONG, P159 VOORHEES E, 1993, ACM SIGIR, P171 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 WALKER S, 1987, IMPROVING SUBJECT RE, V1 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 YU CT, 2001, SIGMOD M WEBSTER ONLINE DIC 2002, COMMUNICATIONS ACM, V45, P39 NR 118 TC 106 Z9 110 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2007 VL 43 IS 4 BP 866 EP 886 DI 10.1016/j.ipm.2006.09.003 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 155UY UT WOS:000245605100002 ER PT J AU Dominich, S Kiezer, T AF Dominich, Sandor Kiezer, Tamas TI A measure theoretic approach to information retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID AUTOMATIC TEXT-RETRIEVAL AB The vector space model of information retrieval is one of the classical and widely applied retrieval models. Paradoxically, it has been characterized by a discrepancy between its formal framework and implementable form. The underlying concepts of the vector space model are mathematical terms: linear space, vector, and inner product. However, in the vector space model, the mathematical meaning of these concepts is not preserved. They are used as mere computational constructs or metaphors. Thus, the vector space model actually does not follow formally from the mathematical concepts on which it has been claimed to rest. This problem has been recognized for more than two decades, but no proper solution has emerged so far. The present article proposes a solution to this problem. First, the concept of retrieval is defined based on the mathematical measure theory. Then, retrieval is particularized using fuzzy set theory. As a result, the retrieval function is conceived as the cardinality of the intersection of two fuzzy sets. This view makes it possible to build a connection to linear spaces. It is shown that the classical and the generalized vector space models, as well as the latent semantic indexing model, gain a correct formal background with which they are consistent. At the same time it becomes clear that the inner product is not a necessary ingredient of the vector space model, and hence of Information Retrieval (IR). The Principle of Object Invariance is introduced to handle this situation. Moreover, this view makes it possible to consistently formulate new retrieval methods: in linear space with general basis, entropy-based, and probability-based. It is also shown that Information Retrieval may be viewed as integral calculus, and thus it gains a very compact and elegant mathematical way of writing. Also, Information Retrieval may thus be conceived as an application of mathematical measure theory. C1 Univ Veszprem, Dept Comp Sci, H-8200 Veszprem, Hungary. RP Dominich, S (reprint author), Univ Veszprem, Dept Comp Sci, Egyetem 10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu CR BaezaYates R, 2003, INT J APPROX REASON, V34, P97, DOI 10.1016/j.ijar.2003.07.002 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELEW RK, 2000, FINDING OUT ABOUT Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Berry W., 1999, UNDERSTANDING SEARCH DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Doob JL, 1994, MEASURE THEORY DUBIN D, 2004, LIB TRENDS SPR FAYNMAN RP, 1964, FEYNMAN LECT PHYS Folland G.B., 1984, REAL ANAL MODERN TEC KOLMOGOROFF A, 1950, FDN PROBABILITY LANCZOS K, 1970, SPACE THROUGH AGES Meadow C. T., 1999, TEXT INFORM RETRIEVA NALLAPATI R, 2004, P ACM SIGIR INT C RE Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rudin W., 1966, REAL COMPLEX ANAL SALTON G, 1975, READINGS AUTOMATIC, V18, P613 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1966, READINGS AUTOMATIC L, P169 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simmonds JG, 1982, BRIEF TENSOR ANAL Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 2004, GEOMETRY IR VONNEUMANN J, 1927, MATH PHYS KLASSE, P1 Wang Z, 1991, FUZZY MEASURE THEORY WON KM, 1996, KANGWEON KYUNGKI MAT, V4, P135 Wong S., 1984, P 7 ANN INT ACM SIGI, P167 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 ZIMMERMAN HJ, 1996, FUZZY SET THEORY AND NR 31 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2007 VL 58 IS 8 BP 1108 EP 1122 DI 10.1002/asi.20586 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 176IO UT WOS:000247078400004 ER PT J AU Abdelali, A Cowie, J Soliman, HS AF Abdelali, Ahmed Cowie, Jim Soliman, Hamdy S. TI Improving query precision using semantic expansion SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE query expansion; information retrieval; precision; latent semantic analysis; Arabic ID WEB AB Query Expansion (QE) is one of the most important mechanisms in the information retrieval field. A typical short Internet query will go through a process of refinement to improve its retrieval power. Most of the existing QE techniques suffer from retrieval performance degradation due to imprecise choice of query's additive terms in the QE process. In this paper, we introduce a novel automated QE mechanism. The new expansion process is guided by the semantics relations between the original query and the expanding words, in the context of the utilized corpus. Experimental results of our "controlled" query expansion, using the Arabic TREC-10 data, show a significant enhancement of recall and precision over current existing mechanisms in the field. (c) :2006 Elsevier Ltd. All rights reserved. C1 New Mexico State Univ, Comp Res Lab, Las Cruces, NM 88003 USA. New Mexico Inst Min & Technol, Dept Comp Sci, Socorro, NM 87801 USA. RP Abdelali, A (reprint author), New Mexico State Univ, Comp Res Lab, Box 30001 MSC 3CRL, Las Cruces, NM 88003 USA. EM ahmed@crl.nmsu.edu; jcowie@crl.nmsu.edu; hss@nmt.edu RI Abdelali, Ahmed/ OI Abdelali, Ahmed/0000-0002-4160-8181 CR Abdelali A., 2005, WORKSH COMP MOD LEX ABDELALI A, 2004, REV IBEROAMERICANA, V8, P103 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chau M, 2005, J AM SOC INF SCI TEC, V56, P1363, DOI 10.1002/asi.20210 Cronen-Townsend S., 2004, P CIKM 04, P236, DOI 10.1145/1031171.1031220 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Goweder A., 2001, AR NLP WORKSH ACL EA Grefenstette G., 1992, P 15 ANN INT ACM SIG, P89, DOI 10.1145/133160.133181 Harman D., 1992, INFORMATION RETRIEVA, P241 IMAI H, 1999, P GEN INF TOK KELALAINEN J, 1998, P 21 ANN INT ACM SIG, P130 Kintsch W, 2002, METAPHOR SYMBOL, V17, P249, DOI 10.1207/S15327868MS1704_1 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 OGDEN W, 1998, DESIGN IMPLEMENTATIO Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R STENMARK D, 2005, P 38 HAW INT C SYST VOGT CC, 2000, P CONT BAS MULT INF, P457 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 21 TC 7 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2007 VL 43 IS 3 BP 705 EP 716 DI 10.1016/j.ipm.2006.06.007 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 141ZV UT WOS:000244618900010 ER PT J AU Chang, YC Chen, SM Liau, CJ AF Chang, Yu-Chuan Chen, Shyi-Ming Liau, Churn-Jung TI A new query expansion method for document retrieval based on the inference of fuzzy rules SO JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS LA English DT Article DE document retrieval; membership function; fuzzy rules; query expansion; term reweighting; user relevance feedback ID FEEDBACK AB Automatic query expansion based on user relevance feedback techniques can improve the performance of document retrieval systems. In this paper, we present a new query expansion method based on the inference of fuzzy rules and user relevance feedback techniques to deal with document retrieval. The proposed method uses membership functions and fuzzy rules to infer relevant degrees of expansion terms and puts the expansion terms with larger relevant degrees into the original user's query. Then, the system calculates the degree of similarity of each document with respect to the expanded user's query. The proposed method gets a higher average precision rate and a higher average recall rate than the existing methods for document retrieval. C1 Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan. Acad Sinica, Inst Sci Informat, Taipei 106, Taiwan. RP Chen, SM (reprint author), Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan. EM smchen@mail.ntust.edu.tw CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 CHANG YC, 2003, P 2003 JOINT C AI FU CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 CROUCH J, 1992, P 1K ANN INT ACM SIG Frakes W. B., 1992, INFORM RETRIEVAL DAT Horng Y. J., 2003, INT J INF MANAGE SCI, V14, P63 Horng YJ, 2005, IEEE T FUZZY SYST, V13, P216, DOI 10.1109/TFUZZ.2004.840134 Ide E., 1971, SMART RETRIEVAL SYST, P337 Lee Joon Ho, 1994, P 17 ANN INT ACM SIG, P182 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1971, SMART RETRIEVAL SYST SUGENO M, 1985, INFORM SCIENCES, V36, P59, DOI 10.1016/0020-0255(85)90026-X Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 19 TC 0 Z9 0 PU CHINESE INST ENGINEERS PI TAIPEI PA #1, 4TH FL, SEC 2, JEN-AI RD, TAIPEI 10019, TAIWAN SN 0253-3839 J9 J CHIN INST ENG JI J. Chin. Inst. Eng. PD MAY PY 2007 VL 30 IS 3 BP 511 EP 515 DI 10.1080/02533839.2007.9671279 PG 5 WC Engineering, Multidisciplinary SC Engineering GA 173FV UT WOS:000246859800015 ER PT J AU Hernandez, N Mothe, J Chrisment, C Egret, D AF Hernandez, Nathalie Mothe, Josiane Chrisment, Claude Egret, Daniel TI Modeling context through domain ontologies SO INFORMATION RETRIEVAL LA English DT Article DE ontology; task; document representation; semantic indexing; browsing interface AB Traditional information retrieval systems aim at satisfying most users for most of their searches, leaving aside the context in which the search takes place. We propose to model two main aspects of context: The themes of the user's information need and the specific data the user is looking for to achieve the task that has motivated his search. Both aspects are modeled by means of ontologies. Documents are semantically indexed according to the context representation and the user accesses information by browsing the ontologies. The model has been applied to a case study that has shown the added value of such a semantic representation of context. C1 UPS, IRIT, IUFM, F-31062 Toulouse 9, France. Observ Paris, F-75014 Paris, France. RP Hernandez, N (reprint author), UPS, IRIT, IUFM, 118 Route Narbonne, F-31062 Toulouse 9, France. EM hernande@irit.fr; mothe@irit.fr; chrisment@irit.fr; egret@obspm.fr RI Egret, Daniel/C-1053-2014 OI Egret, Daniel/0000-0003-1605-7047 CR Allan J., 2003, SIGIR Forum, V37 AMOUS I, 2001, P DATAKON, P249 ANGELOVA G, 2004, P MOV MEAN INT SYST, P700 AUSSENACGILLES N, 2004, P RIAO AV FRANC 26 2, P129 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baziz M., 2005, P SAC, P1020 BENJAMINS R, 1999, P INT WORKSH ONT ENG, P1 BIBOW B, 1999, P INT WORKSH ONT ENG, P25 Bourigaultet D, 2000, CAHIERS GRAMMAIRE, V25, P131 CIMIANO P, 2005, LECT NOTES COMPUTER, P227 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 DKAKI T, 1997, P INT C COMP ASS INF, P32 Englmeier K., 2004, Proceedings. IEEE International Conference on Web Services, DOI 10.1109/ICWS.2004.1314746 Fernandez-Lopez M., 1997, P AAAI97 SPRING S SE, P33 Freund L., 2005, P ANN C CAN ASS INF Gangemi A, 2002, LECT NOTES ARTIF INT, V2473, P166 Guarino N, 2002, COMMUN ACM, V45, P61 Guha R. V., 2003, P 12 INT C WORLD WID, P700 Haav H.M., 2001, P 5 E EUR C ADBIS 20, V2, P29 HERNANDEZ N, 2004, P INT C ART INT METH, P11 Jiang J., 1997, P INT C COMP LING RO, VX JONES G., 2004, P ACM SIGIR 2004 WOR, P20 JONES KS, 1999, P SEARCH INF ART INT, V7, P1 Kiryakov A., 2004, J WEB SEMANT, V2, P49 Lassila O., 2001, KSL0102 STANF U Lord P W, 2003, Pac Symp Biocomput, P601 Maedche A, 2002, P EUR C KNOWL ACQ MA, P251 McGuinness D. L., 2004, OWL WEB ONTOLOGY LAN Mihalcea R., 2000, P ACL 2000 WORKSH RE, P35 MILLER GA, 1988, WORDNET ELECT LEXICI, P23 MOTHE J, 2002, LIB INFORM SERVICES, V4, P233 MOTHE J, 2006, IN PRESS DATA MINING RESNIK P, 1995, P JOINT C ART INT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Soergel Dagobert, 2004, J DIGITAL INFORM, V4 Staab Steffen, 2004, INT HDB INFORM SYSTE, V1 Stuckenschmidt H, 2004, IEEE INTELL SYST, V19, P34, DOI 10.1109/MIS.2004.9 Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6 TAYLOR RS, 1968, COLL RES LIBR, V29, P178 TUDHOPE D, 2001, J DIGITAL INFORM, V41, P1 Turtle H. R., 1991, THESIS U MASSACHUSET VALLET D, 2005, P 2 EUR SEM WEB C, P55, DOI 10.1016/B978-075067888-9/50003-X NR 42 TC 5 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2007 VL 10 IS 2 BP 143 EP 172 DI 10.1007/s10791-006-9018-0 PG 30 WC Computer Science, Information Systems SC Computer Science GA 135XE UT WOS:000244186400003 ER PT J AU Kwok, KL Grunfeld, L Deng, P AF Kwok, Kui-Lam Grunfeld, Laszlo Deng, Peter TI Employing web mining and data fusion to improve weak ad hoc retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT Asia Information Retrieval Symposium (AIRS 2004) CY OCT 18-20, 2004 CL Beijing, PEOPLES R CHINA SP Microsoft Res Asia, Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, LexisNexis DE weak query; robust retrieval; salient term selection; web mining; alternate queries; data fusion ID INFORMATION-RETRIEVAL AB When a user issues a reasonable query to a retrieval system and obtains no relevant documents, he or she is bound to feel frustrated. We call these weak queries and retrievals. Improving their effectiveness is an important issue for ad hoc retrieval and would be most rewarding for these users. We explain why data fusion of sufficiently dissimilar retrieval lists can improve weak query results and confirm this with experiments using short and medium size queries. To realize sufficiently dissimilar retrieval lists, we propose composing alternate queries through web search and mining, employ them for target retrieval, and combine with the original query retrieval list. Methods of forming web probes from longer queries, including salient term selection and query text window rotation, are investigated. When compared with normal ad hoc retrieval, web assistance and data fusion can more than double the original weak query effectiveness. Other queries can also improve along with weak ones. (c) 2006 Elsevier Ltd. All rights reserved. C1 CUNY Queens Coll, Comp Sci Dept, Flushing, NY 11367 USA. RP Kwok, KL (reprint author), CUNY Queens Coll, Comp Sci Dept, 65-30 Kisena Blvd, Flushing, NY 11367 USA. EM kwok@ir.cs.qc.edu CR Amati G, 2004, LECT NOTES COMPUT SC, V2997, P127 Arampatzis AT, 1998, INFORM PROCESS MANAG, V34, P693, DOI 10.1016/S0306-4573(98)00030-2 Buckley C., 2004, ACM SIGIR, P584 Chang C. -C., 2004, LIBSVM LIB SUPPORT V Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval DORR B, 2003, P HLTNAACL 2003 TEXT Fox E. A., 1994, NIST SPECIAL PUBLICA, P243 GRUNFELD L, 2004, NAT I STANDARDS TECH, P510 Harman D., 2004, P 27 ANN INT ACM SIG, P528, DOI 10.1145/1008992.1009104 Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 Katz S.M., 1996, NAT LANG ENG, V2, P15, DOI 10.1017/S1351324996001246 Kwok K. L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291003 KWOK KL, 2005, NIST SPECIAL PUBLICA KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 Kwok Kuilam L., 1996, P 19 ANN INT ACM SIG, P187, DOI 10.1145/243199.243266 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lin Dekang, 1994, P COLING 94, P482 LIN J, 2003, P 11 TEXT RETR C TRE, P447 LIU S, 2005, NIST SPECIAL PUBLICA Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 RADA R, 1989, J AM SOC INFORM SCI, V40, P304, DOI 10.1002/(SICI)1097-4571(198909)40:5<304::AID-ASI2>3.0.CO;2-6 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 Voorhees E., 2004, NIST SPECIAL PUBLICA, V500-261 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 VOORHEES EM, 2004, NAT I STANDARDS TECH, P69 YOMTOV E, 2005, NIST SPECIAL PUBLICA NR 27 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2007 VL 43 IS 2 BP 406 EP 419 DI 10.1016/j.ipm.2006.07.008 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 111AF UT WOS:000242422100010 ER PT J AU Qin, T Liu, TY Zhang, XD Feng, G Wang, DS Ma, WY AF Qin, Tao Liu, Tie-Yan Zhang, Xu-Dong Feng, Guang Wang, De-Sheng Ma, Wei-Ying TI Topic distillation via sub-site retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT Asia Information Retrieval Symposium (AIRS 2004) CY OCT 18-20, 2004 CL Beijing, PEOPLES R CHINA SP Microsoft Res Asia, Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, LexisNexis DE sub-site retrieval; topic distillation; website expansion model; punished-integration algorithm; user interface AB Topic distillation is one of the main information needs when users search the Web. Previous approaches for topic distillation treat single page as the basic searching unit, which has not fully utilized the structure information of the Web. In this paper, we propose a novel concept for topic distillation, named sub-site retrieval, in which the basic searching unit is sub-site instead of single page. A sub-site is the subset of a website, consisting of a structural collection of pages. The key of sub-site retrieval includes (1) extracting effective features for the representation of a sub-site using both the content and structure information, (2) delivering the sub-site-based retrieval results with a friendly and informative user interface. For the first point, we propose Punished Integration algorithm, which is based on the modeling of the growth of websites. For the second point, we design a user interface to better illustrate the search results of sub-site retrieval. Testing on the topic distillation task of TREC 2003 and 2004, sub-site retrieval leads to significant improvement of retrieval performance over the previous methods based on single pages. Furthermore, time complexity analysis shows that sub-site retrieval can be integrated into the index component of search engines. (c) 2006 Elsevier Ltd. All rights reserved. C1 Tsing Hua Univ, MSP Lab, Dept Elect Engn, Beijing 100084, Peoples R China. Microsoft Res Asia, Beijing 100080, Peoples R China. RP Qin, T (reprint author), Tsing Hua Univ, MSP Lab, Dept Elect Engn, Beijing 100084, Peoples R China. EM qinshitao99@mails.thu.edu.cn; tyliu@microsoft.com; zhangxd@tsinghua.edu.cn; fengg03@mails.thu.edu.cn; wangdsh_ee@tsinghua.edu.cn; wyma@microsoft.com CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BHARAT K, 1998, P ACM SIGIR BHARAT K, 2001, P 10 INT WORLD WID W Brin S., 1998, P 7 WWW Broder A., 2002, SIGIR FORUM, V36 Chakrabarti S., 2001, P 24 ANN INT ACM SIG, P208, DOI 10.1145/383952.383990 Chakrabarti S., 2001, 10 INT WORLD WID WEB CRASWELL N, 2003, 12 TEXT RETR C FENG G, 2005, 7 ASIA PAC WEB C Kleinberg J.M., 1999, JACM, V46, P5 Page L., 1998, PAGERANK CITATION RA Qin T., 2005, SIGIR 05, P408 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAKERY A, 2003, 12 TEXT RETR C NR 15 TC 2 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2007 VL 43 IS 2 BP 445 EP 460 DI 10.1016/j.ipm.2006.07.004 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 111AF UT WOS:000242422100013 ER PT J AU Ding, C Patra, JC AF Ding, Chen Patra, Jagdish C. TI User modeling for personalized web search with self-organizing map SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID DOCUMENT-RETRIEVAL; INFORMATION; RELEVANCE AB The widely used Web search engines index and recommend individual Web pages in response to a few keywords queries to assist users in locating relevant documents. However, the Web search engines give different users the same answer set, although the users may have different preferences. A personalized Web search would carry out the search for each user according to his or her preferences. To conduct the personalized Web search, the authors provide a novel approach to model the user profile with a self-organizing map (SOM). Their results indicate that SOM is capable of helping the user to find the related category for each query used in the Web search to make a personalized Web search effective. C1 Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore. RP Ding, C (reprint author), Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore. EM PG04159157@ntu.edu.sg; aspatra@ntu.edu.sg RI Patra, Jagdish/D-2652-2009 CR Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M., 1995, P AAAI SPRING S INF, P13 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BEZDEK JC, 1995, IEEE T NEURAL NETWOR, V6, P1029, DOI 10.1109/72.410347 Bollacker K.D., 1999, P 4 ACM C DIG LIB, P105, DOI 10.1145/313238.313270 Brin S., 1998, COMPUTER NETWORKS IS, V30, P1 BUDZIK J, 1999, P 62 ANN M AM SOC IN, P27 Cetintemel U., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839477 Chen GX, 2002, STAT SINICA, V12, P241 Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 CHOI DI, 1994, IEEE T NEURAL NETWOR, V5, P561 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diamantaras K., 1996, PRINCIPAL COMPONENT DOLIN R, 1998, DLIB MAGAZINE, P558 FINKELSTEIN L, 2002, ACM T INFORM SYST, V20, P1046 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Foltz PW, 1990, P C OFF INF SYST CAM, P40, DOI 10.1145/91474.91486 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 *GOOGL, 2004, GOOGL PERS SEARCH Hou JY, 2003, IEEE T KNOWL DATA EN, V15, P940 Joachims T., 1997, P 15 INT JOINT C ART, P770 Jolliffe IT, 1996, PRINCIPAL COMPONENT Kao B, 2000, IEEE T SYST MAN CY C, V30, P364, DOI 10.1109/5326.885118 Kohonen T., 2001, SELF ORG MAPS Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28 Mauldin ML, 1997, IEEE EXPERT, V12, P8, DOI 10.1109/64.577466 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 Pitkow J, 2002, COMMUN ACM, V45, P50 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1997, READINGS INFORM RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scime A., 2000, Proceedings 2000 Kyoto International Conference on Digital Libraries: Research and Practice, DOI 10.1109/DLRP.2000.942176 STADNYK I, 1992, COMMUN ACM, V35, P49, DOI 10.1145/138859.138864 Sugiyama K., 2004, P 13 INT C WORLD WID, P675, DOI 10.1145/988672.988764 TRAJKOVA J, 2004, RECH INF ASS ORD RIA Yu S., 2003, P 12 INT C WORLD WID, P11 NR 43 TC 12 Z9 12 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB 15 PY 2007 VL 58 IS 4 BP 494 EP 507 DI 10.1002/asi.20497 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 166LF UT WOS:000246379600004 ER PT B AU Church, K Smyth, B AF Church, Karen Smyth, Barry BE Puerta, A Lau, T TI Mobile Content Enrichment SO 2007 INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES LA English DT Proceedings Paper CT International Conference on Intelligent User Interfaces CY JAN 28-31, 2007 CL Hawaii, HI SP ACM SIGART, ACM SIGGHI DE Mobile Search; Mobile Web; Mobile Internet; Content Enrichment; Index Enhancement ID RETRIEVAL; SELECTION AB Delivering an effective mobile search service is challenging for many reasons. Certainly small-screen mobile handsets with limited text input capabilities do not make ideal search devices. In addition, the brevity of Mobile Internet content hampers effective indexing and limits retrieval opportunities. In this paper we focus on this indexing issue and describe an approach that leverages Web search engines as a source of content enrichment. We present an evaluation using a mobile news service that demonstrated significant improvements in search performance compared to a standard benchmark system. C1 [Church, Karen; Smyth, Barry] Univ Coll Dublin, Adapt Informat Cluster Comp Sci & Informat, Dublin 4, Ireland. RP Church, K (reprint author), Univ Coll Dublin, Adapt Informat Cluster Comp Sci & Informat, Dublin 4, Ireland. EM karen.church@ucd.ie; barry.smyth@ucd.ie CR Billerbeck B., 2003, CIKM 03, P2 BRANDOW R, 1995, INFORM PROCESS MANAG, V31, P675, DOI 10.1016/0306-4573(95)00052-I Buckley C., 1994, P 3 TEXT RETR C TREC SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 EFTHIMIADIS EN, 1993, P 2 TEXT RETR C TREC, P278 Fitzpatrick L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P306, DOI 10.1145/258525.258597 Gonzalo J, 1998, P COLING ACL 98 WORK, P38 Hust A., 2002, Proceedings of the IASTED International Conference Information and Knowledge Sharing *IP INS, 2006, MOB PHON COULD SOON Kamvar M, 2006, P SIGCHI C HUM FACT, P701, DOI 10.1145/1124772.1124877 Mihalcea R., 2000, P ACL 2000 WORKSH RE, P35 *MMA, 2006, MOB MARK ASS ANN MOB RAJASHEKAR TB, 1995, J AM SOC INFORM SCI, V46, P272, DOI 10.1002/(SICI)1097-4571(199505)46:4<272::AID-ASI4>3.0.CO;2-T ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Sakai T., 2001, P 24 ANN INT ACM SIG, P190, DOI 10.1145/383952.383987 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R SUMITA E, 1997, P 3 ANN M ASS NAT LA, P353 WASSON M, 2002, P ACL 02 WORKSH AUT, P27 *ZD NET, 2006, MOB INT AR WE THER Y 2006, CELLULAR NEWS SEP NR 27 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-59593-481-9 PY 2007 BP 112 EP 121 PG 10 WC Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BJG98 UT WOS:000265741100018 ER PT B AU Kang, HK Jeon, HS Ko, MC Kim, JS Yang, K AF Kang, Hyun-Kyu Jeon, Heung Seok Ko, Myeong-Cheol Kim, Jin Soo Yang, Kiduk BE Kim, H Song, SS Strzelecki, M Choi, J An, DU Kim, S TI A resolving of word sense ambiguity using two-level document ranking method in information retrieval SO 2007 INTERNATIONAL SYMPOSIUM ON INFORMATION TECHNOLOGY CONVERGENCE, PROCEEDINGS LA English DT Proceedings Paper CT International Symposium on Information Technology Convergence CY NOV 23-24, 2007 CL Jeonju, SOUTH KOREA SP Inst Informat Technol Advancement, Jeollabukdo, BK21, Korea Sci & Engn Fdn, IEEE Sect Seoul AB The resolving of word sense ambiguity is one of important problem in information retrieval. Words can have several meanings or senses, and a use of the word out of context is ambiguous between these senses. Mutual information is a relation measure which represents relation between a word and another word. To disambiguate the lexical ambiguity in the retrieved documents, we re-evaluate relation between terms of retrieved document and terms of query. In this paper, we discuss a resolving of word sense ambiguity of information retrieval that is two-level document ranking method using mutual information. At the first level, we retrieve documents based on an automatically constructed keyword index. At the second-level, we reorder the retrieved documents using mutual information. We show that our method provides a resolving of word sense ambiguity in the retrieved documents. Also, we analyze several formulas and found a formula which to disambiguate the word sense ambiguity in the retrieved documents. C1 [Kang, Hyun-Kyu; Jeon, Heung Seok; Ko, Myeong-Cheol; Kim, Jin Soo] Konkuk Univ, Dept Comp Sci, Chungbuk 380701, South Korea. RP Kang, HK (reprint author), Konkuk Univ, Dept Comp Sci, 322 Danwol-Dong, Chungbuk 380701, South Korea. CR BUCKLEY C, 1985, INT C RES DEV INF RE, P97 CHURCH KW, 1990, COMPUTATIONAL LINGUI, P22 Fano Robert M., 1961, TRANSMISSION INFORM Harman D., 1992, INFORMATION RETRIEVA, P363 KROVERZ R, 1999, ASS COMPUTING MACHIN, V10, P115 LIU S, 2005, P 14 ACM INT C INF K MINKOV E, 2006, INT C RES DEV INF RE, P27 Palermo D. S., 1964, WORD ASS NORMS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SCHUTZE H, 2004, P S DOC AN INF RETR, P161 1992, KEMONG COMP NEW ENCY NR 12 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3045-1 PY 2007 BP 315 EP 319 DI 10.1109/ISITC.2007.39 PG 5 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHG46 UT WOS:000252960000063 ER PT S AU Blanco, R Barreiro, A AF Blanco, Roi Barreiro, Alvaro BE Amati, G Carpineto, C Romano, G TI Static pruning of terms in inverted files SO ADVANCES IN INFORMATION RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 29th European Conference in Information Retrieval Research (ECIR 2007) CY APR 02-05, 2007 CL Rome, ITALY SP Fondaz Ugo Bordoni, BCS IRSG, ACM SIGIR, CNR AB This paper addresses the problem of identifying collection dependent stop-words in order to reduce the size of inverted files. We present four methods to automatically recognise stop-words, analyse the tradeoff between efficiency and effectiveness, and compare them with a previous pruning approach. The experiments allow us to conclude that in some situations stop-words pruning is competitive with respect to other inverted file reduction techniques. C1 Univ A Coruna, Dept Comp Sci, IRLab, La Coruna, Spain. RP Blanco, R (reprint author), Univ A Coruna, Dept Comp Sci, IRLab, La Coruna, Spain. EM rblanco@udc.es; barreiro@udc.es CR BAHLE D, P ACM SIGIR 2002, P215 CARMEL D, P ACM SIGIR 2001, P43 CHURCH K, 1995, NATURAL LANGUAGE ENG, V2, P163 DEMOURA ES, P WWW 2005, P235 Fox C., 1990, SIGIR Forum, V24 LO RTW, 2005, P DIR 05 Moffat A., 2002, COMPRESSION CODING A RENNIE JDM, P ACM SIGIR 2005, P353 ROBERTSON SE, 1996, TEXT RETR C, P21 ROBERTSON SE, 2000, TEXT RETR C, P151 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H Witten I. H., 1999, MANAGING GIGABYTES C NR 14 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-71494-1 J9 LECT NOTES COMPUT SC PY 2007 VL 4425 BP 64 EP 75 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BGC63 UT WOS:000246044300006 ER PT S AU Yamout, F Oakes, M Tait, J AF Yamout, Fadi Oakes, Michael Tait, John BE Amati, G Carpineto, C Romano, G TI Relevance feedback using weight propagation compared with information-theoretic query expansion SO Advances in Information Retrieval SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 29th European Conference in Information Retrieval Research (ECIR 2007) CY APR 02-05, 2007 CL Rome, ITALY SP Fondaz Ugo Bordoni, BCS IRSG, ACM SIGIR, CNR DE relevance feedback; Rocchio; ide; deviation from randomness ID PROBABILISTIC MODELS; RETRIEVAL AB A new Relevance Feedback (RF) technique called Weight Propagation has been developed which provides greater retrieval effectiveness and computational efficiency than previously described techniques. Documents judged relevant by the user propagate positive weights to documents close by in vector similarity space, while documents judged not relevant propagate negative weights to such neighbouring documents. Retrieval effectiveness is improved since the documents are treated as independent vectors rather than being merged into a single vector as is the case with traditional vector model RF techniques, or by determining the documents relevancy based in part on the lengths of all the documents as with traditional probabilistic RF techniques. Improving the computational efficiency of Relevance Feedback by considering only documents in a given neighbourhood means that the Weight Propagation technique can be used with large collections. C1 Univ Sunderland, Sch Comp & Technol, Sunderland SR2 7EE, Durham, England. RP Yamout, F (reprint author), Univ Sunderland, Sch Comp & Technol, Sunderland SR2 7EE, Durham, England. CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 AMATI G, 1992, P CLEF CROSS LANG EV, P182 AMATI G, 2001, NIST SPECIAL PUBLICA, P182 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE CHAKRABARTI S, 1999, IEEE COMPUTER AUG CORTES C, 2001, P IDA 2001 INT DAT A CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 IDE E, 1971, SMART RETRIEVAL SYST, pCH16 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Melnik S., 2002, P 18 ICDE C ROBERTSON SE, 1998, NIST SPECIAL PUBLICA, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO G, 1971, SMART RETRIEVAL SYST, pCH14 ROSE D, 1996, NIST SPECIAL PUBLICA, P279 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Salton G., 1983, INTRO MODERN INFORM SANDER PV, 2002, AAMAS 02 JUL 15 19 B Steinbach M., 2000, KDD WORKSH TEXT MIN Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd YAMOUT F, 2006, P 28 ANN INT ACM SIG YAMOUT F, 2004, TIR 2004 GERM YAMOUT F, 2004, IASTED INT C DAT APP Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhou X. S., 2001, P IEEE C COMP VIS PA NR 27 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-71494-1 J9 LECT NOTES COMPUT SC PY 2007 VL 4425 BP 258 EP 270 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BGC63 UT WOS:000246044300022 ER PT S AU Gong, BH Peng, B Li, XM AF Gong, Bihong Peng, Bo Li, Xiaoming BE Chang, KCC Wang, W Chen, L Ellis, CA Hsu, CH Tsoi, AC Wang, HX TI A Personalized Re-ranking Algorithm Based on Relevance Feedback SO ADVANCES IN WEB AND NETWORK TECHNOLOGIES, AND INFORMATION MANAGEMENT, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Asia-Pacific Web Conference/8th International Conference on Web-Age Information Management (APWeb/WAIM 2007) CY JUN 16-18, 2007 CL Huangshan, PEOPLES R CHINA SP Anhui Univ, Chinese Univ Hong Kong, Univ New S Wales, Assoc Sci & Technol, Anhui Comp Federat, Hohai Univ, Huangshan Univ, Natl Sci Fdn China, Oracle DE Relevance feedback; Re-ranking; Information Retrieval; Personalized AB Relevance feedback is the most popular query reformulation strategy. However, clicking data as user's feedback is not so reliable since the quality of a ranked result will influence the user's feedback. An evaluation method called QR (quality of a ranked result) is proposed in this paper to tell how good a ranked result is. Then use the quality of current ranked result to predict the relevance of different feedbacks. In this way, better feedback document will play a more important role in the process of re-ranking. Experiments show that the QR measure is in direct proportion to DCG measure while QR needs no manual label. And the new re-ranking algorithm (QR-linear) outperforms the other two baseline algorithms especially when the number of feedback is large. C1 [Gong, Bihong; Peng, Bo; Li, Xiaoming] Peking Univ, Comp Network & Distributed Syst Lab, Beijing 100871, Peoples R China. RP Gong, BH (reprint author), Peking Univ, Comp Network & Distributed Syst Lab, Beijing 100871, Peoples R China. EM gbh@net.pku.edu.cn; pb@net.pku.edu.cn; lxm@pku.edu.cn CR Agichtein E., 2006, ANN ACM SIGIR C RES, P3 Beitzel S. M., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009048 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Ide E., 1971, SMART RETRIEVAL SYST, P337 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Joachims T., 2005, ACM C RES DEV INF RE, P154 JOACHIMS T, 2002, P SIGKDD 2002 KELLY D, 2003, IMPLICIT FEEDBACK IN OARD DW, 2001, P 64 ANN M AM SOC IN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 SHEN X, 2005, IMPLICIT USER MODELI VOORHEES EM, 2001, 24 ANN INT ACM SIGIR White R., 2001, TEXT RETR C TREC WHITE RW, 2005, ACM T INFORM SYSTEMS NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-72908-2 J9 LECT NOTES COMPUT SC PY 2007 VL 4537 BP 255 EP 263 PG 9 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BIO33 UT WOS:000261332400030 ER PT S AU Theobald, M Broschart, A Schenkel, R Solomon, S Weikum, G AF Theobald, Martin Broschart, Andreas Schenkel, Ralf Solomon, Silvana Weikum, Gerhard BE Fuhr, N Lalmas, M Trotman, A TI TopX - AdHoc track and feedback task SO Comparative Evaluation of XML Information Retrieval Systems SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-20, 2006 CL Dagstuhl, GERMANY SP DELOS Network Excellence Digital Lib ID QUERY EXPANSION AB This paper describes the setup and results of the Max-Planck-Institut fur Informatik's contributions for the INEX 2006 AdHoc Track and Feedback task. The runs were produced with the TopX system, which is a top-k retrieval engine for text and XML data that uses a combination of BM25-based content and structural scores. C1 Max Planck Inst Informat, Saarbrucken, Germany. CR Bast H., 2006, P 32 INT C VER LARG, P475 Fellbaum C, 1998, WORDNET ELECT LEXICA Pehcevski J, 2006, LECT NOTES COMPUT SC, V3977, P43 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Savoy J, 1997, INFORM PROCESS MANAG, V33, P495, DOI 10.1016/S0306-4573(97)00027-7 SCHENKEL R, 2006, P 28 EU C IR RES ECI, P326 Schenkel R, 2006, LECT NOTES COMPUT SC, V3977, P344 Theobald M, 2006, LECT NOTES COMPUT SC, V3977, P282 Theobald M., 2005, P 31 INT C VER LARG, P625 Theobald M., 2004, P 30 INT C VER LARG, P648, DOI 10.1016/B978-012088469-8/50058-9 Theobald Martin, 2005, P 28 ANN INT ACM SIG, P242, DOI 10.1145/1076034.1076077 NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73887-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4518 BP 233 EP 242 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BGP47 UT WOS:000249566700023 ER PT S AU Larson, RR AF Larson, Ray R. BE Fuhr, N Lalmas, M Trotman, A TI Probabilistic retrieval approaches for thorough and heterogeneous XML retrieval SO Comparative Evaluation of XML Information Retrieval Systems SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-20, 2006 CL Dagstuhl, GERMANY SP DELOS Network Excellence Digital Lib ID FEEDBACK; FUSION AB For this year's INEX UC Berkeley focused on the Heterogeneous track, and submitted only two runs for the Adhoc tasks, a "thorough" run and a "BestInContext" run. For these runs we used the TREC2 probabilistic model with blind feedback. The Thorough run was ranked 21st out of the 106 runs submitted for the Thorough task. The majority of our effort for INEX was on the Heterogeneous (Het) track where we submitted three collection ranking runs and two content-only runs. As the only group to actually submit runs for the Het track, we are guaranteed both the highest, and lowest, effectiveness scores for each task. However, because it was deemed pointless to conduct the actual relevance assessments on the submissions of a single system, we do not know the exact values of these results. C1 Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. CR Callan J., 2000, ADV INFORM RETRIEVAL, P127 Callan J., 1998, P 21 ANN INT ACM SIG, P112, DOI 10.1145/290941.290974 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Chen A, 2004, INFORM RETRIEVAL, V7, P149, DOI 10.1023/B:INRT.0000009444.89549.90 Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Chen AT, 2003, LECT NOTES COMPUT SC, V2785, P28 Cooper W. S., 1994, Second Text REtrieval Conference (TREC-2) (NIST-SP 500-215) COOPER WS, 1992, 15 ANN INT ACM SIGIR, P21 Cooper W.S., 1994, TEXT RETR C TREC 2, P57 Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 GRAVANO L, 1995, INT C VER LARG DAT V, P78 Harman D., 1992, INFORMATION RETRIEVA, P241 Larson RR, 2005, INFORM RETRIEVAL, V8, P601, DOI 10.1007/s10791-005-0749-0 Larson RR, 2006, LECT NOTES COMPUT SC, V3977, P225 Larson R. R., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Larson RR, 2003, LECT NOTES COMPUT SC, V2769, P487 MASS Y, 2005, 3 INT WORKSH IN EV X, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Voorhees E., 1998, 7 TEXT RETR C TREC 7 Voorhees E. M., 1999, 8 TEXT RETR C TREC 8 NR 20 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73887-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4518 BP 318 EP 330 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BGP47 UT WOS:000249566700032 ER PT S AU Hlaoua, L Torjmen, M Pinel-Sauvagnat, K Boughanem, M AF Hlaoua, Lobna Torjmen, Mouna Pinel-Sauvagnat, Karen Boughanem, Mohand BE Fuhr, N Lalmas, M Trotman, A TI XFIRM at INEX 2006. Ad-hoc, relevance feedback and multimedia tracks SO Comparative Evaluation of XML Information Retrieval Systems SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-20, 2006 CL Dagstuhl, GERMANY SP DELOS Network Excellence Digital Lib AB This paper describes experiments carried out with the XFIRM system in the INEX 2006 framework. The XFIRM system uses a relevance propagation method to answer CO and CO+S queries. Runs were submitted to the ad-hoc, relevance feedback and multimedia tracks. C1 IRIT, SIG RFI, Toulouse, France. CR BOUGHANEM M, 1998, P TREC 7 Denoyer L., 2006, WIKIPEDIA XML CORPUS FUHR N, 2005, INEX 2005 WORKSH P HALOUA L, 2007, P RCIS 2007 MASS Y, 2005, EXPT VARIOUS USER MO ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, RELEVANCE FEEDBACK I RUTHVEN I, 1999, P 3 INT C CONC LIB I SAVGAGNAT K, 2006, FLEXIBLE QUERY ANSWE SAVGAGNAT K, 2005, INEX 2005 WORKSH P, P88 THIJS W, 2000, IMAGE RETRIEVAL CONT, P276 NR 11 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73887-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4518 BP 373 EP 386 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BGP47 UT WOS:000249566700036 ER PT S AU Larson, RR Gey, FC AF Larson, Ray R. Gey, Fredric C. BE Peters, C TI GeoCLEF text retrieval and manual expansion approaches SO Evaluation of Multilingual and Multi-modal Information Retrieval SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 7th Workshop of the Cross-Language Evaluation Forum CY SEP 20-22, 2006 CL Alicante, SPAIN AB This paper describes the retrieval algorithms and evaluation results for Berkeley's official submissions for the GeoCLEF track. Two separate systems were used for our runs, although both used the same basic algorithm for retrieval. Instead of the automatic expansion used in last year's GeoCLEF, this year we used manual expansion for a selected subset of queries for only 2 out of the IS runs submitted. The remainder of the runs were automatic without manual intervention in the queries (or translations). We submitted 12 Monolingual runs (2 German, 4 English, 2 Spanish, and 4 Portuguese) and 6 Bilingual runs (2 English double right arrow German, 2 English double right arrow Spanish, and 2 English double right arrow Portuguese). We did not submit any Biligual X double right arrow English runs. This paper first describes the retrieval algorithms used for our submissions, followed by a discussion of the processing used for the runs. We then examine the results obtained for our official runs, and finally present conclusions and future directions for GeoCLEF participation. C1 Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. RP Larson, RR (reprint author), Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. CR Chen A, 2004, INFORM RETRIEVAL, V7, P149, DOI 10.1023/B:INRT.0000009444.89549.90 Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Chen AT, 2003, LECT NOTES COMPUT SC, V2785, P28 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 Cooper W.S., 1994, TEXT RETR C TREC 2, P57 LARSON RR, 2006, LNCS, V3977, P255 RAY R, 2006, LNCS, V4022, P963 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-74998-1 J9 LECT NOTES COMPUT SC PY 2007 VL 4730 BP 970 EP 977 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BGU22 UT WOS:000250568000125 ER PT B AU Jones, KS AF Jones, Karen Sparck GP IEEE COMPUTER SOC TI Statistics and retrieval: past and future SO ICCTA 2007: International Conference on Computing: Theory and Applications, Proceedings LA English DT Proceedings Paper CT International Conference on Computing - Theory and Applications (ICCTA 2007) CY MAR 05-07, 2007 CL Calcutta, INDIA SP Indian Stat Inst ID INFORMATION-RETRIEVAL; RELEVANCE AB Statistical approaches to document indexing and retrieval date back to the beginning of automation. This paper considers early ideas, how they developed, their status now, and the challenges to be tackled in the future. C1 Univ Cambridge, Comp Lab, Cambridge CB3 0FD, England. RP Jones, KS (reprint author), Univ Cambridge, Comp Lab, JJ Thomson Ave, Cambridge CB3 0FD, England. CR Bush V., 1945, ATLANTIC MONTHLY, P101 Croft W.B, 2003, LANGUAGE MODELLING I CROFT WB, ADV INFORM RETRIEVAL, P1000 Dubin D, 2004, LIBR TRENDS, V52, P748 JOYCE T, 1958, AM DOC, V9, P192, DOI 10.1002/asi.5090090305 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1959, RC101 IBM CORP LUHN HP, 1961, INFORMATION RETRIE 2, V3, P1021 LUHN HP, 1957, RC3 IBM CORP LUHN HP, 1958, IBM J RES DEV, V2, P159 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 2004, P 13 ACM C INF KNOWL ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1966, ISR10 HARV COMP LAB SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 Salton G., 1975, THEORY INDEXING Salton G., 1968, AUTOMATIC INFORM ORG SCHULTZ CK, 1968, HP LUHN PIONEER INFO Sneath P.H., 1973, NUMERICAL TAXONOMY Sparck J. K., 2000, INFORMATION PROCESSI, V36, P779 SPARCK JK, 2005, 655 U CAMBR COMP LAB SPARCK JK, 2000, INFORMATION PROCESSI, V36, P809 SPARCKJONES K, 1972, J DOC, V28, P11 STEVENS ME, 1965, PUBLICATION NBS, V269 TENOPIR C, 1994, ONLINE, V18, P31 TURTLE H, 1992, P 13 ANN INT ACM SIG, P279 van Rijsbergen C. J, 1975, INFORM RETRIEVAL VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VICKERY BC, 1961, RETRIEVAL SYSTEM THE Voorhees Ellen M., 2005, TREC EXPT EVALUATION NR 33 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2770-3 PY 2007 BP 396 EP 404 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BGB99 UT WOS:000245973000068 ER PT J AU Toda, H Kataoka, R Oku, M AF Toda, Hiroyuki Kataoka, Ryoji Oku, Masahiro TI Search result clustering using informatively named entities SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION LA English DT Article ID RETRIEVAL AB Clustering the results of a search helps the user to review the information gathered. In this article, we regard the clustering task as indexing the search results. Here, an index means a structured label list that can make it easier for the user to comprehend the labels and search results. To realize this goal, we make three proposals. The first is to use Named Entity Extraction for term extraction. The second is to create a new label-selecting criterion based on importance in the search result and the relation between terms and search queries. The third is a label categorization using category information of labels, which is generated by named entity extraction. We implement a prototype system based on these proposals and find that it offers a much higher performance than existing methods; we focus on news articles in this article, but the system is not topic specific. C1 NTT Corp, NTT Cyber Solut Labs, Kanagawa 2390847, Japan. RP Toda, H (reprint author), NTT Corp, NTT Cyber Solut Labs, 1-1 Hikari No Oka, Kanagawa 2390847, Japan. EM toda.hiroyuki@lab.ntt.co.jp CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 Brin S., 1998, P 7 INT WORLD WID WE, P107 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 EGUCHI K, 2004, NTCIR4 S, V1 FERRAGINA P, 2004, P 4 IEEE INT C DAT M, P395 Grishman R., 1996, P 16 INT C COMP LING, P466 HAYASHI Y, 2000, P ACM SIGIR 2000 WOR, P27 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 HISAMITSU T, 1999, P IRAL 99, P83 Isozaki H., 2002, P COLING 2002, P390 Kim J-D, 2004, P INT JOINT WORKSH N, P70, DOI 10.3115/1567594.1567610 Kummamuru Krishna, 2004, P 13 INT C WORLD WID, P658, DOI 10.1145/988672.988762 Kurland O., 2005, P 28 ANN INT ACM SIG, P19, DOI 10.1145/1076034.1076041 LAUSKI A, 2001, P CIKM 01, P33 Minkov E., 2005, P C HUM LANG TECHN E, P443, DOI 10.3115/1220575.1220631 *NAT I INF RES CTR, 2005, NTCIR PROJ OHTA M, 2004, WORKING NTCIR NII S, V1, P37 Pasca M., 2004, P 13 ACM C INF KNOWL, P137, DOI 10.1145/1031171.1031194 RENNIE J, 2005, P SIGIR 05, P353, DOI 10.1145/1076034.1076095 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Sekine S., 2004, P 4 INT C LANG RES E Sekine S., 2004, NAMED ENTITY HIST FU SEKINE S, 1999, P IREX WORKSH, P7 SHINZATO K, 2004, P COLING 04, P938, DOI 10.3115/1220355.1220490 SQUIRE D, 1999, P 3 INT C VIS INF IN, P549 TAKATA Y, 2000, P 2 INT WORKSH WWW C, P165 *U DORTM COMP SCI, 1999, FREE WAIS SF Yang Y., 2002, P 8 ACM SIGKDD INT C, P688, DOI DOI 10.1145/775047.775150 Zamir O., 1999, P 8 INT C WORLD WID, P1361 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 NR 33 TC 4 Z9 4 PU LAWRENCE ERLBAUM ASSOC INC-TAYLOR & FRANCIS PI PHILADELPHIA PA 325 CHESTNUT STREET, STE 800, PHILADELPHIA, PA 19106 USA SN 1044-7318 J9 INT J HUM-COMPUT INT JI Int. J. Hum.-Comput. Interact. PY 2007 VL 23 IS 1-2 BP 3 EP 23 PG 21 WC Computer Science, Cybernetics; Ergonomics SC Computer Science; Engineering GA 206VQ UT WOS:000249211400002 ER PT J AU Christauskas, C Stunguriene, S AF Christauskas, Ceslovas Stunguriene, Stanislava TI Motivation Factors of Decision Making Person SO INZINERINE EKONOMIKA-ENGINEERING ECONOMICS LA English DT Article DE decision making person; environment factor; information meaning AB Decision making is a creative and knowledge demanding process which is being analyzed in scientific publications and continually improved in practice. The Authors of this article have sought to systematize results of behavior of both motivation factors of decision making person (DMP) and environment factor found in various publications and also to present their own approach to perception of information meaning within diverse time intervals. Environment factor in econometric models is understood as what influences activity of analyzed system but acts independent of the system. Problems of investigating environment factor's influence in the decision support systems are related to receiving exact information about state of this factor. Unlike environment factor which acts as unknowing gambler decision making person is described as a conscious gambler. Market, social relations, nature, non-managed organization and other objects might be considered as environment factor while separate individual, group of decision making persons or managed organization are named as decision making person. Elements, that sophisticated system is made of, have their purposes and problems related to problems and purposes of the whole system to solve. Solution of every problem depends upon elements' available amount of resources. Conflict situations come up because total resources of the whole system are limited but the purposes of different elements vary and it is difficult to combine amount of resources needed for solving problems of these elements with limitations of the whole system. However the biggest problem when making decisions is the estimation of environment factor's influence. Therefore it is very important to pay attention to the circumstances that environment states research should include evaluation of risk factor of receiving exact information from reliable source. If consultancy was paid for reliable information soon, it is possible to incur large losses seeing that, after circumstances have changed this information might lose not only worth but also meaning. Therefore, if time for project implementation is t but managers have to make decision on fixed time moment t(0) they must size up risk of using information about market states favor. According to the opinion of the authors of this article it is considered appropriate to take ratio between time interval of all project implementation and time interval of change of information worth as risk valuation criterion. Time interval within information has lost its meaning in respect of decision making quality and was named as critical time. Critical time shows uncertainty level or, in the other words, it shows time interval in which it is not known how available information could be used. The Authors have analyzed three situations of critical time displacement and have made conclusion that displacement of critical time to the end of project implementation reduces decision making risk. Therefore the main motivation of decision making person should be based on exact information knowing risk of changes of this information meaning. Decision making person is always interested in states of environment factor and price of exact information about states of this factor. C1 [Christauskas, Ceslovas; Stunguriene, Stanislava] Kauno Technol Univ, LT-44309 Kaunas, Lithuania. RP Christauskas, C (reprint author), Kauno Technol Univ, K Donelaicio G 73, LT-44309 Kaunas, Lithuania. CR Andersen TJ, 2004, J MANAGE STUD, V41, P1271, DOI 10.1111/j.1467-6486.2004.00475.x Astebro T, 2005, J PROD INNOVAT MANAG, V22, P322, DOI 10.1111/j.0737-6782.2005.00129.x Bish EK, 2004, OPER RES, V52, P954, DOI 10.1287/opre.1040.0138 BROWNE N, 1992, EAST-WEST FILM J, V6, P5 CHRISTAUSKAS C, 2004, J FOLIA U AGR STETIN, P351 CHRISTAUSKAS C, 2005, RES BUSINESS STRATEG, P297 DUBROV AM, 1999, MODELIROVANIE RISKOV Engerman S, 2005, IMF STAFF PAPERS, V52, P15 HARALICK RM, 1980, ARTIF INTELL, V14, P263, DOI 10.1016/0004-3702(80)90051-X Hinterhuber A, 2004, IND MARKET MANAG, V33, P765, DOI 10.1016/j.indmarman.2003.10.006 HORVITZ EJ, 1995, 11 C MONTR CAN ORG K, P296 HOWARD RA, 1966, IEEE T SYST SCI CYB, VSSC2, P22, DOI 10.1109/TSSC.1966.300074 HOWARD RA, 1977, READINGS DECISION AN, P429 Hurley TM, 2005, J RISK UNCERTAINTY, V30, P169, DOI 10.1007/s11166-005-6565-5 Jeffrey R., 1983, LOGIC DECISION, V2nd Keeney R.L., 1976, DECISIONS MULTIPLE O KIM CV, 2003, INT J INTELL SYST, V7, P733 KOENIG S, 2000, ADV NEURAL INFORM PR Koller D, 1996, GAME ECON BEHAV, V14, P247, DOI 10.1006/game.1996.0051 LI M, 2004, MANAGEMENT SCI GLOBA, V1, P378 LOTFI TA, 2003, J APPL MATH MODELLIN, V27, P913 Luce R. D., 1957, GAMES DECISIONS MAKOWSKI M, 2005, MATH MODELING COPING, P33 MAROSTICA A, 2005, P 8 JOINT C INF SCI, V1, P1103 Moore SC, 2003, PROCEEDINGS OF THE TWENTY-FIFTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, PTS 1 AND 2, P822 Mulligan EJ, 2005, J BEHAV DECIS MAKING, V18, P145, DOI 10.1002/bdm.491 Myserson R., 1991, GAME THEORY ANAL CON Nagurney A, 2006, EUR J OPER RES, V172, P40, DOI 10.1016/j.ejor.2004.09.035 NOVICKAS J, 2001, ENG ECON, P22 Raiffa H, 1968, DECISION ANAL INTRO Render B., 2003, QUANTITATIVE ANAL MA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUTKAUSKAS AV, 1999, FINANC MANAGE, P295 Sauer C., 2003, EUROPEAN MANAGEMENT, V21, P497, DOI 10.1016/S0263-2373(03)00078-1 SMITH JQ, 1988, DECISION ANAL STUNGURENE S, 2004, RESTRUKTUR EKONOM PR, P160 STUNGURIENE S, 2000, FINANCIAL DECISIONS, P434 VILKAS E, 1990, OPTIMALNOSTI IGRAKH von Neumann J., 1947, THEORY GAMES EC BEHA, V2nd von Winterfeldt Detlof, 1986, DECISION ANAL BEHAV WANG H, 2004, J DECISION SUPPORT S, V37, P457 WANG Y, 2005, P 2005 INT C CONSTR, V1, P1371 Wieringa B., 2001, EUROPEAN MANAGEMENT, V19, P228, DOI 10.1016/S0263-2373(01)00020-2 NR 43 TC 4 Z9 4 PU KAUNAS UNIV TECHNOL PI KAUNAS PA LAISVES AL 55, KAUNAS, 44309, LITHUANIA SN 1392-2785 J9 INZ EKON JI Inz. Ekon. PY 2007 IS 3 BP 51 EP 56 PG 6 WC Economics SC Business & Economics GA V11SC UT WOS:000207550300006 ER PT J AU Mountain, D MacFarlane, A AF Mountain, David MacFarlane, Andrew TI Geographic information retrieval in a mobile environment: evaluating the needs of mobile individuals SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE information retrieval; geographic information retrieval; mobile computing; mobile information needs; geographic filters; location-aware computing; location-based services ID LOCATION-BASED SERVICES; RELEVANCE; ARCHITECTURE; REGIONS AB This paper describes research that aims to define the information needs of mobile individuals, to implement a mobile information system that can satisfy those needs, and finally to evaluate the performance of that system with end-users. First a review of the emerging discipline of geographic information retrieval (GIR) is presented as background to the more specific issue of mobile information retrieval. Following this, a user needs study is described evaluating the requirements of potential users of a mobile information system; the study finds that there is a strong geographic component to users' information needs. Next, four geographic post query filters are described which attempt to represent the region of space associated with an individual's query made at some specific spatial location. These filters are spatial proximity (distance in space), temporal proximity (travel time), speed-heading prediction surfaces (likelihood of visiting locations) and visibility (locations that can be seen). Two of these filters - spatial proximity and speed-heading prediction surfaces are implemented in a mobile information system and subsequently evaluated with users in an outdoor setting. The results of evaluation suggest that retrieved information to which post-query geographic filters have been applied is considered more relevant than unfiltered information, and that users find information sorted by spatial proximity to be more relevant than that sorted by a prediction surface of likely future locations. The paper closes with a discussion of the wider implications of these results for developers of mobile information systems and location-based services. C1 City Univ London, Dept Informat Sci, London EC1V 0HB, England. RP Mountain, D (reprint author), City Univ London, Dept Informat Sci, Northampton Sq, London EC1V 0HB, England. EM dmm@soi.city.ac.uk CR AMITAY E, 2004, SIGIR 2004 Arampatzis A, 2006, COMPUT ENVIRON URBAN, V30, P436, DOI 10.1016/j.compenvurbsys.2005.08.001 BATES S, 2000, INT INFORM COMMUNICA, V19, P122 Beard K., 1997, International Journal on Digital Libraries, V1, DOI 10.1007/s007990050011 Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198 Brewster S, 2002, PERS UBIQUIT COMPUT, V6, P188, DOI 10.1007/s007790200019 Brimicombe A., 2006, T GIS, V10, P5, DOI 10.1111/j.1467-9671.2006.00241.x CAIRNCROSS F, 1999, AM Q, V51, P160 *CAM, 2006, CREAT YOUR MOB MULT Chincholle D, 2002, LECT NOTES COMPUT SC, V2411, P211 CLOUGH P, 2005, SIGIR 2005 BREMEN, P25 Davies N, 1999, WMCSA '99, SECOND IEEE WORKSHOP ON MOBILE COMPUTING SYSTEMS AND APPLICATIONS, PROCEEDINGS, P11 EDWARDES A, 2003, 21 INT CART C 12 Fisher P, 1998, GEOGRAPHY, V83, P247 Fisher P, 2004, T I BRIT GEOGR, V29, P106, DOI 10.1111/j.0020-2754.2004.00117.x Gey F. C., 2004, WORKSH GEOGR INF RET Giaglis G M, 2003, MOBILE COMMERCE TECH, P67 OPENSHAW S, 1987, ENVIRON PLANN A, V19, P1423, DOI 10.1068/a191423 Goodchild M. F., 1997, Computers, Environment and Urban Systems, V21, DOI 10.1016/S0198-9715(98)00009-X *GOOGL, 2006, GOOGL LOC HELAL A, 1999, ANY TIME ANYWHERE CO HILL L, 2004, WORKSH GEOGR INF RET Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 INSIGHT BAB, 2005, STRUCTURE EUROPEAN L Jones C. B., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Jones CB, 2004, LECT NOTES COMPUT SC, V3234, P125 Jose JM, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P411, DOI 10.1145/1065385.1065515 Jose R, 2003, MOBILE NETW APPL, V8, P377, DOI 10.1023/A:1024531629631 Keen M., 1966, FACTORS DETERMINING, V1 Kjeldskov J, 2002, LECT NOTES COMPUT SC, V2411, P271 KRUG K, 2004, REPORT TESTING VALID KRUG K, 2002, ANAL DEFINITION USER KULJU M, 2007, MOBILE HCI02 WORKSH Kwan MP, 2000, AREA, V32, P145, DOI 10.1111/j.1475-4762.2000.tb00125.x LARSON R, 2004, SIGIR 2004 LENNTORP B, 1978, TIMING SPACE SPACING, V2 LONGLEY PA, 2001, GEORGAPHICAL INFORM Mountain D, 2001, ASLIB PROC, V53, P404, DOI 10.1108/EUM0000000007074 MOUNTAIN DM, 2005, EXPLORING MOBILE TRA MOUNTAIN DM, 2003, GIS RES UK, P206 MYRTHAUG HI, 2004, LECT NOTES COMPUTER, V3295, P327 Nissim M, 2004, WORKSH GEOGR INF RET Nothegger C., 2004, SPAT COGN COMPUT, V4, P113, DOI [10.1207/s15427633scc0402_1, DOI 10.1207/S15427633SCC0402_] *OP GEOSP CONS, 2005, OP LOC SERV OP OSTREM J, 2002, PALM OS USER INTERFA Passani L, 2002, LECT NOTES COMPUT SC, V2411, P9 PURVES R, 2005, GIS RES UK C, P313 RAPER JF, 2001, M FDN QUEST GISC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGARA T, 2004, WORKSH GEOGR INF RET SANDERSON M, 2004, WORKSH GEOGR INF RE SARACEVIC T, 1996, P 59 ANN M AM SOC IN, P3 Saracevic T., 1996, P 2 C CONC LIB INF S, P201 Silva MJ, 2006, COMPUT ENVIRON URBAN, V30, P378, DOI 10.1016/j.compenvurbsys.2005.08.003 Spink A, 2002, COMPUTER, V35, P107, DOI 10.1109/2.989940 Spink A, 1998, INFORM PROCESS MANAG, V34, P599, DOI 10.1016/S0306-4573(98)00025-9 Vakkari P, 2000, J DOC, V56, P540, DOI 10.1108/EUM0000000007127 van Kreveld M, 2005, GEOINFORMATICA, V9, P61, DOI 10.1007/s10707-004-5622-6 *VOD, 2005, VOD COMP *WEBP, 2005, GEOGR REL INF MON US Worboys M, 2004, GIS COMPUTING PERSPE *YELL GROUP, 2005, UKS LOC SEARCH ENG 2005, UPMYSTREET COM NR 63 TC 21 Z9 21 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PY 2007 VL 33 IS 5 BP 515 EP 530 DI 10.1177/0165551506075333 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 221WZ UT WOS:000250259400001 ER PT B AU Crabtree, D Andreae, P Gao, XY AF Crabtree, Daniel Andreae, Peter Gao, Xiaoying BE Berkhin, P Caruana, R Wu, X Gaffney, S TI Exploiting Underrepresented Query Aspects for Automatic Query Expansion SO KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING LA English DT Proceedings Paper CT 13th International Conference on Knowledge Discovery and Data Mining CY AUG 12-15, 2007 CL San Jose, CA SP ACM SIGKDD, ACM SIGMOD DE query expansion; web search; global document analysis; aspect coverage AB Users attempt to express their search goals through web search queries. When a search goal has multiple components or aspects, documents that represent all the aspects are likely to be more relevant than those that only represent some aspects. Current web search engines often produce result sets whose top ranking documents represent only a subset of the query aspects. By expanding the query using the right keywords, the search engine can find documents that represent more query aspects and performance improves. This paper describes AbraQ, an approach for automatically finding the right keywords to expand the query. AbraQ identifies the aspects in the query, identifies which aspects are underrepresented in the result set of the original query, and finally, for any particularly underrepresented aspect, identifies keywords that would enhance that aspect's representation and automatically expands the query using the best one. The paper presents experiments that show AbraQ significantly increases the precision of hard queries, whereas traditional automatic query expansion techniques have riot improved precision. AbraQ also compared favourably against a range of interactive query expansion techniques that require user involvement including clustering, web-log analysis, relevance feedback, and pseudo relevance feedback. C1 [Crabtree, Daniel; Andreae, Peter; Gao, Xiaoying] Victoria Univ Wellington, Sch Math Stat & Comp Sci, Wellington, New Zealand. RP Crabtree, D (reprint author), Victoria Univ Wellington, Sch Math Stat & Comp Sci, Wellington, New Zealand. EM daniel@danielcrabtree.com; pondy@mcs.vuw.ac.nz; xgao@mcs.vuw.ac.nz CR Berkhin P., 2002, SURVEY CLUSTERING DA BUCKLEY C, 1994, TREC, V3 Buckley C., 2004, ACM SIGIR, P584 Carmel D., 2006, ACM SIGIR, P390 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 COLLINSTHOMPSON K, 2005, CIKM, P704 CRABTREE D, 2006, WEB INTELLIGENCE, P202 CROFT BB, 1991, ACM SIGIR, P32 Cui H., 2002, WWW, P325 DELOUPY C, 2000, USING EVALUATION WIT, P34 Harman D., 1992, RELEVANCE FEEDBACK O, P241 HSU MH, 2006, ACT AS INF RETR S AI, P1 Jansen BJ, 2005, J AM SOC INF SCI TEC, V56, P559, DOI 10.1002/asi.20145 KHOO CSG, 1994, INFORM PROCESS MANAG, V30, P223, DOI 10.1016/0306-4573(94)90066-3 MAGENNIS M, 1997, ACM SIGIR, P324 NEVES FAD, 2005, CIKM, P91 Osinski S, 2004, ADV SOFT COMP, P359 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 SOBOROFF I, 2004, SIGIR 04, P530 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VELEZ B, 1997, ACM SIGIR, P6 VINAY V, 2005, WWW, P1052 XU J, 1996, ACM SIGIR C R D INF, P4 Yom-Tov E., 2005, ACM SIGIR, P512 YU X, 2005, IEEE DATA ENG B, V28, P29 Zamir O., 1998, RES DEV INFORM RETRI, P46 ZHANG J, 2005, TEXT RETRIEVAL C TRE NR 29 TC 1 Z9 1 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-59593-609-7 PY 2007 BP 191 EP 200 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BJK16 UT WOS:000266628300020 ER PT S AU Aramaki, E Imai, T Kajino, M Miyo, K Ohe, K AF Aramaki, Eiji Imai, Takeshi Kajino, Masayuki Miyo, Kengo Ohe, Kazuhiko BE Kuhn, KA Warren, JR Leong, TY TI Statistical Selector of the Best Multiple ICD-coding Method SO MEDINFO 2007: PROCEEDINGS OF THE 12TH WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2 SE Studies in Health Technology and Informatics LA English DT Proceedings Paper CT 12th World Congress on Health (Medical) Informatics CY AUG 20-24, 2007 CL Brisbane, AUSTRALIA SP Hlth Informat Soc Australia DE ICD-10; International Classification of Disease codes; coding; decision trees; natural language processing ID ORTHOPEDICS; INFORMATION; SYSTEM; CODES AB The International Classification of Diseases 10th version (ICD-10) is one of the standard and most important disease classifications. Since computerized ICD-10 coding systems have drawn a great deal of attention in the medical field a great number of different coding systems have been proposed Thist paper proposes a hybrid architecture of different coding systems. First, given an input disease name, three coding systems output codes with their confidence scores. A C4.5-based system selector then selects the best output by using both input statistics and the confidence score from each system. The experimental results demonstrated that the selector significantly boosts the overall performance (+3.4 points). C1 [Aramaki, Eiji] Tokyo Univ Hosp, Bunkyo Ku, Tokyo 1138656, Japan. RP Aramaki, E (reprint author), Tokyo Univ Hosp, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan. EM aramaki@hcc.h.u-tokyo.ac.jp CR ALEXANDER S, 2003, AIN J HLTH SYST PHAR ARAMAKI E, 2006, JOINT C MED INF, P212 BOUCHET C, 1998, MEDINFO, V9, P176 CEUSTERS W, 1996, EL HLTH REC EUR 96 P, P294 Deimel D, 1997, Z ORTHOP GRENZGEB, V135, P528, DOI 10.1055/s-2008-1039740 Deimel D, 1995, UNFALLCHIRURG, V98, P545 DELAMARRE D, 1995, METHOD INFORM MED, V4, P345 Fabry Paul, 2003, Stud Health Technol Inform, V95, P433 Gillick L., 1989, P ICASSP, P532 HATANO K, 2003, AM MED INF ASS AMIA, P597 Hess T, 1999, ORTHOPADE, V28, P212 Hoelzer S, 2003, J AM MED INFORM ASSN, V10, P463, DOI 10.1197/jamia.M1258 Hohnloser JH, 1996, METHOD INFORM MED, V35, P108 Kurohashi Sadao, 1994, P INT WORKSH SHAR NA, P22 LEVENSHT.VI, 1965, DOKL AKAD NAUK SSSR+, V163, P845 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 Pakhomov SVS, 2006, J AM MED INFORM ASSN, V13, P516, DOI 10.1197/jamia.M2077 QUINLAN JR, 1987, INT J MAN MACH STUD, V27, P221, DOI 10.1016/S0020-7373(87)80053-6 Riddick L, 1998, AM J FOREN MED PATH, V19, P269, DOI 10.1097/00000433-199809000-00015 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSSI CR, 1993, MED INFORM, V18, P53 Schnitzer PG, 2005, J PEDIATR PSYCHOL, V30, P413, DOI 10.1093/jpepsy/jsio65 Tagliabue Giovanna, 2006, Popul Health Metr, V4, P10, DOI 10.1186/1478-7954-4-10 World Health Organization, 1992, INT STAT CLASS DIS R NR 25 TC 0 Z9 0 PU I O S PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0926-9630 BN 978-1-58603-774-1 J9 ST HEAL T PY 2007 VL 129 BP 645 EP 649 PG 5 WC Computer Science, Interdisciplinary Applications; Health Care Sciences & Services; Medical Informatics SC Computer Science; Health Care Sciences & Services; Medical Informatics GA BMF03 UT WOS:000272064000130 PM 17911796 ER PT B AU Cai, KK Bu, JJ Chen, C Huang, P AF Cai, Keke Bu, Jiajun Chen, Chun Huang, Peng BE Kim, SS Park, JH Pissinou, N Kim, TH Fang, WC Slezak, D Arabnia, HR Howard, D TI Automatic query type classification for web image retrieval SO MUE: 2007 International Conference on Multimedia and Ubiquitous Engineering, Proceedings LA English DT Proceedings Paper CT International Conference on Multimedia and Ubiquitous Engineering CY APR 26-28, 2007 CL Seoul, SOUTH KOREA SP IEEE AB In this paper, a framework of query classification is proposed for text-based image retrieval. The classification process in this framework consists of three phases. In the first phase, two basic classifiers are employed to classify the image query into certain categories. They are easily implemented but have weak adaptability to various web image queries. Therefore, in the second phase, an expanded classifier is implemented to compensate for the possible incapability of the basic classifiers. In this step, the inferential relationships extracted by Information Flow (IF) are exploited to disclose the meaning underneath the image query, which makes the classification more accurate. In the third phase, the strengths of all three individual classifiers are leveraged together to achieve the most possible efficient classification. The experimental results prove the effectiveness of this framework in dealing with image query classification. C1 Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Peoples R China. RP Cai, KK (reprint author), Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Peoples R China. CR ARWISE J, 1997, CAMBRIDGE TRACTS THE, P44 Bai J., 2005, CIKM, P688 Beitzel S.M., 2005, ICDM 2005, P42 BRUZA P, 2002, CIKM, P260 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 CHUA TS, 2004, TRECVID 2004 WORKSH Enser P. G. B., 1992, ANAL VISUAL INFORM R Jansen B. J., 2004, J WEB ENG, V3, P182 JANSEN BJ, 2002, P E LEARN 2002 C MON, P1 Jorgensen C, 1998, INFORM PROCESS MANAG, V34, P161, DOI 10.1016/S0306-4573(97)00077-0 Kherfi ML, 2004, ACM COMPUT SURV, V36, P35, DOI 10.1145/1013208.1013210 LIAO S, 2005, P WORKSH SCI ART LYNDON S, 2005, ACM MULTIMEDIA, P882 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Song D., 2001, P 24 ANN INT C RES D, P327, DOI 10.1145/383952.384017 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd YAN R, 2004, ACM MULTIMEDIA NR 17 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2777-2 PY 2007 BP 1021 EP 1026 PG 6 WC Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA BGI07 UT WOS:000247093600171 ER PT S AU Yavlinsky, A Ruger, S AF Yavlinsky, Alexei Ruger, Stefan BE Hanjalic, A Schettini, R Sebe, N TI Efficient re-indexing of automatically annotated image collections using keyword combination - art. no. 650608 SO Multimedia Content Access: Algorithms and Systems SE PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) LA English DT Proceedings Paper CT Conference on Multimedia Content Access - Algorithms and Systems CY JAN 31-FEB 01, 2007 CL San Jose, CA SP Soc Imaging Sci & Technol, SPIE ID FEATURES AB This paper presents a framework for improving the image index obtained by automated image annotation. Within this framework, the technique of keyword combination is used for fast image re-indexing based on initial automated annotations. It aims to tackle the challenges of limited vocabulary size and low annotation accuracies resulting from differences between training and test collections. It is useful for situations when these two problems are not anticipated at the time of annotation. We show that based on example images from the automatically annotated collection, it is often possible to find multiple keyword queries that can retrieve new image concepts which are not present in the training vocabulary, and improve retrieval results of those that are already present. We demonstrate that this can be done at a very small computational cost and at an acceptable performance tradeoff, compared to traditional annotation models. We present a simple, robust, and computationally efficient approach for finding an appropriate set of keywords for a given target concept. We report results on TRECVID 2005, Getty Image Archive, and Web image datasets, the last two of which were specifically constructed to support realistic retrieval scenarios. C1 Univ London Imperial Coll Sci Technol & Med, Dept Comp, London SW7 2AZ, England. RP Yavlinsky, A (reprint author), Univ London Imperial Coll Sci Technol & Med, Dept Comp, S Kensington Campus, London SW7 2AZ, England. CR Ahn L., 2004, P SIGCHI C HUM FACT, P319 AMIR A, 2003, P TRECVID BUCKLEY C, 1994, TEXT RETRIEVEAL C Duygulu P., 2002, P EUR C COMP VIS, P97, DOI DOI 10.1007/3-540-47979-1_7 FENG SL, 2004, IEEE C COMP VIS PATT, P1002 HAUPTMAN A, 2004, P TRECVID 2004 Howarth P., 2004, P INT C IM VID RETR, p326 Iyengar G, 2005, P 13 ACM INT C MULT, P21, DOI 10.1145/1101149.1101154 Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Khudanpur S., 2005, P 28 ANN INT ACM SIG, P544, DOI DOI 10.1145/1076034.1076127 Lavrenko V, 2003, P 16 C ADV NEUR INF Lavrenko V., 2004, P IEEE ICASSP INT C, V3, P17 Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 MORI Y, 1999, P INT WORKSH MULT IN Naphade M., 2000, P IEEE INT C MULT NE, P475 Naphade M.R., 1998, P IEEE INT C IM PROC, V3, P536, DOI 10.1109/ICIP.1998.999041 NATSEV A, 2003, P ITN C IM PROC, V3, P625 RASIWASIA N, 2006, P 5 INT C IM VID RET, P51 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Smith J., 2003, P IEEE INT C MULT EX, P445, DOI DOI 10.1109/ICME.2003.1221649 TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999 Vapnik V, 1995, NATURE STAT LEARNING Volkmer T, 2005, P 13 ANN ACM INT C M, P892, DOI DOI 10.1145/1101149.1101341 Wu Y, 2004, P IEEE INT C MULT EX, V2, P1003 YAN R, 2006, IN PRESS P IEEE INT, P51 YAVLINSKY A, 2005, P INT C IM VID RETR, P537 NR 30 TC 0 Z9 0 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 978-0-8194-6619-8 J9 P SOC PHOTO-OPT INS PY 2007 VL 6506 BP 50608 EP 50608 AR 650608 DI 10.1117/12.702913 PG 12 WC Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BGG53 UT WOS:000246677900007 ER PT J AU Christauskas, C Stunguriene, S AF Christauskas, Ceslovas Stunguriene, Stanislava TI Research on difficulties of financial decision making under uncertainty conditions SO TRANSFORMATIONS IN BUSINESS & ECONOMICS LA English DT Article; Proceedings Paper CT 1st International and Interdisciplinary Scientific Conference on Merging Physical Sciences, the Humanities and Social Sciences (FIHUSO-2007) CY MAY 18-19, 2007 CL Vilnius, LITHUANIA HO Vilnius Univ, Kaunas Fac Humanities DE environment factor; decision making; information price; information meaning ID PROJECT EVALUATION; INFORMATION AB The success of subject ruling management situation in economical system depends on its ability to make decisions under uncertainty conditions according to environment factor. Environment factor in econometric models is understood as the force that influences the activity of a researched system but acts irrespective of the system. Research problems of influence of environment factor in decision support system are related to the receiving of exact information about the state of this factor. The benefit of financial decisions is related to long-range future so it is necessary to investigate prospective consequences of decisions made under uncertainty conditions according to the variation of possible risk. Behaviour of decision making person (DMP) and environment factor, methods of price estimation for exact information about the state of environment factor, and possibilities of practical application of received results evaluating risk of financial decisions are researched in this article. C1 [Christauskas, Ceslovas] Kaunas Univ Technol, Fac Social Sci, LT-44239 Kaunas, Lithuania. [Stunguriene, Stanislava] Vilnius State Univ, Int Business Sch, LT-10225 Vilnius, Lithuania. RP Christauskas, C (reprint author), Kaunas Univ Technol, Fac Social Sci, K Donelaicio G 20, LT-44239 Kaunas, Lithuania. EM ceslovas.christauskas@ktu.lt; ststunguriene@centras.lt RI Caposaldo, Marica/I-8659-2014 CR Andersen TJ, 2004, J MANAGE STUD, V41, P1271, DOI 10.1111/j.1467-6486.2004.00475.x Astebro T, 2005, J PROD INNOVAT MANAG, V22, P322, DOI 10.1111/j.0737-6782.2005.00129.x Bish EK, 2004, OPER RES, V52, P954, DOI 10.1287/opre.1040.0138 BROWNE N, 1992, EAST-WEST FILM J, V6, P5 CAOAN K, 2003, INT J INTELL SYST, V7, P733 CHRISTAUSKAS C, 2004, OECONOMICA, V237, P351 CHRISTAUSKAS C, 2005, MIKROEKONOMICZNE ASP, P297 COLLAN M, 2004, SOFT COMPUTING IND A, V17, P79 CUMMINS EJ, 2005, P 8 JOINT C INF SCI, V1, P1076 Dey PK, 2006, INT J PROD ECON, V103, P90, DOI 10.1016/j.ijpe.2004.11.018 DUBROV AM, 1999, MODELIROVANIJE RISKO Duncan W. R., 1996, GUIDE PROJECT MANAGE Edwards W., 1986, DECISION ANAL BEHAV Engerman S, 2005, IMF STAFF PAPERS, V52, P15 GELDER P, 2004, PROBABILISTIC SAFETY, V1, P1936 HARALICK RM, 1980, ARTIF INTELL, V14, P263, DOI 10.1016/0004-3702(80)90051-X Hinterhuber A, 2004, IND MARKET MANAG, V33, P765, DOI 10.1016/j.indmarman.2003.10.006 HORVITZ EJ, 1995, 11 C MONTR CAN ORG K, P296 HOWARD RA, 1966, IEEE T SYST SCI CYB, VSSC2, P22, DOI 10.1109/TSSC.1966.300074 HOWARD RA, 1977, READINGS DECISION AN, P429 Hurley TM, 2005, J RISK UNCERTAINTY, V30, P169, DOI 10.1007/s11166-005-6565-5 JBUEDJ C, 1997, J FINANCIAL PLANNING, P84 Jeffrey R., 1983, LOGIC DECISION, V2nd KEDAITIENE A, 1999, INFORM MARKETING RES Keeney R.L., 1976, DECISIONS MULTIPLE O KOENIG S, 2000, ADV NEURAL INFORM PR Koller D, 1996, GAME ECON BEHAV, V14, P247, DOI 10.1006/game.1996.0051 LI M, 2004, MANAGEMENT SCI GLOBA, V1, P378 LOTFI T, 2003, APPL MATH MODEL, V27, P913 Luce R. D., 1957, GAMES DECISIONS Makowski M, 2005, SYSTEMS AND HUMAN SCIENCE - FOR SAFETY, SECURITY AND DEPENDABILITY, P33, DOI 10.1016/B978-044451813-2/50004-X MAROSTICA A, 2005, P 8 JOINT C INF SCI, V1, P1103 Michael SC, 2004, J MANAGE STUD, V41, P1171, DOI 10.1111/j.1467-6486.2004.00471.x MONGERSTERN O, 1949, SCI AM, P22 MOORE SC, 2003, P 25 ANN C COGN SCI, V2, P822 MOORE SC, 2003, P 25 ANN C COGN SCI, V1, P822 Mulligan EJ, 2005, J BEHAV DECIS MAKING, V18, P145, DOI 10.1002/bdm.491 Myerson R., 1991, GAME THEORY ANAL CON NAGURNEY A, 2005, EUR J OPER RES, V172, P40 Neumann J., 1947, THEORY GAMES EC BEHA NOVICKAS J, 2001, ENG ECON, V5, P22 Borgonovo E, 2004, INT J PROD ECON, V90, P17, DOI 10.1016/S0925-5273(03)00213-5 PLELI N, 2005, P 10 INT C OP RES KO Raiffa H, 1968, DECISION ANAL INTRO RENDER B, 2003, FINANCIAL MANAGEMENT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sauer C., 2003, EUROPEAN MANAGEMENT, V21, P497, DOI 10.1016/S0263-2373(03)00078-1 Schal M, 2005, MATH METHOD OPER RES, V62, P141, DOI 10.1007/s00186-005-0445-2 Shafer G., 1990, READINGS UNCERTAIN R SMITH JQ, 1988, DECISION ANAL SPIEGELHALTER DJ, 1986, UNCERTAINTY ARTIFICI, P47 VILKAS E, 1990, OPTIMALNOSTI IGRACH WANG Y, 2005, P 2005 INT C CONSTR, V1, P1371 WANG Y, 2005, P 2005 INT C CONSTR, V2, P1371 NR 54 TC 3 Z9 3 PU VILNIUS UNIV PI KAUNAS PA MUITINES G 8,, KAUNAS, LT-44280, LITHUANIA SN 1648-4460 J9 TRANSFORM BUS ECON JI Transform. Bus. Econ. PY 2007 VL 6 IS 2 SU A BP 98 EP 113 PG 16 WC Business; Economics SC Business & Economics GA 258FP UT WOS:000252854100006 ER PT J AU Chen, DY Li, X Dong, ZY Chen, X AF Chen, Ding-Yi Li, Xue Dong, Zhao Yang Chen, Xia TI Fitness assessment of document model SO INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE LA English DT Article DE document classification; document model; fitness measurement ID TEXT CATEGORIZATION AB Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, "Resource selection for domain-specific cross-lingual IR", in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification. C1 Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. RP Chen, DY (reprint author), Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. EM dingyi@itee.uq.edu.au CR Akaike H., 1974, 2 INT S INF THEOR, P267 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 CHEN DY, 2005, ADC 2005 16 AUSTR DA, P125 Chickering D., 1997, P 13 C UNC ART INT U, P80 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 Cristianini N., 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dietterich T, 1995, ACM COMPUT SURV, V27, P326, DOI 10.1145/212094.212114 Dobra A., 2002, KDD 2002 P 8 ACM SIG, P481 Dumais S., 1998, CIKM 98, P148 FINCH S, 2003, REUTERS 21578 CORPUS FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Gao Jianfeng, 2004, SIGIR 04 P 27 ANN IN, P170, DOI DOI 10.1145/1008992.1009024 Heckerman D., 1994, KDD WORKSH, P85 Joachims T, 1998, EUR C MACH LEARN ECM, P137 KHMELEV DV, 2003, SIGIR 2003 P 26 ANN, P104 Krovetz R., 1993, SIGIR Forum LARKEY LS, 1998, SIGIR 1998 P 21 ANN, P90 Larkey LS, 2003, AUTOMATED ESSAY SCORING: A CROSS-DISCIPLINARY PERSPECTIVE, P55 Lewis D. D., 1995, SIGIR Forum Lewis David D., 1992, THESIS U MASSACHUSET Lewis D.D, 1998, EUR C MACH LEARN, P4 LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 PORTER MF, 1997, READINGS INFORMATION Quinlan J. R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence Reunanen J., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753715 RIJSBERGEN CJV, 1997, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROGATI M, 2004, SIGIR 2004 P 27 ANN, P154 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 VAPNIK VN, 1995, NATURE STAT LEARNING, P119 WARD G, 1996, MOBY WORD MOBY LEXIC WEISS SM, 1993, IEEE EXPERT, V8, P61, DOI 10.1109/64.248354 Yang Y., 1997, ICML 97, P412 NR 37 TC 0 Z9 0 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 0020-7721 J9 INT J SYST SCI JI Int. J. Syst. Sci. PD OCT 20 PY 2006 VL 37 IS 13 BP 893 EP 903 DI 10.1080/00207720600891539 PG 11 WC Automation & Control Systems; Computer Science, Theory & Methods; Operations Research & Management Science SC Automation & Control Systems; Computer Science; Operations Research & Management Science GA 096EZ UT WOS:000241361700004 ER PT J AU Martinez-Santiago, F Garcia-Cumbreras, MA Urena-Lopez, LA AF Martinez-Santiago, Fernando Garcia-Cumbreras, Miguel A. Urena-Lopez, L. Alfonso TI Does pseudo-relevance feedback improve distributed information retrieval systems? SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE DIR; collection fusion; TREC; CORI; pseudo-relevance feedback ID RESOURCES AB This paper presents a thorough analysis of the capabilities of the pseudo-relevance feedback (PRF) technique applied to distributed information retrieval (DIR). Previous studies have researched the application of PRF to improve the selection process of the best set of collections from a ranked list. This work emphasizes the effectiveness of PRF applied to the collection fusion problem. Usually, DIR systems apply PRF in the same way as traditional Information Retrieval systems. For each collection, local results are improved through PRF. A first question which arises is whether this local improvement is preserved in the final result. In addition, DIR systems merge the documents of rankings that are returned from a set of collections. Since a new global list of documents is available, we could use that list to apply PRF again, but on global level rather than on a local level. In order to apply global PRF, we have developed a merging approach called two-step RSV. Finally, we describe a number of experiments involving the two levels, local and global, of application of the PRF techniques. (c) 2006 Elsevier Ltd. All rights reserved. C1 Univ Jaen, Dept Comp Sci, Jaen, Spain. RP Garcia-Cumbreras, MA (reprint author), Univ Jaen, Dept Comp Sci, Jaen, Spain. EM dofer@ujaen.es; magc@ujaen.es; laurena@ujaen.es RI Urena Lopez, Alfonso/G-9999-2015; Martinez-Santiago, Fernando/H-3454-2015; Garcia Cumbreras, Miguel Angel/H-5708-2015 OI Urena Lopez, Alfonso/0000-0001-7540-4059; Martinez-Santiago, Fernando/0000-0002-1480-1752; Garcia Cumbreras, Miguel Angel/0000-0003-1867-9587 CR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CALLAN J, 2000, CMULTI00162 SCH COMP CALLAN JP, 1999, ACM SIGMOD INT C MAN CALLAN JP, 1995, P 18 INT C ACM SIGIR LIU K, 2001, P 10 ACM INT C INF K MARTIN M, 2005, NEURAL PROCESSING LE, V22 Martinez F, 2003, LECT NOTES COMPUT SC, V2785, P187 MARTINEZSANTIAG.F, 2003, ADV CROSS LANGUAGE I, V2785 Martinez-Santiago F, 2005, LECT NOTES COMPUT SC, V3491, P156 OGILVE P, 2001, P 10 INT C INF KNOWL ROBERSTON SE, 2000, INFORM PROCESSING MA, V1, P95 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST Rogati M, 2003, LECT NOTES COMPUT SC, V3237, P133 Si L., 2003, P 26 ANN INT ACM SIG SI L, 2003, P NAT C DIG GOV RES SI L, 2003, ACM SIGIR INT C RES Voorhees E., 1995, P 3 TEXT RETR C TREC, P95 NR 18 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2006 VL 42 IS 5 BP 1151 EP 1162 DI 10.1016/j.ipm.2006.01.003 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 056UH UT WOS:000238550100002 ER PT J AU Orengo, VM Huyck, C AF Orengo, VM Huyck, C TI Relevance feedback and cross-language information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE cross-language information retrieval; relevance feedback AB This paper presents a study of relevance feedback in a cross-language information retrieval environment. We have performed an experiment in which Portuguese speakers are asked to judge the relevance of English documents; documents hand-translated to Portuguese and documents automatically translated to Portuguese. The goals of the experiment were to answer two questions (i) how well can native Portuguese searchers recognise relevant documents written in English, compared to documents that are hand translated and automatically translated to Portuguese; and (ii) what is the impact of misjudged documents on the performance improvement that can be achieved by relevance feedback. Surprisingly, the results show that machine translation is as effective as hand translation in aiding users to assess relevance in the experiment. In addition, the impact of misjudged documents on the performance of RF is overall just moderate, and varies greatly for different query topics. (c) 2006 Elsevier Ltd. All rights reserved. C1 Middlesex Univ, Sch Comp Sci, London NW4 4BT, England. RP Orengo, VM (reprint author), UFRGS, Inst Informat, Av Bento Goncalves,9500 Bloco 4, BR-91501970 Porto Alegre, RS, Brazil. EM vmorengo@inf.ufrgs.br RI Moreira, Viviane/ OI Moreira, Viviane/0000-0003-4400-054X CR BALLESTEROS L, 1997, AAAI SPRING S TEXT S BALLESTEROS L, 1998, CROSS LANGUAGE INFOR, P23 BATHIE Z, 2002, LECT NOTES COMPUTING, V2406, P332 BIRON PV, 1995, ACM S APPL COMP NASH BRASCHLER M, 2003, LECT NOTES COMPUTING, V2785 CRESTANI F, 2000, UNCERTAINTY INTELLIG Deerwester S., 1990, J AM SOC INFORM SCI, V41, P1 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DRUCKER H, 2001, 18 INT C MACH LEARN Dumais S., 1995, 3 TEXT RETR C TREC3 DUMAIS S, 1992, 15 ANN INT ACM SIGIR DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 EFTHIMIADIS EN, 1989, PERSPECTIVES INFORMA, P257 Efthimiadis EN, 2000, J AM SOC INFORM SCI, V51, P989, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1002>3.0.CO;2-B HARMAN D, 1992, 15 ANN INT ACM SIGIR Harman D., 1992, INFORMATION RETRIEVA, P241 Ide E., 1971, SMART RETRIEVAL SYST KARLGREN J, 2003, LECT NOTES COMPUTING, V2785, P255 LANDAUER T, 1990, 6 ANN C UW CTR NEW O, P31 LESK ME, 1968, INFORM STORAGE RET, V4, P343, DOI 10.1016/0020-0271(68)90029-6 LOPEZOSTENERO F, 2001, LECT NOTES COMPUTING, V2406 MANDL T, 2003, LECT NOTES COMPUTING, V2785 MCNAMEE P, 2002, LECT NOTES COMPUTER, V2406 MCNAMEE P, 2003, LECT NOTES COMPUTING, V2785 OARD DW, 2001, LECT NOTES COMPUTER, V2406 OARD DW, 2003, LECT NOTES COMPUTING, V2785, P245 OARD DW, 2003, CLEF 2003 WORKSH TRO ORENGO VM, 2003, LECT NOTES COMPUTING, V2785 QU Y, 2000, P CONT BAS MULT INF ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 Salton G., 1997, READINGS INFORM RETR, ppp SHEN X, 2005, 28 ANN INT ACM SIGIR SPINK A, 1994, 17 ANN INT ACM SIG C, P81 Voorhees E.M., 1998, 21 ANN INT ACM SIGIR, P315 WANG J, 2001, LECT NOTES COMPUTING, V2406 Xu J., 1996, 19 ANN INT ACM SIGIR, P4 YANG Y, 1997, 15 NT JOINT C ART IN NR 39 TC 5 Z9 6 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2006 VL 42 IS 5 BP 1203 EP 1217 DI 10.1016/j.ipm.2005.12.003 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 056UH UT WOS:000238550100006 ER PT J AU Vechtomova, O AF Vechtomova, Olga TI Noun phrases in interactive query expansion and document ranking SO INFORMATION RETRIEVAL LA English DT Article; Proceedings Paper CT 27th European Conference on Information Retrieval Research (ECIR 2005) CY MAR 21-23, 2005 CL Santiago Compostela, SPAIN SP Microsoft Res, SHARP, ERCIM, CEPIS, BCS HO Univ Santiago Compostele, Tech Sch Engn DE interactive query expansion; noun phrases; document ranking ID INFORMATION-RETRIEVAL; MODEL AB The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance feedback and a new phrase-based document ranking method. A combined syntactico-statistical method was used for the selection of phrases for query expansion. Several statistical measures of phrase selection were evaluated. Experiments were also conducted studying the effectiveness of noun phrases in document ranking. One of the major problems in phrase-based document retrieval is weighting of overlapping and non-contiguous word sequences in documents. The paper presents a new method of phrase weighting, which addressed this problem, and its evaluation on the TREC dataset. C1 Univ Waterloo, Fac Engn, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. RP Vechtomova, O (reprint author), Univ Waterloo, Fac Engn, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. EM ovechtom@engmail.uwaterloo.ca CR ALLAN J, 2005, P 13 TEXT RETR C NIS Anick P. G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312670 Banerjee S., 2003, P 4 INT C INT TEXT P Beaulieu M, 1998, INTERACT COMPUT, V10, P237, DOI 10.1016/S0953-5438(98)00008-3 BELY N, 1970, PROCEDURES ANAL SEMA Brill E, 1995, COMPUT LINGUIST, V21, P543 Bruza Peter, 2000, P 23 ANN INT ACM SIG, P280, DOI 10.1145/345508.345598 CLARKE CLA, 1995, CS9507 U WAT COMP SC Dunning T., 1993, Computational Linguistics, V19 EVANS DA, 1996, P ACL 96 34 ANN M AS Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Frantzi K., 1996, P 16 INT C COMP LING, P41 HULL D, 1996, P 5 TEXT RETR C GAIT, P167 Jones K. S., 2000, Information Processing & Management, V36 MARCHIONINI G, 1992, J AM SOC INFORM SCI, V43, P156, DOI 10.1002/(SICI)1097-4571(199203)43:2<156::AID-ASI8>3.0.CO;2-U Mitra M, 1997, P RIAO97 COMP ASS IN, P200 PICKENS J, 2000, P RIAO Ramshaw L.A., 1995, P 3 ACL WORKSH VER L ROBERTSON SE, 2004, P 13 C INF KNOWL MAN ROBERTSON SE, 2004, P 12 TEXT RETR C, P418 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruthven L, 2003, P 26 ANN INT ACM SIG, P213 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1998, P 20 BCS IRSG C GREN STRZALKOWSKI T, 1999, NATURAL LANGUAGE INF, P113 STRZALKOWSKI T, 1995, INFORM PROCESS MANAG, V31, P397, DOI 10.1016/0306-4573(94)00055-8 VECHTOMOVA O, 2003, P 12 TEXT RETR C NIS VECHTOMOVA O, 2005, P 13 TEXT RETR C NIS Vintar S., 2004, P MEMURA 2004 WORKSH, P54 VOORHEES E, 2003, P 12 TEXT RETR C NIS Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 33 TC 4 Z9 4 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD SEP PY 2006 VL 9 IS 4 BP 399 EP 420 DI 10.1007/s10791-006-6390-8 PG 22 WC Computer Science, Information Systems SC Computer Science GA 067RZ UT WOS:000239321600002 ER PT J AU Vinay, V Cox, IJ Milic-Frayling, N Wood, K AF Vinay, Vishwa Cox, Ingemar J. Milic-Frayling, Natasa Wood, Ken TI Can constrained relevance feedback and display strategies help users retrieve items on mobile devices? SO INFORMATION RETRIEVAL LA English DT Article; Proceedings Paper CT 27th European Conference on Information Retrieval Research (ECIR 2005) CY MAR 21-23, 2005 CL Santiago Compostela, SPAIN SP Microsoft Res, SHARP, ERCIM, CEPIS, BCS HO Univ Santiago Compostele, Tech Sch Engn DE relevance feedback; display strategies; small displays AB Searching online information resources using mobile devices is affected by small screens which can display only a fraction of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ) and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking that attempts to maximize information gain from the user's choices and the top-D ranked documents. Experimental results indicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. C1 UCL, Dept Comp Sci, London WC1E 6BT, England. Microsoft Res Ltd, Cambridge, England. RP Vinay, V (reprint author), UCL, Dept Comp Sci, London WC1E 6BT, England. EM v.vinay@cs.ucl.ac.uk; ingemar@ieee.org; natasamf@microsoft.com; krw@microsoft.com CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 Buyukkokten O., 2001, P 10 INT WORLD WID W BUYUKKOKTEN O, 2000, P ACM C COMP HUM INT Campbell I., 1996, P COLIS, V2, P251 CHEN Y, 2003, P 12 WORLD WID WEB C Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 HARMAN, 1997, 5 TEXT RETR C TREC 5 HARMAN D, 1992, P 15 ANN INT AM SIGI JONES M, 1997, IEEE C ISSUES NETWOR, V239, P1 JONES M, 1999, P 8 WORLD WID WEB C Levene M, 1999, INFORM SCIENCES, V114, P165, DOI 10.1016/S0020-0255(98)10055-5 MAGENNIS M, 1997, P 20 ANN INT ACM SIG ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RODDEN K, 2003, P HCI C BATH RUVINI JD, 2003, IUI 03 MIAM FLOR Sellen A. J., 2002, P SIGCHI C HUM FACT, P227 WHITE RW, 2004, P ECIR NR 19 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD SEP PY 2006 VL 9 IS 4 BP 435 EP 453 DI 10.1007/s10791-006-6391-7 PG 19 WC Computer Science, Information Systems SC Computer Science GA 067RZ UT WOS:000239321600004 ER PT J AU Hassan, M Brown, RD Varma-O'Brien, S Rogers, D AF Hassan, Moises Brown, Robert D. Varma-O'Brien, Shikha Rogers, David TI Cheminformatics analysis and learning in a data pipelining environment SO MOLECULAR DIVERSITY LA English DT Review DE Bayesian models; bioactivity prediction; data mining; data pipelining; maximal common substructure search; molecular fingerprints; molecular similarity; virtual screening ID CYCLIN-DEPENDENT KINASES; BIOACTIVE REFERENCE STRUCTURES; MOLECULAR SIMILARITY; INHIBITORS; ANTICANCER; SEARCH; DESCRIPTORS; GENERATION; ALGORITHM; CLASSIFIER AB Workflow technology is being increasingly applied in discovery information to organize and analyze data. SciTegic's Pipeline Pilot is a chemically intelligent implementation of a workflow technology known as data pipelining. It allows scientists to construct and execute workflows using components that encapsulate many cheminformatics based algorithms. In this paper we review SciTegic's methodology for molecular fingerprints, molecular similarity, molecular clustering, maximal common subgraph search and Bayesian learning. Case studies are described showing the application of these methods to the analysis of discovery data such as chemical series and high throughput screening results. The paper demonstrates that the methods are well suited to a wide variety of tasks such as building and applying predictive models of screening data, identifying molecules for lead optimization and the organization of molecules into families with structural commonality. C1 SciTegic Inc, San Diego, CA 92121 USA. Accelrys Inc, San Diego, CA 92121 USA. RP Hassan, M (reprint author), SciTegic Inc, 10188 Telesis Court,Suite 100, San Diego, CA 92121 USA. EM mhassan@scitegic.com CR AVIDON VV, 1978, KHIM FARM ZH+, V12, P88 Bemis GW, 1996, J MED CHEM, V39, P2887, DOI 10.1021/jm9602928 Bender A, 2004, J CHEM INF COMP SCI, V44, P170, DOI 10.1021/ci034207y Bender A, 2004, ORG BIOMOL CHEM, V2, P3204, DOI 10.1039/b409813g Bradley EK, 2003, J MED CHEM, V46, P4360, DOI 10.1021/jm020472j Breiman L., 1984, CLASSIFICATION REGRE Bremser W., 1978, ANAL CHIM ACTA, V103, P355, DOI 10.1016/S0003-2670(01)83100-7 Buolamwini JK, 2000, CURR PHARM DESIGN, V6, P379, DOI 10.2174/1381612003400948 DUBOIS JE, 1976, CHEM APPL GRAPH THEO, P161 EVERITT, 1997, CLUSTER ANAL Fischer PM, 2000, CURR MED CHEM, V7, P1213 Fischer P M, 2001, Curr Opin Drug Discov Devel, V4, P623 Hassan M, 1996, MOL DIVERS, V2, P64, DOI 10.1007/BF01718702 HERT J, 2006, IN PRESS J CHEM INF Hert J, 2004, J CHEM INF COMP SCI, V44, P1177, DOI 10.1021/ci034231b Hert J, 2004, ORG BIOMOL CHEM, V2, P3256, DOI 10.1039/b409865j Johnson M. A., 1990, CONCEPTS APPL MOL SI Kaufman L, 1990, FINDING GROUPS DATA Klon AE, 2004, J MED CHEM, V47, P4356, DOI 10.1021/jm049970d Klon AE, 2004, J MED CHEM, V47, P2743, DOI 10.1021/jm030363k Mani S, 2000, EXPERT OPIN INV DRUG, V9, P1849, DOI 10.1517/13543784.9.8.1849 MARIE T, 1979, J CHEM INF COMP SCI, V19, P251 McGregor MJ, 1997, J CHEM INF COMP SCI, V37, P443, DOI 10.1021/ci960151e Meijer L, 2000, DRUG RESIST UPDATE, V3, P83, DOI 10.1054/drup.2000.0129 MORGAN HL, 1965, J CHEM DOC, V5, P107, DOI 10.1021/c160017a018 Parker CN, 2005, J BIOMOL SCREEN, V10, P647, DOI 10.1177/1087057105281268 RANDIC M, 1978, J CHEM INF COMP SCI, V18, P101, DOI 10.1021/ci60014a014 Raymond JW, 2002, COMPUT J, V45, P631, DOI 10.1093/comjnl/45.6.631 Raymond JW, 2002, J CHEM INF COMP SCI, V42, P305, DOI 10.1021/ci010381f ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROGERS D, EXTENEDE CONNECTIVIT Rogers D, 2005, J BIOMOL SCREEN, V10, P682, DOI 10.1177/1087057105281365 Roy KK, 2001, CURR PHARM DESIGN, V7, P1669, DOI 10.2174/1381612013397230 Sausville EA, 2000, ANN NY ACAD SCI, V910, P207 Schneider G, 1999, ANGEW CHEM INT EDIT, V38, P2894, DOI 10.1002/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F SCHUBERT W, 1978, J AM CHEM SOC, V100, P37, DOI 10.1021/ja00469a006 Schuffenhauer A, 2003, J CHEM INF COMP SCI, V43, P391, DOI 10.1021/ci025569t Senderowicz A M, 2001, Cancer Chemother Biol Response Modif, V19, P165 Senderowicz AM, 2001, LEUKEMIA, V15, P1, DOI 10.1038/sj.leu.2401994 Senderowicz AM, 2000, ONCOGENE, V19, P6600, DOI 10.1038/sj.onc.1204085 Sielecki TM, 2000, J MED CHEM, V43, P1, DOI 10.1021/jm990256j Todeschini R, 2000, HDB MOL DESCRIPTORS WEININGER D, 1989, J CHEM INF COMP SCI, V29, P97, DOI 10.1021/ci00062a008 WILLETT P, 1979, J CHEM INF COMP SCI, V19, P159, DOI 10.1021/ci60019a009 WILLETT P, 1979, J CHEM INF COMP SCI, V19, P253, DOI 10.1021/ci60020a017 Xia XY, 2004, J MED CHEM, V47, P4463, DOI 10.1021/jm0303195 NR 46 TC 106 Z9 108 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1381-1991 J9 MOL DIVERS JI Mol. Divers. PD AUG PY 2006 VL 10 IS 3 BP 283 EP 299 DI 10.1007/s11030-006-9041-5 PG 17 WC Biochemistry & Molecular Biology; Chemistry, Applied; Chemistry, Medicinal; Chemistry, Multidisciplinary SC Biochemistry & Molecular Biology; Chemistry; Pharmacology & Pharmacy GA 106YJ UT WOS:000242137600003 PM 17031533 ER PT J AU Jeffery, IB Higgins, DG Culhane, AC AF Jeffery, Ian B. Higgins, Desmond G. Culhane, Aedin C. TI Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data SO BMC BIOINFORMATICS LA English DT Article ID CLASSIFICATION; CANCER; PREDICTION; SURVIVAL; TUMOR AB Background: Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, classical t-statistic and moderated t-statistics. Even though these methods return gene lists that are often dissimilar, few direct comparisons of these exist. We present an empirical study in which we compare some of the most commonly used feature selection methods. We apply these to 9 publicly available datasets, and compare, both the gene lists produced and how these perform in class prediction of test datasets. Results: In this study, we compared the efficiency of the feature selection methods; significance analysis of microarrays (SAM), analysis of variance (ANOVA), empirical bayes t-statistic, template matching, maxT, between group analysis (BGA), Area under the receiver operating characteristic (ROC) curve, the Welch t-statistic, fold change, rank products, and sets of randomly selected genes. In each case these methods were applied to 9 different binary ( two class) microarray datasets. Firstly we found little agreement in gene lists produced by the different methods. Only 8 to 21% of genes were in common across all 10 feature selection methods. Secondly, we evaluated the class prediction efficiency of each gene list in training and test cross-validation using four supervised classifiers. Conclusion: We report that the choice of feature selection method, the number of genes in the genelist, the number of cases (samples) and the noise in the dataset, substantially influence classification success. Recommendations are made for choice of feature selection. Area under a ROC curve performed well with datasets that had low levels of noise and large sample size. Rank products performs well when datasets had low numbers of samples or high levels of noise. The Empirical bayes t-statistic performed well across a range of sample sizes. C1 Natl Univ Ireland Univ Coll Dublin, Conway Inst, Dublin 4, Ireland. Dana Farber Canc Inst, Dept Biostat & Computat Biol, Boston, MA 02115 USA. RP Jeffery, IB (reprint author), Natl Univ Ireland Univ Coll Dublin, Conway Inst, Dublin 4, Ireland. EM Ian.Jeffery@ucd.ie; Des.Higgins@ucd.ie; aedin@jimmy.harvard.edu RI Higgins, des/J-6314-2012; Jeffery, Ian/I-4010-2013; Higgins, Des/ OI Higgins, Des/0000-0002-3952-3285 CR Alon U, 1999, P NATL ACAD SCI USA, V96, P6745, DOI 10.1073/pnas.96.12.6745 BHATTACHARYYA PVS, 2001, P 1 SIAM INT C DAT M BREITLING R, 2005, J BIOINFORM COMPUT B, V3, P171 Breitling R, 2004, FEBS LETT, V573, P83, DOI 10.1016/j.febslet.2004.07.055 Chiaretti S, 2004, BLOOD, V103, P2771, DOI 10.1182/blood-2003-09-3243 Choe SE, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2005-6-2-r16 Culhane AC, 2002, BIOINFORMATICS, V18, P1600, DOI 10.1093/bioinformatics/18.12.1600 Culhane AC, 2005, BIOINFORMATICS, V21, P2789, DOI 10.1093/bioinformatics/bti394 Furey TS, 2000, BIOINFORMATICS, V16, P906, DOI 10.1093/bioinformatics/16.10.906 Ge YC, 2003, TEST, V12, P1, DOI 10.1007/BF02595811 Gentleman RC, 2004, GENOME BIOL, V5, DOI 10.1186/gb-2004-5-10-r80 Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 Irizarry RA, 2003, BIOSTATISTICS, V4, P249, DOI 10.1093/biostatistics/4.2.249 Long AD, 2001, J BIOL CHEM, V276, P19937, DOI 10.1074/jbc.M010192200 Lonnstedt I, 2002, STAT SINICA, V12, P31 Margalit O, 2005, BLOOD REV, V19, P223, DOI 10.1016/j.blre.2004.11.003 Martin DE, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-148 MASSART DL, 1988, DATA HANDLING SCI TE, V2, P395 Mukherjee S, 2005, 9 ANN INT C RES COMP, P237 Mutch DM, 2002, BMC BIOINFORMATICS, V3, DOI 10.1186/1471-2105-3-17 Nutt CL, 2003, CANCER RES, V63, P1602 Pan W, 2002, BIOINFORMATICS, V18, P546, DOI 10.1093/bioinformatics/18.4.546 Parodi S, 2003, CYTOGENET GENOME RES, V101, P90, DOI 10.1159/000074404 Pavlidis P, 2001, GENOME BIOL, V2, DOI 10.1186/gb-2001-2-10-research0042 Pepe MS, 2003, BIOMETRICS, V59, P133, DOI 10.1111/1541-0420.00016 Pomeroy SL, 2002, NATURE, V415, P436, DOI 10.1038/415436a ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahai H., 2000, ANAL VARIANCE FIXED Shipp MA, 2002, NAT MED, V8, P68, DOI 10.1038/nm0102-68 Singh D, 2002, CANCER CELL, V1, P203, DOI 10.1016/S1535-6108(02)00030-2 Smyth G. K., 2004, STAT APPL GENET MOL, V3, DOI DOI 10.2202/1544-6115.1027 Sneath P.H., 1973, NUMERICAL TAXONOMY Statnikov A, 2005, BIOINFORMATICS, V21, P631, DOI 10.1093/bioinformatics/bti033 Thioulouse J, 1997, STAT COMPUT, V7, P75, DOI 10.1023/A:1018513530268 Tian E, 2003, NEW ENGL J MED, V349, P2483, DOI 10.1056/NEJMoa030847 Tsai Chen-An, 2004, J Biopharm Stat, V14, P985, DOI 10.1081/BIP-200035475 Tusher VG, 2001, P NATL ACAD SCI USA, V98, P5116, DOI 10.1073/pnas.091062498 Vapnik VN, 1998, STAT LEARNING THEORY Wu BL, 2005, BIOINFORMATICS, V21, P1565, DOI 10.1093/bioinformatics/bti217 NR 39 TC 168 Z9 176 PU BIOMED CENTRAL LTD PI LONDON PA MIDDLESEX HOUSE, 34-42 CLEVELAND ST, LONDON W1T 4LB, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD JUL 26 PY 2006 VL 7 AR 359 DI 10.1186/1471-2105-7-359 PG 16 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 074FS UT WOS:000239798700002 PM 16872483 ER PT J AU Arampatzis, A van Kreveld, M Reinbacher, I Jones, CB Vaid, S Clough, P Joho, H Sanderson, M AF Arampatzis, Avi van Kreveld, Marc Reinbacher, Iris Jones, Christopher B. Vaid, Subodh Clough, Paul Joho, Hideo Sanderson, Mark TI Web-based delineation of imprecise regions SO COMPUTERS ENVIRONMENT AND URBAN SYSTEMS LA English DT Article; Proceedings Paper CT Workshop on Geographic Information Retrieval CY JUL 25-29, 2004 CL Sheffield, ENGLAND SP SIGIR DE geographical information systems (GIS); World-Wide Web (WWW); imprecise regions; fuzzy boundaries; geometric algorithms ID GEOGRAPHICAL INFORMATION-RETRIEVAL AB This paper describes several steps in the derivation of boundaries of imprecise regions using the Web as the information source. We discuss how to use the Web to obtain locations that are part of and locations that are not part of the region to be delineated, and then we propose methods to compute the region algorithmically. The methods introduced are evaluated to judge the potential of the approach. (c) 2005 Elsevier Ltd. All rights reserved. C1 Univ Cardiff Wales, Sch Comp Sci, Cardiff, Wales. Univ Utrecht, Inst Informat & Comp Sci, NL-3508 TB Utrecht, Netherlands. Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. RP Jones, CB (reprint author), Univ Cardiff Wales, Sch Comp Sci, Cardiff, Wales. EM avgerino@cs.uu.nl; marc@cs.uu.nl; iris@cs.uu.nl; c.b.jones@cs.cardiff.ac.uk; subodh.vaid@cs.cardiff.ac.uk; p.d.clough@sheffield.ac.uk; h.joho@sheffield.ac.uk; m.sanderson@sheffield.ac.uk RI Sanderson, Mark/ OI Sanderson, Mark/0000-0003-0487-9609 CR Alani H, 2001, INT J GEOGR INF SCI, V15, P287, DOI 10.1080/13658810110038942 Cunningham H., 2002, P 40 ANN M ASS COMP Dumais S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval EDELSBRUNNER H, 1983, IEEE T INFORM THEORY, V29, P551, DOI 10.1109/TIT.1983.1056714 JOHO HL, 2001, P 1 HUM LANG TECHN C, P219 Jones CB, 2003, LECT NOTES COMPUT SC, V2888, P934 Jones C. B., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval LARSON RR, 1996, GIS LIB PATRONS MAPS Li H, 2003, PAYMENT TECHNOLOGIES FOR E-COMMERCE, P39 MacEachren A. M., 1995, HOW MAPS WORK MARKOWETZ A, 2003, ISPRS WG 4 WORKSH NE Montello D., 2003, SPAT COGN COMPUT, V2, P185, DOI 10.1207/S15427633SCC032&3_06 MOON R, 1998, FIXED EXPRESSION IDI OSULLIVAN D, 2003, GEOGRAPHIC INFORM AN Purves R., 2005, P GISRUK 2005 GLASG, P313 Rauch E, 2003, P HLT NAACL 2003 WOR, P50 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Joho H., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354817 Smith D.A., 2003, P HLT NAACL 2003 WOR, P45 STMIH D, 2002, P 25 ANN INT ACM SIG, P73 NR 20 TC 24 Z9 25 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0198-9715 J9 COMPUT ENVIRON URBAN JI Comput. Environ. Urban Syst. PD JUL PY 2006 VL 30 IS 4 BP 436 EP 459 DI 10.1016/j.compenvurbsys.2005.08.001 PG 24 WC Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Studies; Geography; Operations Research & Management Science SC Computer Science; Engineering; Environmental Sciences & Ecology; Geography; Operations Research & Management Science GA 113TI UT WOS:000242619900005 ER PT J AU Kim, BM Li, Q Park, CS Kim, SG Kim, JY AF Kim, Byeong Man Li, Qing Park, Chang Seok Kim, Si Gwan Kim, Ju Yeon TI A new approach for combining content-based and collaborative filters SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE information filtering; collaborative filtering; content-based filtering; recommendation system ID RETRIEVAL AB With the development of e-commerce and the proliferation of easily accessible information, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendations, including content-based and collaborative techniques. Content-based filtering selects information based on semantic content, whereas collaborative filtering combines the opinions of other users to make a prediction for a target user. In this paper, we describe a new filtering approach that combines the content-based filter and collaborative filter to capitalize on their respective strengths, and thereby achieves a good performance. We present a series of recommendations on the selection of the appropriate factors and also look into different techniques for calculating user-user similarities based on the integrated information extracted from user profiles and user ratings. Finally, we experimentally evaluate our approach and compare it with classic filters, the result of which demonstrate the effectiveness of our approach. C1 Kumoh Natl Inst Technol, Dept Software Engn, Gumi 730701, Gyeongbuk, South Korea. Informat & Commun Univ, Dept Engn, Taejon 305732, South Korea. Bucheon Coll, Puchon 421735, Gveonggi Do, South Korea. RP Li, Q (reprint author), Kumoh Natl Inst Technol, Dept Software Engn, 1 Yangho Dong, Gumi 730701, Gyeongbuk, South Korea. EM liqing@icu.ac.kr RI Qing, Li/H-2398-2014 CR ANICK PG, 1990, P 13 ANN INT ACM SIG, P135, DOI 10.1145/96749.98015 BAEZAYATES R, 1999, MODERN INFORM RETRIE, P30 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Basu C., 1998, P AAAI 98 Bradley P. S., 1998, P 15 INT C MACH LEAR, P91 Breese J S, 1998, P 14 C UNC ART INT, P43 Claypool M., 1999, ACM SIGIR 99 WORKSH DELGADO J, 1998, P INT WORKSH COOP IN, P206 DUDA RO, 2000, PATTERN CLASSIFICATI, P528 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 GOOD N, 1999, P AAAI 99 GUPTA D, 1999, P ACM SIGIR 99 WORKS HAUVER DB, 2001, P WEB DEL MUS HERLOCKER J, 1999, P SIGIR 99 KIM M, 2000, P ACM SIGIR 2000 WOR LEE JH, 1993, INFORM PROCESSING MA, V30, P79 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 OARD DW, 1996, EETR9625 U MAR OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H POPESCUL A, 2001, P UAI 2001 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 TERRY DB, 1993, P ACM C ORG COMP SYS, P21, DOI 10.1145/168555.168558 UPENDRA S, 1995, P ACM CHI 95 C, P210 VERHOEFF J, 1961, COMMUN ACM, V4, P594 VERHOEFF J, 1961, COMMUN ACM, V4, P557, DOI 10.1145/366853.366861 Wasfi A.M. Ahmad, 1999, P 1999 INT C INT US, P57 Wilkinson R., 1991, P ACM SIGIR C RES DE, P202, DOI 10.1145/122860.122880 WITTENBURG K, 1995, P 4 INT WORLD WID WE, P51 NR 32 TC 22 Z9 35 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD JUL PY 2006 VL 27 IS 1 BP 79 EP 91 DI 10.1007/s10844-006-8771-2 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 082HT UT WOS:000240378000004 ER PT J AU Bodoff, D Wong, SPS AF Bodoff, David Wong, Samuel Po-Shing TI Documents and queries as random variables: History and implications SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID WORLD-WIDE-WEB; INFORMATION-RETRIEVAL; PROBABILISTIC MODELS; MAXIMUM-LIKELIHOOD; RELEVANCE; SEARCH AB The view of documents and/or queries as random variables is gaining importance in the theory of information retrieval. We argue that traditional probabilistic models consider documents and queries as random variables, but that newer models such as language modeling and our unified model take this one step further. The additional step is called error in predictors. Such models consider that we don't observe the document and query random variables that are modeled to predict relevance probabilistically. Rather, there are additional random variables, which are the observed documents and queries. We discuss some important implications of this idea for parameter estimation, relevance prediction, and even test-collection construction. By clarifying the positions of various probabilistic models on this question, and presenting in one place many of its implications, this article aims to deepen our common understanding of the theories behind traditional probabilistic models, and to strengthen the theoretical basis for further development of more recent approaches such as language modeling. C1 Univ Haifa, Grad Sch Business, IL-31999 Haifa, Israel. Chinese Univ Hong Kong, Dept Stat, Hong Kong, Hong Kong, Peoples R China. RP Bodoff, D (reprint author), Univ Haifa, Grad Sch Business, IL-31999 Haifa, Israel. EM dbodoff@gsb.haifa.ac.il RI bodoff, david/A-3775-2009 CR AMATI G, 1998, INFORM RETRIEVAL UNC Barabasi AL, 2000, PHYSICA A, V281, P69, DOI 10.1016/S0378-4371(00)00018-2 Bodoff D, 2001, J AM SOC INF SCI TEC, V52, P785, DOI 10.1002/asi.1137.abs Bodoff D, 2003, J AM SOC INF SCI TEC, V54, P1050, DOI 10.1002/asi.10300 Bodoff D, 2004, J AM SOC INF SCI TEC, V55, P471, DOI 10.1002/asi.10398 BODOFF D, 2004, ACM T INFORM SYST, V22, P1 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BRODER A, 2000, 9 INT WORLD WID WEB Cooper MD, 2001, J AM SOC INF SCI TEC, V52, P813, DOI 10.1002/asi.1140.abs Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Fuhr N., 1990, P 13 INT C RES DEV I, P45, DOI 10.1145/96749.98008 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 GLASSMAN S, 1994, COMPUT NETWORKS ISDN, V27, P165, DOI 10.1016/0169-7552(94)90130-9 HARTER SP, 1975, J AM SOC INFORM SCI, P197 Lafferty J., 2003, KLUWER INT SERIES IN, V13, P258 LAFFERTY J, 2001, SIGIR 2001 Lavrenko V., 2001, SIGIR 01 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McCullagh P, 1989, GEN LINEAR MODELS NAYMAN J, 1948, ECONOMETRICA, V16, P1 PONTE J, 1998, LANGUAGE MODELING AP ROBERTSON S, 2003, WORKSH MATH FDN INF ROBERTSON S, 2000, BAYESIAN MODELS EVEN Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1981, INFORM RETRIEVAL RES ROBERTSON SE, 1982, UNIFIED PROBABILISTI ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Soboroff I., 2002, SIGIR Forum, V36 SOBOROFF I, 2002, ANN ACM C RES DEV IN Tanner M. A., 1996, TOOLS STAT INFERENCE, V3rd WONG S, 2003, MCMC UNIFIED MODEL Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 NR 33 TC 0 Z9 0 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUL PY 2006 VL 57 IS 9 BP 1138 EP 1154 DI 10.1002/asi.20378 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 056JT UT WOS:000238519600001 ER PT J AU Fragos, K Maistros, Y AF Fragos, K Maistros, Y TI A goodness of fit test approach in Information Retrieval SO INFORMATION RETRIEVAL LA English DT Article DE goodness of fit tests; Information Retrieval AB In many probabilistic modeling approaches to Information Retrieval we are interested in estimating how well a document model "fits" the user's information need (query model). On the other hand in statistics, goodness of fit tests are well established techniques for assessing the assumptions about the underlying distribution of a data set. Supposing that the query terms are randomly distributed in the various documents of the collection, we actually want to know whether the occurrences of the query terms are more frequently distributed by chance in a particular document. This can be quantified by the so-called goodness of fit tests. In this paper, we present a new document ranking technique based on Chi-square goodness of fit tests. Given the null hypothesis that there is no association between the query terms q and the document d irrespective of any chance occurrences, we perform a Chi-square goodness of fit test for assessing this hypothesis and calculate the corresponding Chi-square values. Our retrieval formula is based on ranking the documents in the collection according to these calculated Chi-square values. The method was evaluated over the entire test collection of TREC data, on disks 4 and 5, using the topics of TREC-7 and TREC-8 (50 topics each) conferences. It performs well, outperforming steadily the classical OKAPI term frequency weighting formula but below that of KL-Divergence from language modeling approach. Despite this, we believe that the technique is an important non-parametric way of thinking of retrieval, offering the possibility to try simple alternative retrieval formulas within goodness-of-fit statistical tests' framework, modeling the data in various ways estimating or assigning any arbitrary theoretical distribution in terms. C1 Natl Tech Univ Athens Iroon, Dept Elect & Comp Engineers, Zografos, Greece. RP Fragos, K (reprint author), Natl Tech Univ Athens Iroon, Dept Elect & Comp Engineers, Polytexneniou 9, Zografos, Greece. EM kfragos@mail.ntua.gr; maistros@cs.ntua.gr CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 BROGLIO J, 1995, OVERVIEW 3 RETRIEVAL, P500 DAGOSTINO BR, 1986, GOODNESS FIT TECHNIQ Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop MILLER HD, 1999, P 1999 ACM SIGIR C R, P214 MILLER HD, 1999, P 7 TEXT RETR C TREC, P133 OAKES M, 2001, 24 ACM SIGIR C RES D Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST SHANNON C, 1948, BELL SYST TECH J, V27, pR30 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VOORHESS E, 2001, P TEXT RETR C TRECI ZHAI C, 2001, 24 ACM SIGIR C RES D ZHAI C, 2001, SCH COMP SCI CARN ME NR 18 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2006 VL 9 IS 3 BP 331 EP 342 DI 10.1007/s10791-006-3609-7 PG 12 WC Computer Science, Information Systems SC Computer Science GA 050PP UT WOS:000238100700006 ER PT J AU Choi, J Kim, M Raghavan, VV AF Choi, J Kim, M Raghavan, VV TI Adaptive relevance feedback method of extended Boolean model using hierarchical clustering techniques SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE relevance feedback; extended Boolean model; hierarchical clustering; multi-layer perceptron ID INFORMATION-RETRIEVAL; QUERY EXPANSION; PERFORMANCE; SYSTEMS AB The relevance feedback process uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. Salton et al. proposed a relevance feedback method, called DNF (disjunctive normal form) method, for a well established extended Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulated Boolean queries. In this study, we investigate the problems of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve those problems. We also propose a neural network model in which the term weights used in extended Boolean queries can be adjusted by the users' relevance feedbacks. (c) 2005 Published by Elsevier Ltd. C1 Ajou Univ, Dept Comp Engn, Suwon 443749, South Korea. Univ Louisiana, Ctr Adv Comp Studies, Lafayette, LA 70504 USA. RP Choi, J (reprint author), Ajou Univ, Dept Comp Engn, Suwon 443749, South Korea. EM cjp@ajou.ac.kr; minkoo@ajou.ac.kr; raghavan@cacs.louisiana.edu CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BELEW RK, 1984, P 12 INT C RES DEV I, P11 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P275 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chang Y, 1971, SMART RETRIEVAL SYST, P355 CHOI J, 2001, P ACM SIGIR WORKSH M, P42 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Haykin S., 1994, NEURAL NETWORKS COMP Ide E., 1971, SMART RETRIEVAL SYST, P337 KWOK KL, 1989, SIGIR FORUM, V23, P21, DOI 10.1145/75334.75338 Lee Chi Ho, 1994, Archives of Pharmacal Research (Seoul), V17, P182, DOI 10.1007/BF02974256 Lippmann R.P., 1987, IEEE ASSP MAG, V3, P4 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P193, DOI 10.1016/0306-4573(94)90064-7 PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RUTHVEN I, 2003, KNOWLEDGE ENG REV, V18 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SHUTZE H, 1995, P SIGIR 95, P229 Smith M.E., 1990, THESIS CORNELL U JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X Wilkinson R., 1991, P ACM SIGIR C RES DE, P202, DOI 10.1145/122860.122880 Wong S. K. M., 1993, P 16 ANN INT ACM SIG, P107, DOI 10.1145/160688.160703 NR 30 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2006 VL 42 IS 2 BP 331 EP 349 DI 10.1016/j.ipm.2005.05.009 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 981BF UT WOS:000233061300001 ER PT J AU Kuflik, T Boger, Z Shoval, P AF Kuflik, T Boger, Z Shoval, P TI Filtering search results using an optimal set of terms identified by an artificial neural network SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information filtering; feature selection; user profile; artificial neural network ID INFORMATION-RETRIEVAL; TEXT CATEGORIZATION; RELEVANCE; CLASSIFICATION; SYSTEMS; AGENTS; MODEL AB Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user's interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm. (c) 2005 Elsevier Ltd. All rights reserved. C1 Univ Haifa, Dept Management Informat Syst, IL-31905 Haifa, Israel. Ind Neural Syst Ltd, OPTIMAL, IL-84243 Beer Sheva, Israel. Opt Neural Informat LLC, Rockville, MD 20852 USA. Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. RP Kuflik, T (reprint author), Univ Haifa, Dept Management Informat Syst, IL-31905 Haifa, Israel. EM tsvikak@mis.hevra.haifa.ac.il; zboger@bgumail.bgu.ac.il; shoval@bgumail.bgu.ac.il RI Shoval, Peretz/F-1229-2012; Kuflik, Tsvi/M-5115-2014 OI Shoval, Peretz/0000-0002-0183-2832; CR AHMED K, 1999, P INT JOINT C NEUR N, V4, P2816 Baba K., 1990, P INT JOINT C NEUR N, V3, P155 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bishop CM, 1995, NEURAL NETWORKS PATT Boger Z, 2003, ANAL CHIM ACTA, V490, P31, DOI 10.1016/S0003-2670(03)00349-0 BOGER Z, 1997, P IEEE INT C SYST MA, P3030 Boger Z, 2001, INFORM PROCESS MANAG, V37, P187, DOI 10.1016/S0306-4573(00)00030-3 BOGER Z, 2002, P INT JOINT C NEUR N BOGER Z, 1992, ISA T, V31, P25, DOI 10.1016/0019-0578(92)90007-6 Boger Z, 1997, INFORM SCIENCES, V101, P203, DOI 10.1016/S0020-0255(97)00010-8 Booker A, 1999, COMPUT SCI ENG, V1, P26, DOI 10.1109/5992.774838 CARUANA R, 2000, OVERFITTING NEURAL N Creput JC, 1997, CYBERNETICA, V40, P127 Dasigi V, 2001, PATTERN RECOGN, V34, P2413, DOI 10.1016/S0031-3203(00)00171-0 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Drucker H, 2002, INFORM PROCESS MANAG, V38, P305, DOI 10.1016/S0306-4573(01)00037-1 Egecioglu O., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354822 ETZIONI O, 1994, COMMUN ACM, V37, P7 ETZIONI O, 1995, IEEE EXPERT, V10, P44, DOI 10.1109/64.403956 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GORENBAR D, 2004, J AM SOC INFORM SCI, V56, P345 Greenberg S, 1996, APPL OPTICS, V35, P4598, DOI 10.1364/AO.35.004598 Guterman H., 1994, Neural, Parallel & Scientific Computations, V2 Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Hull D. A., 1994, P 17 ACM SIGIR C, P282 KARYPIS G, 2002, P 9 ACM INT C INF KN, P12 Koehn P., 2002, Advances in Natural Language Processing. Third International Conference, PorTAL 2002. Proceedings (Lecture Notes in Artificial Intelligence Vol.2389) KOHONEN T, 1997, P IEEE INT C NEUR NE, V1, P1 KUFLIK T, 2003, THESIS U NEGEV KURBEL K, 1998, P 1998 IACIS C CANC, P75 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 Lam S. L. Y., 1999, Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, DOI 10.1109/DASFAA.1999.765752 Lawrence S., 1997, P 14 NAT C ART INT A, P540 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 RAUBER A, 2000, J AUSTRIAN SOC ARTIF, V13, P17 ROBERTS RJ, 1998, NEB TRANSCRIPT, V9, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, SMART RETRIEVAL SYST, P57 Ruiz ME, 2002, INFORM RETRIEVAL, V5, P87, DOI 10.1023/A:1012782908347 Ruiz M. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312700 RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0 SALTON G, 1983, INTRO MODERN INFORMA Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 TOMSICH P, 2000, P 8 EUR C HIGH PERF, P136 VANDAELE A, 1983, APPL TIME SERIES BOX VANREIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VLAJIC N, 1999, J NETW COMPUT APPL, V21, P91 Werbos P.J., 1974, THESIS HARVARD U WERBOS PJ, 1993, ROOTS BACK PROPAGATI Wermter S, 2000, INFORM RETRIEVAL, V3, P87, DOI 10.1023/A:1009942513170 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 YU ES, 1999, P INT JOINT C NEUR N, V4, P2924 NR 54 TC 6 Z9 6 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2006 VL 42 IS 2 BP 469 EP 483 DI 10.1016/j.ipm.2005.03.020 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 981BF UT WOS:000233061300009 ER PT J AU Hert, J Willett, P Wilton, DJ Acklin, P Azzaoui, K Jacoby, E Schuffenhauer, A AF Hert, J Willett, P Wilton, DJ Acklin, P Azzaoui, K Jacoby, E Schuffenhauer, A TI New methods for ligand-based virtual screening: Use of data fusion and machine learning to enhance the effectiveness of similarity searching SO JOURNAL OF CHEMICAL INFORMATION AND MODELING LA English DT Article; Proceedings Paper CT 7th International Conference on Chemical Structures CY JUN 05-09, 2005 CL Noordwijkerhout, NETHERLANDS SP ASC, CINF, CSA Trust, CSJ, GDCh, KNCV ID BIOACTIVE REFERENCE STRUCTURES; LEAD-DISCOVERY PROGRAMS; NEAREST-NEIGHBOR; SUBSTRUCTURAL ANALYSIS; MOLECULAR SIMILARITY; BIOLOGICAL-ACTIVITY; MEDICINAL CHEMISTS; DRUG DESIGN; DESCRIPTORS; INFORMATION AB Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching. C1 Univ Sheffield, Krebs Inst Biomolec Res, Sheffield S10 2TN, S Yorkshire, England. Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. Novartis Inst BioMed Res, CH-4002 Basel, Switzerland. RP Willett, P (reprint author), Univ Sheffield, Krebs Inst Biomolec Res, Sheffield S10 2TN, S Yorkshire, England. EM p.willett@sheffield.ac.uk RI Hert, Jerome/A-8158-2008 CR AVIDON VV, 1978, KHIM FARM ZH+, V12, P88 Bender A, 2004, J CHEM INF COMP SCI, V44, P170, DOI 10.1021/ci034207y Bender A, 2004, ORG BIOMOL CHEM, V2, P3204, DOI 10.1039/b409813g Bohl M, 2002, QUANT STRUCT-ACT REL, V21, P590, DOI 10.1002/qsar.200290001 Bƶhm Hans-Joachim, 2004, Drug Discov Today Technol, V1, P217, DOI 10.1016/j.ddtec.2004.10.009 Brown RD, 1996, J CHEM INF COMP SCI, V36, P572, DOI 10.1021/ci9501047 CARHART RE, 1985, J CHEM INF COMP SCI, V25, P64, DOI 10.1021/ci00046a002 Charifson PS, 1999, J MED CHEM, V42, P5100, DOI 10.1021/jm990352k CHEN B, UNPUB EFFECT NOISY T Chen X, 2002, J CHEM INF COMP SCI, V42, P1407, DOI 10.1021/ci025531g Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X Cramer RD, 2004, J MED CHEM, V47, P6777, DOI 10.1021/jm049501b CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 Ginn CMR, 2000, PERSPECT DRUG DISCOV, V20, P1, DOI 10.1023/A:1008752200506 Harper G, 2001, J CHEM INF COMP SCI, V41, P1295, DOI 10.1021/ci000397q He L, 2005, J MOL GRAPH MODEL, V23, P503, DOI 10.1016/j.jmgm.2005.03.003 HERT J, 2005, THESIS U SHEFFIELD S Hert J, 2004, J CHEM INF COMP SCI, V44, P1177, DOI 10.1021/ci034231b Hert J, 2005, J MED CHEM, V48, P7049, DOI 10.1021/jm050316n Hert J, 2004, ORG BIOMOL CHEM, V2, P3256, DOI 10.1039/b409865j Jenkins JL, 2004, J MED CHEM, V47, P6144, DOI 10.1021/jm049654z JOHNSON MA, 1990, CONCEPTS APPL MOL SU Jorissen RN, 2005, J CHEM INF MODEL, V45, P549, DOI 10.1021/ci049641u Klon AE, 2004, J MED CHEM, V47, P2743, DOI 10.1021/jm030363k Kubinyi H, 1998, PERSPECT DRUG DISCOV, V9-11, P225, DOI 10.1023/A:1027221424359 Lajiness MS, 2004, J MED CHEM, V47, P4891, DOI 10.1021/jm049740z Lewell XQ, 2003, J MED CHEM, V46, P3257, DOI 10.1021/jm0300429 Martin YC, 2002, J MED CHEM, V45, P4350, DOI 10.1021/jm020155c Nikolova N., 2003, QSAR COMB SCI, V22, P1006, DOI DOI 10.1002/QSAR.200330831 ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 Patani GA, 1996, CHEM REV, V96, P3147, DOI 10.1021/cr950066q Patterson DE, 1996, J MED CHEM, V39, P3049, DOI 10.1021/jm960290n ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schneider G, 1999, ANGEW CHEM INT EDIT, V38, P2894, DOI 10.1002/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F Schuffenhauer A, 2002, J CHEM INF COMP SCI, V42, P947, DOI 10.1021/co010385k Sheridan RP, 2004, J CHEM INF COMP SCI, V44, P1912, DOI 10.1021/ci049782w Sheridan RP, 2002, DRUG DISCOV TODAY, V7, P903, DOI 10.1016/S1359-6446(02)02411-X Stanton DT, 1999, J CHEM INF COMP SCI, V39, P21, DOI 10.1021/ci9801015 Takaoka Y, 2003, J CHEM INF COMP SCI, V43, P1269, DOI 10.1021/ci0340431 Whittle M, 2004, J CHEM INF COMP SCI, V44, P1840, DOI 10.1021/ci049867x Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 WILLETT P, 1986, J CHEM INF COMP SCI, V26, P36, DOI 10.1021/ci00049a008 Wilton D, 2003, J CHEM INF COMP SCI, V43, P469, DOI 10.1021/ci025586i Xia XY, 2004, J MED CHEM, V47, P4463, DOI 10.1021/jm0303195 NR 44 TC 125 Z9 128 PU AMER CHEMICAL SOC PI WASHINGTON PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA SN 1549-9596 J9 J CHEM INF MODEL JI J. Chem Inf. Model. PD MAR-APR PY 2006 VL 46 IS 2 BP 462 EP 470 DI 10.1021/ci050348j PG 9 WC Chemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Pharmacology & Pharmacy; Chemistry; Computer Science GA 028XT UT WOS:000236522800003 PM 16562973 ER PT J AU Ng, HS Toukourou, A Soibelman, L AF Ng, H. S. Toukourou, A. Soibelman, L. TI Knowledge Discovery in a Facility Condition Assessment Database Using Text Clustering SO JOURNAL OF INFRASTRUCTURE SYSTEMS LA English DT Article DE Data analysis; Maintenance; Infrastructure; Facilities; Databases AB Knowledge discovery in databases (KDD) has been applied in many different areas of study including DNA sequence analysis, pattern discovery, document classification, image recognition, and speech recognition. This paper presents the application of KDD in the analysis of a facility condition assessment (FCA) database. The FCA database contains information on facilities located at three campuses within a statewide university system. The case study utilizes cluster analysis for text mining. Cluster analysis is the grouping of objects that are similar within the same cluster and dissimilar to the other clusters. In this analysis, deficiency descriptions from a university's FCA database are the objects being grouped together into clusters. Deficiency descriptions were gathered from 15 housing facilities and 15 academic facilities located at 3 campuses. The results show how some clusters of facility deficiencies are unique with respect to the type of facility and the influence of location on deficiencies of academic facilities. The paper begins with a presentation of background on clustering approaches in KDD. Next, a case study based on a higher education FCA database is presented. Last, the paper concludes by exploring other potential areas of application of the described clustering approach. C1 [Ng, H. S.; Toukourou, A.] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA. [Soibelman, L.] Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA 15213 USA. RP Ng, HS (reprint author), Univ Illinois, Dept Civil & Environm Engn, 3142 Newmark CE Lab, Urbana, IL 61801 USA. FU National Science Foundation [0093841] FX This material is based upon work supported by the National Science Foundation under Grant No. 0093841 (CAREER). CR Baeza-Yates R., 1999, MODERN INFORM TETRIE Caldas C. H., 2002, P COMP CIV ENG ASCE, P194 Cheng D., 2002, RECURSIVE SPECTRAL A Chi E., 2002, LUMBERJACK INTELLIGE Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Ding H., 2002, CLUSTER MERGING SPLI Fayyad G., 1996, AI MAG, V17, P37 Frakes W. B., 1992, INFORM RETRIEVAL DAT Han E.-H., 1998, B TECHNICAL COMMITTE, V21, P15 Han J., 2000, DATA MINING CONCEPTS Han J., 2001, GEOGRAPHIC DATA MINI Jain A.K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kaufman L, 1990, FINDING GROUPS DATA Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Mannila H., 1997, P INT C DAT THEOR DE Ng R. T., 1994, P 20 INT C VER LARG, P144 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rush S. C., 1988, DECAYING AM CAMPUS T Salton G., 1989, AUTOMATIC TEXT PROCE Salton G., 1998, MACH LEARN, V39, P135 Soibelman L, 2002, J COMPUT CIVIL ENG, V16, P39, DOI 10.1061/(ASCE)0887-3801(2002)16:1(39) Steinbach M., 2000, KNOWL DISC DAT KDD W Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Yang Y., 2002, CLOPE FAST EFFECTIVE Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Zhao Y., 2002, 0216 U MINN DEP COMP Zhao Y., 2001, 0140 U MINN ARM HPC NR 29 TC 3 Z9 4 PU ASCE-AMER SOC CIVIL ENGINEERS PI RESTON PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA SN 1076-0342 J9 J INFRASTRUCT SYST JI J. Infrastruct. Syst. PD MAR PY 2006 VL 12 IS 1 BP 50 EP 59 DI 10.1061/(ASCE)1076-0342(2006)12:1(50) PG 10 WC Engineering, Civil SC Engineering GA V25VN UT WOS:000208505600006 ER PT B AU Wen, M Huang, XJ AF Wen, Miao Huang, Xiangji BE Zhang, YQ Lin, TY TI A multilevel searching and re-ranking framework for information retrieval SO 2006 IEEE International Conference on Granular Computing LA English DT Proceedings Paper CT IEEE International Conference on Granular Computing CY MAY 10-12, 2006 CL Atlanta, GA SP IEEE Computat Intelligence Soc, Georgia State Univ DE Information Retrieval; index; ranking; probability; model AB In this paper, we propose a multilevel searching and re-ranking framework (MLSRF) for information retrieval. In this framework, indexes are constructed at multi-level such as document level and passage level. For each topic, we search against each index independently and generate a sub-result for corresponding index. The final result is generated by re-ranking and integrating the sub-results for all the indexes. Experiments on 2004 and 2005 HARD data sets show that the proposed framework can make a positive contribution on both document level retrieval and passage level retrieval. We also investigate the effect of the blind feedback method on TREC HARD data sets. The results show that the performance of blind feedback material depends on the topic it works on. C1 York Univ, Dept Comp Sci, N York, ON M3J 1P3, Canada. RP Wen, M (reprint author), York Univ, Dept Comp Sci, N York, ON M3J 1P3, Canada. CR Allan J, 2004, NIST SPECIAL PUBLICA ALLAN J, 1995, RES DEV INFORM RETRI ALLAN J, 2005, NIST SPECIAL PUBLICA, P132 BEAULIEU MM, 1996, NIST SPECIAL PUBLICA CALLAN J, 1994, P 17 ANN INT ACM SIG HUANG XJ, 2005, P 28 ANN INT ACM SIG Huang XJ, 2003, INFORM RETRIEVAL, V6, P333, DOI 10.1023/A:1026028229881 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTOIN G, 1968, AUTOMATIC INFORM ORG SALTON G, 1993, P 16 ANN INT ACM SIG Voorhees E., 2004, NIST SPECIAL PUBLICA, V500-261 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 1-4244-0133-X PY 2006 BP 619 EP 622 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BEN81 UT WOS:000238353400137 ER PT S AU Herschel, S Heese, R Bleiholder, J AF Herschel, Sven Heese, Ralf Bleiholder, Jens BE Roddick, JF Benjamins, VR Cherfi, SSS Chiange, R Claramunt, C Elmasri, R Grandi, F Han, H Hepp, M Lytras, M Misic, VB Poels, G Song, IY Trujillo, J Vangenot, C TI An architecture for emergent semantics SO Advances in Conceptual Modeling - Theory and Practice, Proceedings SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 25th International Conference on Conceptual Modeling CY NOV 06-09, 2006 CL Tucson, AZ SP ER Inst, ACM SIGMIS, ACM SIGMOD, Univ Arizona, Eller Coll Management AB Emergent Semantics is a new paradigm for inferring semantic meaning from implicit feedback by a sufficiently large number of users of an object retrieval system. In this paper, we introduce a universal architecture for emergent semantics using a central repository within a multi-user environment, based on solid linguistic theories. Based on this architecture, we have implemented an information retrieval system supporting keyword queries on standard information retrieval corpora. Contrary to existing query refinement strategies, feedback on the retrieval results is incorporated directly into the actual document representations improving future retrievals. An evaluation yields higher precision values at the standard recall levels and thus demonstrates the effectiveness of the emergent semantics approach for typical information retrieval problems. C1 Humboldt Univ, D-10099 Berlin, Germany. RP Herschel, S (reprint author), Humboldt Univ, Unter Linden 6, D-10099 Berlin, Germany. CR Aberer K., 2003, P 12 INT WORLD WID W, P197 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Grosky WI, 2002, SIGMOD RECORD, V31, P54 Heflin J., 1999, CSTR4078 U MAR JACOB C, 2006, P INT C INT COMP Morris Charles W., 1938, FDN THEORY SIGNS Oard DW, 1997, USER MODEL USER-ADAP, V7, P141, DOI 10.1023/A:1008287121180 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Salton G., 1984, INTRO MODERN INFORM Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-47703-9 J9 LECT NOTES COMPUT SC PY 2006 VL 4231 BP 425 EP 434 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA BFM89 UT WOS:000243127500050 ER PT S AU Song, RH Xin, GM Shi, SM Wen, JR Ma, WY AF Song, Ruihua Xin, Guomao Shi, Shuming Wen, Ji-Rong Ma, Wei-Ying BE Lalmas, M MacFarlane, A Ruger, S Tombros, A Tsikrika, T Yavlinsky, A TI Exploring URL hit priors for web search SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 28th European Conference on Information Retrieval (ECIR 2006) CY APR 10, 2005-APR 12, 2006 CL London, ENGLAND SP Queen Mary Univ London, City Univ, Engn & Phys Sci Res Council, CEPIS, Google, GCHQ, Microsoft Res, Yahoo Res, Sharp, Apriorie, Lemur Consulting, MMKM, Elsevier HO Imperial Coll London, S Kensington AB URL usually contains meaningful information for measuring the relevance of a Web page to a query in Web search. Some existing works utilize URL depth priors (i.e. the probability of being a good page given the length and depth of a URL) for improving some types of Web search tasks. This paper suggests the use of the location of query terms occur in a URL for measuring how well a web page is matched with a user's information need in web search. First, we define and estimate URL hit types, i.e. the priori probability of being a good answer given the type of query term hits in the URL. The main advantage of URL hit priors (over depth priors) is that it can achieve stable improvement for both informational and navigational queries. Second, an obstacle of exploiting such priors is that shortening and concatenation are frequently used in a URL. Our investigation shows that only 30% URL hits are recognized by an ordinary word breaking approach. Thus we combine three methods to improve matching. Finally, the priors are integrated into the probabilistic model for enhancing web document retrieval. Our experiments were conducted using 7 query sets of TREC2002, TREC2003 and TREC2004, and show that the proposed approach is stable and improve retrieval effectiveness by 4%similar to 11% for navigational queries and 10% for informational queries. C1 Microsoft Res Asia, Sigma Ctr, Beijing 100080, Peoples R China. RP Song, RH (reprint author), Microsoft Res Asia, Sigma Ctr, 5F,49 Zhichun Rd, Beijing 100080, Peoples R China. EM rsong@microsoft.com; guomxin@microsoft.com; shumings@microsoft.com; jrwen@microsoft.com; wyma@microsoft.com CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Berger J. O., 1985, STAT DECISION THEORY Bharat Krishna, 1998, 21 INT ACM SIGIR C R, P104 BORDER A, 2002, SIGIR FORUM, V36 CHI CH, 1999, CIKM 99 CRASWELL N, 2005, P ACM SIGIR 05 SALV HAWKING D, 2001, 8 TEXT TRET C TREC8 HU Y, 2005, P SIGIR 05 SAVL BRAZ KRAAIJ W, 2001, SIGIR 02 Lee U., 2005, P 14 INT WORLD WID W MARCHIONINI G, 1992, J AM SOC INFORM SCI, V43, P156, DOI 10.1002/(SICI)1097-4571(199203)43:2<156::AID-ASI8>3.0.CO;2-U OGILVIE P, 2003, TREC2003 QIN T, 2005, P 28 ANN INT ACM SIG RA DY, 2002, 10 TEXT RETR C TREC ROBERTSON SE, 1999, 8 TEXT RETR C TREC 8, P151 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE DE, 2004, P 13 INT WORLD WID W WEN JR, 2003, 12 TEXT RETR C WESTERVELD T, 2001, TREC2001 NR 19 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-33347-9 J9 LECT NOTES COMPUT SC PY 2006 VL 3936 BP 277 EP 288 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BEM01 UT WOS:000238083200025 ER PT S AU Schenkel, R Theobald, M AF Schenkel, Ralf Theobald, Martin BE Lalmas, M MacFarlane, A Ruger, S Tombros, A Tsikrika, T Yavlinsky, A TI Structural feedback for keyword-based XML retrieval SO ADVANCES IN INFORMATION RETRIEVAL SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 28th European Conference on Information Retrieval (ECIR 2006) CY APR 10-12, 2005-2006 CL London, ENGLAND SP Queen Mary Univ London, City Univ, Engn & Phys Sci Res Council, CEPIS, Google, GCHQ, Microsoft Res, Yahoo Res, Sharp, Apriorie, Lemur Consulting, MMKM, Elsevier HO Imperial Coll London, S Kensington ID SEARCH TERMS; SELECTION AB Keyword-based queries are an important means to retrieve information from XML collections with unknown or complex schemas. Relevance Feedback integrates relevance information provided by a user to enhance retrieval quality. For keyword-based XML queries, feedback engines usually generate an expanded keyword query from the content of elements marked as relevant or nonrelevant. This approach that is inspired by text-based IR completely ignores the semistructured nature of XML. This paper makes the important step from pure content-based to structural feedback. It presents a framework that expands a keyword query into a full-fledged content-and-structure query. Extensive experiments with the established INEX benchmark and our TopX search engine show the feasibility of our approach. C1 Max Planck Inst Informat, Saarbrucken, Germany. RP Schenkel, R (reprint author), Max Planck Inst Informat, Saarbrucken, Germany. EM schenkel@mpi-inf.mpg.de; mtb@mpi-inf.mpg.de CR AMATI G, 2004, INEX WORKSH 2004, P77 BALMIN A, 2003, VLDB 2003, P1069 Blanken H., 2003, LNCS, V28 CROUCH C, 2005, SIGIR 2005 FORUM CROUCH CJ, 2004, INEX 2004 WORKSH, P149 Efthimiadis E. N., 1993, SIGIR Forum GRUST T, 2002, SIGMOD, P109 Guo L., 2003, SIGMOD, P16 HLAOUA L, 2005, WORKSH OP SOURC WEB Hsu W, 2004, PROC INT C TOOLS ART, P526 LIU S, 2004, SIGIR 2004, P88 MASS Y, 2004, INEX 2004 WORKSH, P154 MIHAJLOVIC V, 2004, INEX 2004 WORKSH, P141 PAN H, 2004, ER 2004, P854 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 RAMIREZ G, 2005, INSE0508 CWI CTR MAT RAMIREZ G, 2005, CIKM 2005 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RUTHVEN I, 2003, KNOWLEDGE ENG REV, V18 SIGURBJORNSSON B, 2004, INEX 2004 WORKSH, P104 THEOBALD M, 2005, P 2005 INEX WORKSH D THEOBALD M, 2005, VLDB 2005, V2005, P625 TROTMAN A, 2004, NARROWED EXTENDED XP VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VITTAUT JN, 2004, INEX WORKSH 2004, P58 Xu Y., 2005, SIGMOD C, P537 NR 28 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-33347-9 J9 LECT NOTES COMPUT SC PY 2006 VL 3936 BP 326 EP 337 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BEM01 UT WOS:000238083200029 ER PT S AU Zhou, XH Zhang, XD Hu, XH AF Zhou, Xiaohua Zhang, Xiaodan Hu, Xiaohua BE Lalmas, M MacFarlane, A Ruger, S Tombros, A Tsikrika, T Yavlinsky, A TI Using concept-based indexing to improve language modeling approach to genomic IR SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 28th European Conference on Information Retrieval (ECIR 2006) CY APR 10, 2005-APR 12, 2006 CL London, ENGLAND SP Queen Mary Univ London, City Univ, Engn & Phys Sci Res Council, CEPIS, Google, GCHQ, Microsoft Res, Yahoo Res, Sharp, Apriorie, Lemur Consulting, MMKM, Elsevier HO Imperial Coll London, S Kensington AB Genomic IR, characterized by its highly specific information need, severe synonym and polysemy problem, long term name and rapid growing literature size, is challenging IR community. In this paper, we are focused on addressing the synonym and polysemy issue within the language model framework. Unlike the ways translation model and traditional query expansion techniques approach this issue, we incorporate concept-based indexing into a basic language model for genomic IR. In particular, we adopt UMLS concepts as indexing and searching terms. A UMLS concept stands for a unique meaning in the biomedicine domain; a set of synonymous terms will share same concept ID. Therefore, the new approach makes the document ranking effective while maintaining the simplicity of language models. A comparative experiment on the TREC 2004 Genomics Track data shows significant improvements are obtained by incorporating concept-based indexing into a basic language model. The MAP (mean average precision) is significantly raised from 29.17% (the baseline system) to 36.94%. The performance of the new approach is also significantly superior to the mean (21.72%) of official runs participated in TREC 2004 Genomics Track and is comparable to the performance of the best run (40.75%). Most official runs including the best run extensively use various query expansion and pseudo-relevance feedback techniques while our approach does nothing except for the incorporation of concept-based indexing, which evidences the view that semantic smoothing, i.e. the incorporation of synonym and sense information into the language models, is a more standard approach to achieving the effects traditional query expansion and pseudo-relevance feedback techniques target. C1 Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. RP Zhou, XH (reprint author), Drexel Univ, Coll Informat Sci & Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA. EM xiaohua.zhou@drexe1.edu; xzhang@cis.drexel.edu; thu@cis.drexel.edu CR Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 HERSH W, 2004, 13 TEXT RETR C Jones K. S., 2000, Information Processing & Management, V36 LAFFERTY J, 2001, 2001 ACM SIGIR C RES LAFFERTY J, 2003, LANGUAGE MODELIGN IN, V13 LESK M, 1986, P SIGDOC 86 C ACM MOONEY R, 2005, SIGKDD EXPLORATIONS, V7, P3 Palakal M., 2002, Proceedings IEEE Computer Society Bioinformatics Conference, DOI 10.1109/CSB.2002.1039333 PONTE JM, P 21 ANN INT ACM SIG ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Soderland Stephen, 1995, P 14 INT JOINT C ART, P1314 STOKOE C, 2004, P 12 TEXT RETR C TRE SUBRAMANIAM L, 2003, P ACM C INF KNOWL MA ZHAI C, 2004, ACM T INFORMATION SY, V2 ZHOU X, 2005, P INT WORKSH BIOM DA NR 18 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-33347-9 J9 LECT NOTES COMPUT SC PY 2006 VL 3936 BP 444 EP 455 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BEM01 UT WOS:000238083200039 ER PT S AU Grunfeld, L Kwok, KL AF Grunfeld, L. Kwok, K. L. GP Springer BE Strzalkowski, T Harabagiu, S TI SENTENCE RANKING USING KEYWORDS AND META-KEYWORDS EXPERIENCE WITH TREC-9 AND TREC-2001 USING PIRCS SYSTEM SO ADVANCES IN OPEN DOMAIN QUESTION ANSWERING SE Text Speech and Language Technology LA English DT Article; Book Chapter ID INFORMATION-RETRIEVAL AB This paper describes our approach and experience with the question-answering tasks of TREC-9 and TREC-2001. Our approach employed techniques from IR, pattern matching and meta-keyword detection with little linguistic analysis and no natural language understanding. It involved the following four steps: 1) retrieval of top-ranked subdocuments using content keywords of a question as query; 2) weighting of sentences from retrieved subdocuments based on heuristic rules and matching with question keywords; 3) refined weighting and ranking of sentences using agreement with expected answer type suggested by question analysis; and 4) extraction of answer strings from top-ranked sentences based on expected answer type and sentence word pattern rules. The blind experiments in TREC showed that the approach returned reasonably good results, excluding those questions with NIL answer. It works because the questions are mainly factoid, definitional types. Analysis shows that our system improves with more subdocuments retrieved, and if answer candidates from two different retrieval lists are combined by confirmation. It can identify sentences containing answers quite well, but it often fails when answers need to be extracted correctly within a 50-byte output. These experiments may serve as examples of how far one can attain in open domain question-answering without making use of external resources (e.g. the web) to find answers, and without deeper natural language analysis. C1 [Grunfeld, L.; Kwok, K. L.] CUNY Queens Coll, Flushing, NY 11367 USA. RP Grunfeld, L (reprint author), CUNY Queens Coll, Flushing, NY 11367 USA. EM grunfeld@ir.cs.qc.edu; kwok@ir.cs.qc.edu CR ALLAN J, 2001, NIST SPECIAL PUBLICA, P551 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BRILL E, 2001, NIST SPECIAL PUBLICA, P393 BURGER A, 1999, P 22 ANN INT ACM SIG, P222 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 CLARKE CLA, 2002, NIST SPECIAL PUBLICA, P673 CORMACK G, 2000, NIST SPECIAL PUBLICA, P735 HARABAGIU S, 2002, NIST SPECIAL PUBLICA, P355 HOVY E, 2001, NIST SPECIAL PUBLICA, P655 HULL DA, 2000, NIST SPECIAL PUBLICA, P743 KATZ B, 1997, AAAI SPRING S NLP WW KRAFT DH, 1983, INT J MAN MACH STUD, V19, P45, DOI 10.1016/S0020-7373(83)80041-8 Kwok K. L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291003 KWOK KL, 1993, NIST500207 SPEC PUBL, P153 KWOK KL, 2002, NIST SPECIAL PUBLICA, P452 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 KWOK KL, 2001, NIST SPECIAL PUBLICA, P417 KWOK KL, 2000, NIST SPECIAL PUBLICA, P217 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 LAVENKO V, 2001, P 24 ANN INT ACM SIG, P120 Licklider J. C. R., 1965, LIB FUTURE Moldovan D., 2000, P 38 ANN M ASS COMP, P563, DOI 10.3115/1075218.1075289 OCONNOR J, 1975, INFORM PROCESS MANAG, V11, P155, DOI 10.1016/0306-4573(75)90004-7 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PRAGER J, 2002, NIST SPECIAL PUBLICA, P250 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM SOUBBOTIN MM, 2002, NIST SPECIAL PUBLICA, P293 SRIHARI RK, ADV OPEN DOMAIN QUES Tague-Sutcliffe J., 1992, P 15 ANN INT ACM SIG, p23 , DOI 10.1145/133160.133171 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1994, P 17 ANN INT ACM SIG, P212 VOORHEES EM, 2002, NIST SPECIAL PUBLICA, P42 VOORHEES EM, 2001, NIST SPECIAL PUBLICA, P71 WINSTON PH, 1977, ARTIFICIAL INTELLIGE Woods W., 1977, LINGUISTIC STRUCTURE, P521 NR 38 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1386-291X BN 978-1-4020-4746-6 J9 TEXT SPEECH LANG TEC PY 2006 VL 32 BP 229 EP 258 D2 10.1007/978-1-4020-4746-6 PG 30 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Language & Linguistics SC Computer Science; Linguistics GA BLA56 UT WOS:000269770600009 ER PT S AU Larson, RR AF Larson, Ray R. BE Fuhr, N Lalmas, M Malik, S Kazai, G TI Probabilistic retrieval, component fusion and blind feedback for XML retrieval SO ADVANCES IN XML INFORMATION RETRIEVAL AND EVALUATION SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 4th International Workshop of the Initiative for the Evaluation of XML Retrieval CY NOV 28-30, 2005 CL Dagstuhl, GERMANY SP DELOS Network Digital Lib ID CHESHIRE-II AB This paper describes the retrieval approaches used by UC Berkeley in our official submissions for the various Adhoc tasks. As in previous INEX evaluations, the main technique we are testing is the fusion of multiple probabilistic searches against different XML components using different probabilistic retrieval algorithms. In addition this year we began to use a different fusion/combination method from previous years. This year we also continued to use re-estimated Logistic Regression (LR) parameters for different components of the IEEE document collection, estimated using relevance judgements from the INEX 2003 evaluation. All of our runs were fully automatic with no manual editing or interactive submission of queries, and all used only the title elements of the INEX topics. C1 Univ Calif Berkeley, Sch Informat Management & Syst, Berkeley, CA 94720 USA. RP Larson, RR (reprint author), Univ Calif Berkeley, Sch Informat Management & Syst, Berkeley, CA 94720 USA. EM ray@sims.berkeley.edu CR Chen A, 2004, INFORM RETRIEVAL, V7, P149, DOI 10.1023/B:INRT.0000009444.89549.90 Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Chen AT, 2003, LECT NOTES COMPUT SC, V2785, P28 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 COOPER WS, 1994, NIST SPECIAL PUBLICA, P57 Cooper W.S., 1994, TEXT RETR C TREC 2, P57 Harman D., 1992, INFORMATION RETRIEVA, P241 Larson RR, 2005, LECT NOTES COMPUT SC, V3493, P322 Larson RR, 2005, INFORM RETRIEVAL, V8, P601, DOI 10.1007/s10791-005-0749-0 Larson RR, 2001, INFORM PROCESS MANAG, V37, P485, DOI 10.1016/S0306-4573(00)00058-3 Larson R.R., 2002, SIGIR 2002, P399 LEE JH, 1997, SIGIR 97 P 20 ANN IN, P267 Mass Y, 2005, LECT NOTES COMPUT SC, V3493, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAW JA, 1994, NATL I STANDARDS TEC, P243 Voorhees E., 1998, 7 TEXT RETR C TREC 7 Voorhees E. M., 1999, 8 TEXT RETR C TREC 8 NR 17 TC 7 Z9 7 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34962-6 J9 LECT NOTES COMPUT SC PY 2006 VL 3977 BP 225 EP 239 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BET37 UT WOS:000239425500017 ER PT S AU Schenkel, R Theobald, M AF Schenkel, Ralf Theobald, Martin BE Fuhr, N Lalmas, M Malik, S Kazai, G TI Relevance feedback for structural query expansion SO ADVANCES IN XML INFORMATION RETRIEVAL AND EVALUATION SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 4th International Workshop of the Initiative for the Evaluation of XML Retrieval CY NOV 28-30, 2005 CL Dagstuhl, GERMANY SP DELOS Network Digital Lib ID SEARCH AB Keyword-based queries are an important means to retrieve information from XML collections with unknown or complex schemas. Relevance Feedback integrates relevance information provided by a user to enhance retrieval quality. For keyword-based XML queries, feedback engines usually generate an expanded keyword query from the content of elements marked as relevant or nonrelevant. This approach that is inspired by text-based IR completely ignores the semistructured nature of XML. This paper makes the important step from pure content-based to structural feedback. It presents two independent approaches that include structural dimensions in a feedback-driven query evaluation: The first approach reranks the result list of a keyword-based search engine, using structural features derived from results with known relevance. The second approach expands a keyword query into a full-fledged content-and-structure query with weighted conditions. C1 Max Planck Inst Informat, Saarbrucken, Germany. RP Schenkel, R (reprint author), Max Planck Inst Informat, Saarbrucken, Germany. EM schenkel@mpi-inf.mpg.de; mtb@mpi-inf.mpg.de CR AMATI G, 2004, INEX WORKSH 2004, P77 BALMIN A, 2003, VLDB 2003, P1069 Blanken H., 2003, LNCS, V28 CROUCH CJ, 2004, INEX 2004 WORKSH, P149 GONCALVES MA, 2004, 4 ACM IEEE CS JOINT, P98 GRUST T, 2002, SIGMOD, P109 Guo L., 2003, SIGMOD, P16 HLAOUA L, 2005, WORKSH OP SOURC WEB Hsu W, 2004, PROC INT C TOOLS ART, P526 LIU S, 2004, SIGIR 2004, P88 MASS Y, 2004, INEX 2004 WORKSH, P154 MIHAJLOVIC V, 2004, INEX 2004 WORKSH, P141 PAN H, 2004, ER 2004, P854 RAMIREZ G, 2005, INSE0508 CWI CTR MAT RAMIREZ G, 2005, CIKM 2005 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RUTHVEN I, 2003, KNOWLEDGE ENG REV, V18 Schenkel R, 2005, INFORM RETRIEVAL, V8, P521, DOI 10.1007/s10791-005-0746-3 SCHENKEL R, 2006, 28 EUR C INF RETR EC SCHENKEL R, 2006, 10 INT C EXT DAT TEC SIGURBJORNSSON B, 2004, INEX 2004 WORKSH, P104 Theobald M., 2005, VLDB, P625 TROTMAN A, 2004, NARROWED EXTENDED XP VITTAUT JN, 2004, INEX WORKSH 2004, P58 Xu Y., 2005, SIGMOD C, P537 NR 27 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34962-6 J9 LECT NOTES COMPUT SC PY 2006 VL 3977 BP 344 EP 357 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BET37 UT WOS:000239425500026 ER PT B AU Lv, YH Sun, L Zhang, JL Nie, JY Chen, W Zhang, W AF Lv, Yuanhua Sun, Le Zhang, Junlin Nie, Jian-Yun Chen, Wan Zhang, Wei GP COLING TI An Iterative Implicit Feedback Approach to Personalized Search SO COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE LA English DT Proceedings Paper CT 21st International Conference on Computational Linguistics/44th Annual Meeting of the Association for Computational Linguistics CY JUL 17-21, 2006 CL Sydney, AUSTRALIA SP Assoc Computat Linguist ID WEB SEARCH AB General information retrieval systems are designed to serve all users without considering individual needs. In this paper, we propose a novel approach to personalized search. It can, in a unified way, exploit and utilize implicit feedback information, such as query logs and immediately viewed documents. Moreover, our approach can implement result re-ranking and query expansion simultaneously and collaboratively. Based on this approach, we develop a client-side personalized web search agent PAIR (Personalized Assistant for Information Retrieval), which supports both English and Chinese. Our experiments on TREC and HTRDP collections clearly show that the new approach is both effective and efficient. C1 [Lv, Yuanhua] Chinese Acad Sci, Inst Software, Beijing 100080, Peoples R China. RP Lv, YH (reprint author), Chinese Acad Sci, Inst Software, Beijing 100080, Peoples R China. EM lvyuanhua@gmail.com; sunle@iscas.cn; junlin01@iscas.cn; nie@iro.umontreal.ca; chenwan@nus.edu.sq; zhangwei04@iscas.cn CR Beaulieu M, 1998, INTERACT COMPUT, V10, P237, DOI 10.1016/S0953-5438(98)00008-3 Bharat K, 2000, COMPUT NETW, V33, P493, DOI 10.1016/S1389-1286(00)00047-5 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Hawking D, 1999, COMPUT NETW, V31, P1321, DOI 10.1016/S1389-1286(99)00024-9 Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 KLEINBERG J, 1999, ACM, V46, P604 Kritikopoulos A., 2003, P ITWP WORKSH INT TE, P229 Liu F., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Morita M., 1994, P 17 ANN INT ACM SIG, P272 Pitkow J, 2002, COMMUN ACM, V45, P50 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1983, INTRO MODERN INFORM Shen C.X, 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747 Speretta M., 2005, WEB INTELLIGENCE, P622 Sugiyama K., 2004, P 13 INT C WORLD WID, P675, DOI 10.1145/988672.988764 TANUDJAJA F, 2002, PERSONA CONTEXTUAL I Teevan J., 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111 WHITE RW, 2002, P 24 BCS IRSG EUR C, P93 NR 19 TC 2 Z9 2 PU ASSOC COMPUTATIONAL LINGUISTICS-ACL PI STROUDSBURG PA 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA BN 978-1-932432-65-7 PY 2006 BP 585 EP 592 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BNG36 UT WOS:000274500200074 ER PT S AU Amati, G AF Amati, Giambattista BE Larsen, HL Pasi, G OrtizArroyo, D Andreasen, T Christiansen, H TI Information theoretic approach to information extraction SO FLEXIBLE QUERY ANSWERING SYSTEMS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th International Conference on Flexible Query Answering Systems CY JUN 07-10, 2006 CL Milan, ITALY SP Assoc Italiana Informat Calcolo Automat, Univ Degli Studi Milano Bicoca, Univ Milano, Dipartment Tecnol Informaz ID RETRIEVAL AB We use the hypergeometric distribution to extract relevant information from documents. The hypergeometric distribution gives the probability estimate of observing a given term frequency with respect to a prior. The lower the probability the higher the amount of information is carried by the term. Given a subset of documents, the information items are weighted by using the inversely related function of of the hypergeometric distribution. We here provide an exemplifying introduction to a topic-driven information extraction from a document collection based on the hypergeometric distribution. C1 Fdn Ugo Bordoni, Rome, Italy. RP Amati, G (reprint author), Fdn Ugo Bordoni, Rome, Italy. CR Amati G, 2006, LECT NOTES COMPUT SC, V3936, P13 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Cai D., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 CROFT W, 1993, P 16 ANN INT ACM SIG, P2 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Ide E., 1971, SMART RETRIEVAL SYST, P337 Kwok Kuilam L., 1996, P 19 ANN INT ACM SIG, P187, DOI 10.1145/243199.243266 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lafferty J.D., 2001, ICML, P282 ROBERTSON S, 1986, J DOC, V42, P288 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1990, J AM SOC INFORM SCI, V41, P182 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 WELLNER B, 2004, C UNC ART INT UAI Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34638-4 J9 LECT NOTES COMPUT SC PY 2006 VL 4027 BP 519 EP 529 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BEP51 UT WOS:000238575000044 ER PT S AU Huang, XJ Huang, YR Wen, M An, AJ Liu, Y Poon, J AF Huang, Xiangji Huang, Yan Rui Wen, Miao An, Aijun Liu, Yang Poon, Josiah BE Clifton, CW Zhong, N Liu, JM Wah, BW Wu, XD TI Applying data mining to pseudo-relevance feedback for high performance text retrieval SO ICDM 2006: Sixth International Conference on Data Mining, Proceedings SE IEEE International Conference on Data Mining LA English DT Proceedings Paper CT 6th IEEE International Conference on Data Mining CY DEC 18-22, 2006 CL Hong Kong, PEOPLES R CHINA SP IEEE ID INFORMATION AB In this paper we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of labeled passages obtained from the blind feedback of a retrieval system. The data mining results are used to expand query terms and to re-estimate some of the parameters used in a probabilistic weighting function. We evaluate the data mining based feedback method on the TREC HARD data set. The results show that data mining can be successfully applied to improve the text retrieval performance. We report our experimental findings in detail. C1 York Univ, Sch Informat Technol, Toronto, ON M3J 2R7, Canada. York Univ, Dept Comp Sci, Toronto, ON M3J 2R7, Canada. Univ Sydney, Sch Informat Technologies, Sydney, NSW 2006, Australia. RP Huang, XJ (reprint author), York Univ, Sch Informat Technol, Toronto, ON M3J 2R7, Canada. CR Allan J, 2004, NIST SPECIAL PUBLICA Allan J., 2003, SIGIR Forum, V37 BEAULIEU M, 1997, P TREC, V5, P143 Bellegarda JR, 2000, P IEEE, V88, P1279, DOI 10.1109/5.880084 Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 Breiman L., 1984, CLASSIFICATION REGRE Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 CHAN J, 2004, P 9 AUSTR DOC COMP S, P47 Cohen W.W., 1996, P AAAI SPRING S MACH, P18 Craven M, 2000, ARTIF INTELL, V118, P69, DOI 10.1016/S0004-3702(00)00004-7 Fan W., 2004, P 27 ANN INT ACM SIG, P138, DOI 10.1145/1008992.1009018 GOLDMAN S, 2000, P 17 ICML He J., 2004, P PAC RIM C MULT HUANG X, 2005, P 28 ACM SIGIR HUANG X, 2006, P 29 ACM SIGIR HUANG X, 2004, NIST SPECIAL PUBLICA Huang XJ, 2003, INFORM RETRIEVAL, V6, P333, DOI 10.1023/A:1026028229881 IWAYAMA M., 2000, P 23 ANN INT ACM SIG, P10, DOI 10.1145/345508.345538 Joachims T., 1999, P 16 INT C MACH LEAR, V99, P200 Kiritchenko S., 2001, P 2001 C CTR ADV STU Lynam T. R., 2004, P 13 ACM INT C INF K, P261, DOI 10.1145/1031171.1031229 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Platt JC, 1999, ADVANCES IN KERNEL METHODS, P185 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Raskutti B., 2002, P 8 ACM SIGKDD INT C, P620 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SU Z, 2000, P IEEE INT C TOOLSL VALIANT LG, 1984, COMMUN ACM, V27, P1134, DOI 10.1145/1968.1972 Witten I. H., 2005, DATA MINING PRACTICA Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 YAN R, 2003, P ACM MULTIMEDIA 200 Yu S., 2003, P 12 INT C WORLD WID, P11 Zelikovitz S., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management ZHONG M, 2006, P 29 ACM SIGIR NR 36 TC 8 Z9 8 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1550-4786 BN 978-0-7695-2701-7 J9 IEEE DATA MINING PY 2006 BP 295 EP 306 PG 12 WC Computer Science, Information Systems SC Computer Science GA BFZ37 UT WOS:000245601900031 ER PT J AU Rolleke, T Tsikrika, T Kazai, G AF Rolleke, T Tsikrika, T Kazai, G TI A general matrix framework for modelling Information Retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT 4th ACM SIGIR Workshop on Mathematical/Formal Methods in IR CY 2003 CL Toronto, CANADA SP ACM DE Information Retrieval; content; structure; semantics; matrix spaces; frequency-based operations; tf-idf; evaluation measures; IR models; eigenvectors ID IR AB In this paper, we present a well-defined general matrix framework for modelling Information Retrieval (IR). In this framework, collections, documents and queries correspond to matrix spaces. Retrieval aspects, such as content, structure and semantics, are expressed by matrices defined in these spaces and by matrix operations applied on them. The dualities of these spaces are identified through the application of frequency-based operations on the proposed matrices and through the investigation of the meaning of their eigenvectors. This allows term weighting concepts used for content-based retrieval, such as term frequency and inverse document frequency, to translate directly to concepts for structure-based retrieval. In addition, concepts such as pagerank, authorities and hubs, determined by exploiting the structural relationships between linked documents, can be defined with respect to the semantic relationships between terms. Moreover, this mathematical framework can be used to express classical and alternative evaluation measures, involving, for instance, the structure of documents, and to further explain and relate IR models and theory. The high level of reusability and abstraction of the framework leads to a logical layer for IR that makes system design and construction significantly more efficient, and thus, better and increasingly personalised systems can be built at lower costs. (c) 2004 Published by Elsevier Ltd. C1 Queen Mary Univ London, Dept Comp Sci, London E1 4NS, England. RP Rolleke, T (reprint author), Queen Mary Univ London, Dept Comp Sci, London E1 4NS, England. EM thor@dcs.qmul.ac.uk; theodora@dcs.qmul.ac.uk; gabs@dcs.qmul.ac.uk CR AMATI G, 1998, INFORMATION RETRIEVA, P189 AMATI G, 2002, P 24 BCS IRSG EUR C, P183 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELEW RK, 2000, FINDING ABOUT CROFT B, 2003, LANGUAGE MODELING IN Golub G. H., 1996, MATRIX COMPUTATIONS Grossman D. A., 1998, INFORM RETRIEVAL ALG Hawking D., 1999, PHARM PATHOPHYSIOLOG, P131 Kazai G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009008 Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LAFFERTY J, 2003, PROBABILISTIC RELEVA, pCH1 Page L., 1998, PAGERANK CITATION RA ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROELLEKE T, 2002, P BCS IRSG EUR C INF, P284 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 TURTLE H, 1991, P RIAO, V91, P644 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 23 TC 11 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2006 VL 42 IS 1 BP 4 EP 30 DI 10.1016/j.ipm.2004.11.006 PG 27 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 971BE UT WOS:000232355300002 ER PT J AU Zhai, CX Lafferty, J AF Zhai, CX Lafferty, J TI A risk minimization framework for information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT 4th ACM SIGIR Workshop on Mathematical/Formal Methods in IR CY 2003 CL Toronto, CANADA SP ACM DE retrieval models; risk minimization; statistical language models; bayesian decision theory ID PROBABILISTIC MODELS; RELEVANCE; SPACE; IR AB This paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models, user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. We discuss how this framework can unify existing retrieval models and accommodate systematic development of new retrieval models. As an example of using the framework to model non-traditional retrieval problems, we derive retrieval models for subtopic retrieval, which is concerned with retrieving documents to cover many different subtopics of a general query topic. These new models differ from traditional retrieval models in that they relax the traditional assumption of independent relevance of documents. (c) 2004 Elsevier Ltd. All rights reserved. C1 Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. RP Zhai, CX (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. EM czhai@cs.uiuc.edu CR Allan James, 2001, P 24 ANN INT ACM SIG, P10, DOI 10.1145/383952.383954 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Carbonell J., 1998, P SIGIR 1998 Cooper W., 1991, P 14 ANN INT ACM SIG, P57, DOI 10.1145/122860.122866 COOPER WS, 1994, P 17 ANN INT ACM C R, P242 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1981, J AM SOC INFORM SCI, P451 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dominich S, 2000, J AM SOC INFORM SCI, V51, P614, DOI 10.1002/(SICI)1097-4571(2000)51:7<614::AID-ASI4>3.0.CO;2-S Dominich S., 2002, P ACM SIGIR 2002 WOR, P43 DOMINICH S, 2001, MATH FDN INFORMATION Evans D. A., 1996, P 34 ANN M ASS COMP, P17, DOI 10.3115/981863.981866 FOX EA, 1983, THESIS CORNELL U FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 FUHR N, 2001, P LANG MOD IR WORKSH FUNG R, 1995, COMMUN ACM, V38, P42, DOI 10.1145/203330.203340 Gey FC, 1994, P 17 ANN INT ACM SIG, P222 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Hiemstra D., 1998, P 7 TEXT RETR C TREC Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 KALT T, 1996, 78 CIIR U MASS KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 LAFFERTY J, 2003, LANGUAGE MODELING IN Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 LEWIS DD, 1992, 9193 U MASS Lewis D.D, 1998, EUR C MACH LEARN, P4 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Mittendorf E., 1994, P 17 ANN INT ACM SIG, P318 Over P., 1998, Sixth Text REtrieval Conference (TREC-6) (NIST SP 500-240) Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 RIBEIRONETO B, 2000, SOFT COMPUTING INFOR, P259 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 STRZALKOWSKI T, 1997, P 5 TEXT RETR C TREC TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJBERGEN CJ, 1977, J DOC, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, COMPUTER J, V29 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1995, ACM T INFORM SYST, V13, P69 WONG SKM, 1991, INT J GEN SYST, V19, P101 Zhai, 2002, THESIS CARNEGIE MELL Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zhai C., 2001, 10 INT C INF KNOWL M, P403 Zhai C., 2002, P 25 ANN INT ACM SIG, P49 ZHAI C, 1997, K C APPL NAT LANG PR, P312 NR 67 TC 35 Z9 36 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2006 VL 42 IS 1 BP 31 EP 55 DI 10.1016/j.ipm.2004.11.003 PG 25 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 971BE UT WOS:000232355300003 ER PT J AU Yang, H Zhang, MJ AF Yang, H Zhang, MJ TI Two-stage statistical language models for text database selection SO INFORMATION RETRIEVAL LA English DT Article DE database language model; text database selection; distributed information retrieval; hierarchical topics; statistical language modeling; query expansion ID CATEGORIZATION; COLLECTION; ALGORITHM AB As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches. C1 Univ Wollongong, Sch Informat Technol & Comp Sci, Wollongong, NSW 2500, Australia. RP Yang, H (reprint author), Univ Wollongong, Sch Informat Technol & Comp Sci, Wollongong, NSW 2500, Australia. EM hy92@uow.edu.au; minjie@uow.edu.au CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 BAUMGARTEN, 1997, P 20 ANN INT ACM SIG, P258 Baumgarten C., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312685 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Callan J., 2000, ADV INFORM RETRIEVAL, P127 Callan J, 2001, ACM T INFORM SYST, V19, P97, DOI 10.1145/382979.383040 Cancedda N, 2003, J MACH LEARN RES, V3, P1059, DOI 10.1162/153244303322533197 Craswell N., 2000, P 5 ACM C DIG LIB AC, P37, DOI 10.1145/336597.336628 DALESSION S, 1998, P 3 C EMP METH NAT L DAVID RH, 1999, P 22 ANN INT ACM SIG, P214 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DSOUZA D, 2000, P 11 AUSTR DAT C ADC, P28 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 French J. C., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312684 Gauch S., 1996, J UNIVERS COMPUT SCI, V2, P637 Gravano L, 2003, ACM T INFORM SYST, V21, P1, DOI 10.1145/635484.635485 Gravano L., 1997, P ACM SIGMOD INT C M, P207, DOI 10.1145/253260.253299 GRAVANO L., 2004, P 2004 ACM SIGMOD IN, P767, DOI 10.1145/1007568.1007655 Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 Ipeirotis P. G., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases IPEIROTIS PG, 2001, P 2001 ACM SIGMOD IN, P67, DOI 10.1145/375663.375671 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Jin R., 2002, P 25 ANN INT ACM SIG, P42 Koller D., 1997, P 14 INT C MACH LEAR, P170 Kullback S., 1951, ANN MATH STAT, V22, P76 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lewis DD, 2004, J MACH LEARN RES, V5, P361 MANBER U, 1997, P USENIX S INT TECHN Meng W., 1998, P 24 INT C VER LARG, P14 MENG W, 2002, J KNOWL INF SYST, V4, P132 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Mood A.M., 1963, INTRO THEORY STAT PONTE JM, 1998, P 21 ANN INT ACM SIG, P214 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Powell AL, 2003, ACM T INFORM SYST, V21, P412, DOI 10.1145/944012.944016 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Si L., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 SI L, 2003, P SIGIR 2003 WORKSH, P31 SONG F, 1998, P 8 INT C INF KNOWL, P316 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 Voorhees Ellen M., 1995, P 18 ANN INT ACM SIG, P172, DOI 10.1145/215206.215357 Weigend A. S., 1999, Information Retrieval, V1, DOI 10.1023/A:1009983522080 WONG SKM, 1987, ACM T DB SYST, V12, P229 Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 YANG H, 2004, INT J COMPUTERS THEI, V11, P118 Yu C, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P217, DOI 10.1145/319950.320005 YU C, 1999, METHODOLOGY RETRIEVE Yuwono B., 1997, P 5 INT C DAT SYST A, P41 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 ZOBEL J, 1997, P 2 AUSTR DOC COMP S, P74 NR 57 TC 2 Z9 3 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JAN PY 2006 VL 9 IS 1 BP 5 EP 31 DI 10.1007/s10791-005-5719-z PG 27 WC Computer Science, Information Systems SC Computer Science GA 011ZK UT WOS:000235307800001 ER PT J AU Chen, YL Wei, JJ Wu, SY Hu, YH AF Chen, Yen-Liang Wei, Jhong-Jhih Wu, Shin-Yi Hu, Ya-Han TI A similarity-based method for retrieving documents from the SCI/SSCI database SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE information retrieval; similarity; neural network ID INFORMATION-RETRIEVAL; MODEL; RELEVANCE; PERFORMANCE; CLASSIFICATION; ALGORITHM AB As more and more documents become electronically available, finding documents in large databases that fit users' needs is becoming increasingly important. In the past, the document search problem was dealt with using the database query approach or the text-based search approach. In this paper, we investigate this problem, focusing on the SCI/SSCI databases from ISI. Specifically, we design our search methodology based on the four fields commonly seen in a scientific research document: abstract, title, keywords, and reference list. Of these four, only the abstract field can be viewed as a normal text, while the other three have their own characteristics to differentiate them from texts. Therefore, we first develop a method to compute the similarity value for each field. Our next problem is combining the four similarity values into a final value. One approach is to assign weights to each and compute the weighted sum. We have not adopted this simple weighting method, however, because it is difficult to determine appropriate weights. Instead, we use the back propagation neural network to combine them. Finally, extensive experiments have been carried out using real documents drawn from TKDE journal, and the results indicate that in all situations our method has a much higher accuracy than the traditional text-based search approach. C1 Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. RP Chen, YL (reprint author), Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. EM ylchen@mgt.ncu.edu.tw CR Adami G, 2005, DATA KNOWL ENG, V54, P301, DOI 10.1016/j.datak.2004.11.003 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Choi J, 2006, INFORM PROCESS MANAG, V42, P331, DOI 10.1016/j.ipm.2005.05.009 Desai M, 2005, INFORM PROCESS MANAG, V41, P1035, DOI 10.1016/j.ipm.2004.05.003 Frakes W. B., 1992, INFORM RETRIEVAL DAT Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Kalczynski PJ, 2005, INFORM PROCESS MANAG, V41, P635, DOI 10.1016/j.ipm.2004.01.002 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Korfhage R., 1997, INFORM STORAGE RETRI Lee Joon Ho, 1994, P 17 ANN INT ACM SIG, P182 Lim CS, 2005, INFORM PROCESS MANAG, V41, P1263, DOI 10.1016/j.ipm.2004.06.004 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P193, DOI 10.1016/0306-4573(94)90064-7 Meadow C., 2000, TEXT INFORM RETRIEVA Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 Ripley B., 1994, STAT NEURAL NETWORKS, P105 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA, P52 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 Salton G., 1988, AUTOMATIC TEXT PROCE Salton G., 1971, SMART RETRIEVAL SYST SONG F, 1999, P 22 INT C RES DEV I, P206 SPARCKJONES K, 1972, J DOC, V28, P11 Tai XY, 2002, INFORM PROCESS MANAG, V38, P749, DOI 10.1016/S0306-4573(01)00053-X TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Vechtomova O, 2006, INFORM PROCESS MANAG, V42, P191, DOI 10.1016/j.ipm.2004.10.006 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 2005, ELSEVIER SDOS LIBR 2005, IEEE IEE ELECT LIBR 2005, ISI WEB KNOWLEDGE 2005, ENG INFORM 2005, ABI INFORM PROQUEST 2005, GOOGLE SEARCH ENGINE 2005, FREE DOWNLOAD NEURAL NR 44 TC 4 Z9 4 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PY 2006 VL 32 IS 5 BP 449 EP 464 DI 10.1177/0165551506065814 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 106GX UT WOS:000242090100005 ER PT B AU Song, M Song, IY Allen, RB Obradovic, Z AF Song, Min Song, Il-Yeol Allen, Robert B. Obradovic, Zoran GP IEEE TI Keyphrase extraction-based query expansion in digital libraries SO Opening Information Horizons LA English DT Proceedings Paper CT 6th ACM/IEEE Joint Conference on Digital Libraries (JCDL) CY JUN 11-15, 2006 CL Chapel Hill, NC SP ACM SIGIR, IEEE TC DL DE information gain; keyphrase extraction; query expansion; POS; WordNet ID FULL-TEXT RETRIEVAL; INFORMATION-RETRIEVAL; RANKING ALGORITHMS; CLASSIFICATION; APPLICABILITY; FORMULATIONS; DATABASES; IMPROVE AB In pseudo-relevance feedback, the two key factors affecting the retrieval performance most are the source from which expansion terms are generated and the method of ranking those expansion terms. In this paper, we present a novel unsupervised query expansion technique that utilizes keyphrases and POS phrase categorization. The keyphrases are extracted from the retrieved documents and weighted with an algorithm based on information gain and co-occurrence of phrases. The selected keyphrases are translated into Disjunctive Normal Form (DNF) based on the POS phrase categorization technique for better query refomulation. Furthermore, we study whether ontologies such as WordNet and MeSH improve the retrieval performance in conjunction with the keyphrases. We test our techniques on TREC 5, 6, and 7 as well as a MEDLINE collection. The experimental results show that the use of keyphrases with POS phrase categorization produces the best average precision. C1 Temple Univ, Ctr Informat Sci & Technol, Philadelphia, PA 19122 USA. CR Agichtein E, 2003, PROC INT CONF DATA, P113, DOI 10.1109/ICDE.2003.1260786 BILLERBECK B, 2004, P 15 AUSTR DAT C ADC, P69 Chang KCC, 1996, IEEE T KNOWL DATA EN, V8, P515 Cohen W. W., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Dougherty J., 1995, P 12 INT C MACH LEAR, P194 Frank E, 1999, P 16 INT JOINT C ART, P668 FRANTS VI, 1991, J AM SOC INFORM SCI, V42, P16, DOI 10.1002/(SICI)1097-4571(199101)42:1<16::AID-ASI3>3.0.CO;2-I French JC, 1997, J AM SOC INFORM SCI, V48, P694 Gauch S, 1999, ACM T INFORM SYST, V17, P250, DOI 10.1145/314516.314519 HEARST MA, 1996, P S DOC AN INF RETR HU X, 2004, P 2004 IEEE ACM WEB, P77 Lam-Adesina AM, 2001, P 24 ANN INT ACM SIG, P1, DOI 10.114/383952.383953 Liu S, 2004, P 27 ANN INT ACM SIG, P266, DOI DOI 10.1145/1008992.1009039 MIHALCEA R, 2000, ACL WORKSH IR NLP MITRA CU, 1998, P 21 ANN INT ACM SIG, P206 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Quinlan J. R., 1993, PROGRAMS MACHINE LEA RO JS, 1988, J AM SOC INFORM SCI, V39, P147, DOI 10.1002/(SICI)1097-4571(198805)39:3<147::AID-ASI1>3.0.CO;2-O RO JS, 1988, J AM SOC INFORM SCI, V39, P73, DOI 10.1002/(SICI)1097-4571(198803)39:2<73::AID-ASI1>3.0.CO;2-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGER WKH, 1976, INT FORUM INFORM DOC, V1, P12 SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 Singhal A., 2001, P 10 INT WORLD WID W, P708, DOI 10.1145/371920.372186 SONG M, 2004, ASIST ANN M PROV R, V1, P367 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Van der Pol R, 2003, INFORM RETRIEVAL, V6, P21, DOI 10.1023/A:1022944313947 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 VOORHEES EM, 1998, WORDNET ELECT LEXICA, P285 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 29 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 1-59593-354-9 PY 2006 BP 202 EP 209 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BEQ50 UT WOS:000238914700036 ER PT J AU Westerveld, T de Vries, AP AF Westerveld, T de Vries, AP TI Generative probabilistic models for multimedia retrieval: query generation against document generation SO IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING LA English DT Article; Proceedings Paper CT 3rd International Conference on Image and Video Retrieval (CIVR 2004) CY JUL 21-23, 2004 CL Dublin, IRELAND SP Dublin City Univ, Ctr Digital Video Proc, NDP, Sfi, DCU, IEE, ACM SIGIR, IPRCS, HEMA, BCS ID INFORMATION-RETRIEVAL; FRAMEWORK; RELEVANCE AB This paper presents the use of generative probabilistic models for multimedia retrieval. Gaussian mixture models are estimated to describe the visual content of images (or video) and are explored in different ways of using them for retrieval. So-called query generation (how likely is the query given the document model) and document generation (how likely is the document given the query model) approaches are considered and how both fit in a common probabilistic framework is explained. Query generation is shown to be theoretically superior, and confirmed experimentally on the TRECVID search task. However, it is found that in some cases a document generation approach gives better results. Especially in the cases where queries are narrow and visual results are combined with textual results, the document generation approach seems to be better at setting a visual context than the query generation variant. C1 CWI, INSI, NL-1090 GB Amsterdam, Netherlands. RP Westerveld, T (reprint author), CWI, INSI, POB 94079, NL-1090 GB Amsterdam, Netherlands. EM thijs@cwi.nl RI de Vries, Arjen/ OI de Vries, Arjen/0000-0002-2888-4202 CR Blei D.M., 2003, P 26 ANN INT ACM SIG CALLAN J, 2003, P 26 ANN INT ACM SIG DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Greenspan H, 2004, IEEE T PATTERN ANAL, V26, P384, DOI 10.1109/TPAMI.2004.1262334 Greenspan H, 2002, LECT NOTES COMPUT SC, V2353, P461 Greenspan H, 2001, COMPUT VIS IMAGE UND, V84, P384, DOI 10.1006/cviu.2001.0946 HIEMSTRA D, 1998, LECT NOTES COMPUTER, V513, P569 IANEVA T, 2004, IEEE INT C MULT EXP, V3, P1607 Jelinek F., 1980, P WORKSH PATT REC PR Jeon J., 2003, P 26 ANN INT ACM SIG Jones K. S., 2000, Information Processing & Management, V36 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval LAFFERTY J, 2003, KLUWER INT SERIES IN, V13 Gauvain JL, 2002, SPEECH COMMUN, V37, P89, DOI 10.1016/S0167-6393(01)00061-9 Lavrenko V., 2004, THESIS U MASSACHUSET LUO H, 2003, IEEE INT C MULT EXP MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMEATON AF, 2003, TRECVID WORKSH VASCONCELOS N, 2000, THESIS MIT Vasconcelos N, 1998, P SOC PHOTO-OPT INS, V3527, P134, DOI 10.1117/12.325807 WESTERVELD T, 2003, P 26 ANN INT ACM SIG WESTERVELD T, 2004, P INT C IM VID RETR WESTERVELD T, 2003, EURASIP J APPL SIG P, P186 WESTERVELD T, 2004, THESIS U TWENTE Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 NR 27 TC 1 Z9 1 PU IEE-INST ELEC ENG PI HERTFORD PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND SN 1350-245X J9 IEE P-VIS IMAGE SIGN JI IEE Proc.-Vis. Image Signal Process. PD DEC PY 2005 VL 152 IS 6 BP 852 EP 858 DI 10.1049/ip-vis:20045196 PG 7 WC Engineering, Electrical & Electronic SC Engineering GA 998HA UT WOS:000234308000025 ER PT J AU Cummins, R O'riordan, C AF Cummins, R O'riordan, C TI Evolving general term-weighting schemes for information retrieval: Tests on larger collections SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article; Proceedings Paper CT 15th Artificial Intelligence and Cognitive Science Conference (AICS 2004) CY SEP, 2004 CL Castlebar, IRELAND DE genetic programming; information retrieval; term-weighting schemes ID GENETIC ALGORITHMS; DOCUMENT-RETRIEVAL AB Term-weighting schemes are vital to the performance of Information Retrieval models that use term frequency characteristics to determine the relevance of a document. The vector space model is one such model in which the weights assigned to the document terms are of crucial importance to the accuracy of the retrieval system. This paper describes a genetic programming framework used to automatically determine term-weighting schemes that achieve a high average precision. These schemes are tested on standard test collections and are shown to perform as well as, and often better than, the modern BM25 weighting scheme. We present an analysis of the schemes evolved to explain the increase in performance. Furthermore, we show that the global (collection wide) part of the evolved weighting schemes also increases average precision over idf on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as middle frequency terms are assigned the highest weight. However, the complete weighting schemes evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections. C1 Natl Univ Ireland Univ Coll Galway, Dept Informat Technol, Galway, Ireland. RP Cummins, R (reprint author), Natl Univ Ireland Univ Coll Galway, Dept Informat Technol, Galway, Ireland. EM ronan.cummins@nuigalway.ie CR CUMMINS R, 2004, 15 ART INT COGN SCI CUMMINS R, 2004, LAT BREAK PAP 2004 G Darwin C., 1859, ORIGIN SPECIES MEANS FAN W, 2004, INFORMATION PROCESSI Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GRIEFF W, 1998, P 21 INT ACM SIGIR C Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X KIM S, 2001, P 2001 C EV COMP CEC Koza J, 1992, GENETIC PROGRAMMING KUSCU I, 2000, P 2000 C EV COMP CEC, P1393 Luhn H. P., 1958, IBM J RES DEV, P159 OREN N, 2002, P SAICSIT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1997, P 20 ANN INT ACM SIG ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Trotman A., 2004, SIGIR, P603 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VRAJITORU D, 2000, SOFT COMPUTING INFOR, P199 Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 YANG JJ, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P603 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 28 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0269-2821 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD NOV PY 2005 VL 24 IS 3-4 BP 277 EP 299 DI 10.1007/s10462-005-9001-y PG 23 WC Computer Science, Artificial Intelligence SC Computer Science GA 994GI UT WOS:000234018800004 ER PT J AU Possas, B Ziviani, N Meira, W Ribeiro-Neto, B AF Possas, B Ziviani, N Meira, W Ribeiro-Neto, B TI Set-based vector model: An efficient approach for correlation-based ranking SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE information retrieval models; association rule mining; weighting index term co-occurrences; data mining; correlation-based ranking ID INFORMATION-RETRIEVAL SYSTEMS; CO-OCCURRENCE DATA; RELEVANCE AB This work presents a new approach for ranking documents in the vector space model. The novelty lies in two fronts. First, patterns of term co-occurrence are taken into account and are processed efficiently. Second, term weights are generated using a data mining technique called association rules. This leads to a new ranking mechanism called the set-based vector model. The components of our model are no longer index terms but index termsets, where a termset is a set of index terms. Termsets capture the intuition that semantically related terms appear close to each other in a document. They can be efficiently obtained by limiting the computation to small passages of text. Once termsets have been computed, the ranking is calculated as a function of the termset frequency in the document and its scarcity in the document collection. Experimental results show that the set-based vector model improves average precision for all collections and query types evaluated, while keeping computational costs small. For the 2-gigabyte TREC-8 collection, the set-based vector model leads to a gain in average precision figures of 14.7% and 16.4% for disjunctive and conjunctive queries, respectively, with respect to the standard vector space model. These gains increase to 24.9% and 30.0%, respectively, when proximity information is taken into account. Query processing times are larger but, on average, still comparable to those obtained with the standard vector model (increases in processing time varied from 30% to 300%). Our results suggest that the set-based vector model provides a correlation-based ranking formula that is effective with general collections and computationally practical. C1 Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, Brazil. Akwan Informat Technol, BR-31275000 Belo Horizonte, MG, Brazil. RP Possas, B (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, Brazil. EM bavep@dcc.ufmg.hr; nivio@dcc.ufmg.hr; meira@dcc.ufmg.hr; berthier@dcc.ufmg.hr CR AGRAWAL R, 1993, P ACM SIGMOD INT C M, P204 Alsaffar AH, 2000, J INTELL INF SYST, V14, P155, DOI 10.1023/A:1008783718847 [Anonymous], 1994, P 20 INT C VER LARG, P487 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 BOLLMANNSDORRA P, 2001, LECT NOTES COMPUTER, V2114, P21 Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P249, DOI 10.1016/0306-4573(81)90019-4 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HAWKING D, 2001, P 10 TEXT RETR C TRE, P61 HAWKING D, 1998, 7 TEXT RETR C TREC 7, P91 Hawking D, 1999, COMPUT NETW, V31, P1321, DOI 10.1016/S1389-1286(99)00024-9 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 KIM M, 2000, P INT S METH INT SYS, P453 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Kaszkiel M, 1999, ACM T INFORM SYST, V17, P406, DOI 10.1145/326440.326445 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Nallapati R., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 POSSAS B, 2004, LECT NOTES COMPUTER, P171 Possas B., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval POSSAS B, 2002, P 9 INT S STRING PRO, P104 Raghavan V. V., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320071.320081 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 Salton G., 1982, P 5 ANN INT ACM SIGI, P151 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1968, J ACM, V15, P68 Silva A. S. d., 1999, P STRING PROC INF RE, P184 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 SPINK A, 2002, IEEE COMPUT, V35, P107 Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Voorhees E.M., 1999, P TREC, P1 Witten I. H., 1999, MANAGING GIGABYTES C Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 Zaki M. J., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347101 ZOBEL J, 1995, INFORM PROCESS MANAG, V31, P361, DOI 10.1016/0306-4573(94)00052-5 Zobel J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291014 NR 50 TC 13 Z9 15 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD OCT PY 2005 VL 23 IS 4 BP 397 EP 429 DI 10.1145/1095872.1095874 PG 33 WC Computer Science, Information Systems SC Computer Science GA 987TP UT WOS:000233540500002 ER PT J AU Diaz-Hermida, F Losada, DE Bugarin, A Barro, S AF Diaz-Hermida, F Losada, DE Bugarin, A Barro, S TI A probabilistic quantifier fuzzification mechanism: The model and its evaluation for information retrieval SO IEEE TRANSACTIONS ON FUZZY SYSTEMS LA English DT Article DE fuzzy quantification; information retrieval; quantifier fuzzification mechanisms ID AGGREGATION OPERATORS; FUZZY QUANTIFIERS; SENTENCES; FRAMEWORK AB In this paper, we propose a new quantifier fuzzification mechanism which is deeply rooted in the theory of probability. This quantifier fuzzification mechanism skips the nested assumption, which is inherent to other probabilistic quantification methods. The new quantification approach complies with the properties required for determiner fuzzification schemes (DFS) with finite sets and, hence, its good behavior is assured. Moreover, this new approach is suitable for some application domains. In particular, the use of fuzzy quantifiers for implementing query quantified statements for information retrieval exemplifies the adequacy of the new proposal. The new quantifier fuzzification mechanism has been efficiently implemented and empirically tested for a retrieval task. This practical evaluation followed the standard methodology in the field of information retrieval and was conducted against a popular benchmark consisting of a large collection of documents. The retrieval performance evaluation made evident that: 1) the new method can work in realistic scenarios, and 2) it can overcome recent proposals for applying fuzzy quantifiers in information retrieval. C1 Univ Santiago de Compostela, Dept Elect & Comp Sci, Santiago De Compostela 15782, Spain. RP Diaz-Hermida, F (reprint author), Univ Santiago de Compostela, Dept Elect & Comp Sci, Santiago De Compostela 15782, Spain. EM felixdh@dec.usc.es; dlosada@dec.usc.es; alberto@dec.usc.es; senen@dec.usc.es RI Losada, David/A-3202-2009; Bugarin Diz, Alberto/B-4268-2009; Diaz, Felix/A-7511-2014 OI Bugarin Diz, Alberto/0000-0003-3574-3843; CR Baldwin JF, 1996, FUZZY SET SYST, V83, P353, DOI 10.1016/0165-0114(95)00297-9 Barro S, 2003, IEEE T FUZZY SYST, V11, P89, DOI 10.1109/TFUZZ.2002.806319 BORDOGNA G, 1995, INT J INTELL SYST, V10, P233, DOI 10.1002/int.4550100205 BOSC P, 1995, FUZZINESS DATABASE M, V5, P275 CRESTANI F, 1998, INFORM RETRIEVAL UNC Delgado M, 2002, STUD FUZZ SOFT COMP, V83, P286 Delgado M, 2000, INT J APPROX REASON, V23, P23, DOI 10.1016/S0888-613X(99)00031-6 Diaz-Hermida F., 2004, GSI0401 U SANT COMP Diaz-Hermida F, 2004, FUZZY SET SYST, V146, P97, DOI 10.1016/j.fss.2004.03.012 Diaz-Hermida F, 2003, INT J APPROX REASON, V34, P49, DOI 10.1016/S0888-613X(03)00053-7 Glockner I, 2004, INT J APPROX REASON, V37, P93, DOI 10.1016/j.ijar.2003.09.005 Glockner I, 2001, STUD FUZZ SOFT COMP, V70, P215 GLOCKNER I, 2003, THESIS U BIELEFELD B Glockner I., 1997, TR9706 U BIEL HARMAN D, 1994, P TREC 3 3 TEXT RETR Jones K. S., 2000, Information Processing & Management, V36 Kruse R., 1994, FDN FUZZY SYSTEMS Losada D.E., 2004, P SAC 04 19 ACM S AP, P1059, DOI 10.1145/967900.968116 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Porter M. F., 1997, READINGS INFORM RETR, P313 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1983, INTRO MODERN INFORM Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Thomas S., 1995, FUZZINESS PROBABILIT VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 YAGER RR, 1984, IEEE T SYST MAN CYB, V14, P636 YAGER RR, 1988, IEEE T SYST MAN CYB, V18, P183, DOI 10.1109/21.87068 ZADEH LA, 1983, COMPUT MATH APPL, V9, P149, DOI 10.1016/0898-1221(83)90013-5 NR 29 TC 10 Z9 10 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA SN 1063-6706 J9 IEEE T FUZZY SYST JI IEEE Trans. Fuzzy Syst. PD OCT PY 2005 VL 13 IS 5 BP 688 EP 700 DI 10.1109/TFUZZ.2005.856557 PG 13 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 974PT UT WOS:000232604600010 ER PT J AU Trotman, A AF Trotman, A TI Learning to rank SO INFORMATION RETRIEVAL LA English DT Article DE searching; document ranking; genetic programming; machine learning ID TEXT RETRIEVAL AB New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learned functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32% observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learned function. C1 Univ Otago, Dept Comp Sci, Dunedin, New Zealand. RP Trotman, A (reprint author), Univ Otago, Dept Comp Sci, Dunedin, New Zealand. EM andrew@cs.otago.ac.nz RI Trotman, Andrew/A-4918-2008 CR ANH VN, 2002, AUSTR COMPUTER SCI C, V24, P41 Buckley C, 1991, TREC EVAL Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 DeJong K., 1975, THESIS U MICHIGAN FAN W, 2004, P 37 ANN HAW INT C S FAN W, 1999, P 1999 AM C INF SYST Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 GREFENSTETTE JJ, 1986, IEEE T SYST MAN CYB, V16, P122, DOI 10.1109/TSMC.1986.289288 Harman D., 1992, INFORMATION RETRIEVA, P363 Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 HART WE, 1996, ADAPTIVE INDIVIDUALS, P483 HAWKING D, 1999, P 8 INT WORLD WID WE, P1321 HOLLAND JH, 1975, ADAPTATION NATURAL A Igel C., 1999, P GEN EV COMP C GECC, P1061 JONES S, 1999, P 8 TEXT RETRIEVAL C Kaszkiel M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291031 Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137 Khuri S., 1994, P 22 ANN ACM COMP SC, P66, DOI 10.1145/197530.197558 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Koza J, 1992, GENETIC PROGRAMMING OREN N, 2002, THESIS U WITWATERSRA OREN N, 2002, P SAICSIT, P224 Page L., 1998, PAGERANK CITATION RA Possas B., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Raghavan V. V., 1983, Proceedings of the Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 1994, P 3 TEXT RETR C TREC, P109 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1995, P 4 TEXT RETRIEVAL C, P537 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SMART W, 2004, P 2 WORKSH AUSTR INF TONGCHIM S, 2000, P 5 INT S ART LIF RO, P251 Williams HE, 1999, COMPUT J, V42, P193, DOI 10.1093/comjnl/42.3.193 Witten I.H., 1994, MANAGING GIGABYTES C Zobel J, 1998, ACM T DATABASE SYST, V23, P453, DOI 10.1145/296854.277632 Zobel J., 1998, SIGIR Forum, V32 ZOBEL J, 1995, SOFTWARE PRACT EXPER, V25, P891, DOI 10.1002/spe.4380250804 NR 40 TC 25 Z9 25 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD SEP PY 2005 VL 8 IS 3 BP 359 EP 381 DI 10.1007/s10791-005-6991-7 PG 23 WC Computer Science, Information Systems SC Computer Science GA 907NW UT WOS:000227725800001 ER PT J AU Fan, WG Gordon, MD Pathak, P AF Fan, WG Gordon, MD Pathak, P TI Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison SO DECISION SUPPORT SYSTEMS LA English DT Article DE personalization; profiling; information push; information routing; selective dissemination of information; information retrieval ID QUERY EXPANSION; WEB; CONTEXT AB Due to the overwhelming volume of information that is increasingly available, many people rely on current awareness systems to keep abreast of the latest developments in the fields that they are interested in, as evidenced in the popularity of subscriptions to news-monitoring and digital library services. The success of these services, however, often requires effective acquisition of users' personal standing interests as represented in personal profiles. Our objective in this paper is twofold. First, we have introduced a new method for profile generation and compared it against other well-known methods. We have found promising results. Second, although there are various methods proposed in information retrieval and machine learning literature to address the issue of profiling, a unified framework and systematic cross-system comparison to help users, especially service providers, to determine the most effective way of profiling consumers is still lacking in the literature. In this paper, we try to fill the gap by looking at these methods from a more integrated point of view based on statistical contingency theory. Variations of these methods are then systematically tested on three well-known routing systems and results are analyzed and reported. (c) 2004 Elsevier B.V. All rights reserved. C1 Virginia Tech, Accounting & Informat Syst, Blacksburg, VA 24061 USA. Univ Michigan, Ann Arbor, MI 48109 USA. Univ Florida, Gainesville, FL 32611 USA. RP Fan, WG (reprint author), Virginia Tech, Accounting & Informat Syst, 3007 Pamplin Hall, Blacksburg, VA 24061 USA. EM wfan@vt.edu RI Fan, Weiguo/E-6343-2012 OI Fan, Weiguo/0000-0003-1272-5538 CR Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Dietterich TG, 1997, AI MAG, V18, P97 Dumais S, 1998, IEEE INTELL SYST APP, V13, P21 FAN W, 2003, IN PRESS INFORM PROC Fan Weiguo, 2000, P 2000 INT C INF SYS, P20 FRANKLIN M, 1998, P ACM SIGMOD INT C M, P516, DOI 10.1145/276304.276360 FURNAS GW, 1987, COMMUN ACM, V30, P947 Fan WG, 2004, IEEE T KNOWL DATA EN, V16, P523, DOI 10.1109/TKDE.2004.1269663 HULL D, 1999, NIST SPECIAL PUBLICA, V500, P33 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Menczer F, 2000, MACH LEARN, V39, P203, DOI 10.1023/A:1007653114902 MITCHELL T, 1994, COMMUN ACM, V37, P81 Mitchell T.M., 1997, MACHINE LEARNING Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 NG HT, 1999, NIST SPECIAL PUBLICA, V500, P267 PACKER KH, 1979, J AM SOC INFORM SCI, V30, P125, DOI 10.1002/asi.4630300303 Pathak P., 2000, P 33 HAW INT C SYST Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 PEDHAZUR EJ, 1991, MEASUREMENT DESIGN A ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 Robertson S.E., 1996, NIST SPECIAL PUBLICA, p[73, 500] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST Salton G., 1971, SMART RETRIEVAL SYST Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 30 TC 26 Z9 26 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD AUG PY 2005 VL 40 IS 2 BP 213 EP 233 DI 10.1016/j.dss.2004.02.003 PG 21 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA 973LM UT WOS:000232524100005 ER PT J AU Balinski, J Danilowicz, C AF Balinski, J Danilowicz, C TI Re-ranking method based on inter-document distances SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE document ranking; re-ranking; inter-document relationships; metric distance ID INFORMATION-RETRIEVAL; SEARCH ENGINES; SYSTEMS; WEB; SIMILARITY; SEEKING; LINKS AB Lately there has been intensive research into the possibilities of using additional information about documents (such as hyperlinks) to improve retrieval effectiveness. It is called data fusion, based on the intuitive principle that different document and query representations or different methods lead to a better estimation of the documents' relevance scores. In this paper we propose a new method of document re-ranking that enables us to improve document scores using inter-document relationships. These relationships are expressed by distances and can be obtained from the text, hyperlinks or other information. The method formalizes the intuition that strongly related documents should not be assigned very different weights. (c) 2004 Elsevier Ltd. All rights reserved. C1 Wroclaw Tech Univ, Dept Informat Syst, PL-50370 Wroclaw, Poland. RP Balinski, J (reprint author), Wroclaw Tech Univ, Dept Informat Syst, Wybrzeze Wyspianskiego 27, PL-50370 Wroclaw, Poland. EM balinski@pwr.wroc.pl; danilowicz@pwr.wroc.pl CR Allan J, 2001, INFORM PROCESS MANAG, V37, P435, DOI 10.1016/S0306-4573(00)00056-X ANICK PG, 1994, P 17 ANN INT ACM SIG, P349 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BALABANOVIC M, 1998, P 21 INT ACM SIGIR C Belkin N. J., 1993, P 16 ANN INT ACM SIG, P339, DOI 10.1145/160688.160760 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BHARAT K, 2003, Patent No. 6526440 Bharat K, 2002, ACM T INFORM SYST, V20, P47, DOI 10.1145/503104.503107 BICHTELER J, 1980, J AM SOC INFORM SCI, V31, P278, DOI 10.1002/asi.4630310408 Brin S., 1998, P 7 INT WORLD WID WE, P107 Buckley C., 1995, P 18 ANN INT ACM SIG, P351, DOI 10.1145/215206.215383 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley C., 1995, NIST SPECIAL PUBLICA, P69 Butler D, 2000, NATURE, V405, P112, DOI 10.1038/35012148 CHAKRABARTI S, 1999, SCI AM JUN CHAN PK, 1999, KDD 99 WORKSH WEB US, P7 CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 DABROWSKI M, 1978, INFORMATION RETRIEVA Danilowicz C, 2001, INFORM PROCESS MANAG, V37, P623, DOI 10.1016/S0306-4573(00)00038-8 DANILOWICZ C, 1994, INFORM PROCESS MANAG, V30, P363, DOI 10.1016/0306-4573(94)90050-7 Danilowicz C, 2002, LECT NOTES ARTIF INT, V2366, P223 DING C, 2000, P 23 ANN INT ACM SIG, P375, DOI 10.1145/345508.345663 EFTHIMIADIS E, 1994, NIST SPECIAL PUBLICA, P279 FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 FREI HP, 1995, INFORM PROCESS MANAG, V31, P1, DOI 10.1016/0306-4573(94)E0005-M Glover E, 2001, COMMUN ACM, V44, P97, DOI 10.1145/501317.501319 Gordon M, 1999, INFORM PROCESS MANAG, V35, P141, DOI 10.1016/S0306-4573(98)00041-7 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 Harman D., 1992, INFORMATION RETRIEVA, P363 HARMAN D, 1993, 1 TEXT RETRIEVAL C Hawking D, 2001, INFORM RETRIEVAL, V4, P33, DOI 10.1023/A:1011468107287 Jansen B. J., 1998, SIGIR Forum, V32 Joachims T., 2002, P ACM C KNOWL DISC D Katzer J., 1982, Information Technology: Research and Development, V1 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 KOCHTANEK TR, 1982, INFORM PROCESS MANAG, V18, P33, DOI 10.1016/0306-4573(82)90049-8 Lawrence S, 1999, IEEE COMMUN MAG, V37, P116, DOI 10.1109/35.739314 MARCHIONINI G, 1992, J AM SOC INFORM SCI, V43, P156, DOI 10.1002/(SICI)1097-4571(199203)43:2<156::AID-ASI8>3.0.CO;2-U Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 NOREAULT T, 1981, INFORMATION RETRIEVA, P57 PAO ML, 1989, J AM SOC INFORM SCI, V40, P226, DOI 10.1002/(SICI)1097-4571(198907)40:4<226::AID-ASI2>3.0.CO;2-6 PICARD J, 2000, SOFT COMPUTING INFOR, P225 Pitkow J, 2002, COMMUN ACM, V45, P50 Pretschner A., 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence, DOI 10.1109/TAI.1999.809829 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A Savoy J, 1997, J AM SOC INFORM SCI, V48, P235, DOI 10.1002/(SICI)1097-4571(199703)48:3<235::AID-ASI5>3.0.CO;2-Y Savoy J, 1996, INFORM PROCESS MANAG, V32, P155, DOI 10.1016/S0306-4573(96)85003-5 SILVERSTEIN C, 1998, 1998014 COMP SYST RE SOERGEL D, 1967, INFORM STORAGE RET, V3, P129, DOI 10.1016/0020-0271(67)90006-X Voorhees E., 1998, NIST SPECIAL PUBLICA, V500-242, P1 WILBUR WJ, 1994, INFORM PROCESS MANAG, V30, P253, DOI 10.1016/0306-4573(94)90068-X Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 ZHANG D, 2000, P 9 INT WORLD WID WE ZHU J, 2001, 12 ACM C HYP HYP AAR NR 64 TC 22 Z9 22 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2005 VL 41 IS 4 BP 759 EP 775 DI 10.1016/j.ipm.2004.01.006 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 908CG UT WOS:000227764200003 ER PT J AU Lim, JH Jin, JS AF Lim, JH Jin, JS TI Combining intra-image and inter-class semantics for consumer image retrieval SO PATTERN RECOGNITION LA English DT Article DE unconstrained photographs; consumer images; image understanding; image semantics; image retrieval; image indexing ID CLASSIFICATION; QUERY AB Unconstrained consumer photos pose great challenge for content-based image retrieval. Unlike professional images or domain-specific images, consumer photos vary significantly. More often than not, the objects in the photos are ill-posed, occluded, and cluttered with poor lighting, focus and exposure. In this paper, we propose a cascading framework for combining intra-image and inter-class similarities in image retrieval, motivated from probabilistic Bayesian principles. Support vector machines are employed to learn local view-based semantics based on just-in-time fusion of color and texture features. A new detection-driven block-based segmentation algorithm is designed to extract semantic features from images. The detection-based indexes also serve as input for support vector learning of image classifiers to generate class-relative indexes. During image retrieval, both intra-image and inter-class similarities are combined to rank images. Experiments using query-by-example on 2400 genuine heterogeneous consumer photos with 16 semantic queries show that the combined matching approach is better than matching with single index. It also outperformed the method of combining color and texture features by 55 % in average precision. (c) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. C1 Inst Infocomm Res, Singapore 119613, Singapore. Univ Newcastle, Callaghan, NSW, Australia. RP Lim, JH (reprint author), Inst Infocomm Res, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. EM joohwee@i2r.a-star.edu.sg; jesse.jin@newcastle.edu.au CR Adams WH, 2003, EURASIP J APPL SIG P, V2003, P170, DOI 10.1155/S1110865703211173 AMIR A, 2003, TRECVID WORKSH 2003 BARNARD K, 2003, P CVPR Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Barnard K., 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, DOI 10.1109/ICCV.2001.937654 Bishop CM, 1995, NEURAL NETWORKS PATT Bradshaw B., 2000, Proceedings ACM Multimedia 2000, DOI 10.1145/354384.354456 Brodley C., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Chen YX, 2002, IEEE T PATTERN ANAL, V24, P1252 Duygulu P., 2002, P EUR C COMP VIS, P97, DOI DOI 10.1007/3-540-47979-1_7 HSU WHM, 2004, P IEEE ICME 2004 Joachims T, 1999, ADV KERNEL METHODS S Johnson R. A., 1988, APPL MULTIVARIATE ST Kapur J. N., 1992, ENTROPY OPTIMIZATION KUMAR S, 2002, 1 INT WORKSH GEN MOD LI J, 2003, IEEE T PAMI, V25, P1 LIM JH, 2003, P IJCAI 2003, P1413 Lim JH, 2001, PATTERN ANAL APPL, V4, P125, DOI 10.1007/PL00014574 Lim JH, 2003, IEEE MULTIMEDIA, V10, P28 LIM JH, 2000, P ACM MULTIMEDIA 200, P407, DOI 10.1145/354384.376298 LIM JH, 2003, P IEEE ICIP 2003, P515 LIN C, 2003, P IEEE JCME 2003 Lipson P, 1997, PROC CVPR IEEE, P1007, DOI 10.1109/CVPR.1997.609453 LIU W, 2000, P ACM MULTIMEDIA, P479 Lu Y, 2000, P ACM MULT, P31, DOI 10.1145/354384.354403 Maenpaa T., 2002, Proceedings 16th International Conference on Pattern Recognition, DOI 10.1109/ICPR.2002.1044840 Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 MOJSILOVIC A, 2002, P IEEE ICIP NAPHADE MR, 2003, P IEEE ICME, P437 NAPHADE MR, 2003, P IEEE ICIP 2003 Naphade MR, 2002, IEEE T CIRC SYST VID, V12, P40 Ortega M., 1997, Proceedings ACM Multimedia 97, DOI 10.1145/266180.266394 PAPAGEORGIOU PC, 1997, P ICCV, P555 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RODDEN K, 2003, P ACM CHI 2003 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith J., 2003, P IEEE INT C MULT EX, P445, DOI DOI 10.1109/ICME.2003.1221649 SMITH JR, 2001, NIST SPECIAL PUBLICA, V500, P240 Smith JR, 1999, MULTIMEDIA SYST, V7, P129, DOI 10.1007/s005300050116 Sung KK, 1998, IEEE T PATTERN ANAL, V20, P39, DOI 10.1109/34.655648 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487 Szummer M., 1998, Proceedings. 1998 IEEE International Workshop on Content-Based Access of Image and Video Database (Cat. No.98EX125), DOI 10.1109/CAIVD.1998.646032 TIEU K, 2000, P CVPR 2000, P1228 TOWN C, 2000, 200014 AT T RES Vailaya A, 2001, IEEE T IMAGE PROCESS, V10, P117, DOI 10.1109/83.892448 Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109 WU Y, 2000, P CVPR 2000, P1222 NR 47 TC 9 Z9 10 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0031-3203 J9 PATTERN RECOGN JI Pattern Recognit. PD JUN PY 2005 VL 38 IS 6 BP 847 EP 864 DI 10.1016/j.patcog.2004.11.002 PG 18 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 909JL UT WOS:000227856100007 ER PT J AU Kishida, K AF Kishida, K TI Technical issues of cross-language information retrieval: a review SO INFORMATION PROCESSING & MANAGEMENT LA English DT Review DE cross-language information retrieval; machine translation; word sense disambiguation; language model ID CLEF 2001; QUERY-TRANSLATION; CORPORA; DISAMBIGUATION; RESOURCES; CLEF-2001; ENGLISH; SYSTEM; CORPUS; TRACK AB This paper reviews state-of-the-art techniques and methods for enhancing effectiveness of cross-language information retrieval (CLIR). The following research issues are covered: (1) matching strategies and translation techniques, (2) methods for solving the problem of translation ambiguity, (3) formal models for CLIR such as application of the language model, (4) the pivot language approach, (5) methods for searching multilingual document collection, (6) techniques for combining multiple language resources, etc. (C) 2004 Elsevier Ltd. All rights reserved. C1 Surudadai Univ, Fac Cultural Informat Resources, Hanno, Saitama 3578555, Japan. RP Kishida, K (reprint author), Surudadai Univ, Fac Cultural Informat Resources, 698 Azu, Hanno, Saitama 3578555, Japan. EM kishida@surugadai.ac.jp CR Adriani M, 2002, LECT NOTES COMPUT SC, V2406, P219 ADRIANI M, 2001, LNCS, V2069, P156 Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 BALLESTEROS LA, 2000, ADV INFORMATION RETR, P203 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Berry MW, 1995, COMPUT HUMANITIES, V29, P413, DOI 10.1007/BF01829874 BIAN GW, 2001, P 2 NTCIR WORKSH RES BOUGHANEM M, 2001, LNCS, V2069, P202 Boughanem M, 2002, LECT NOTES COMPUT SC, V2406, P158 Braschler M, 2000, INFORM RETRIEVAL, V3, P273, DOI 10.1023/A:1026525127581 BRASCHLER M, 2001, LNCS, V2069, P140 Brown P. F., 1993, Computational Linguistics, V19 BUCKLEY C, 1994, P TREC 2 GAITH NAT I BUCKLEY C, 1998, P TREC 6 GAITH MD NA CALLAN JP, 1995, INFORM PROCESS MANAG, V31, P327, DOI 10.1016/0306-4573(94)00050-D Capstick J, 2000, INFORM PROCESS MANAG, V36, P275, DOI 10.1016/S0306-4573(99)00058-8 CHEN A, 2003, P 3 NTCIR WORKSH RES CHEN A, 1999, P NTCIR WORKSH RES J Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 COOPER WS, 1994, P TREC 2 GAITH MD NA DARWISH K, 2003, P TREC 2002 MD NAT I DAVIS M, 1995, P TREC 4 GAITH MD NA DAVIS M, 1997, P TREC 5 GAITH MD NA Davis MW, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P92, DOI 10.1145/258525.258542 Davis MW, 1998, CROSS LANGUAGE INFOR, P12 Davis MW, 2000, INFORM RETRIEVAL, V3, P339, DOI 10.1023/A:1009907816505 DIEKEMA A, 1999, P TREC 7 GAITH MD NA Dumais ST, 1996, P ACM SIGIR WORKSH C, P16 Dunning T., 1993, Computational Linguistics, V19 Federico M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Fox C, 1989, ACM SIGIR FORUM, V24, P19 Frakes W., 1992, INFORMATION RETRIEVA, P131 FRANZ M, 2000, P TREC 8 GAITH MD NA FRANZ M, 2003, P TREC 2002 GAITH MD FRANZ M, 1999, P TREC 7 GAITH MD NA FRASER A, 2003, P TREC 2002 GAITH MD Fujii A, 2001, COMPUT HUMANITIES, V35, P389, DOI 10.1023/A:1011856202986 GAO FC, 2002, P 25 ACM SIGIR C RES, P183 Gao J., 2001, P 24 ANN INT ACM SIG, P96, DOI 10.1145/383952.383966 GAO J, 2001, P TREC 9 GAITH MD NA GEY F, 2002, WORKSH SIGIR 2002 GEY FC, 1999, P TREC 7 GAITH MD NA GEY FC, 2001, LNCS, V2069, P83 Gollins T., 2001, P 24 ANN INT ACM SIG, P90, DOI 10.1145/383952.383965 Hedlund T, 2001, INFORM PROCESS MANAG, V37, P147, DOI 10.1016/S0306-4573(00)00024-8 Hedlund T, 2002, LECT NOTES COMPUT SC, V2406, P118 HEDLUND T, 2001, LECT NOTES COMPUTER, V2069, P210 Hiemstra D., 2001, LECT NOTES COMPUTER, V2069, P102 Hiemstra D, 1998, LNCS, V1513, P569 HIEMSTRA D, 1999, P TREC 7 GAITH MD NA HULL D, 1998, CROSS LANGUAGE INFOR, P119 Hull D. A., 1996, P 19 ANN INT ACM SIG, P49, DOI 10.1145/243199.243212 Ide N, 1998, COMPUT LINGUIST, V24, P1 Jones GJF, 2002, LECT NOTES COMPUT SC, V2406, P59 Kim T, 1999, INFORM PROCESS MANAG, V35, P559, DOI 10.1016/S0306-4573(98)00057-0 Knight K, 1998, COMPUT LINGUIST, V24, P599 Kraaij W, 2002, LECT NOTES COMPUT SC, V2406, P78 KRAAIJ W, 2000, P TREC 8 GAITH MD NA KWOK KL, 2001, P TREC 9 GAITH MD NA KWOK KL, 1996, ACM T INFORM SYST, V12, P325 LANDAUER T, 1990, P 6 ANN C UW CTR NEW Lavrenko V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval LIN CJ, 1999, P 1 NTCIR WORKSH RES LIN WC, 2003, P 3 NTCIR WORKSH RE Littman M., 1998, CROSS LANGUAGE INFOR, P51 Maeda A, 2000, P 5 INT WORKSH INF R, P25, DOI 10.1145/355214.355218 Manning C. D., 1999, FDN STAT NATURAL LAN McNamee P, 2002, LECT NOTES COMPUT SC, V2406, P193 MCNAMEE P, 2001, LNCS, V2069, P129 McNamee P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 MILLER DRH, 1999, P TREC 7 GAITH MD NA Molina-Salgado H, 2002, LECT NOTES COMPUT SC, V2406, P226 MORI T, 2001, P 2 NTCIER WORKSH M MOULINIER I, 2002, CLEF 2002 WORKSH, P91 Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 Nie JY, 2003, LECT NOTES COMPUT SC, V2785, P101 Nie JY, 2002, LECT NOTES COMPUT SC, V2406, P137 NIE JY, 2001, LNCS, V2069, P188 NIE JY, 2000, P TREC 8 GAITH MD NA NIE JY, 1999, P TREC 7 GAITH MD NA Oard D. W., 1996, SURVEY MULTILINGUAL OARD DW, 2001, LNCS, V2069, P57 Oard DW, 1998, ANNU REV INFORM SCI, V33, P223 Oard DW, 2002, LECT NOTES COMPUT SC, V2406, P308 Oard DW, 2001, LNCS, V2069, P176 OARD DW, 1998, P TREC 6 GAITH MD NA Peters C., 2001, LNCS, V1980, P51, DOI 10.1007/3-540-45368-7_3 Pirkola A, 2001, INFORM RETRIEVAL, V4, P209, DOI 10.1023/A:1011994105352 Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Porter Martin, 2001, SNOWBALL LANGUAGE ST PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Powell A. L., 2000, P 23 ANN INT ACM SIG, P232, DOI 10.1145/345508.345584 Qu Y, 2003, LECT NOTES COMPUT SC, V2785, P223 REHDER B, 1998, P TREC 6 GAITH MD NA RESNIK P, 1999, 37 ANN M ASS COMP LI ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sadat F, 2002, LECT NOTES COMPUT SC, V2406, P177 SAKAI T, 2003, P 3 NTCIR WORKSH RES Salton G, 1970, J AM SOC INFORM SCI, V21, P187, DOI 10.1002/asi.4630210305 Savoy J, 2003, INFORM PROCESS MANAG, V39, P75, DOI 10.1016/S0306-4573(02)00018-3 Savoy J, 2003, LECT NOTES COMPUT SC, V2785, P66 Savoy J, 2002, LECT NOTES COMPUT SC, V2406, P27 Savoy J, 1999, J AM SOC INFORM SCI, V50, P944, DOI 10.1002/(SICI)1097-4571(1999)50:10<944::AID-ASI9>3.3.CO;2-H SEO HC, 2003, P 3 NTCIR WORKSH RES Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 Sperer R., 2000, P 23 ANN INT ACM SIG, P120, DOI 10.1145/345508.345562 Stalls B., 1998, P COLING ACL WORKSH TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Xu J., 2001, P 24 ANN INT ACM SIG, P105, DOI 10.1145/383952.383968 YAMABANA K, 1998, CROSS LANGUAGE INFOR, P93 Yang YM, 1998, ARTIF INTELL, V103, P323, DOI 10.1016/S0004-3702(98)00063-0 NR 115 TC 28 Z9 28 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2005 VL 41 IS 3 BP 433 EP 455 DI 10.1016/j.ipm.2004.06.007 PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 887HY UT WOS:000226297800002 ER PT J AU Larkey, LS Connell, ME AF Larkey, LS Connell, ME TI Structured queries, language modeling, and relevance modeling in cross-language information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE cross-lingual information retrieval; language modeling; structured query translation; query expansion ID CONTINUOUS SPEECH; RECOGNITION AB Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast. language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries-one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus. We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage. (C) 2004 Elsevier Ltd. All rights reserved. C1 Univ Massachusetts, Dept Comp Sci, Ctr Intelligence Informat Retrieval, Amherst, MA 01003 USA. RP Larkey, LS (reprint author), Univ Massachusetts, Dept Comp Sci, Ctr Intelligence Informat Retrieval, Amherst, MA 01003 USA. EM larkey@cs.umass.edu; connell@cs.umass.edu CR BAHL LR, 1983, IEEE T PATTERN ANAL, V5, P179 Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 BROGLIO J, 1995, NIST SPECIAL PUBLICA, P29 Brown P. F., 1990, Computational Linguistics, V16 Brown P. F., 1993, Computational Linguistics, V19 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Federico M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 GEY FC, 2001, NATL I STANDARDS TEC, P16 HARMAN D, 1996, NATL I STANDARDS TEC Hiemstra D., 1999, P 3 EUR C RES ADV TE, P274 HIEMSTRA D, 2000, TRCTIT0009 U TWENT Jelinek F., 1997, STAT METHODS SPEECH JELINEK F, 1975, IEEE T INFORM THEORY, V21, P250, DOI 10.1109/TIT.1975.1055384 Khoja S., 1999, STEMMING ARABIC TEXT KOEHN P, 2002, EUROPARL MULTILINGUA Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 LARKEY LS, 2003, NATL I STANDARDS TEC, P721 Lavrenko V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 *LDC, 1998, LDC98T30 Liu X., 2002, CIKM 2002, P375 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 *NAT I INF, 2001, NTCIR WORKSH 2 P 2 N OARD DW, 2002, NATL I STANDARDS TEC, P17 Och F., 2000, P 38 ANN M ASS COMP, P440, DOI 10.3115/1075218.1075274 PETERS C, 2002, 2 WORKSH CROSS LANG PETERS C, 2001, CLEF 2000 LISB PORT Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957 Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST Siegel S, 1956, NONPARAMETRIC STAT B SMEATON A, 1997, NIST SPECIAL PUBLICA, P57 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Xu J., 2001, P 24 ANN INT ACM SIG, P105, DOI 10.1145/383952.383968 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 NR 43 TC 11 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2005 VL 41 IS 3 BP 457 EP 473 DI 10.1016/j.ipm.2004.06.008 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 887HY UT WOS:000226297800003 ER PT J AU Fujita, S AF Fujita, S TI NTCIR-2 as a Rosetta stone in laboratory experiments of IR systems SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE cross-language information retrieval; parallel test collection; query translation approach AB This paper presents a laboratory based evaluation study of cross-language information retrieval technologies, utilizing partially parallel test collections, NTCIR-2 (used together with NTCIR-1), where Japanese-English parallel document collections, parallel topic sets and their relevance judgments are available. These enable us to observe and compare monolingual retrieval processes in two languages as well as retrieval across languages. Our experiments focused on (1) the Rosetta stone question (whether a partially parallel collection helps in cross-language information access or not?) and (2) two aspects of retrieval difficulties namely "collection discrepancy" and "query discrepancy". Japanese and English monolingual retrieval systems are combined by dictionary based query translation modules so that a symmetrical bilingual evaluation environment is implemented. (C) 2004 Elsevier Ltd. All rights reserved. C1 Patolis Corp, Koto Ku, Tokyo 1350043, Japan. RP Fujita, S (reprint author), Patolis Corp, Koto Ku, Sumitomo Fudosan Kiba Bldg,2-4-29 Shiohama, Tokyo 1350043, Japan. EM s_fujita@patolis.co.jp CR Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 BRASCHLER M, 2001, P 2 NTCIR WORKSH M R, P15 CHEN A, 1999, P 1 NTCIR WORKSH RES, P49 *CLEF, 2000, CROSS LANG EV FOR WO Dumais S. T., 1996, SIGIR 96 WORKSH CROS, P16 EVANS DA, 1993, NIST SPECIAL PUBLICA, P494 FUJITA S, 2000, P 6 RIAO C CONT BAS, P1089 FUJITA S, 2001, P 2 NTCIR WORKSH M R, P181 FUJITA S, 2000, P ACL 2000 WORKSH RE, P47 Fujita S, 1999, P SOC PHOTO-OPT INS, V4017, P101, DOI 10.1117/12.373691 Greiff W. R., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290948 Hull D. A., 1996, P 19 ANN INT ACM SIG, P49, DOI 10.1145/243199.243212 KANDO N, 2001, P 2 NTCIR WORKSH M R, P35 *NTCIR 3, 2002, 3 NTCIR WORKSH 2001 *NTCIR 4, 2003, NTCIR WORKSH 2003 20 OAED D. W., 1996, UMIACSTR9619 OARD D, 1998, P 3 C ASS MACH TRANS, P472 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 XU J, 2000, NIST SPECIAL PUBLICA, P106 NR 19 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2005 VL 41 IS 3 BP 489 EP 506 DI 10.1016/j.ipm.2004.06.010 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 887HY UT WOS:000226297800005 ER PT J AU Dominich, S Lalmas, M Van Rijsbergen, CJ AF Dominich, S Lalmas, M Van Rijsbergen, CJ TI Guest editorial: Spaces, logic, and link analysis in IR: Recent advances from a mathematical and logical perspective SO INFORMATION RETRIEVAL LA English DT Editorial Material ID INFORMATION-RETRIEVAL; RELEVANCE; SCIENCE C1 Univ Veszprem, Dept Comp Sci, H-8200 Veszprem, Hungary. Univ London Queen Mary Coll, Dept Comp Sci, London E1 4NS, England. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Dominich, S (reprint author), Univ Veszprem, Dept Comp Sci, Egyetem U 10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu; mounia@dcs.qmw.ac.uk; keith@dcs.gla.ac.uk CR Bacchin M, 2005, INFORM PROCESS MANAG, V41, P121, DOI 10.1016/j.ipm.2004.04.006 BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 COOPER WS, 1964, J ACM, V11, P117, DOI 10.1145/321217.321218 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 DOMINICH S, 1999, P WORKSH LOG UNC MOD, P12 DOMINICH S, 2001, MATH FDN INFORMATION Egghe L, 1998, J AM SOC INFORM SCI, V49, P1144, DOI 10.1002/(SICI)1097-4571(1998)49:13<1144::AID-ASI2>3.0.CO;2-Z FUHR N, 1998, P 6 INT C EXT DAT TE, P24 GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471 GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108 GELLER NL, 1978, INFORM PROCESS MANAG, V14, P93, DOI 10.1016/0306-4573(78)90066-3 He DQ, 2002, INFORM PROCESS MANAG, V38, P727, DOI 10.1016/S0306-4573(01)00060-7 Lalmas M, 1998, INFORM PROCESS MANAG, V34, P19, DOI 10.1016/S0306-4573(97)00041-1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1967, INFORMATION RETRIEVA, P211 Page L., 1998, PAGERANK CITATION RA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST van Rijsbergen CJ, 1986, P 1986 ACM C RES DEV, P194, DOI 10.1145/253168.253208 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA vanRijsbergen CJ, 1996, J AM SOC INFORM SCI, V47, P385 VANRIJSBERGEN CJ, 2004, GEOMETRY IR NR 23 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2005 VL 8 IS 2 BP 175 EP 179 DI 10.1007/s10791-005-6047-z PG 5 WC Computer Science, Information Systems SC Computer Science GA 899XJ UT WOS:000227178800001 ER PT J AU Robertson, S AF Robertson, S TI On event spaces and probabilistic models in information retrieval SO INFORMATION RETRIEVAL LA English DT Article; Proceedings Paper CT Workshop on Mathematical/Formal Methods in Information Retrieval CY 2002 CL Tampere, FINLAND SP ACM SIGIR DE probabilistic models; event space; information retrieval ID RELEVANCE AB A basic notion of probability theory is the event space, on which the probability measure is defined. A probabilistic model needs an event space. However, some classes of events (which we may want to model probabilistically) exhibit structure which does not fit well into the traditional event space notion. A simple one-to-many example is discussed at length. The information retrieval case, involving queries, documents and relevance, is analysed. The event space issue makes for some difficulty in comparing different probabilistic models in IR. C1 Microsoft Res, Cambridge CB3 0FB, England. RP Robertson, S (reprint author), Microsoft Res, 7 JJ Thomson Ave, Cambridge CB3 0FB, England. EM ser@microsoft.com CR Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Croft W.B, 2003, LANGUAGE MODELLING I FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Lafferty J., 2003, PROBABILISTIC RELEVA, P1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1983, RES DEV INFORMATION, P108 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sparck Jones K, 2003, LANGUAGE MODELLING R, P57 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 11 TC 8 Z9 8 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2005 VL 8 IS 2 BP 319 EP 329 DI 10.1007/s10791-005-5665-9 PG 11 WC Computer Science, Information Systems SC Computer Science GA 899XJ UT WOS:000227178800009 ER PT J AU Sparck-Jones, K AF Sparck-Jones, K TI Some points in a time SO COMPUTATIONAL LINGUISTICS LA English DT Editorial Material ID INFORMATION AB This article offers a personal perspective on the development of language and information processing over the last half century, focusing on the use of statistical methods. Introduced, with computers, in the 1950s, these have not always been highly regarded, but were revived in the 1990s. They have proved effective in more ways than might have been expected, and encourage new thinking about what language and information processing involve. C1 Univ Cambridge, Comp Lab, Cambridge CB3 0FD, England. RP Sparck-Jones, K (reprint author), Univ Cambridge, Comp Lab, William Gates Bldg,JJ Thomson Ave, Cambridge CB3 0FD, England. EM ksj@cl.cam.ac.uk CR Jones K. S., 1996, EVALUATING NATURAL L, V1083 JONES KS, 1967, SP2714 SYST DEV CORP JONES KS, 1999, ADV AUTOMATIC TEXT S, P112 JONES KS, 1995, TRAVAUX CERCLE LINGU, V1, P201 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 2000, PHILOS T ROY SOC A, V358, P1225 JONES KS, 1991, NATURAL LANGUAGE GEN, P201 JONES KS, 2004, LANGUAGE MODELLINGS JONES KS, 1986, 170 ML U CAMBR CAMBR JONES KS, 1967, 196 ML CAMBR LANG RE JONES KS, 1983, IJCAI 83 P 8 INT JOI, P621 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1958, INT C SCI INF WASH D MASTERMAN M, 1959, P INT C SCI INF 1958, V2, P917 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1966, ISR10 HARV U COMP LA SPARCKJONES K, 1972, J DOC, V28, P11 VOORHEES EM, IN PRESS TREC EXPT E WOODS WA, 1978, ADV COMPUT, V17, P2 NR 20 TC 1 Z9 1 PU M I T PRESS PI CAMBRIDGE PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA SN 0891-2017 J9 COMPUT LINGUIST JI Comput. Linguist. PD MAR PY 2005 VL 31 IS 1 BP 1 EP 14 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA 922KK UT WOS:000228836600001 ER PT J AU Lee, C Lee, GG AF Lee, C Lee, GG TI Probabilistic information retrieval model for a dependency structured indexing system SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; term dependence; chow expansion; dependency parse tree; probabilistic model; 2-Poisson model ID TERM DEPENDENCE; BOOLEAN QUERIES AB Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems. (C) 2003 Elsevier Ltd. All rights reserved. C1 Pohang Univ Sci & Technol, Dept Comp Sci & Engn, Pohang 790784, South Korea. RP Lee, C (reprint author), Pohang Univ Sci & Technol, Dept Comp Sci & Engn, San 31 Hyoja Dong, Pohang 790784, South Korea. EM leeck@postech.ac.kr CR Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 COOPER WS, 1991, 14 ANN INT ACM SIGIR, P57 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, 10 ANN INT ACM SIGIR Duda R.O., 1973, PATTERN CLASSIFICATI, P111 HAYS DG, 1964, LANGUAGE, V40, P511, DOI 10.2307/411934 JONES KS, 1998, 446 U CAMBR COMP LAB KEMONG, 1992, KEMONG CO NEW ENCY Kurohashi S., 1994, P WORKSH SHAR NAT LA, P48 LIN D, 1994, P COLING 94 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X Losee RM, 2001, J AM SOC INF SCI TEC, V52, P1019, DOI 10.1002/asi.1155 LOSEE RM, 1988, INFORM PROCESS MANAG, V24, P315, DOI 10.1016/0306-4573(88)90097-0 LOSEE RM, 1991, INFORM PROCESS MANAG, V27, P1, DOI 10.1016/0306-4573(91)90027-J LUHN HP, 1958, IBM J RES DEV, V2, P159 Pauly M. V., 1984, J HLTH EC, V3, P88 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1997, 20 ANN INT ACM SIGIR, P16 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 *TREC EV, 1992, TREC EV PROGR AV VIA TURTLE HR, 1990, INT C RES DEV INF RE, P1 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 33 TC 9 Z9 9 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2005 VL 41 IS 2 BP 161 EP 175 DI 10.1016/j.ipm.2003.11.001 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 874AK UT WOS:000225323100001 ER PT J AU Trotman, A AF Trotman, A TI Choosing document structure weights SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE structured information retrieval; genetic algorithms; vector space model; probability model ID GENETIC ALGORITHMS; INFORMATION-RETRIEVAL; OPTIMIZATION; RANKING AB Existing ranking schemes assume all term occurrences in a given document are of equal influence. Intuitively, terms occurring in some places should have a greater influence than those elsewhere. An occurrence in an abstract may be more important than an occurrence in the body text. Although this observation is not new, there remains the issue of finding good weight: for each structure. Vector space, probability, and Okapi BM25 ranking are extended to include structure weighting. Weights are then selected for the TREC WSJ collection using a genetic algorithm. The learned weights are then tested on an evaluation set of queries. Structure weighted vector :space inner product and structure weighted probabilistic retrieval show an about 5% improvement in mean average precision over their unstructured counterparts. Structure weighted BM25 shows nearly no improvement. Analysis suggests BM25 cannot be improved using structure weighting. (C) 2003 Elsevier Ltd. All rights reserved. C1 Univ Otago, Dept Comp Sci, Dunedin, New Zealand. RP Trotman, A (reprint author), Univ Otago, Dept Comp Sci, Dunedin, New Zealand. EM andrew@cs.otago.ac.nz RI Trotman, Andrew/A-4918-2008 CR Baeza-Yates R., 1998, Proceedings. String Processing and Information Retrieval: A South American Symposium (Cat. No.98EX207), DOI 10.1109/SPIRE.1998.712977 Bartell B. T., 1994, P 17 ANN INT ACM SIG, P173 Boughanem M, 2002, J AM SOC INF SCI TEC, V53, P934, DOI 10.1002/asi.10119 Boyan J., 1996, P AAAI WORKSH INT BA BRAY T, 1988, EXTENSIBLE MARKUP LA Callan James P., 1994, P 17 ANN INT ACM SIG, P302 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S Chinenyanga T., 2001, P 24 ANN INT C RES D, P163, DOI 10.1145/383952.383982 DeJong K., 1975, THESIS U MICHIGAN Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 FUHR N, 2000, P ACM SIGIR 200 WORK FUHR N, 2002, P ACM SIGIR 2000 WOR Fuller M, 1993, P 16 ANN INT ACM SIG, P204, DOI 10.1145/160688.160720 Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GREFENSTETTE JJ, 1986, IEEE T SYST MAN CYB, V16, P122, DOI 10.1109/TSMC.1986.289288 Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 HOLLAND JH, 1975, ADAPTATION NATURAL A Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X Kaszkiel M, 2001, J AM SOC INF SCI TEC, V52, P344, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1075>3.3.CO;2-R Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 Khuri S., 1994, P 22 ANN ACM COMP SC, P66, DOI 10.1145/197530.197558 Kim S, 2000, P IEEE INT ASIC C&E, P13 KIM S, 2001, P 2001 C EV COMP CEC, P1253 KIM YH, 2000, P 9 TEXT RETR C TREC, P392 Kotsakis E., 2002, P 2002 ACM S APPL CO, P663 MEUSS H, 1999, P 21 ANN C IR RES IR Moody J., 1994, STAT NEURAL NETWORKS Morgan J., 1996, POLYMODEL 16 APPL AR, P142 Pathak P., 2000, P 33 HAW INT C SYST Rapela J, 2001, P 3 INT WORKSH WEB I, P61 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1996, EVALUATION LEARNING SCHLIEDER T, 2000, P DELOS WORKSH INF S Schlieder T, 2002, J AM SOC INF SCI TEC, V53, P489, DOI 10.1002/asi.10060 Shin D, 1998, P 3 ACM INT C DIG LI, P235, DOI 10.1145/276675.276702 THOM JA, 1995, DESIGN INDEXES STRUC TROTMAN A, IN PRESS INFORMATION VRAJITORU D, 2000, SOFT COMPUTING INFOR, P199 Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 Weisss S. M., 1991, COMPUTER SYSTEMS LEA WILKINSON R, 1994, P 3 TEXT RETR C TREC, P81 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 WOLFF J, 1999, IAITR9912 U BONN Wolff J. E., 2000, Proceedings IEEE Advances in Digital Libraries 2000, DOI 10.1109/ADL.2000.848377 Yang Jun, 1992, Proceedings of the IEEE International Symposium on Industrial Electronics (Cat. No.92TH0371-5), DOI 10.1109/ISIE.1992.279625 NR 48 TC 22 Z9 24 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2005 VL 41 IS 2 BP 243 EP 264 DI 10.1016/j.ipm.2003.10.003 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 874AK UT WOS:000225323100005 ER PT J AU Fan, WG Gordon, MD Pathak, P AF Fan, WG Gordon, MD Pathak, P TI Genetic programming-based discovery of ranking functions for effective Web search SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS LA English DT Article DE business intelligence; genetic programming; information retrieval; machine learning; ranking function; search engine; text mining; Web mining ID INFORMATION-RETRIEVAL; RELEVANCE AB Web search engines have become an integral part of the daily life of a knowledge worker, who depends on these search engines to retrieve relevant information from the Web or from the company's vast document databases. Current search engines are very fast in terms of their response time to a user query. But their usefulness to the user in terms of retrieval performance leaves a lot to be desired. Typically, the user has to sift through a lot of nonrelevant documents to get only a few relevant ones for the user's information needs. Ranking functions play a very important role in the search engine retrieval performance. In this paper, we describe a methodology using genetic programming to discover new ranking functions for the Web-based information-seeking task. We exploit the content as well as structural information in the Web documents in the discovery process. The discovery process is carried out for both the ad hoc task and the routing task in retrieval. For either of the retrieval tasks, the retrieval performance of these newly discovered ranking functions has been found to be superior to the performance obtained by well-known ranking strategies in the information retrieval literature. C1 Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA. Univ Michigan, Ross Sch Business, Ann Arbor, MI 48109 USA. Univ Florida, Warrington Coll Business, Gainesville, FL 32611 USA. RP Fan, WG (reprint author), Virginia Polytech Inst & State Univ, Blacksburg, VA 24061 USA. RI Fan, Weiguo/E-6343-2012 OI Fan, Weiguo/0000-0003-1272-5538 CR Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 BARTELL BT, 1994, P 17 ANN INT ACM SIG, V56, P173 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X CHEN H, 1994, J MANAGEMENT INFORMA, V11, P7 Chen HC, 1998, J AM SOC INFORM SCI, V49, P604 Dworman Garett O., 1995, J MANAGE INFORM SYST, V12, P97 Fan WG, 2004, J AM SOC INF SCI TEC, V55, P628, DOI 10.1002/asi.20009 Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 FUHR N, 1994, ACM T INFORM SYST, V12, P92, DOI 10.1145/174608.174612 GAO J, 2002, P 10 TEXT RETR C GAI, P384 Gey FC, 1994, P 17 ANN INT ACM SIG, P222 Gordon A, 1999, GLQ-J LESBIAN GAY ST, V5, P1 Gordon M, 1999, INFORM PROCESS MANAG, V35, P141, DOI 10.1016/S0306-4573(98)00041-7 Gordon M., 1988, COMMUN ACM, V31, P152 Fan WG, 2004, IEEE T KNOWL DATA EN, V16, P523, DOI 10.1109/TKDE.2004.1269663 Harman D. K., 1993, P 3 TEXT RETRIEVAL C, P1 HARMAN DK, 1992, P 11 ACM SIGIR C, P321 HARMAN DK, 1996, P 4 TEXT RETR C TREC, P1 HAWKING D, 2001, P 10 TEXT RETR C TRE, P61 HAWKING D, 2000, P 9 TEXT RETR C GAIT, P86 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Koza J, 1992, GENETIC PROGRAMMING LANCASTER F, 1993, INFORMATION RETRIEVA Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Martin-Bautista MJ, 1999, J AM SOC INFORM SCI, V50, P760, DOI 10.1002/(SICI)1097-4571(1999)50:9<760::AID-ASI4>3.3.CO;2-F Pathak P., 2000, P 33 HAW INT C SYST Raghavan V., 1987, P 2 INT C GEN ALG TH, P241 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Sun A., 2002, P 4 INT WORKSH WEB I, P96 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 YANG J, 1993, P 1 TEXT RETR C TREC, P31 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 40 TC 34 Z9 35 PU M E SHARPE INC PI ARMONK PA 80 BUSINESS PARK DR, ARMONK, NY 10504 USA SN 0742-1222 J9 J MANAGE INFORM SYST JI J. Manage. Inform. Syst. PD SPR PY 2005 VL 21 IS 4 BP 37 EP 56 PG 20 WC Computer Science, Information Systems; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA 916CG UT WOS:000228362400004 ER PT B AU Huang, XJ Huang, YR AF Huang, XJ Huang, YR BE Hu, XH Liu, Q Skowron, A Lin, TY Yager, RR Zhang, B TI Using contextual information to improve retrieval performance SO 2005 IEEE International Conference on Granular Computing, Vols 1 and 2 LA English DT Proceedings Paper CT IEEE International Conference on Granular Computing CY JUL 25-27, 2005 CL Beijing, PEOPLES R CHINA SP IEEE Comp Intelligence Soc, Natl Nat Sci Fdn China ID SEARCH AB In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM50 is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets. C1 York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. RP Huang, XJ (reprint author), York Univ, Sch Informat Technol, Toronto, ON M3J 1P3, Canada. CR Abdul-jaleel N., 2004, NIST SPECIAL PUBLICA Adar E, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P413, DOI 10.1145/319950.323231 Allan J, 2004, NIST SPECIAL PUBLICA Allan J., 2003, SIGIR Forum, V37 BEAULIEU MM, 1996, NIST SPECIAL PUBLICA Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 BHARAT K, P 9 INT WORLD WID WE, P493 Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 HARPER D, 2004, NIST SPECIAL PUBLICA HONG M, 2004, NIST SPECIAL PUBLICA HUANG X, 2004, NIST SPECIAL PUBLICA HUANG X, 2000, P BCS IRSG 2000 22 A, P178 Huang XJ, 2003, INFORM RETRIEVAL, V6, P333, DOI 10.1023/A:1026028229881 JIANG J, 2004, NIST SPECIAL PUBLICA JONES G., 2004, P ACM SIGIR 2004 WOR, P20 Lawrence S., 2000, IEEE DATA ENG B, V23, P25 LEVOW G, 2004, NIST SPEICAL PUBLICA PRETSCHNER A, 1999, INT C TOOLS ART INT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RODE H, 2004, NIST SPECIAL PUBLICA SALTON G, 1968, AUTOMATIC INFORMATIO SUN L, 2004, NIST SPECIAL PUBLICA Voorhees E., 2004, NIST SPECIAL PUBLICA, V500-261 Wen J., 2004, P 27 ANN INT C RES D, P57, DOI 10.1145/1008992.1009005 NR 25 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9017-2 PY 2005 BP 474 EP 481 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BCZ83 UT WOS:000232157200107 ER PT S AU Vinay, V Cox, IJ Milic-Frayling, N Wood, K AF Vinay, V Cox, IJ Milic-Frayling, N Wood, K BE Losada, DE FernandezLuna, JM TI Evaluating relevance feedback algorithms for searching on small displays SO ADVANCES IN INFORMATION RETRIEVAL SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 27th European Conference on Information Retrieval Research (ECIR 2005) CY MAR 21-23, 2005 CL Santiago Compostela, SPAIN SP Microsoft Res, SHARP, ERCIM, CEPIS, BCS HO Univ Santiago Compostele, Tech Sch Engn ID INFORMATION-RETRIEVAL; PROBABILISTIC MODEL AB Searching online information resources using mobile devices is affected by displays on which only a small fraction of the set of ranked documents can be displayed. In this paper, we ask whether the search effort can be reduced, on average, by user feedback indicating a single most relevant document in each display. For small display sizes and limited user actions, we are able to construct a tree representing all possible outcomes. Examination of the tree permits us to compute an upper limit on relevance feedback performance. Three standard feedback algorithms are considered - Rocchio, Robertson/Sparck-Jones and a Bayesian algorithm. Two display strategies are considered, one based on maximizing the immediate information gain and the other on most likely documents. Our results bring out the strengths and weaknesses of the algorithms, and the need for exploratory display strategies with conservative feedback algorithms. C1 UCL, Dept Comp Sci, London, England. Microsoft Res Ltd, Cambridge, England. RP Vinay, V (reprint author), UCL, Dept Comp Sci, London, England. EM v.vinay@cs.ucl.ac.uk; ingemar@ieee.org; natasamf@microsoft.com; krw@microsoft.com CR BUYUKKOKTEN O, 2001, P TENTHS INT WORLD W BUYUKKOKTEN O, 2000, P ACM C COMP HUM INT Camp G., 1996, MED ED ONLINE, V1, P2 CHEN Y, 2003, IN PRESS P 12 WORLD Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 Evans D.A., 1995, INFORMATION PROCESSI, V31, P494 GROSSMAN DA, 1997, J AM SOC INFORMATION, V48 HARMAN D, 1992, INFORMATION RETRIEVA, P131 HARMAN D, 1992, P 15 ANN INT ACM SIG HARMAN DK, 1997, OVERVIEW 5 TEXT RETR Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 JONES M, 1997, IEEE C ISSUES NETWOR, V239, P1 JONES M, 1999, P 8 WORLD WID WEB C LEWIS, 1994, S DOC AN INF RETR MAGENNIS M, 1997, P 29 ANN INT ACM SIG Milic-Frayling N., 2002, MSRTR2002114 OVER P, 1996, P 5 TEXT RETR C Robertson S., 1995, NIST SPECIAL PUBLICA ROBERTSON S, 2000, NISH SPECIAL PUBLICA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RODDEN K, 2003, P HCI C BATH SEPT RUVINI JD, 2003, IUI03 MIAMI FLO 0112 Sellen A. J., 2002, P SIGCHI C HUM FACT, P227 VINAY V, 2004, EVALUATING RELEVANCE WHITE RW, 2004, SIMULATED STUDY IMPL NR 27 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25295-9 J9 LECT NOTES COMPUT SC PY 2005 VL 3408 BP 185 EP 199 PG 15 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCF33 UT WOS:000229020900014 ER PT S AU Vechtomova, O AF Vechtomova, O BE Losada, DE FernandezLuna, JM TI The role of multi-word units in interactive information retrieval SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 27th European Conference on Information Retrieval Research (ECIR 2005) CY MAR 21-23, 2005 CL Santiago Compostela, SPAIN SP Microsoft Res, SHARP, ERCIM, CEPIS, BCS HO Univ Santiago Compostele, Tech Sch Engn ID PROBABILISTIC MODEL AB The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance feedback and a new phrase search method. A combined syntactico-statistical method was used for the selection of phrases. First, noun phrases were selected using a part-of-speech tagger and a noun-phrase chunker, and secondly, different statistical measures were applied to select phrases for query expansion. Experiments were also conducted studying the effectiveness of noun phrases in document ranking. We analyse the problems of phrase weighting and suggest new ways of addressing them. A new method of phrase matching and weighting was developed, which specifically addresses the problem of weighting overlapping and non-contiguous word sequences in documents. C1 Univ Waterloo, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. RP Vechtomova, O (reprint author), Univ Waterloo, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. EM ovechtom@engmail.uwaterloo.ca CR ALLAN J, 2004, P 12 TEXT RETR C NIS, P24 Banerjee S., 2003, P 4 INT C INT TEXT P Beaulieu M, 1998, INTERACT COMPUT, V10, P237, DOI 10.1016/S0953-5438(98)00008-3 BELY N, 1970, PROCEDURES ANAL SEMA Brill E, 1995, COMPUT LINGUIST, V21, P543 CLARKE CLA, 1995, CS9507 U WAT Dunning T., 1993, Computational Linguistics, V19 Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Frantzi K., 1996, P 16 INT C COMP LING, P41 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Manning C. D., 1999, FDN STAT NATURAL LAN MARCHIONINI G, 1992, J AM SOC INFORM SCI, V43, P156, DOI 10.1002/(SICI)1097-4571(199203)43:2<156::AID-ASI8>3.0.CO;2-U Mitra M, 1997, P RIAO97 COMP ASS IN, P200 RAMSHAW L, 1995, P 3 ACL WOKSH VER LA ROBERTSON SE, 2004, P 12 TEXT RETR C, P418 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruthven L, 2003, P 26 ANN INT ACM SIG, P213 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1968, COMPUTER EVALUATION, V15, P8 SMEATON AF, 1998, P 20 BCS IRSG C GREN STRZALKOWSKI T, 1999, NATURAL LANGUAGE INF, P113 VECHTOMOVA O, 2004, P 12 TEXT RETR C, P820 VECHTOMOVA O, 2005, IN PRESS P 13 TEXT R Vintar S., 2004, P MEMURA 2004 WORKSH, P54 VOORHEES E, 2004, P 12 TEXT RETR C NIS Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 28 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25295-9 J9 LECT NOTES COMPUT SC PY 2005 VL 3408 BP 403 EP 420 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCF33 UT WOS:000229020900029 ER PT J AU Liu, XY Croft, WB AF Liu, XY Croft, WB TI Statistical language modeling for information retrieval SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID PROBABILISTIC MODEL; RELEVANCE C1 Univ Massachusetts, Ctr Intelligent Retrieval, Dept Comp Sci, Amherst, MA 01003 USA. RP Liu, XY (reprint author), Univ Massachusetts, Ctr Intelligent Retrieval, Dept Comp Sci, Amherst, MA 01003 USA. CR ALLAN J, 2003, SIGIR FORUM, V37 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Bell T.C., 1990, TEXT COMPRESSION Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Chen S.F., 1998, TR1098 HARV U CTR RE Chen Stanley F., 1996, P 34 ANN M ASS COMP, P310, DOI 10.3115/981863.981904 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Cover T. M., 1991, ELEMENTS INFORMATION CROFT WB, 2001, SIGIR FORUM, V35, P4 CROFT WB, 2003, LANGUAGE MODELING IN CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Croft W.B., 2001, P DELOS NSF WORKSH P, P49 Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Cronen-Townsend S., 2002, P HCL 2002, P94 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Fuhr N., 2001, P WORKSH LANG MOD IN, P6 GOOD IJ, 1953, BIOMETRIKA, V40, P237, DOI 10.2307/2333344 GREIFF W, 2001, P WORKSH LANG MOD IN, P26 Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 Hiemstra D., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Hiemstra D., 1999, P 3 EUR C RES ADV TE, P274 HIEMSTRA D, 1999, 7 TEXT RETR C GAITH, P174 HIEMSTRA D, 2000, TRCTIT0009 U TWENTE HOFMANN T, 1999, P WOKRSH LANG MOD IN, P42 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jeffreys H., 1948, THEORY PROBABILITY, V2nd Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Jelinek F., 1997, STAT METHODS SPEECH Jin R., 2002, P 25 ANN INT ACM SIG, P42 JOHNSON WE, 1932, MIND, V41, P421 Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 2001, P WORKSH LANG MOD IN, P12 Jones K. S., 2000, Information Processing & Management, V36 KATZ SM, 1987, IEEE T ACOUST SPEECH, V35, P400, DOI 10.1109/TASSP.1987.1165125 Kneser R, 1995, P IEEE INT C AC SPEE, V1, P181 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Lafferty J., 2001, P WORKSH LANG MOD IN, P1 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Laplace P.-S., 1995, PHILOS ESSAY PROBABI LAVRENKO V, 2002, P 24 EUR C IR RES, P193 Lavrenko V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval LAVRENKO V, 2000, IR222 U MASS Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Li Xia, 2003, Proceedings of 2003 International Conference on Neural Networks and Signal Processing (IEEE Cat. No.03EX641) Lidstone George James, 1920, T FACULTY ACTUARIES, V8, P182 Liu X., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 MacKay D. J. C., 1995, NATURAL LANGUAGE ENG, V1, P1 Manning C. D., 1999, FDN STAT LANGUAGE PR MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 NADAS A, 1984, IEEE T ACOUST SPEECH, V32, P859, DOI 10.1109/TASSP.1984.1164378 NEY H, 1994, COMPUT SPEECH LANG, V8, P1, DOI 10.1006/csla.1994.1001 NEY H, 1993, EUROSPEECH 93, V3, P2239 OGILVIE P, 2000, UNPUB NEAREST NEIGHB PETERS J, 2001, P WORKSH LANG MOD IN, P55 PONTE J, 2000, ADV INFORMATION RETR, P73 PONTE J, 2001, P WORKSH LANG MOD IN, P37 Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rosenfeld R, 2000, P IEEE, V88, P1270, DOI 10.1109/5.880083 SHANNON CE, 1951, AT&T TECH J, V30, P50 Si L., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 Turtle H., 1991, P RIA0 91 C, P644 Turtle H. R., 1990, THESIS U MASSACHUSET VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WITTEN IH, 1991, IEEE T INFORM THEORY, V37, P1085, DOI 10.1109/18.87000 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 XU J, 2000, P 9 TEXT RETRIEVAL C, P106 Xu J., 2001, P 24 ANN INT ACM SIG, P105, DOI 10.1145/383952.383968 Zhai, 2002, THESIS CARNEGIE MELL Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2002, P 25 ANN INT ACM SIG, P49 ZHAI C, 2001, P WORKSH LANG MOD IN, P31 ZHU JX, 2001, P WORKSH LANG MOD IN, P47 Zipf G., 1929, HARVARD STUDIES CLAS, V40, P1, DOI 10.2307/310585 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL Zipf G. K., 1932, SELECTED STUDIES PRI Zipf G.-K., 1965, PSYCHOBIOLOGY LANGUA NR 86 TC 5 Z9 5 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 USA SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 2005 VL 39 BP 3 EP 31 PG 29 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 879UI UT WOS:000225744000002 ER PT S AU Chen, DY Li, X Dong, ZY Chen, X AF Chen, DY Li, X Dong, ZY Chen, X BE Tjoa, AM Trujillo, J TI Effectiveness of document representation for classification SO DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2005) CY AUG 22-26, 2005 CL Copenhagen, DENMARK ID TEXT CATEGORIZATION; MODEL AB Conventionally, document classification researches focus on improving the learning capabilities of classifiers. Nevertheless, according to our observation, the effectiveness of classification is limited by the suitability of document representation. Intuitively, the more features that are used in representation, the more comprehensive that documents are represented. However, if a representation contains too many irrelevant features, the classifier would suffer from not only the curse of high dimensionality, but also overfitting. To address this problem of suitableness of document representations, we present a classifier-independent approach to measure the effectiveness of document representations. Our approach utilises a labelled document corpus to estimate the distribution of documents in the feature space. By looking through documents in this way, we can clearly identify the contributions made by different features toward the document classification. Some experiments have been performed to show how the effectiveness is evaluated. Our approach can be used as a tool to assist feature selection, dimensionality reduction and document classification. C1 Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. RP Chen, DY (reprint author), Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. EM dingyi@itee.uq.edu.au; xueli@itee.uq.edu.au; zdong@itee.uq.edu.au; chenxia@itee.uq.edu.au RI gao, xiaodan/K-3594-2015 CR Akaike H., 1974, 2 INT S INF THEOR, P267 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 CHEN DY, 2005, 16 AUSTR DAT C NEWC, P125 Chickering D., 1997, P 13 C UNC ART INT U, P80 Cristianini N., 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dietterich T, 1995, ACM COMPUT SURV, V27, P326, DOI 10.1145/212094.212114 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Heckerman D., 1994, KDD WORKSH, P85 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS DD, REUTERS CORPUS Liu T., 2003, ICML, P488 Porter M. F., 1997, READINGS INFORM RETR Quinlan J. R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 VAPNIK VN, 1995, NATURE STAT LEARNING, P119 WARD G, MOBY WORK MOBY LEXIC WEISS SM, 1993, IEEE EXPERT, V8, P61, DOI 10.1109/64.248354 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 28 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28558-X J9 LECT NOTES COMPUT SC PY 2005 VL 3589 BP 368 EP 377 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCY28 UT WOS:000231850500036 ER PT B AU Zakos, J Verma, B AF Zakos, J Verma, B BE Selvaraj, H Verma, B DeCarvalho, A TI Concept-based term weighting for web information retrieval SO ICCIMA 2005: Sixth International Conference on Computational Intelligence and Multimedia Applications, Proceedings LA English DT Proceedings Paper CT 6th International Conference on Computational Intelligence and Multimedia Applications CY AUG 16-18, 2005 CL Las Vegas, NV SP IEEE Comp Soc AB In this paper we present a novel technique for determining term importance by exploiting concept-based information found in ontologies. Calculating term importance is a significant and fundamental aspect of most information retrieval approaches and it is traditionally determined through inverse document frequency (IDF). We propose concept-based term weighting (CBW), a technique that is fundamentally different to IDF in that it calculates term importance by intuitively interpreting the conceptual information in ontologies. We show that when CBW is used in an approach for web information retrieval on benchmark data, it performs comparatively to IDF, with only a 3.5% degradation in retrieval accuracy. While this small degradation has been observed the significance of this technique is that 1) unlike IDF, CBW is independent of document collection statistics, 2) it presents a new way of interpreting ontologies for retrieval, and 3) it introduces an additional source of term importance information that can be used for term weighting. C1 Griffith Univ, Sch Informat & Comm Technol, Southport, Qld 4215, Australia. RP Zakos, J (reprint author), Griffith Univ, Sch Informat & Comm Technol, Southport, Qld 4215, Australia. CR ANH V, 2003, P 12 TEXT RETR C TRE, P726 Anh V.N., 2002, P 25 ANN INT ACM SIG, P3 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Craswell N., 2003, P 12 TEXT RETR C TRE, P193 Li X., 1995, P IJCAI 95, P1368 Mihalcea R, 1998, P COLING ACL WORKSH, P16 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 NAMBA I, 1999, P 8 TEXT RETR C TREC, P275 PLACHOURIS V, 2002, WORKSH MATH FORM MET, P105 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1999, P 8 TEXT RETR C TREC, P151 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck Jones K., 1972, J DOC, V28, P111 VOORHEES EM, 1998, WORDNET ELECT LEXICA, P285 WEN J, 2003, P 12 TEXT RETR, P408 Zobel J., 1998, SIGIR Forum, V32 NR 19 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2358-7 PY 2005 BP 173 EP 178 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BDC11 UT WOS:000232529100027 ER PT S AU Ikeda, T Mano, H Itoh, H Takegawa, H Hiraoka, T Horibe, S Ogawa, Y AF Ikeda, T Mano, H Itoh, H Takegawa, H Hiraoka, T Horibe, S Ogawa, Y GP IEEE Comp Soc TI TRMeister: a DBMS with high-performance full-text search functions SO ICDE 2005: 21ST INTERNATIONAL CONFERENCE ON DATA ENGINEERING, PROCEEDINGS SE IEEE International Conference on Data Engineering LA English DT Proceedings Paper CT 21st International Conference on Data Engineering (ICDE 2005) CY APR 05-08, 2005 CL Tokyo, JAPAN SP IEEE Comp Soc, Database Soc Japan, Informat Proc Soc Japan, Inst Elect Informat & Commun Engineers, DBSJ, IPS, EIC, ORACLE, Microsoft, hp Invent, Google, Hitachi Inspire the Next, IBM, MItsubishi Elect, Fujitsu, KDDI, NEC, ntt, Ricoh, sgi, Sun, Toshiba AB TRMeister is a DBMS with high-performance full-text search functions. With TRMeister, high-speed full-text search, including high-precision ranking search in addition to Boolean search, is possible. Further, in addition to search, high-speed insert and delete are possible, allowing full-text search to be used in the same way as other types of database search in which data can be searched right after data is inserted This makes it easy to combine normal attribute search with full-text search and thus easily create text search applications. CR Barbara D, 1996, PROC INT CONF DATA, P172, DOI 10.1109/ICDE.1996.492103 Brown EricW., 1994, P 20 INT C VER LARG, P192 FRAKES WR, 1992, INFORMATION RETRIEVA Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 MANO H, 2003, 29 RICOH, P21 Ogawa Y., 1998, P 21 ANN INT ACM SIG, P367, DOI 10.1145/290941.291057 OGAWA Y, 2000, P 8 TEXT RETR C TREC, P541 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Tomasic A., 1994, P 1994 ACM SIGMOD IN, P289, DOI 10.1145/191839.191896 NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1084-4627 BN 0-7695-2285-8 J9 PROC INT CONF DATA PY 2005 BP 958 EP 967 PG 10 WC Computer Science, Information Systems SC Computer Science GA BCI52 UT WOS:000229594000108 ER PT S AU Kwok, KL Grunfeld, L Deng, P AF Kwok, KL Grunfeld, L Deng, P BE Lee, GG Yamada, A Meng, H Myaeng, SH TI Improving weak ad-hoc retrieval by web assistance and data fusion SO INFORMATION RETRIEVAL TECHNOLOGY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd Asia Information Retrieval Symposium CY OCT 13-15, 2005 CL Jeju Isl, SOUTH KOREA ID INFORMATION-RETRIEVAL AB Users experience frustration when their reasonable queries retrieve no relevant documents. We call these weak queries and retrievals. Improving their effectiveness is an important issue in ad-hoc retrieval and will be most rewarding for these users. We offer an explanation (with experimental support) why data fusion of sufficiently different retrieval lists can improve weak query results. This approach requires sufficiently different retrieval lists for an ad-hoc query. We propose various ways of selecting salient terms from longer queries to probe the web, and define alternate queries from web results. Target retrievals by the original and alternate queries are combined. When compared with normal ad-hoc retrieval, web assistance and data fusion can improve weak query effectiveness by over 100%. Another benefit of this approach is that other queries also improve along with weak ones, unlike pseudo-relevance feedback which works mostly for non-weak queries. C1 CUNY Queens Coll, Dept Comp Sci, Flushing, NY 11367 USA. RP Kwok, KL (reprint author), CUNY Queens Coll, Dept Comp Sci, Flushing, NY 11367 USA. EM kwok@ir.cs.qc.edu CR AMATI G., 2004, P 25 EUR C INF RETR, P127 Arampatzis AT, 1998, INFORM PROCESS MANAG, V34, P693, DOI 10.1016/S0306-4573(98)00030-2 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009132 Chang C. -C., 2004, LIBSVM LIB SUPPORT V Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval GOOGLE B, 2003, P HLT NAACL 2003 TEX GRUNFELD L, 2004, NAT I STANDARDS TECH, P510 Harman D., 2004, P 27 ANN INT ACM SIG, P528, DOI 10.1145/1008992.1009104 Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Kwok K. L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291003 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 Kwok Kuilam L., 1996, P 19 ANN INT ACM SIG, P187, DOI 10.1145/243199.243266 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lin Dekang, 1994, P COLING 94, P482 LIN J, 2002, NATL I STANDARDS TEC, P447 Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 RADA R, 1989, J AM SOC INFORM SCI, V40, P304, DOI 10.1002/(SICI)1097-4571(198909)40:5<304::AID-ASI2>3.0.CO;2-6 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Scholkopf B, 2000, NEURAL COMPUT, V12, P1207, DOI 10.1162/089976600300015565 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 VOORHEES EM, 2004, NAT I STANDARDS TECH, P69 YOMTOV E, 2004, WORK NOT TEXT RETR C, P393 NR 24 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29186-5 J9 LECT NOTES COMPUT SC PY 2005 VL 3689 BP 17 EP 30 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDF93 UT WOS:000233302700002 ER PT S AU Qin, T Liu, TY Zhang, XD Feng, G Ma, WY AF Qin, T Liu, TY Zhang, XD Feng, G Ma, WY BE Lee, GG Yamada, A Meng, H Myaeng, SH TI Subsite retrieval: A novel concept for topic distillation SO INFORMATION RETRIEVAL TECHNOLOGY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd Asia Information Retrieval Symposium CY OCT 13-15, 2005 CL Jeju Isl, SOUTH KOREA AB Topic distillation is one of the main information needs when users search the Web. In previous approaches to topic distillation, the single page was treated as the basic searching unit. This strategy is inherited from general information retrieval, which has not fully utilized the structure information of the Web. In this paper, we propose a novel concept for topic distillation, named subsite retrieval, in which the basic searching unit is the subsite instead of the single page. As indicated by the name, the subsite is a subset of website, consisting of a structural collection of pages. The key of subsite retrieval is to extract effective features to represent a subsite by utilizing both the content in each page and the structural information in the subsite. Specifically, we propose a so-called PI algorithm for this purpose, which is based on the modeling of website growth. Testing on the topic distillation task of TREC 2003 and TREC 2004, subsite retrieval gets significant improvement of retrieval performance over the previous single page based methods. C1 Tsing Hua Univ, MSP Lab, Dept Elect Engn, Beijing 100084, Peoples R China. Microsoft Res Asia, Beijing 100080, Peoples R China. RP Qin, T (reprint author), Tsing Hua Univ, MSP Lab, Dept Elect Engn, Beijing 100084, Peoples R China. EM qinshitao99@mails.tsinghua.edu.cn; t-tyliu@microsoft.com; zhangxd@tsinghua.edu.cn; fengg03@mails.tsinghua.edu.cn; wyma@microsoft.com CR AMITAY E, 2002, 11 TEXT C TREC 2002 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BHARAT K, 1998, P ACM SIGIR BHARAT K, 2001, P 10 INT WORLD WID W Broder A., 2002, SIGIR FORUM, V36 Chakrabarti S., 2001, P 24 ANN INT ACM SIG, P208, DOI 10.1145/383952.383990 Chakrabarti S., 2001, 10 INT WORLD WID WEB CRASWELL N, 2003, 12 TEXT RETR C TRECT FENG G, 2005, 7 AS PAC WEB C APWEB Golub G. H., 1996, MATRIX COMPUTATIONS HAWKING D, 2000, 9 TEXT RETR C TRECT INGONGNGAM P, 2003, 12 TEXT RETR C TRECT Kleinberg J, 2001, SCIENCE, V294, P1849, DOI 10.1126/science.1067014 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Page L., 1998, PAGERANK CITATION RA Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAKERY A, 2003, 12 TEXT RETR C TRECT WEN JR, 2003, 12 TEXT RETR C TREC NR 19 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29186-5 J9 LECT NOTES COMPUT SC PY 2005 VL 3689 BP 388 EP 400 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDF93 UT WOS:000233302700030 ER PT B AU Lamirel, JC Al Shehabi, S Francois, C AF Lamirel, JC Al Shehabi, S Francois, C BE Ingwersen, P Larsen, B TI Evaluation of collaboration between European universities using dynamic interaction between multiple sources SO ISSI 2005: Proceedings of the 10th International Conference of the International Society for Scientometrics and Informetrics, Vols 1 and 2 LA English DT Proceedings Paper CT 10th International Conference of the International-Society-for-Scientometrics-and-Informetrics CY JUL 24-28, 2005 CL Stockholm, SWEDEN SP Int Soc Scientometr & Informetr AB This paper presents a new approach whose aim is to extent the scope of Webometrics by providing it with new knowledge extraction and visualization capabilities. An application of this approach on a dataset of websites issued from European universities is presented. The basic model which is considered in this paper is a multi-topographic neural network model. The powerful features of this model are its generalization mechanism and its mechanism of communication between topographies. These two mechanisms allow rule extraction to be performed whenever a single viewpoint or multiple viewpoints on the same data are considered. The association rule extraction is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. The visualization of the results of the analyses is based on an original hyperbolic approach. C1 Lab Orrain Rech Informat & Applicat, Cortex Res Team, F-54506 Vandoeuvre Les Nancy, France. CR ALSHEHABI S, 2005, P INT JOINT C NEUR N CHERFI H, 2004, THESIS U NANCY 1 FRA *EISTES PROJ, 1999, IST199920350 EISTES HAMMER B, 2002, UNPUB INT CF NEUR NE Han J., 2001, GEOGRAPHIC DATA MINI Kohonen T., 2001, SELF ORG MAPS Lamirel JC, 2004, SCIENTOMETRICS, V60, P445, DOI 10.1023/B:SCIE.0000034386.05278.e8 Lamirel JC, 2004, SCIENTOMETRICS, V61, P427, DOI 10.1023/B:SCIE.0000045119.88828.ce LAMIREL JC, 2003, P 16 INT FLOR ART IN LAMIREL JC, 1995, THESIS U NANCY 1 FRA LAMIREL JC, 2003, P INT C COMP LING AC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 MARTINETZ T, 1991, ARTIFICIAL NEURAL NETWORKS, VOLS 1 AND 2, P397 NR 13 TC 0 Z9 0 PU KAROLINSKA UNIV PRESS AB PI STOCKHOLM PA BOX 200, STOCKHOLM, SE-171 77, SWEDEN BN 91-7140-339-6 PY 2005 BP 740 EP 749 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BDC93 UT WOS:000232759800122 ER PT J AU Griffiths, KM Tang, TT Hawking, D Christensen, H AF Griffiths, KM Tang, TT Hawking, D Christensen, H TI Automated assessment of the quality of depression websites SO JOURNAL OF MEDICAL INTERNET RESEARCH LA English DT Article DE quality indicators; depression; Internet; World Wide Web; validity; information retrieval ID CONSUMER HEALTH INFORMATION; RANDOMIZED CONTROLLED-TRIAL; WORLD-WIDE-WEB; INTERNET; ACCURACY; ACCESSIBILITY; INTERVENTIONS; INDICATORS AB Background: Since health information on the World Wide Web is of variable quality, methods are needed to assist consumers to identify health websites containing evidence-based information. Manual assessment tools may assist consumers to evaluate the quality of sites. However, these tools are poorly validated and often impractical. There is a need to develop better consumer tools, and in particular to explore the potential of automated procedures for evaluating the quality of health information on the web. Objective: This study (1) describes the development of an automated quality assessment procedure (AQA) designed to automatically rank depression websites according to their evidence-based quality; (2) evaluates the validity of the AQA relative to human rated evidence-based quality scores; and (3) compares the validity of Google PageRank and the AQA as indicators of evidence-based quality. Method: The AQA was developed using a quality feedback technique and a set of training websites previously rated manually according to their concordance with statements in the Oxford University Centre for Evidence-Based Mental Health's guidelines for treating depression. The validation phase involved 30 websites compiled from the DMOZ, Yahoo! and LookSmart Depression Directories by randomly selecting six sites from each of the Google PageRank bands of 0, 1-2, 3-4, 5-6 and 7-8. Evidence-based ratings from two independent raters (based on concordance with the Oxford guidelines) were then compared with scores derived from the automated AQA and Google algorithms. There was no overlap in the websites used in the training and validation phases of the study. Results: The correlation between the AQA score and the evidence-based ratings was high and significant (r=0.85, P <.001). Addition of a quadratic component improved the fit, the combined linear and quadratic model explaining 82 percent of the variance. The correlation between Google PageRank and the evidence-based score was lower than that for the AQA. When sites with zero PageRanks were included the association was weak and non-significant (r=0.23, P=.22). When sites with zero PageRanks were excluded, the correlation was moderate (r=.61, P=.002). Conclusions: Depression websites of different evidence-based quality can be differentiated using an automated system. If replicable, generalizable to other health conditions and deployed in a consumer-friendly form, the automated procedure described here could represent an important advance for consumers of Internet medical information. C1 Australian Natl Univ, Mental Hlth Res Ctr, Depress & Anxiety Consumer Res Unit, Canberra, ACT 0200, Australia. Australian Natl Univ, Dept Comp Sci, Canberra, ACT 0200, Australia. CSIRO, ICT Ctr, Canberra, ACT, Australia. RP Griffiths, KM (reprint author), Australian Natl Univ, Mental Hlth Res Ctr, Depress & Anxiety Consumer Res Unit, Canberra, ACT 0200, Australia. EM kathy.griffiths@anu.edu.au RI Griffiths, Kathleen /A-9872-2012; Christensen, Helen/F-5053-2012 CR *AUSTR BUR STAT, 2005, HOUS US INF TECHN AU Berland GK, 2001, JAMA-J AM MED ASSOC, V285, P2612, DOI 10.1001/jama.285.20.2612 Brin S., 1998, P 7 INT C WORLD WID, P107 Charnock D, 1999, J EPIDEMIOL COMMUN H, V53, P105 Christensen H, 2004, BRIT MED J, V328, P265, DOI 10.1136/bmj.37945.566632.EE *CTR EV BAS MENT H, SYST GUID MAN DEPR P Curro V, 2004, MED INFORM INTERNET, V29, P95, DOI 10.1080/14639230410001684396 Eysenbach G, 2002, JAMA-J AM MED ASSOC, V287, P2691, DOI 10.1001/jama.287.20.2691 Fallis D, 2002, J AM MED INFORM ASSN, V9, P73 FOX S, HLTH INFORM ONLINE Fricke M, 2005, AM J MED, V118, P168, DOI 10.1016/j.amjmed.2004.04.032 Griffiths KM, 2005, J MED INTERNET RES, V7, P53, DOI 10.2196/jmir.7.5.e55 Griffiths KM, 2004, BRIT J PSYCHIAT, V185, P342, DOI 10.1192/bjp.185.4.342 Griffiths KM, 2000, BRIT MED J, V321, P1511, DOI 10.1136/bmj.321.7275.1511 Griffiths KM, 2002, MED J AUSTRALIA, V176, pS97 Hibbard J., 2003, MED CARE, V41, P161, DOI DOI 10.1097/00005650-200301001-00007 JORM AF, 2001, HELP DEPRESSION Kohn R, 2004, B WORLD HEALTH ORGAN, V82, P858 Kunst H, 2002, BRIT MED J, V324, P581, DOI 10.1136/bmj.324.7337.581 Lopez A.D., 1996, GLOBAL BURDEN DIS CO *NAT TEL INF ADM, 2004, NAT ONL ENT BROADB A *OFF NAT STAT, FAM EXP SURV 2005 Price S L, 1999, Proc AMIA Symp, P911 Risk A, 2002, JAMA-J AM MED ASSOC, V287, P2713, DOI 10.1001/jama.287.20.2713 Robertson S, 1994, NIST SPECIAL PUBLICA ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SPSS, 2003, SPSS WIND REL 12 0 1 Suarez H H, 1997, Proc AMIA Annu Fall Symp, P824 TANG TT, IN PRESS J INFORM RE NR 30 TC 16 Z9 16 PU JOURNAL MEDICAL INTERNET RESEARCH PI TORONTO PA TORONTO GENERAL HOSPITAL, R FRASER ELLIOTT BLDG, 4TH FL, R 4S435, 190 ELIZABETH ST, TORONTO, ON M5G 2C4, CANADA SN 1438-8871 J9 J MED INTERNET RES JI J. Med. Internet Res. PY 2005 VL 7 IS 5 BP 80 EP 90 AR e59 DI 10.2196/jmir.7.5.e59 PG 11 WC Health Care Sciences & Services; Medical Informatics SC Health Care Sciences & Services; Medical Informatics GA 010SL UT WOS:000235218100009 PM 16403723 ER PT S AU Rajman, M Vesely, M AF Rajman, M Vesely, M BE Sirmakessis, S TI Combining text mining and information retrieval techniques for enhanced access to statistical data on the web: A preliminary report SO Knowledge Mining SE STUDIES IN FUZZINESS AND SOFT COMPUTING LA English DT Proceedings Paper CT NEMIS Final Conference on Knowledge Mining CY OCT 25, 2004 CL Athens, GREECE AB In this contribution, we present the StatSearch prototype, a search engine that enables an enhanced access to domain specific data available on the Web. The StatSearch engine proposes a hybrid search interface combining query-based search with automated navigation through a tree-like hierarchical structure. The goal of such an interface is to allow a more natural and intuitive control over the information access process, thus improving the speed and quality of the access to information. An algorithm for automated navigation is proposed that requires natural language pre-processing of the documents, including language identification, tokenization, Part-of-Speech (PoS) tagging, lemmatization and entity extraction. Structural transformation of the available data collection is also performed to reorganize the nodes in the information space (the Web site) from a graph into a tree-like hierarchical structure. This structural pre-processing (transformation of a graph structure into a tree-like hierarchy) can be done either by document clustering, or, alternatively, derived from existing structure of the document collection by splitting, shifting, or merging of nodes where necessary. The clustering approach is more straightforward but requires that the intermediate nodes in the created tree axe assigned understandable descriptions, which corresponds to a difficult task. Target documents are represented by weighted lexical profiles the components of which correspond to triples of the form (surface form, lemma, PoS). The extracted and normalized terms and entities are weighted using the TF.IDF weighting scheme. Document relevance is computed as the textual similarity between the query and document profiles. Several well known similarity functions from the field of information retrieval have been tested, including the Cosine and Okapi BM25 similarity measures. In addition to the similarity score, the contributions of all the query terms to the computed document similarities are also provided. The principle of the presented algorithm for automated navigation is to compute a score distribution on the documents (leaves of the tree), and to propagate the obtained scores upwards in the tree. The node scores are then used to guide a faster, partially automatic, downward navigation in the tree. In particular, user intervention for node selection is only required for nodes with children corresponding to a score distribution where no clearly good candidate can be identified. Otherwise, the (possible partial) traversal of the tree is performed automatically. Several approaches axe compared for the automation of the navigation. They include decision rules based on relative (resp. absolute) minimum best score differences, as well as on information theoretic measures. The automated navigation algorithm also allows a more reliable document ranking by giving to the user the possibility to restrict the search to the set of documents dominated by a specific node or to the documents matching a limited set of document types. The presented hybrid search technique has been implemented in the StatSearch prototype that has been realized in collaboration between EPFL, Statistics Sweden (SCB), and CERN, in the framework of the NEMIS network of excellence. The prototype focuses on domain of official statistics, and currently uses a database of over 5000 full text documents, tables and graphs in English accessible at the SCB Web site. C1 Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland. RP Rajman, M (reprint author), Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland. RI Rajman, Martin/ OI Rajman, Martin/0000-0002-1521-4920 CR ALF FBF, 2004, COMPSTAT 2004 16 S I BOYNTON IM, 2005, IN PRESS P HCI 2005 FORLER E, 2000, THESIS ECOLE POLYTEC GREFENSTETTE G, 1995, JADT 95 3 INT C STAT GUHL U, 1995, THESIS RHEINISCH WES ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 6 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1434-9922 BN 3-540-25070-0 J9 STUD FUZZ SOFT COMP PY 2005 VL 185 BP 213 EP 222 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDK35 UT WOS:000233987800016 ER PT S AU Kojima, Y AF Kojima, Y BE Peters, C Clough, P Gonzalo, J Jones, GJF Kluck, M Magnini, B TI Ricoh at CLEF 2004 SO MULTILINGUAL INFORMATION ACCESS FOR TEXT, SPEECH AND IMAGES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 5th Workshop of the Cross-Language Evaluation Forum CY SEP 15-17, 2004 CL Bath, ENGLAND SP Ist Sci Tecnol Informaz AB This paper describes Ricoh's participation is monolingual and bilingual information retrieval tasks done on the German Indexing and Retrieval Testdatabase (GIRT) at the Cross-Language Evaluation Forum (CLEF) 2004. We used a commercial morphological analyzer to decompound words and parallel corpora to retrieve bi-lingual information. While monolingual information retrieval was improved by using the analyzer, bi-lingual information retrieval still has room for improvement. C1 RICOH Co Ltd, Software R&D Grp, Bunkyo Ku, Tokyo 1120002, Japan. RP Kojima, Y (reprint author), RICOH Co Ltd, Software R&D Grp, Bunkyo Ku, 1-1-17 Koishikawa, Tokyo 1120002, Japan. EM ykoji@src.ricoh.co.jp CR ITOH H, 2001, 10 TEXT RETR C TREC, P457 KOJIMA Y, 2004, COMOP EVALUATION FOR, V3237, P367, DOI 10.1007/978-3-540-30222-3_35 KOJIMA Y, RICOH NTCIR 4 CLIR T OGAWA Y, 2001, P 2 NTCIR WORKSH M, P121 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 7 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-27420-0 J9 LECT NOTES COMPUT SC PY 2005 VL 3491 BP 292 EP 297 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCT55 UT WOS:000231117600030 ER PT S AU Al Shehabi, S Lamirel, JC AF Al Shehabi, S Lamirel, JC GP IEEE TI Multi-topographic neural network communication and generalization for multi-viewpoint analysis SO Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT IEEE International Joint Conference on Neural Networks (IJCNN 2005) CY JUL 31-AUG 04, 2005 CL Montreal, CANADA SP Int Neural Network Soc, IEEE Computat Intelligence Soc AB This paper presents a new generic multitopographic neural network model whose main area of application is clustering and knowledge extraction tasks on documentary data. The most powefull features of this model are its generalization mechanism and its mechanism of communication between topographies. This paper shows how these mechanisms can be exploited within the framework of the SOM and NG models. An evaluation of the generalization mechanism based on original quality and propagation coherency measures is also proposed. A secondary result of this evaluation is to proof that the generalization mechanism could significantly reduce the well-known border effect of the SOM map. C1 LORIA, F-54500 Vandoeuvre Les Nancy, France. RP Al Shehabi, S (reprint author), LORIA, Campus Sci,BP 239, F-54500 Vandoeuvre Les Nancy, France. CR ALSHEHABI S, 2004, 8 WORLD MULT SYST CY CAMPANARIO JM, 1995, SCIENTOMETRICS, V33, P23, DOI 10.1007/BF02020773 DITTENBACH ARM, 2000, ESANN 2000 P EUR S A, P7 Dittenbach M., 2000, P INT JOINT C NEUR N, V6, P15 Fritzke B., 1997, SOME COMPETITIVE LEA Kohonen T., 2001, SELF ORG MAPS Lamirel JC, 2004, SCIENTOMETRICS, V60, P445, DOI 10.1023/B:SCIE.0000034386.05278.e8 Lamirel JC, 2004, SCIENTOMETRICS, V61, P427, DOI 10.1023/B:SCIE.0000045119.88828.ce LAMIREL JC, 2003, ACL 2003 WORKSH PAT, P7 LAMIREL JC, 1995, THESIS U N H POINCAR LAMIREL JC, 2000, RIAO 2000 Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Martinetz T., 1991, ARTIFICIAL NEURAL NE, VI, P397 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 White HD, 1998, P 5 INT ISKO C WURZB, P57 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 0-7803-9048-2 J9 IEEE IJCNN PY 2005 BP 1564 EP 1569 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BDS40 UT WOS:000235178002044 ER PT B AU Kammoun, H Lamirel, JC Ben Ahmed, M AF Kammoun, Hager Lamirel, Jean Charles Ben Ahmed, Mohamed BE Ardil, C TI Neural-Symbolic Machine-Learning for Knowledge Discovery and Adaptive Information Retrieval SO PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 6 LA English DT Proceedings Paper CT Conference of the World-Academy-of-Science-Engineering-and-Technology CY JUN 24-26, 2005 CL Sydney, AUSTRALIA DE Information Retrieval Systems; machine learning; classification; Galois lattices; Self Organizing Map ID TEXT RETRIEVAL; SYSTEM AB In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, one qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build "long term" knowledge about past queries and concepts in a collection of documents. The "long term" knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure. C1 [Kammoun, Hager; Ben Ahmed, Mohamed] RIADI GDL, Ecole Natl Sci Informat, Tunis, Tunisia. RP Kammoun, H (reprint author), RIADI GDL, Ecole Natl Sci Informat, Campus Univ Manouba, Tunis, Tunisia. EM hager.kammoun@isd.rnu.tn; lamirel@loria.fr; Mohmed.Benahmed@riadi.rnu.tn CR GODIN R, 1993, INT J MAN MACH STUD, V38, P747, DOI 10.1006/imms.1993.1035 BASTIDE Y, 2000, THESIS U BLAISE PASC Carpineto C, 2000, J AM SOC INFORM SCI, V51, P587 CLUZEAUCIRY M, 1988, DOCUMENTALISTE, V25, P155 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DANIELS PJ, 1995, P C RES DEV INF RETR, P238 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GODIN R, 1989, SIGIR FORUM, V23, P32, DOI 10.1145/75334.75339 Harman D., 1992, INFORMATION RETRIEVA, P241 Kohonen T, 1989, SELF ORG ASS MEMORY LAMIREL JC, 1995, THESIS U HENRIPOINCA Lee JH, 1998, INFORM PROCESS MANAG, V34, P681, DOI 10.1016/S0306-4573(98)00034-X Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Nie J., 1988, P ACM SIGIR GREN FRA, P495, DOI 10.1145/62437.62493 Rijsbergen V., 1979, INFORM RETRIEVAL, V2nd ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1971, ISR9 U CORN COMP SCI SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST THOMPSON RH, 1989, INT J MAN MACH STUD, V30, P639, DOI 10.1016/S0020-7373(89)80014-8 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VOORHEES EM, 1993, OV 6 TEXT RETR C TRE WALKER S, 1997, P 6 TEXT RETR C TREC XU J, P 19 INT C RES DEV I, P4 ZHANG H, 1995, MULTIMEDIA COMPUTING, V2417 NR 26 TC 0 Z9 0 PU WORLD ACAD SCI, ENG & TECH-WASET PI CANAKKALE PA PO BOX 125, CANAKKALE, 17100, TURKEY PY 2005 BP 295 EP 299 PG 5 WC Computer Science, Interdisciplinary Applications SC Computer Science GA BIN47 UT WOS:000261051500071 ER PT J AU Milic-Frayling, N AF Milic-Frayling, N. BE Zanasi, A TI Text processing and information retrieval SO TEXT MINING AND ITS APPLICATIONS TO INTELLIGENCE, CRM AND KNOWLEDGE MANAGEMENT SE Advances in Management Information LA English DT Article; Book Chapter ID VECTOR-SPACE MODEL; DOCUMENT-RETRIEVAL; RELEVANCE; ALGORITHM; FEEDBACK; SYSTEM AB This chapter introduces the reader to various issues related to gathering, pre-processing, and retrieving textual information. It provides an overview of statistical natural language processing (NLP) techniques that are commonly used as a pre-cursor to text mining or integral parts of text mining applications. It also discusses various aspects of information retrieval, covering the main research issues and common practices. C1 [Milic-Frayling, N.] Microsoft Res Ltd, Cambridge, England. RP Milic-Frayling, N (reprint author), Microsoft Res Ltd, Cambridge, England. EM natasamf@microsoft.com CR ADAMSON GW, 1974, INFORM STORAGE RET, V10, P253, DOI 10.1016/0020-0271(74)90020-5 Allen J., 1995, NATURAL LANGUAGE UND [Anonymous], 1995, P 6 MESS UND C MUC 6 [Anonymous], 1991, P 3 MESS UND C MUC 3 [Anonymous], 1993, P 5 MESS UND C MUC 5 [Anonymous], 1992, P 4 MESS UND C MUC 4 [Anonymous], 1998, P 7 MESS UND C MUC 7 [Anonymous], DOCUMENT UNDERSTANDI Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bahl L.R., 1976, INT S INF THEOR RONN Baker JK, 1975, SPEECH RECOGNITION, P521 Beauliue M. M., 1995, J AM SOC INFORM SCI, V47, P85 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 Bowman M.C., 1994, P 2 INT WORLD WID WE, P763 Brants Th., 1998, P NEMLAP 98 SYDN AUS Brill E, 1995, COMPUT LINGUIST, V21, P543 Brin S., 1998, P 7 WWW C SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chakrabarti S., 1998, P 7 WWW C CHARNIAK E, 1993, PROCEEDINGS OF THE ELEVENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, P784 Cho J., 1998, P 7 WWW C BRISB AUST Church K., 1989, ACL, V27, P76 Church K., 1991, LEXICAL ACQUISITION, P115 Church K.W., 1988, ANLP, V2, P136 Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 Dawson J., 1982, WRITING EFFICIENT PR Demers A.J., 1977, P 4 ACM SIGACT SIGPL, P170, DOI 10.1145/512950.512966 DeRose S. J., 1988, Computational Linguistics, V14 DEROUAULT AM, 1986, IEEE T PATTERN ANAL, V8, P742 Doran Ch., 1994, COLING, V15, P922 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W ELLIS D, 1989, J INFORM SCI, V15, P237, DOI 10.1177/102831538901500406 ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 Evans D.A., 1995, INFORMATION PROCESSI, V31, P494 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Fox C., 1990, SIGIR Forum, V24 Frakes W. B., 1992, INFORM RETRIEVAL DAT Francis W. N., 1964, MANUAL INFORM ACCOMP Francis W. N., 1982, FREQUENCY ANAL ENGLI Francis W. N., 1967, COMPUTATIONAL ANAL P Garside Roger, 1987, COMPUTATIONAL ANAL E Goldfarb C.F., 1998, XML HDB Goldfarb Charles F., 1990, SGML HDB HAFER MA, 1974, INFORM STORAGE RET, V10, P371, DOI 10.1016/0020-0271(74)90044-8 HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P Harman D., 1995, Hypertext - Information Retrieval - Multimedia. Proceedings HIM '95 Harman D.K., 1994, NIST SPECIAL PUBLICA Harman D.K., 1997, NIST SPECIAL PUBLICA HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Hauser R., 1998, FDN COMPUTATIONAL LI Ide E., 1971, SMART RETRIEVAL SYST, P337 Ingwersen P., 1992, INFORM RETRIEVAL INT Jelinek F., 1985, NATO ASI SERIES E, VE91, P569 Johansson S., 1978, MANUAL INFORM ACCOMP Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Justeson J. S., 1995, NATURAL LANGUAGE ENG, V1, P9 Kleinberg J.M., 1999, P 5 ANN INT C COMP C Lauer M., 1995, ACL, V33, P47 Lauer M., 1995, THESIS MARCQUARIE U Lewis DD, 1996, COMMUN ACM, V39, P92, DOI 10.1145/234173.234210 LEWIS DD, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P37 LIDDY ED, 1991, INFORM PROCESS MANAG, V27, P55, DOI 10.1016/0306-4573(91)90031-G LOVINS JB, 1968, MECH TRANSL, V11, P22 Manning D., 1999, FDN STAT NATURAL LAN MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Marshall I., 1987, COMPUTATIONAL ANAL E, P42 Paice C. D., 1990, SIGIR Forum, V24 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCHE E, 1995, COMPUT LINGUIST, V21, P227 Rosenkrantz S.J., 1970, IEEE 11 ANN S SWITCH, P139 Salton G., 1981, INFORM RETRIEVAL EXP, P316 Salton G., 1968, AUTOMATIC INFORM ORG Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1990, INFORM PROCESS MANAG, V26, P73, DOI 10.1016/0306-4573(90)90010-Y Sparck Jones K., 1998, READINGS INFORM RETR Sparck Jones K., 1976, J DOC, V32, P59 Sparck Jones K., 1981, INFORM RETRIEVAL EXP Sparck Jones K., 1995, INFORMATION PROCESSI, V31, P291 Strzalkowski T., 1994, P 4 C APPL NAT LANG, P168, DOI 10.3115/974358.974396 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 Ullman J.D., 1979, INTRO AUTOMATA THEOR Van Rijsbergen C. J., 1979, INFORM RETRIEVAL, V2nd Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Zobel J., 1998, SIGIR Forum, V32 NR 91 TC 0 Z9 0 PU WIT PRESS/COMPUTATIONAL MECHANICS PUBLICATIONS PI SOUTHAMPTON PA ASHURST LODGE, ASHURST, SOUTHAMPTON SO40 7AA, ENGLAND SN 1742-0172 BN 978-1-84564-131-3 J9 ADV MANAG INFORM PY 2005 VL 2 BP 1 EP 45 D2 10.2495/978-1-85312-995-7 PG 45 WC Computer Science, Information Systems SC Computer Science GA BWX86 UT WOS:000295277400002 ER PT J AU Chung, YM Lee, JY AF Chung, YM Lee, JY TI Optimization of some factors affecting the performance of query expansion SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE query expansion; similarity measures; query term weighting; term weights ID DOCUMENT-RETRIEVAL SYSTEMS; INFORMATION; THESAURUS; TERMS; USERS AB This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed. (C) 2003 Elsevier Ltd. All rights reserved. C1 Yonsei Univ, Dept Lib & Informat Sci, Sodaemun Ku, Seoul, South Korea. RP Chung, YM (reprint author), Yonsei Univ, Dept Lib & Informat Sci, Sodaemun Ku, 134 Shinchondong, Seoul, South Korea. EM ymchung@yonsei.ac.kr RI Lee, Jae Yun/A-5558-2010 OI Lee, Jae Yun/0000-0002-9263-6131 CR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY GJ, 1994, TECHNOL DISABIL, V3, P69 Chung YM, 2001, J AM SOC INF SCI TEC, V52, P283, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1073>3.3.CO;2-X CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DELOUPY C, 1998, TREC 7, P443 FENICHEL CH, 1981, J AM SOC INFORM SCI, V32, P23, DOI 10.1002/asi.4630320104 Gauch S., 1997, Proceedings of the Sixth International Conference on Information and Knowledge Management. CIKM'97, DOI 10.1145/266714.266910 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 HARMAN D, 1995, TREC 4, P1 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Jing Y., 1994, P RIAO 94, P146 JONES KS, 1971, AUTOMATIC KEYWORD CL Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 MANDALA R, 1998, INFORMATION PROCESSI, V36, P361 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 Park YC, 1996, INFORM PROCESS MANAG, V32, P543, DOI 10.1016/0306-4573(96)00026-X PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 Qiu Y., 1995, THESIS ETH ZURICH Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 Voorhees E.M., 1996, TREC 5, P1 Wei J, 2000, P 1 INT C WEB INF SY, V1, P366 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 27 TC 10 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2004 VL 40 IS 6 BP 891 EP 917 DI 10.1016/j.ipm.2003.11.003 PG 27 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 855PC UT WOS:000223985200002 ER PT J AU Peng, FC Schuurmans, D Wang, SJ AF Peng, FC Schuurmans, D Wang, SJ TI Augmenting naive Bayes classifiers with statistical language models SO INFORMATION RETRIEVAL LA English DT Article; Proceedings Paper CT 25th European Conference on Information Retrieval Research (ECIR 2003) CY APR 14-16, 2003 CL PISA, ITALY SP Elsevier, Assoc Italiana Informat Calcolo Automat, CEPIS, LIBERO, Microsoft Res, CRE, fast, Sharp, IBM, Data Port DE naive Bayes; text classification; n-gram language models; smoothing ID TEXT CATEGORIZATION; TERMS AB We augment naive Bayes models with statistical n-gram language models to address short-comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we refer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the independence assumptions of naive Bayes - allowing a local Markov chain dependence in the observed variables - while still permitting efficient inference and learning. Second, they permit straightforward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text classification problems - authorship attribution, text genre classification, and topic detection - in several languages - Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model. C1 Univ Massachusetts, Dept Comp Sci, Ctr Intelligent Informat Retrieval, Amherst, MA 01003 USA. Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2E8, Canada. Univ Waterloo, Sch Comp Sci, Waterloo, ON N2L 3G1, Canada. RP Peng, FC (reprint author), Univ Massachusetts, Dept Comp Sci, Ctr Intelligent Informat Retrieval, 140 Governors Dr, Amherst, MA 01003 USA. EM fuchun@cs.umass.edu; dale@cs.ualberta.ca; swang@cs.ualberta.ca CR Aizawa A., 2001, P 6 NAT LANG PROC PA, P307 Bell T.C., 1990, TEXT COMPRESSION Benedetto D., 2002, PHYS REV LETT, V88 CAVNAR WB, 1994, 3 ANN S DOC AN INF R Chen Stanley F., 1998, TR1098 HARV U COMP S CHURCH K, 1991, COMPUTER SPEECH LANG, V5 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Duda R., 1973, PATTERN CLASSIFICATI Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 EYHERAMENDY S, 2003, P ART INT STAT 2003 FEDERICO M, 1998, SPOKEN DIALOGUES COM, pCH7 Friedman N., 1997, MACH LEARN, V29, P31 GOODMAN J, 2002, UNPUB COMMENT LANGUA He J., 2000, P PRICAI 2000 INT WO, P24 HE J, 2001, APPL INTELLIGENCE Hiemstra D., 2001, THESIS U TWENTE Holmes D. I., 1995, LIT LINGUISTIC COMPU, V10, P111, DOI 10.1093/llc/10.2.111 Howard P.G., 1993, THESIS BROWN U HUFFMAN S, 1995, P TREC 4 4 TEXT RETR, P359 Jelinek F., 1990, READINGS SPEECH RECO, P450 Joachims T., 1998, P 10 EUR C MACH LEAR KATZ SM, 1987, IEEE T ACOUST SPEECH, V35, P400, DOI 10.1109/TASSP.1987.1165125 KEOGH E, 1999, P ART INT STAT 1999 KESSLER B, 1997, P 35 ANN M ASS COMP Kneser R, 1995, P IEEE INT C AC SPEE, V1, P181 Kwok KL, 1999, J AM SOC INFORM SCI, V50, P709, DOI 10.1002/(SICI)1097-4571(1999)50:8<709::AID-ASI8>3.0.CO;2-V Kwok KL, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P34, DOI 10.1145/258525.258531 Lang K., 1995, P 12 INT C MACH LEAR, P331 LEE Y, 2002, P 25 ANN INT ACM SIG Lewis D., 1998, P 10 EUR C MACH LEAR Lewis David D., 1992, THESIS U MASSACHUSET MANNING C, 1999, FDN STAT NASTURAL LA McCallum A, 1998, P AAAI 98 WORKSH LEA MOFFAT A, 1990, IEEE T COMMUN, V38, P1917, DOI 10.1109/26.61469 NEY H, 1994, COMPUT SPEECH LANG, V8, P1, DOI 10.1006/csla.1994.1001 Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rennie J D, 2001, THESIS MIT Rish I., 2001, P IJCAI 01 WORKSH EM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHMITT JC, 1991, Patent No. 5062143 Scott S., 1999, P 16 INT C MACH LEAR, P379 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Stamatatos E, 2000, COMPUT LINGUIST, V26, P471, DOI 10.1162/089120100750105920 Stamatatos E, 2001, COMPUT HUMANITIES, V35, P193, DOI 10.1023/A:1002681919510 TEAHAN W, 2003, IN PRESS LANGUAGE MO TEAHAN WJ, 2001, P WORKSH LANG MOD IN TEAHAN WJ, 1998, THESIS U WAIKATO Turney P., 2002, P 40 ANN M ASS COMP TURPIN A, 1999, 22 ACM SIGIR C RES D WITTEN I, 1991, IEEE T INFORMATION T, V37 WITTEN I, 1999, P IEEE DAT COMPR C 1 Yang Y, 1999, INFORMATION RETRIEVA, V1, P67 ZHANG H, 2001, P 18 INT C MACH LEAR NR 56 TC 66 Z9 69 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD SEP-DEC PY 2004 VL 7 IS 3-4 BP 317 EP 345 DI 10.1023/B:INRT.0000011209.19643.e2 PG 29 WC Computer Science, Information Systems SC Computer Science GA 761RP UT WOS:000187927500005 ER PT J AU Bodoff, D AF Bodoff, D TI Relevance models to help estimate document and query parameters SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE theory; algorithms; probabilistic models; language models ID MAXIMUM-LIKELIHOOD; RETRIEVAL AB A central idea of Language Models is that documents (and perhaps queries) are random variables, generated by data-generating functions that are characterized by document (query) parameters. The key new idea of this paper is to model that a relevance judgment is also generated stochastically, and that its data generating function is also governed by those same document and query parameters. The result of this addition is that any available relevance judgments are easily incorporated as additional evidence about the true document and query model parameters. An additional aspect of this approach is that it also resolves the long-standing problem of document-oriented versus query-oriented probabilities. The general approach can be used with a wide variety of hypothesized distributions for documents, queries, and relevance. We test the approach on Reuters Corpus Volume 1, using one set of possible distributions. Experimental results show that the approach does succeed in incorporating relevance data to improve estimates of both document and query parameters, but on this data and for the specific distributions we hypothesized, performance was no better than two separate one-sided models. We conclude that the model's theoretical contribution is its integration of relevance models, document models, and query models, and that the potential for additional performance improvement over one-sided methods requires refinements. C1 Hong Kong Univ Sci & Technol, ISMT Dept, Hong Kong, Peoples R China. RP Bodoff, D (reprint author), Hong Kong Univ Sci & Technol, ISMT Dept, Hong Kong, Peoples R China. EM dbodoff@ust.hk RI bodoff, david/A-3775-2009 CR Bodoff D, 2001, J AM SOC INF SCI TEC, V52, P785, DOI 10.1002/asi.1137.abs Bodoff D, 2003, J AM SOC INF SCI TEC, V54, P1050, DOI 10.1002/asi.10300 Brauen T., 1971, SMART RETRIEVAL SYST, P456 COOPER WS, 1995, P TREC3 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 FUHR N, 1994, P TREC3 Hansen LK, 1996, ADV COMPUT MATH, V5, P269, DOI 10.1007/BF02124747 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1994, J DOC, V50, P233, DOI 10.1108/eb026932 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 TEEVAN J, 2003, P SIGIR 2003 TOR CAN, P18 Yang Y., 1997, P ICML 97 14 INT C M ZHAI C, 2001, P CIKM 2001 ATL GA N NR 19 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 2004 VL 22 IS 3 BP 357 EP 380 DI 10.1145/1010614.1010615 PG 24 WC Computer Science, Information Systems SC Computer Science GA 837IF UT WOS:000222621600001 ER PT J AU Tarko, L AF Tarko, L TI Virtual fragmentation of molecules and similarity evaluation SO REVISTA DE CHIMIE LA English DT Article DE molecular fragment; functional group; similarity; outlier ID SUBSTRUCTURAL ANALYSIS; PROGRAM; SELECTION; ALGORITHM; FEATURES; DESIGN AB It is proposed a computer-assisted procedure for the fragmentation of molecules based on two ideas: a) two heavy atoms, bonded by a chemical bond with bond order B, are part of the same fragment if the bond order B is larger than a certain limit value; b) the hydrogen atoms bonded to a heavy atom belong to the same fragment as the heavy atom. Examples are given on how to identify fragments in molecules, ions, radicals, ion-radicals and compare these fragments to the classical functional groups. There are. computed certain properties (descriptors) of the identified fragments as functions of atom's net charge, bond orders etc. Checkings are made if the fragments from one analyzed molecule (M or M) form a representative sample for the join set of all fragments of M-1 and M-2. If the function of the representative sample has a large value, the molecules M-1 and M-2 have a significant chemical similarity. Examples of finding chemical outliers in a given group of molecules are presented. C1 Romanian Acad, Ctr Organ Chem CD Nenitzescu, Bucharest 060023, Romania. RP Tarko, L (reprint author), Romanian Acad, Ctr Organ Chem CD Nenitzescu, 202B Spl Independentai, Bucharest 060023, Romania. EM ltarko@cco.ro CR ADAMSON GW, 1976, J CHEM SOC PERK T 1, P168, DOI 10.1039/p19760000168 ADAMSON GW, 1974, NATURE, V248, P406, DOI 10.1038/248406a0 ANDREI T, 1995, STAT TEORIE APLICATI, P122 [Anonymous], SERENA SOFTWARE CARHART RE, 1985, J CHEM INF COMP SCI, V25, P64, DOI 10.1021/ci00046a002 Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 HODES L, 1981, J CHEM INF COMP SCI, V21, P132, DOI 10.1021/ci00031a004 HODES L, 1977, J MED CHEM, V20, P469, DOI 10.1021/jm00214a002 Ivanciuc O, 2002, INTERNET ELECT J MOL, V1, P559 Japertas P, 2002, QUANT STRUCT-ACT REL, V21, P23, DOI 10.1002/1521-3838(200205)21:1<23::AID-QSAR23>3.0.CO;2-E KLOPMAN G, 1986, MOL PHARMACOL, V136, P67 KLOPMAN G, 1995, TOXICOL LETT, V79, P145, DOI 10.1016/0378-4274(95)03366-S ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SATOH K, 1997, J CHEM SOFTWARE, V4, P101 SAUNDERS M, 1990, J AM CHEM SOC, V112, P1419, DOI 10.1021/ja00160a020 Shemetulskis NE, 1996, J CHEM INF COMP SCI, V36, P862, DOI 10.1021/ci950169+ STEWART JJP, 1989, J COMPUT CHEM, V10, P209, DOI 10.1002/jcc.540100208 STEWART JP, QCMP175 SOFTWARE MOP TARKO L, 1997, REV CHIM BUCURESTI, V48, P676 Tarko L, 2000, REV ROUM CHIM, V45, P809 Tarko L, 2001, MATCH-COMMUN MATH CO, P201 NR 23 TC 21 Z9 21 PU CHIMINFORM DATA S A PI BUCHAREST PA CALEA PLEVNEI NR 139, SECTOR 6, BUCHAREST R-77131, ROMANIA SN 0034-7752 J9 REV CHIM-BUCHAREST JI Rev. Chim. PD JUL PY 2004 VL 55 IS 7 BP 539 EP 546 PG 8 WC Chemistry, Multidisciplinary; Engineering, Chemical SC Chemistry; Engineering GA 844LU UT WOS:000223160500016 ER PT J AU Taghva, K Borsack, J Nartker, T Condit, A AF Taghva, K Borsack, J Nartker, T Condit, A TI The role of manually-assigned keywords in query expansion SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE query expansion; document keywords; relevance feedback; retrieval effectiveness; user satisfaction ID INFORMATION-RETRIEVAL; SEARCHERS SELECTION; RELEVANCE FEEDBACK; TERMS; KEYS; SYSTEM; USERS; WEB AB We report on two types of experiments with respect to manually-assigned keywords to documents in a collection. The first type of experiment examines the usefulness of manually-assigned keywords to automatic feedback. The second type of experiment considers the potential benefits of these keywords to the user as an interactive tool. Several experiments were run and compared. The results of these experiments indicate that there is no gain in average precision when manually-assigned keywords are used for query expansion. Further, manually-assigned keywords did not aid the user as an interactive tool for document understanding. (C) 2004 Elsevier Ltd. All rights reserved. C1 Univ Nevada, Informat Sci Res Inst, Las Vegas, NV 89154 USA. RP Taghva, K (reprint author), Univ Nevada, Informat Sci Res Inst, Las Vegas, NV 89154 USA. EM taghva@isri.unlv.edu CR ALLEN B, 1994, INFORM PROCESS MANAG, V30, P177, DOI 10.1016/0306-4573(94)90063-9 BATES M, 1981, ANN REV INFORMATION, V60, P139 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H EFTHIMIADIS E, 1989, FEEDBACK INTERACTION, P257 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Efthimiadis EN, 2000, J AM SOC INFORM SCI, V51, P989, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1002>3.0.CO;2-B FIDEL R, 1991, J AM SOC INFORM SCI, V42, P501, DOI 10.1002/(SICI)1097-4571(199108)42:7<501::AID-ASI5>3.0.CO;2-V FIDEL R, 1991, J AM SOC INFORM SCI, V42, P490, DOI 10.1002/(SICI)1097-4571(199108)42:7<490::AID-ASI4>3.0.CO;2-V FIDEL R, 1991, J AM SOC INFORM SCI, V42, P515, DOI 10.1002/(SICI)1097-4571(199108)42:7<515::AID-ASI6>3.0.CO;2-F HARPER DJ, 1980, THESIS JESUS COLL CA Harter S. P., 1986, ONLINE INFORMATION R *HUMM COMM LTD, 1999, FULCR SEARCHSQL REF Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 JONES KS, 1980, 5553 BRIT LIB Lucas W, 2002, J AM SOC INF SCI TEC, V53, P95, DOI 10.1002/asi.10013 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SPINK A, 2002, COMMUNICATION Spink A, 2001, J AM SOC INF SCI TEC, V52, P161, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1564>3.3.CO;2-C Spink A, 1997, J AM SOC INFORM SCI, V48, P382 Spink A, 1997, J AM SOC INFORM SCI, V48, P741, DOI 10.1002/(SICI)1097-4571(199708)48:8<741::AID-ASI7>3.0.CO;2-S SPINK A, 1995, INFORMATION PROCESSI, V31, P1 SPINK A, 1994, P 17 ANN INT ACM SIG, P81 SU LT, 1992, INFORM PROCESS MANAG, V28, P503, DOI 10.1016/0306-4573(92)90007-M VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 NR 27 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2004 VL 40 IS 3 BP 441 EP 458 DI 10.1016/j.ipm.2003.12.005 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 818PX UT WOS:000221258100003 ER PT J AU Dominich, S Goth, J Kiezer, T Szlavik, Z AF Dominich, S Goth, J Kiezer, T Szlavik, Z TI An entropy-based interpretation of retrieval status value-based retrieval, and its application to the computation of term and query discrimination value SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID MAXIMUM-ENTROPY; SYSTEMS; COMMUNICATION AB The concepts of Shannon information and entropy have been applied to a number of information retrieval tasks such as to formalize the probabilistic model, to design practical retrieval systems, to cluster documents, and to model texture in image retrieval. In this report, the concept of entropy is used for a different purpose. It is shown that any positive Retrieval Status Value (RSV)-based retrieval system may be conceived as a special probability space in which the amount of the associated Shannon information is being reduced; in this view, the retrieval system is referred to as Uncertainty Decreasing Operation (UDO). The concept of UDO is then proposed as a theoretical background for term and query discrimination power, and it is applied to the computation of term and query discrimination values in the vector space retrieval model. Experimental evidence is given as regards such computation; the results obtained compare well to those obtained using vector-based calculation of term discrimination values. The UDO-based computation, however, presents advantages over the vector-based calculation: It is faster, easier to assess and handle in practice, and its application is not restricted to the vector space model. Based on the ADI test collection, it is shown that the UDO-based Term Discrimination Value (TDV) weighting scheme yields better retrieval effectiveness than using the vector-based TDV weighting scheme. Also, experimental evidence is given to the intuition that the choice of an appropriate weighting scheme and similarity measure depends on collection properties, and thus the UDO approach may be used as a theoretical basis for this intuition. C1 Univ Veszprem, Dept Comp Sci, H-8200 Veszprem, Hungary. RP Dominich, S (reprint author), Univ Veszprem, Dept Comp Sci, Egyetem U 10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu; goth@dcs.vein.hu; kiezer@dcs.vein.hu; szlavik@dcs.vein.hu CR Baclawski K, 1996, INFORM PROCESS LETT, V57, P211, DOI 10.1016/0020-0190(96)00003-8 BELEW KB, 2000, FINDING OUT Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Berry M., 1999, UNDERSTANDING SEARCH COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 DOMINICH S, 2001, MATH FDN INFORMATION DUBIN D, 1995, P 18 ANN INT ACM SIG, P199, DOI 10.1145/215206.215360 FUJII A, 2001, P NTCIR M EV CHIN JA, P250 Greiff WR, 2000, ACM T INFORM SYST, V18, P246, DOI 10.1145/352595.352597 GUAZZO M, 1977, INFORM PROCESS MANAG, V13, P155, DOI 10.1016/0306-4573(77)90034-6 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 KANTOR PB, 1998, J AM SOC INFORM SCI, V46, P557 Kolmogoroff A N, 1933, GRUNDBEGRIFFE WAHRSC MCCLEAN M, 2002, USING ADV MATH IMPRO MEADOW CT, 1999, TEXT INFORMATION RET MEETHAM AR, 1969, INFORM STORAGE RET, V5, P129, DOI 10.1016/0020-0271(69)90017-5 Nigam K., 1999, P IJCAI 99 WORKSH MA, P61 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1974, INFORMATION PROCESSI, V74, P584 SHANNON CE, 1948, AT&T TECH J, V27, P379 Tan CM, 2002, INFORM PROCESS MANAG, V38, P529, DOI 10.1016/S0306-4573(01)00045-0 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 Yoo HW, 2002, PATTERN RECOGN, V35, P749, DOI 10.1016/S0031-3203(01)00072-3 YU CT, 1977, COMMUN ACM, V20, P135, DOI 10.1145/359436.359441 NR 27 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD MAY PY 2004 VL 55 IS 7 BP 613 EP 627 DI 10.1002/asi.20008 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 812GJ UT WOS:000220827700004 ER PT J AU Goncalves, MA Fox, EA Watson, LT Kipp, NA AF Goncalves, MA Fox, EA Watson, LT Kipp, NA TI Streams, structures, spaces, scenarios, societies (5S): A formal model for digital libraries SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Review DE theory; foundations; taxonomy; definitions; applications ID INFORMATION-RETRIEVAL; SYSTEMS; TEXT; SCIENCE; RELEVANCE; FRAMEWORK; LANGUAGE; SEARCH; WEB AB Digital libraries (DLs) are complex information systems and therefore demand formal foundations lest development efforts diverge and interoperability suffers. In this article, we propose the fundamental abstractions of Streams, Structures, Spaces, Scenarios, and Societies (5S), which allow us to define digital libraries rigorously and usefully. Streams are sequences of arbitrary items used to describe both static and dynamic (e.g., video) content. Structures can be viewed as labeled directed graphs, which impose organization. Spaces are sets with operations on those sets that obey certain constraints. Scenarios consist of sequences of events or actions that modify states of a computation in order to accomplish a functional requirement. Societies are sets of entities and activities and the relationships among them. Together these abstractions provide a formal foundation to define, relate, and unify concepts - among others, of digital objects, metadata, collections, and services required to formalize and elucidate "digital libraries". The applicability, versatility, and unifying power of the 5S model are demonstrated through its use in three distinct applications: building and interpretation of a DL taxonomy, informal and formal analysis of case studies of digital libraries (NDLTD and OAI), and utilization as a formal basis for a DL description language. C1 Virginia Polytech Inst & State Univ, Dept Comp Sci, Blacksburg, VA 24061 USA. RP Goncalves, MA (reprint author), Virginia Polytech Inst & State Univ, Dept Comp Sci, Blacksburg, VA 24061 USA. EM mgoncalv@vt.edu; fox@vt.edu; ltw@vt.edu; kipp@vt.edu CR Abiteboul S., 1999, DATA WEB RELATIONS S ABITEBOUL S, 1997, J DIGITAL LIB, V1, P5 Abrams M, 1999, COMPUT NETW, V31, P1695, DOI 10.1016/S1389-1286(99)00044-4 Anderson K. M., 2003, Proceedings 2003 Joint Conference on Digital Libraries ATKINS A, 2001, INTEROPERABILITY MET Atkins DL, 1999, IEEE T SOFTWARE ENG, V25, P334, DOI 10.1109/32.798323 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BAEZAYATES R, 2000, P ACM SIGIR 2000 WOR BAYLEY KD, 1994, TYPOLOGIES TAXONOMIE Bayraktar M., 1998, Digital 98 Libraries. Third ACM Conference on Digital Libraries Beeri C., 1990, Data & Knowledge Engineering, V5, DOI 10.1016/0169-023X(90)90020-E BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 Berners-Lee Tim, 2001, SCI AM, V284, P5 Booch G, 1999, COMMUN ACM, V42, P26, DOI 10.1145/317665.317672 Borgman CL, 1999, INFORM PROCESS MANAG, V35, P227 Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198 CASTELLI D, 2002, LECT NOTES COMPUTER, V2458, P251 Cattell R.G.G., 1994, OBJECT DATABASE STAN CLARKE CLA, 1995, COMPUT J, V38, P43 Codd E. F., 1970, COMMUN ACM, V13, P6 COOMBS JH, 1987, COMMUN ACM, V30, P933, DOI 10.1145/32206.32209 Cormen TH, 1990, INTRO ALGORITHMS CRABTREE A, 1997, P 2 ACM INT C DIG LI, P221, DOI 10.1145/263690.263824 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 DAVIS L, 2001, JCDL 01 P 1 JOINT C, P146 DAVIS MD, 1994, COMPUTATION COMPLEXI de Moura ES, 2000, ACM T INFORM SYST, V18, P113 De Oliveira MCF, 2001, ACM T INFORM SYST, V19, P28, DOI 10.1145/366836.366869 *DOMTEAM, 2003, DOC OBJ MOD DOM HOM Dong A., 2001, P 1 ACM IEEE CS JOIN, P314, DOI 10.1145/379437.379699 DUSHAY N, 2001, CSDL0112017 CORR ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 FLANAGAN JW, 2003, MANAGING COMPLEX INF FOSKETT DJ, 1980, ENCY LIB INFORMATION, P416 Fox E. A., 1995, Communications of the ACM, V38, DOI 10.1145/205323.205325 FOX EA, 1997, D LIB MAG, V3, P9 Fox EA, 1998, COMMUN ACM, V41, P28 FRANCE RK, 2002, MARIAN DIGITAL LIB S FUHR N, 2000, P ACM SIGIR 2000 WOR FUHR N, 2004, XIRQL XML QUERY LANG FURUTA R, 1994, P 1 ANN C THEOR PRAC FURUTA R, 1989, ELECT PUB ORIGINATIO, V1, P19 GARZASALAZAR DA, 2001, PHRONESIS Gladney H., 1994, P 1 ANN C THEOR PRAC, P101 Gladney HM, 2001, COMMUN ACM, V44, P63, DOI 10.1145/374308.374343 GODEMENT R, 1969, ALGEBRA GOH D, 2000, DL 00 P 5 ACM INT C, P153 Goldfarb C.F., 1998, XML HDB Goncalves M. A., 2002, P 2 ACM IEEE CS JOIN, P263 GONCALVES MA, 2001, P JOINT DESLOS NSF W GONCALVES MA, 2003, DIGITAL LIB FORMAL T GONCALVES MA, 2003, 0304 VIRG TECH GONCALVES MA, 2002, P 9 INT S STRING PRO, P194 GONCALVES MA, 2001, P 5 EUR C RES ADV TE, P173 HEATH LS, 1995, COMMUN ACM, V38, P52, DOI 10.1145/205323.376383 HSIA P, 1994, IEEE SOFTWARE, V11, P33, DOI 10.1109/52.268953 JONES KS, 1997, READINGS INFORMATION KALINICHENKO LA, 2000, P 2 RUSS SCI C DIG L Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 KELAPURE R, 2003, LECT NOTES COMPUTER, V2760 Kochtanek TR, 1999, INFORM PROCESS MANAG, V35, P245, DOI 10.1016/S0306-4573(98)00060-0 KYING M, 1995, SCENARIO BASED DESIG Laender AHF, 2002, DATA KNOWL ENG, V40, P121, DOI 10.1016/S0169-023X(01)00047-7 Lagoze C., 2001, P 1 ACM IEEE CS JOIN, P54, DOI 10.1145/379437.379449 LAGOZE C, 1996, D LIB MAG, V2, P7 Lamsweerde A., 1998, IEEE T SOFTWARE ENG, V24, P1089 Lesk M., 1999, P INT S DIG LIB 1999 LEVY DM, 1995, COMMUN ACM, V38, P77, DOI 10.1145/205323.205346 *LIB C, 2003, METS MET ENC TRANS S Licklider J. C. R., 1965, LIB FUTURE Lorie Raymond A., 2001, P 1 ACM IEEE CS JOIN, P346, DOI 10.1145/379437.379726 Lucarella D, 1996, ACM T INFORM SYST, V14, P3, DOI 10.1145/214174.214175 MACKAY WE, 1998, P CHI 98, P416, DOI 10.1145/274644.274701 MILLER T, 1999, ANNOTATION SYSTEM CO Navarro G, 1997, ACM T INFORM SYST, V15, P400, DOI 10.1145/263479.263482 *NDLTD, 2003, NETW DIG LIB THES DI Nelson ML, 2001, COMMUN ACM, V44, P60, DOI 10.1145/374308.374342 Nestorov S., 1997, SIGMOD Record, V26 NEVES FD, 2000, P 5 ACM C DIG LIB AC, P103 *OAI, 2001, OP ARCH IN PROT MET OARD D, 1999, D LIB MAG, V5, P10 Oberweis A, 1996, ACM T INFORM SYST, V14, P380, DOI 10.1145/237496.237498 OGAWA R, 1990, P ECHT 90 1 EUR C HY, P38 PRINCE R, 1999, P INT JOINT C WORK A, P217, DOI 10.1145/295665.295692 Ramakrishnan N, 2000, IEEE INTERNET COMPUT, V4, P21, DOI 10.1109/4236.895012 RANGANATHAN SR, 1965, DESCRIPTIVE ACCOUNT Reddy R., 2001, DIGITAL LIB UNIVERSA ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rossman AY, 1999, STUD MYCOL, P1 ROSSON MB, 1996, P ACM CHI 96 C HUM F, P342 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SCHAUBLE P, 1998, SUMMARY REPORT SERIE SCHWARTZ MF, 1993, COMMUN ACM, V36, P78, DOI 10.1145/163381.163402 SHACKELFORD DE, 1993, P ACM C HYP ECHT 93, P1, DOI 10.1145/168750.168753 SHANNON CE, 1948, AT&T TECH J, V27, P379 Singhal M, 1994, ADV CONCEPTS OPERATI Spivey J.M., 1988, INTRO Z SPECIFICATIO STAPLES T, 2003, D LIB MAGA, V9, P4 Sutcliffe A., 1997, Proceedings of the Third IEEE International Symposium on Requirements Engineering (Cat. No.97TB100086), DOI 10.1109/ISRE.1997.566843 Sutcliffe AG, 1998, IEEE T SOFTWARE ENG, V24, P1072, DOI 10.1109/32.738340 Tague J., 1991, P 14 ANN INT ACM SIG, P14, DOI 10.1145/122860.122862 Tansley R., 2003, Proceedings 2003 Joint Conference on Digital Libraries TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Ullman J. D., 1988, PRINCIPLES DATABASE Varian H. R., 2000, INTERNET PUBLISHING VICKERY BC, 1965, RUTGERS SERIES INTEL, V5 *W3C, 2001, MATH MARK LANG MATHM *W3C, 2000, XML SCHEM PART 0 PRI *W3C, 1998, RES DESCR FRAM RDF M *W3C, 2000, RES DESCR FRAM RDF S WANG B, 1999, INT J DIGITAL LIB, V2, P91, DOI 10.1007/s007990050040 Wang WP, 1998, ACM T INFORM SYST, V16, P372, DOI 10.1145/291128.291132 WIEDERHOLD G, 1995, COMMUN ACM, V38, P85, DOI 10.1145/205323.205347 WILKINSON R, 1996, INFORM RETRIEVAL HYP, P257 Winskel G., 1993, FDN COMPUTING SERIES Witten I. H., 2000, P 5 ACM C DIG LIB SA, P113, DOI 10.1145/336597.336650 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 ZHU Q, 2003, LECT NOTES COMPUTER, V2769 Zia LL, 2001, COMMUN ACM, V44, P83, DOI 10.1145/374308.375359 Ziviani N, 2000, COMPUTER, V33, P37, DOI 10.1109/2.881693 ZUBAIR M, 2000, P 9 INT WORLD WID WE NR 124 TC 79 Z9 80 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 2004 VL 22 IS 2 BP 270 EP 312 DI 10.1145/984321.984325 PG 43 WC Computer Science, Information Systems SC Computer Science GA 812QK UT WOS:000220853800004 ER PT J AU Sartori, G Lombardi, L AF Sartori, G Lombardi, L TI Semantic relevance and semantic disorders SO JOURNAL OF COGNITIVE NEUROSCIENCE LA English DT Article ID CATEGORY-SPECIFIC DEFICITS; DOUBLE DISSOCIATION; LIVING THINGS; KNOWLEDGE; BRAIN; IMPAIRMENT; SYSTEMS; INFORMATION; RECOGNITION; SIMILARITY AB Semantic features are of different importance in concept representation. The concept elephant may be more easily identified from the feature than from the feature . As propose a new model of semantic memory to measure the relevance of semantic features for a concept and use this model to investigate the controversial issue of category specificity. Category-specific patients have an impairment in one domain of knowledge (e.g., living), whereas the other domain (e.g., nonliving) is relatively spared. We show that categories differ in the level of relevance and that, when concepts belonging to living and nonliving categories are equated to this parameter, the category-specific disorder disappears. Our findings suggest that category specificity, as well as other semantic-related effects, may be explained by a semantic memory model in which concepts are represented by semantic features with associated relevance values. C1 Univ Padua, Dipartimento Psicol Gen, I-35100 Padua, Italy. Univ Trent, Trent, Italy. RP Sartori, G (reprint author), Univ Padua, Dipartimento Psicol Gen, Via Venezia 8, I-35100 Padua, Italy. EM giuseppe-sartori@unipd.it RI Sartori, Giuseppe/B-8140-2008; Lombardi, Luigi/J-4639-2012 CR Humphreys GW, 2001, BEHAV BRAIN SCI, V24, P453 Capitani E, 2003, COGN NEUROPSYCHOL, V20, P213, DOI 10.1080/02643290244000266 Caramazza A, 1998, J COGNITIVE NEUROSCI, V10, P1, DOI 10.1162/089892998563752 Dell'acqua R, 2000, BEHAV RES METH INS C, V32, P588, DOI 10.3758/BF03200832 DERENZI E, 1994, CORTEX, V30, P3 Devlin JT, 1998, J COGNITIVE NEUROSCI, V10, P77, DOI 10.1162/089892998563798 DIXON MJ, 2001, BRAIN COGNITION, V43, P158 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Durrant-Peatfield MR, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P193 Forde EME, 1997, COGNITIVE NEUROPSYCH, V14, P403 FUNNELL E, 1992, COGNITIVE NEUROPSYCH, V9, P135, DOI 10.1080/02643299208252056 Funnell E, 1996, NEUROCASE, V2, P461, DOI 10.1080/13554799608402422 Garrard P, 2001, COGNITIVE NEUROPSYCH, V18, P125, DOI 10.1080/02643290125857 HILLIS AE, 1991, BRAIN, V114, P2081, DOI 10.1093/brain/114.5.2081 HODGES JR, 1995, MEMORY, V3, P463, DOI 10.1080/09658219508253161 Kintsch W, 2001, COGNITIVE SCI, V25, P173, DOI 10.1207/s15516709cog2502_1 LUHN HP, 1958, IBM J RES DEV, V2, P159 Martin A, 1996, NATURE, V379, P649, DOI 10.1038/379649a0 MARTIN A, 1995, SCIENCE, V270, P102, DOI 10.1126/science.270.5233.102 MCKHANN G, 1984, NEUROLOGY, V34, P939 PERANI D, 1995, NEUROREPORT, V6, P1637, DOI 10.1097/00001756-199508000-00012 Ralph MAL, 1998, NEUROCASE, V4, P311, DOI 10.1080/13554799808410630 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SACCHETT C, 1992, COGNITIVE NEUROPSYCH, V9, P73, DOI 10.1080/02643299208252053 Sartori G., 2002, CATEGORY SPECIFICITY, P25 SARTORI G, 1988, COGNITIVE NEUROPSYCH, V5, P105, DOI 10.1080/02643298808252928 Silveri MC, 1997, NEUROPSYCHOLOGIA, V35, P359 SMALL SL, 1995, BRAIN, V118, P441, DOI 10.1093/brain/118.2.441 STEWART F, 1992, Q J EXP PSYCHOL-A, V44, P261 TANAKA JW, 1991, COGNITIVE PSYCHOL, V23, P457, DOI 10.1016/0010-0285(91)90016-H TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 Tyler LK, 2000, BRAIN LANG, V75, P195, DOI 10.1006/brln.2000.2353 Tyler LK, 1997, COGNITIVE NEUROPSYCH, V14, P511 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WARRINGTON EK, 1984, BRAIN, V107, P829, DOI 10.1093/brain/107.3.829 NR 35 TC 42 Z9 43 PU M I T PRESS PI CAMBRIDGE PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA SN 0898-929X J9 J COGNITIVE NEUROSCI JI J. Cogn. Neurosci. PD APR PY 2004 VL 16 IS 3 BP 439 EP 452 DI 10.1162/089892904322926773 PG 14 WC Neurosciences; Psychology, Experimental SC Neurosciences & Neurology; Psychology GA 807GT UT WOS:000220490700009 PM 15072679 ER PT J AU Bodoff, D Robertson, S AF Bodoff, D Robertson, S TI A new unified Probabilistic model SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; MAXIMUM-LIKELIHOOD; RELEVANCE; LOGIC AB This paper proposes a new unified probabilistic model. Two previous models, Robertson et al.'s "Model 0" and "Model 3," each have strengths and weaknesses. The strength of Model 0 not found in Model 3, is that it does not require relevance data about the particular document or query, and, related to that, its probability estimates are straightforward. The strength of Model 3 not found in Model 0 is that it can utilize feedback information about the particular document and query in question. In this paper we introduce a new unified probabilistic model that combines these strengths: the expression of its probabilities is straightforward, it does not require that data must be available for the particular document or query in question, but it can utilize such specific data if it is available. The model is one way to resolve the difficulty of combining two marginal views in probabilistic retrieval. C1 Hong Kong Univ Sci & Technol, ISMT Dept, Hong Kong, Hong Kong, Peoples R China. Microsoft Res Cambridge, Cambridge CB3 0FB, England. RP Bodoff, D (reprint author), Hong Kong Univ Sci & Technol, ISMT Dept, Clear Water Bay, Hong Kong, Hong Kong, Peoples R China. EM dbodoff@ust.hk; ser@microsoft.com RI bodoff, david/A-3775-2009 CR Bodoff D, 2001, J AM SOC INF SCI TEC, V52, P785, DOI 10.1002/asi.1137.abs Bodoff D, 2003, J AM SOC INF SCI TEC, V54, P1050, DOI 10.1002/asi.10300 Brauen T., 1971, SMART RETRIEVAL SYST, P456 COOPER WS, 1995, TREC3 NAT I STAND TE Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GREEN R, 1995, J AM SOC INFORM SCI, V46, P646, DOI 10.1002/(SICI)1097-4571(199510)46:9<646::AID-ASI2>3.0.CO;2-1 LAFFERTY J, 2001, DOCUMENT LANGUAGE MO Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTSON S, 2000, SIGIR FOR, V34, P1 Robertson S, 1994, NIST SPECIAL PUBLICA Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 NR 25 TC 9 Z9 9 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD APR PY 2004 VL 55 IS 6 BP 471 EP 487 DI 10.1002/asi.10398 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 804HW UT WOS:000220290800001 ER PT B AU Nanas, N Uren, V de Roeck, A AF Nanas, N Uren, V de Roeck, A GP ieee computer society TI A comparative evaluation of term weighting methods for information filtering SO 15TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT 15th International Conference on Database and Expert Systems Applications (DEXA 2004) CY AUG 30-SEP 03, 2004 CL Zaragoza, SPAIN SP FAW, DEXA Assoc, Austrian Comp Soc HO Univ Zaragoza ID RELEVANCE AB Users of information filtering systems can not be expected to provide large amounts of information to initialize a profile. Therefore, term weighting methods for information filtering have somewhat different requirements to those for information retrieval and text categorization. We present a comparative evaluation of term weighting methods, including a new method, relative document frequency, designed specifically for information filtering. The best weighting methods appear to be those that favor information provided by the user, over information from a general collection. C1 Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England. RP Nanas, N (reprint author), Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England. CR ARAMPATZIS A, 2003, COMMUNICATION Breiman L., 1984, CLASSIFICATION REGRE CALLAN J, 1998, 21 ANN INT ACM SIGIR, P224 EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Kageura K., 1996, Terminology, V3, DOI 10.1075/term.3.2.03kag Manning C. D., 1999, FDN STAT NATURAL LAN Moens MF, 2000, INFORM PROCESS MANAG, V36, P841, DOI 10.1016/S0306-4573(00)00012-1 Nanas N., 2003, 26 ANN INT ACM SIGIR, P198 PAZZANI MJ, 2000, INT C INT US INT NEW Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE T, 2002, 3 IN C LANG RES EV Sebastiani F., 2002, ACM COMPUTING SURVEY, V34 SPARCKJONES K, 1972, J DOC, V28, P11 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Yang Y., 1997, 14 INT C MACH LEARN NR 18 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2195-9 PY 2004 BP 13 EP 17 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BBB00 UT WOS:000224459100003 ER PT B AU Al Shehabi, S Lamirel, JC AF Al Shehabi, S Lamirel, JC BE Callaos, N Sanchez, M Pineda, JM TI Unsupervised neural networks of topographic and gas families for documentary data classification SO 8TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS, AND INFORMATICS, VOL XVI, PROCEEDINGS LA English DT Proceedings Paper CT 8th World Multi-Conference on Systemics, Cybernetics and Informatics CY JUL 18-21, 2004 CL Orlando, FL SP Int Inst Informat & System, Amer Soc Cybernet, Acad Non Linear Sci, Univ Las Palmas Gran Canaria, Telemat Engn Dept, Concurrency & Architecture Grp, CUST, Blaise Pascal Univ, Engn Sci Inst, Cybernet & Human Knowing, Int Federat Syst Res, Int Syst Inst, Int Soc Syst Sci, Italian Soc System, Univ Nacl San Luis, Lab Res Computac Intelligence, Dept Informat, Polish Syst Soc, Slovenian Artificial Intelligence Soc, Soc Appl Syst Res, Syst Soc Poland, Ctr Syst Studies, Tunisian Sci Soc, World Org System & Cybernet, IEEE Comp Soc, Venezuela Chapter, IEEE, Venezuela Chapter, Natl Res Council Canada, Steacie Inst Mol Sci DE self-organizing map (SOM); MultiSOM neural gas (NG); growing neural gas (GNG); unsupervised data clustering; quality criteria and viewpoint-oriented-analysis ID SELF-ORGANIZING-MAPS AB The unsupervised neural networks are excellent tools for the analysis of high-dimensional input data as in data mining applications. In this paper different methods of data clustering are considered as the self-organizing map (SOM), neural gas (NG) and growing neural gas (GNG). This paper demonstrates, in one side, the efficiency of a viewpoint-oriented-analysis as compared to a global analysis, and in the other side, it compare these three unsupervised neural clustering methods as classifier for documentary data. For that, two quality criteria are taken into account for quality evaluation. These criteria are used as well for highlighting the clustering methods internal operation. C1 LORIA, INRIA, F-54506 Vandoeuvre Les Nancy, France. RP Al Shehabi, S (reprint author), LORIA, INRIA, Campus Sci,BP 239, F-54506 Vandoeuvre Les Nancy, France. CR Fritzke B., 1997, SOME COMPETITIVE LEA HINTON GE, 1989, ARTIF INTELL, V40, P185, DOI 10.1016/0004-3702(89)90049-0 HOLMSTROM J, THESIS *IST, IST199920350 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 Kohonen T., 1997, SELF ORG MAPS KOHONEN T, 1991, ARTIFICIAL NEURAL NETWORKS, VOLS 1 AND 2, P981 KOHONEN T, 1993, IEEE INT C NEUR NETW, V1, P1147 Kohonen T., 1984, SELF ORG ASSOCIATIVE KOHONEN T, 2000, IEEE T NEURAL NETWOR LAMIREL JC, P 1 DELOS NETW EXC W LAMIREL JC, P ACL SAPP JAP LAMIREL JC, 2000, C P RIAO, V2, P1657 LAMIREL JC, 2001, P ISSI 2001 SYDN AUS LAMIREL JC, 1995, THESIS U NANCY HENRI LAMIREL JC, 2004, INT J SCIENTOMETRICS, P4 Lebart L, 1982, TRAITEMENT DONNEES S OULD MMA, 1997, MEMOIRE STAGE DESS I Polanco X, 2001, SCIENTOMETRICS, V51, P267, DOI 10.1023/A:1010537316758 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST MARTINETZ T, 1991, ARTIFICIAL NEURAL NETWORKS, VOLS 1 AND 2, P397 VARSIS A, 1992, P 3 INT WORKSH PAR A NR 24 TC 0 Z9 0 PU INT INST INFORMATICS & SYSTEMICS PI ORLANDO PA 14269 LORD BARCLAY DR, ORLANDO, FL 32837 USA BN 980-6560-13-2 PY 2004 BP 354 EP 358 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBT44 UT WOS:000227693100075 ER PT J AU Tarko, L AF Tarko, L TI A procedure for virtual fragmentation of molecules into functional groups SO ARKIVOC LA English DT Article DE molecular fragment; functional group; bond order ID SUBSTRUCTURAL ANALYSIS; PROGRAM; SELECTION; FEATURES; DESIGN AB This article presents a computer assisted procedure for virtual fragmentation of molecules. The proposed algorithm defines fragments which usually coincide with the classical functional groups. The fragmentation criteria are the bond order's value of the chemical bonds and the type of the connected atoms ( hydrogen, carbon, heteroatom). C1 Romanian Acad, Ctr Organ Chem CD Nenitzescu, Bucharest, Romania. RP Tarko, L (reprint author), Romanian Acad, Ctr Organ Chem CD Nenitzescu, Spl Independentei 202B,Sect 6,POB 15-258, Bucharest, Romania. EM ltarko@cco.ro CR ADAMSON GW, 1976, J CHEM SOC PERK T 1, P168, DOI 10.1039/p19760000168 ADAMSON GW, 1974, NATURE, V248, P406, DOI 10.1038/248406a0 BLANKLEY CJ, 1996, CHEM INF COMPUT SCI, V36, P862 CARHART RE, 1985, J CHEM INF COMP SCI, V25, P64, DOI 10.1021/ci00046a002 *CCO, DESCRIPT SOFTW Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 HODES L, 1981, J CHEM INF COMP SCI, V21, P132, DOI 10.1021/ci00031a004 HODES L, 1977, J MED CHEM, V20, P469, DOI 10.1021/jm00214a002 Japertas P, 2002, QUANT STRUCT-ACT REL, V21, P23, DOI 10.1002/1521-3838(200205)21:1<23::AID-QSAR23>3.0.CO;2-E KLOPMAN G, 1986, MOL PHARMACOL, V136, P67 KLOPMAN G, 1995, TOXICOL LETT, V79, P145, DOI 10.1016/0378-4274(95)03366-S ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SATOH K, 1997, J CHEM SOFTWARE, V4, P101 SAUNDERS M, 1990, J AM CHEM SOC, V112, P1419, DOI 10.1021/ja00160a020 STEWART JJP, 1989, J COMPUT CHEM, V10, P209, DOI 10.1002/jcc.540100208 STEWART JP, QCMP175 SOFTWARE MOP Tarko L, 2004, REV CHIM-BUCHAREST, V55, P539 Tarko L, 2003, REV ROUM CHIM, V48, P745 Tarko L, 2000, REV ROUM CHIM, V45, P809 Tarko L, 2001, MATCH-COMMUN MATH CO, P201 NR 22 TC 12 Z9 12 PU ARKAT USA INC PI GAINESVILLE PA C/O ALAN R KATRITZKY, UNIV FLORIDA, DEPT CHEMISTRY, PO BOX 117200, GAINESVILLE, FL 32611 USA J9 ARKIVOC JI Arkivoc PY 2004 BP 74 EP 82 PN 14 PG 9 WC Chemistry, Organic SC Chemistry GA 904KH UT WOS:000227496100007 ER PT J AU MacFarlane, A Robertson, SE McCann, FA AF MacFarlane, A Robertson, SE McCann, FA TI Parallel computing for passage retrieval SO ASLIB PROCEEDINGS LA English DT Article DE text retrieval; retrieval performance evaluation; computers AB In this paper methods for both speeding up passage processing and examining more passages using parallel computers are explored. The number of passages processed are varied in order to examine the effect on retrieval effectiveness and efficiency. The particular algorithm applied has previously been used to good effect in Okapi experiments at TREC. This algorithm and the mechanism for applying parallel computing to speed up processing are described. C1 City Univ London, Ctr Interact Res, London EC1V 0HB, England. Univ London Imperial Coll Sci Technol & Med, Dept Comp, London, England. RP MacFarlane, A (reprint author), City Univ London, Ctr Interact Res, London EC1V 0HB, England. CR FRAKES WR, 1992, INFORMATION RETRIEVA HAWKING D, 1999, NIST SPECIAL PUBLICA Kaszkiel M, 2001, J AM SOC INF SCI TEC, V52, P344, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1075>3.3.CO;2-R MACFARLANE A, 2000, SPIRE 2000 MACFARLANE A, 2000, NIST SPECIAL PUBLICA MACFARLANE A, 2000, THESIS CITY U LONDON ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 9 TC 0 Z9 0 PU EMERALD GROUP PUBLISHING LIMITED PI BRADFORD PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND SN 0001-253X J9 ASLIB PROC JI Aslib Proc. PY 2004 VL 56 IS 4 BP 201 EP 211 DI 10.1108/00012530410549231 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 847KD UT WOS:000223390200001 ER PT J AU Losee, RM Church, L AF Losee, RM Church, L TI Information retrieval with distributed databases: Analytic models of performance SO IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS LA English DT Article DE collection fusion; information retrieval; metasearch engines; distributed processing; analytic performance models; digital libraries ID SEARCH-ENGINE QUALITY; RELEVANCE; METASEARCH; SELECTION AB The major emphasis of this paper is on analytical techniques for predicting the performance of various collection fusion scenarios. Knowledge of analytical models of information retrieval system performance, both with single processors and with multiple processors, increases our understanding of the parameters (e.g., number of documents, ranking algorithms, stemming algorithms, stop word lists, etc.) affecting system behavior. While there is a growing literature on the implementation of distributed information retrieval systems and digital libraries, little research has focused on analytic models of performance. We analytically describe the performance for single and multiple processors, both when different processors have the same parameter values and when they have different values. The use of different ranking algorithms and parameter values at different sites is examined. C1 Univ N Carolina, Chapel Hill, NC 27599 USA. RP Losee, RM (reprint author), Univ N Carolina, Manning Hall,CB 3360, Chapel Hill, NC 27599 USA. CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BAUMGARTEN C, 1999, THESIS U TECHNOLOGY Baumgarten C., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312685 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 Callan J., 1998, P 21 ANN INT ACM SIG, P112, DOI 10.1145/290941.290974 Chidlovskii B., 1998, Proceedings. IDEAS'98. International Database Engineering and Applications Symposium (Cat. No.98EX156), DOI 10.1109/IDEAS.1998.694381 COOPER WS, 1994, P 17 ANN INT ACM C R, P242 CRASWELL N, 2000, THESIS AUSTR NATL U CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 FRENCH JC, 1999, RES DEV INFORMATION, P238 FRENCH JC, 1998, ACM DIGITAL LIB, P283 FRENCH JC, 1999, CS9919 U VIRG FRENCH JC, 1999, CS9908 U VIRG Fuhr N, 1999, ACM T INFORM SYST, V17, P229, DOI 10.1145/314516.314517 Garcia-Molina H, 2002, DATABASE SYSTEMS COM Gravano L, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P196 Gravano L., 1997, P ACM SIGMOD INT C M, P207, DOI 10.1145/253260.253299 GRAVANO L, 1998, DATA ENG B, V21, P28 Green N., 2001, P ACM IEEE JOINT C D, p207 , DOI 10.1145/379437.379496 HARMAN DK, 1993, INFORM PROCESS MANAG, V29, P411, DOI 10.1016/0306-4573(93)90037-E HAWKING D, 1998, ACM SIGIR FORUM, V32, P16, DOI 10.1145/305110.305119 Hawking D, 2001, INFORM RETRIEVAL, V4, P33, DOI 10.1023/A:1011468107287 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 KIM D, 2000, CSTR2000147 KOR I SC Kleinrock L., 1975, QUEUEING SYSTEMS, V1 Korfhage R., 1997, INFORMATION STORAGE Losee RM, 1996, J AM SOC INFORM SCI, V47, P95 Losee RM, 1999, J AM SOC INFORM SCI, V50, P882, DOI 10.1002/(SICI)1097-4571(1999)50:10<882::AID-ASI5>3.0.CO;2-6 Maglaughlin KL, 2002, J AM SOC INF SCI TEC, V53, P327, DOI 10.1002/asi.10049 Liu KL, 2002, IEEE T KNOWL DATA EN, V14, P1422 Meng WY, 1999, PROC INT CONF DATA, P146 Meng WJ, 2001, ACM T INFORM SYST, V19, P310, DOI 10.1145/502115.502120 Meng WY, 2002, ACM COMPUT SURV, V34, P48, DOI 10.1145/505282.505284 PAEPCKE A, 2000, D LIB MAGAZINE NOV, V6 Pratt JW, 1995, INTRO STAT DECISION Rasolofo Y., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAVOY J, 1997, P 5 TEXT RETR C TREC, P489 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C SPARCKJONES K, 1972, J DOC, V28, P11 SWANSON DR, 1986, LIBR QUART, V56, P389 Tang R, 1998, INFORM PROCESS MANAG, V34, P237, DOI 10.1016/S0306-4573(97)00081-2 Tang R, 1999, J AM SOC INFORM SCI, V50, P254, DOI 10.1002/(SICI)1097-4571(1999)50:3<254::AID-ASI8>3.0.CO;2-Y TOWELL G, 1995, P 12 INT C MACH LEAR, P540 Valduriez Patrick, 1999, PRINCIPLES DISTRIBUT VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES E, 1995, P 3 TEXT RETR C TREC, P95 Voorhees E. M., 2000, P 8 TEXT RETR C TREC WALPOLE RE, 1998, PROBABLITY STAT ENG WU Z, 2001, WORLD WIDE WEB, P386 Yu C., 1998, PRINCIPLES DATABASE YU CT, 1977, COMMUN ACM, V20, P135, DOI 10.1145/359436.359441 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 1998, TEXT RETRIEVAL FILTE NR 54 TC 1 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1045-9219 J9 IEEE T PARALL DISTR JI IEEE Trans. Parallel Distrib. Syst. PD JAN PY 2004 VL 15 IS 1 BP 18 EP 27 DI 10.1109/TPDS.2004.1264782 PG 10 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 757QH UT WOS:000187572600002 ER PT J AU Lee, KS Kageura, K Choi, KS AF Lee, KS Kageura, K Choi, KS TI Implicit ambiguity resolution using incremental clustering in cross-language information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE implicit ambiguity resolution; cross-language information retrieval; incremental clustering; document context; document re-rank AB This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in cross-language information retrieval (CLIR) such as Korean-to-English and Japanese-to-English CLIR. The main objective of this paper shows that document clusters can effectively resolve the ambiguities tremendously increased in translated queries as well as take into account the context of all the terms in a document. In the framework we propose, a query in Korean/Japanese is first translated into English by looking up bilingual dictionaries, then documents are retrieved for the translated query terms based on the vector space retrieval model or the probabilistic retrieval model. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is recalculated by using the clusters. In the experiment based on TREC CLIR test collection, our method achieved 39.41% and 36.79% improvement for translated queries without ambiguity resolution in Korean-to-English CLIR, and 17.89% and 30.46% improvements in Japanese-to-English CLIR, on the vector space retrieval and on the probabilistic retrieval, respectively. Our method achieved 12.30% improvement for all translation queries, compared with blind feedback for the probabilistic retrieval in Korean-to-English CLIR. These results indicate that cluster analysis help to resolve ambiguity. (C) 2003 Elsevier Ltd. All rights reserved. C1 NII, Chiyoda Ku, Tokyo 1018430, Japan. Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Taejon 305701, South Korea. RP Lee, KS (reprint author), NII, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan. RI Choi, Key-Sun/C-1978-2011 CR Allan J., 1998, P DARPA BROADC NEWS, P194 ANICK PG, 1997, P 20 ACM SIGIR C SIG BALLESTEROS L, 1998, P 21 ACM SIGIR C SIG BREEN J, 2003, EDICT JAPANESE ENGLI CHUN JH, 2000, THESIS KAIST CHURCH K, 1990, COMPUTATIONAL LINGUI, V16, P23 DAIVS M, 1996, P 5 TEXT RETR C TREC Dumais S., 1997, P AAAI S CROSS LANG EICHMANN D, 1998, P 21 ACM SIGIR C SIG FRAKES WB, 1992, INFORMATION RETRIEVA, P435 GAO J, 2002, P 25 ACM SIGIR C SIG GILARRANZ J, 1997, P AAAI SPR S CROSS L HEARST MA, 1996, P 19 ACM SIGIR C SIG HULL DA, 1996, P 19 ACM SIGIR C SIG JANG MG, 1999, P 37 ANN M ASS COMP KUPIEC J, 1993, P 31 ANN M ASS COMP KWON OW, 1997, P NLPRS 97, P101 Lee KS, 2001, INFORM PROCESS MANAG, V37, P1, DOI 10.1016/S0306-4573(00)00017-0 OARD DW, 1997, P 6 TEXT RETR C TREC PAUL O, 2001, P 10 TEXT RETR C TRE REHDER B, 1997, P 6 TEXT RETR C TREC ROBERTSON G, 1999, AUTOMATIC TEXT PROCE ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Smadja F, 1996, COMPUT LINGUIST, V22, P1 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA XU J, 1996, P 19 ACM SIGIR C SIG YANG Y, 1998, ARTIF INTELL, P323 NR 27 TC 5 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2004 VL 40 IS 1 BP 145 EP 159 DI 10.1016/S0306-4573(03)00028-1 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 759LC UT WOS:000187738300010 ER PT J AU Savoy, J AF Savoy, J TI Combining multiple strategies for effective monolingual and cross-language retrieval SO INFORMATION RETRIEVAL LA English DT Article DE cross-language information retrieval; bilingual information retrieval; collection merging strategies; evaluation; learning curve ID TRANSLATION RESOURCES; INFORMATION-RETRIEVAL; CLEF 2001; ALGORITHM; QUERIES AB This paper describes and evaluates different retrieval strategies that are useful for search operations on document collections written in various European languages, namely French, Italian, Spanish and German. We also suggest and evaluate different query translation schemes based on freely available translation resources. In order to cross language barriers, we propose a combined query translation approach that has resulted in interesting retrieval effectiveness. Finally, we suggest a collection merging strategy based on logistic regression that tends to perform better than other merging approaches. C1 Univ Neuchatel, Inst Interfac Informat, CH-2000 Neuchatel, Switzerland. RP Savoy, J (reprint author), Univ Neuchatel, Inst Interfac Informat, CH-2000 Neuchatel, Switzerland. CR AMATI G, 2003, CROSS LANGUAGE INFOR, P257 Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 BRAND R, 2003, CROSS LANGUAGE INFOR, P59 Braschler M, 2002, LECT NOTES COMPUT SC, V2406, P394 Braschler M, 2002, LECT NOTES COMPUT SC, V2406, P102 BRASCHLER M, 2003, CROSS LANGUAGE INFOR, P164 BRASCHLER M, 2001, LNCS, V2069, P140 Buckley C., 1996, NIST PUBLICATION, P25 Callan J., 2000, ADV INFORM RETRIEVAL, P127 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 CHEN A, 2003, CROSS LANGUAGE INFOR, P28 Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Dumais S., 1994, NATL I STANDARDS TEC, V500-215, P105 Efron B, 1993, INTRO BOOTSTRAP Figuerola CG, 2002, LECT NOTES COMPUT SC, V2406, P253 Fox E. A., 1994, NIST SPECIAL PUBLICA, P243 GACHOT DA, 1998, CROSS LANGUAGE INFOR, P105 GEY F, 2001, LECT NOTES COMPUTER, V2069, P116 Gey FC, 2002, LECT NOTES COMPUT SC, V2406, P235 HARTER PS, 1975, J ASIS 1, V26, P197 Hiemstra D., 2001, LECT NOTES COMPUTER, V2069, P102 Hosmer D, 2000, APPL LOGISTIC REGRES Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Kleinbaum DG, 2002, LOGISTIC REGRESSION Kraaij W, 2002, LECT NOTES COMPUT SC, V2406, P78 KWOK KL, 1995, NIST PUBLICATION, P247 KWOK KL, 2001, NIST SPECIAL PUBLICA, P417 Le Calve A, 2000, INFORM PROCESS MANAG, V36, P341, DOI 10.1016/S0306-4573(99)00036-9 LOVINS JB, 1968, MECH TRANSL, V11, P22 MACFARLANE A, 2003, CROSS LANGUAGE INFOR, P321 MARTINEZSANTIAG.F, 2003, CROSS LANGUAGE INFOR, P187 McNamee P, 2002, LECT NOTES COMPUT SC, V2406, P193 MCNAMEE P, 2001, LNCS, V2069, P129 Molina-Salgado H, 2002, LECT NOTES COMPUT SC, V2406, P226 Monz C, 2002, LECT NOTES COMPUT SC, V2406, P262 Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 Nie JY, 2002, LECT NOTES COMPUT SC, V2406, P137 NIE JY, 2001, LNCS, V2069, P188 Oard DW, 2001, LNCS, V2069, P176 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Powell A. L., 2000, P 23 ANN INT ACM SIG, P232, DOI 10.1145/345508.345584 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Savoy J., 2002, Technique et Science Informatiques, V21 SAVOY J, 2003, CROSS LANGUAGE INFOR, P66 Savoy J, 1997, INFORM PROCESS MANAG, V33, P495, DOI 10.1016/S0306-4573(97)00027-7 SAVOY J, 2001, LECT NOTES COMPUTER, V2406, P27 SAVOY J, 2001, NIST PUBLICATION, P579 Savoy J, 1999, J AM SOC INFORM SCI, V50, P944, DOI 10.1002/(SICI)1097-4571(1999)50:10<944::AID-ASI9>3.3.CO;2-H SINGHAL A, 1999, NIST SPECIAL PUBLICA, P239 Sproat Richard W., 1992, MORPHOLOGY COMPUTATI VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, 1995, NIST PUBLICATION, P95 NR 56 TC 25 Z9 26 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JAN-APR PY 2004 VL 7 IS 1-2 BP 121 EP 148 DI 10.1023/B:INRT.0000009443.51912.e7 PG 28 WC Computer Science, Information Systems SC Computer Science GA 755DF UT WOS:000187387900006 ER PT J AU Chen, A Gey, FC AF Chen, A Gey, FC TI Multilingual information retrieval using machine translation, relevance feedback and decompounding SO INFORMATION RETRIEVAL LA English DT Article DE multilingual information retrieval; cross-language information retrieval; relevance feedback; decompounding; results merging ID DOCUMENT TRANSLATION; RESOURCES; CLEF AB Multilingual retrieval ( querying of multiple document collections each in a different language) can be achieved by combining several individual techniques which enhance retrieval: machine translation to cross the language barrier, relevance feedback to add words to the initial query, decompounding for languages with complex term structure, and data fusion to combine monolingual retrieval results from different languages. Using the CLEF 2001 and CLEF 2002 topics and document collections, this paper evaluates these techniques within the context of a monolingual document ranking formula based upon logistic regression. Each individual technique yields improved performance over runs which do not utilize that technique. Moreover the techniques are complementary, in that combining the best techniques outperforms individual technique performance. An approximate but fast document translation using bilingual wordlists created from machine translation systems is presented and evaluated. The fast document translation is as effective as query translation in multilingual retrieval. Furthermore, when fast document translation is combined with query translation in multilingual retrieval, the performance is significantly better than that of query translation or fast document translation. C1 Univ Calif Berkeley, Sch Informat Management & Syst, Berkeley, CA 94720 USA. Univ Calif Berkeley, UC Data Arch & Tech Assistance UC DATA, Berkeley, CA 94720 USA. RP Chen, A (reprint author), Univ Calif Berkeley, Sch Informat Management & Syst, Berkeley, CA 94720 USA. CR BALLESTEROS L, 1998, CROSS LANGUAGE INFOR Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 Braschler M, 2002, LECT NOTES COMPUT SC, V2406, P102 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 CHEN A, 2002, CLEF 2002 WORKSH, P5 Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Cooper W. S., 1994, Second Text REtrieval Conference (TREC-2) (NIST-SP 500-215) GEY F, 2001, LECT NOTES COMPUTER, V2069, P116 GEY FC, 1999, NIST SPECIAL PUBLICA, P527 Grefenstette G., 1998, CROSS LANGUAGE INFOR Harman D., 1992, INFORMATION RETRIEVA, P241 Hiemstra D., 2001, LECT NOTES COMPUTER, V2069, P102 Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Kraaij W, 2002, LECT NOTES COMPUT SC, V2406, P78 LEZIUS W, 1988, COLING ACL 98, P743 McCarley JS, 1998, LECT NOTES ARTIF INT, V1529, P150 McNamee P, 2002, LECT NOTES COMPUT SC, V2406, P193 MONZ C, 2002, LECT NOTES COMPUTER, V2406, P263 Oard DW, 1998, LECT NOTES ARTIF INT, V1529, P472 Oard DW, 1998, ANNU REV INFORM SCI, V33, P223 PETERS C, 2002, CLEF 2002 WORKSH 19 PETERS C, 2002, LECT NOTES COMPUTER, V2406 PETERS C., 2001, LECT NOTES COMPUTER, V2069 PICCHI E, 1998, CROSS LANGUAGE INFOR Pirkola A, 2001, INFORM RETRIEVAL, V4, P209, DOI 10.1023/A:1011994105352 Porter Martin, 2001, SNOWBALL LANGUAGE ST Rice J. A., 1995, MATH STAT DATA ANAL Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAVOY J, 2002, LECT NOTES COMPUTER, V2069, P27 SAVOY J, 2002, CLEF 2002 WORKSH, P31 Voorhees E., 1998, 7 TEXT RETR C TREC 7 Voorhees E. M., 1999, 8 TEXT RETR C TREC 8 XU J, 2001, NIST SPECIAL PUBLICA, P106 XU J, 2002, NIST SPECIAL PUBLICA, P68 Yang YM, 1998, ARTIF INTELL, V103, P323, DOI 10.1016/S0004-3702(98)00063-0 NR 36 TC 21 Z9 21 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JAN-APR PY 2004 VL 7 IS 1-2 BP 149 EP 182 DI 10.1023/B:INRT.0000009444.89549.90 PG 34 WC Computer Science, Information Systems SC Computer Science GA 755DF UT WOS:000187387900007 ER PT B AU Vickery, B Vickery, A AF Vickery, B Vickery, A TI Information Science in Theory and Practice, Third Revised and Englarged Edition SO INFORMATION SCIENCE IN THEORY AND PRACTICE, THIRD REVISED AND ENGLARGED EDITION LA English DT Book ID WORLD-WIDE-WEB; SOCIAL-SERVICES DEPARTMENTS; AUTHOR COCITATION ANALYSIS; MEASURING READERS FAILURE; SCIENTIFIC COMMUNICATION; SCHOLARLY COMMUNICATION; COST-EFFECTIVENESS; RETRIEVAL SYSTEMS; CITATION ANALYSIS; BRADFORDS LAW CR [Anonymous], 1973, STUDY HUMAN COMMUNIC Bales RF, 1951, AM SOCIOL REV, V16, P461, DOI 10.2307/2088276 BARNARD CC, 1938, LIBRARY ASSOCIATION, V40, P549 Barr A., 1981, HDB ARTIFICIAL INTEL, V1 BARR D, 1971, BOOK SERIAL PUBLISHI Barton A. H., 1955, LANGUAGE SOCIAL RES, P40 Bass F., 1969, MANAGEMENT SCI, V15 BATES M, 1983, ACM SIGIR FORUM, V17, P81, DOI 10.1145/1013230.511804 BATES MJ, 2000, J AM SOC INFORM SCI, V50 BATES MJ, 1981, ANNU REV INFORM SCI, V16, P139 Bawden D, 2001, J DOC, V57, P218, DOI 10.1108/EUM0000000007083 Bawden D., 1990, USER ORIENTED EVALUA BEGHTOL C, 1986, J DOC, V42, P84, DOI 10.1108/eb026788 BELKIN NJ, 1990, ACM SIGIR 90 13 INT BELKIN NJ, 1976, J AM SOC INFORM SCI, V27, P197, DOI 10.1002/asi.4630270402 BELKIN NJ, 1978, J DOC, V34, P55, DOI 10.1108/eb026653 BELKIN NJ, 1985, 35 BRIT LIB LIB INF Bell D., 1976, COMING POSTINDUSTRIA BELL D, 1980, MICROELECTRONICS REV, P540 BENNION BC, 1976, J AM SOC INFORM SCI, V27, P53 Benoit G, 2002, ANNU REV INFORM SCI, V36, P265, DOI 10.1002/aris.1440360107 Bensman S. J., 1982, LIBR RES, V4, P279 Berelson B., 1964, HUMAN BEHAV Bergman M.K., 2001, J ELECT PUBLISHING, V7 BERNAL JD, 1960, ASLIB PROC, V12, P432, DOI 10.1108/eb049779 BERNIER CL, 1978, INFORM PROCESS MANAG, V14, P445, DOI 10.1016/0306-4573(78)90008-0 BERUL LH, 1965, DEP DEFENSE USER NEE BISWAS SC, 1989, LIBR INFORM SCI RES, V11, P109 BLAGDEN JF, 1980, DO WE REALLY NEED LI BLAIR DC, 1992, LANGUAGE REPRESENTAT Blaug M., 1972, INTRO EC ED BLICK AR, 1977, ASLIB PROC, V29, P310, DOI 10.1108/eb050606 Bliss HE, 1929, ORG KNOWLEDGE SYSTEM Boisot M., 1995, INFORM SPACE BOLINGER D., 1975, ASPECTS LANGUAGE BOLT GJ, 1981, MARKET SALES FORECAS BOOKSTEIN A, 1976, LIBR QUART, V46, P416 Borgman C. L., 1990, SCHOLARLY COMMUNICAT Borgman CL, 2002, ANNU REV INFORM SCI, V36, P3 Borgman CL, 2000, GUTENBERG GLOBAL INF Borgman CL, 2000, J DOC, V56, P412, DOI 10.1108/EUM0000000007121 Bosak J, 1999, SCI AM, V280, P89 BOWER CA, 1976, BLL REV, V4, P31 Bowker G., 1999, SORTING THINGS OUT BOYLE PJ, 1973, SURVEY WORLD AGR DOC Bradford SC, 1934, ENGINEERING-LONDON, V137, P85 BRAJNIK G, 1990, IEEE T SYST MAN CYB, V20, P166, DOI 10.1109/21.47819 Briggs A, 2002, SOCIAL HIST MEDIA GU *BRIGHTPL, 2000, DEEP WEB SURF HIDD V Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BRITTAIN JM, 1970, INFORM ITS USERS BRITTAIN JM, 1973, J DOC, V29, P72 BROADUS RN, 1983, J AM SOC INFORM SCI, V34, P132, DOI 10.1002/asi.4630340206 BRODIE ML, 1983, CONCEPTUAL MODELLING Brookes B. C., 1970, Journal of Documentation, V26, DOI 10.1108/eb026500 BROOKES BC, 1975, RES THEORETICAL BASI BROOKES BC, 1969, NATURE, V224, P953, DOI 10.1038/224953a0 BROOKES BC, 1970, INFORM STORAGE RET, V6, P127, DOI 10.1016/0020-0271(70)90056-2 Brookes BC, 1970, J AM SOC INFORM SCI, V21, P320, DOI 10.1002/asi.4630210503 BROOKES BC, 1977, J DOC, V33, P180, DOI 10.1108/eb026641 BROOKES BC, 1970, J LIBR, V2, P14, DOI 10.1177/096100067000200102 Brooks Terrence A., 2003, INFORM RES, V8 Brophy P, 2000, J DOC, V56, P161, DOI 10.1108/EUM0000000007113 BROPHY P, 1976, READER OPERATIONS RE BRUCE B, 1975, ARTIF INTELL, V6, P327, DOI 10.1016/0004-3702(75)90020-X BRUTCHER C, 1964, LIBR RESOUR TECH SER, V8, P418 *BSO PAN, 1993, BROAD SYST ORD Buchanan B.G., 1984, RULE BASED EXPERT SY BUCHANAN RH, 1976, WORLD MAN BUCKLAND M, 1983, INFORM INFORM SYSTEM Buckland Michael K., 1975, BOOK AVAILABILITY LI BUCKLAND MK, 1983, LIB SERVICES THEORY BUCKLAND MK, 1978, SOCIO ECON PLAN SCI, V12, P333, DOI 10.1016/0038-0121(78)90038-1 BUCKLAND MK, 1970, SYSTEMS ANAL U LIB BULICK S, 1976, J AM SOC INFORM SCI, V27, P175, DOI 10.1002/asi.4630270308 Bunch A., 1982, COMMUNITY INFORM SER BUNDY A, 1985, RES DEVEOPMENT EXPER, P193 BURKETT J, 1979, LIB INFORM NETWORKS BURRELL Q, 1980, J DOC, V36, P115, DOI 10.1108/eb026693 Busha C., 1980, RES METHODS LIBRARIA *CAB OFF INF TECHN, 1981, MAK BUS INF CARLSON G, 1961, REPORT ORG LARGE FIL CAUSEY A, 1998, ESSENCE ARTIFICIAL CAWKELL AE, 1972, INFORM SCIENTIST, V6, P143 Chan L. M., 1985, THEORY SUBJECT ANAL CHAPANIS A, 1971, AM PSYCHOL, V26, P949, DOI 10.1037/h0032325 *CHART I PUBL FIN, PUBL LIB STAT ACT Chen C-C, 1982, INFORM SEEKING Cherry C., 1957, HUMAN COMMUNICATION Cherry Colin, 1971, WORLD COMMUNICATION CHERRY SM, 2002, WEAVING WEB IDEAS Childers T., 1975, INFORM POOR AM Choffray J. M., 1980, MARKET PLANNING NEW Chowdhury G.G., 1999, INTRO MODERN INFORM CIPOLLA CM, 1969, LITERACY DEVLOPMENT CLARK LH, 1985, CONSUMER BEHAV CLEMENTS DW, 1967, J DOC, V23, P131, DOI 10.1108/eb026426 CLEVERDO.C, 1970, J DOC, V26, P55, DOI 10.1108/eb026487 Coates E.J., 1960, SUBJECT CATALOGUES H COCHRAN PA, 1981, P 2 NAT ONLIN EMM, P133 COLE PF, 1963, J DOC, V19, P1 COLE PF, 1962, J DOC, V18, P58 COLE PF, 1958, J DOC, V14, P197 COLE RC, 1977, J AM SOC INFORM SCI, V28, P366 Coleman J. S., 1966, MED INNOVATION DIFFU COLLIER H, 1981, MONITOR SEP, P10 COLLINS AM, 1969, J VERB LEARN VERB BE, V8, P240, DOI 10.1016/S0022-5371(69)80069-1 Coombs Philip Hall, 1974, ATTACKING RURAL POVE COOPER MD, 1983, LIB INFORMATION SCI, V5, P207 COOPER MD, 1978, INFORM PROCESS MANAG, V14, P419, DOI 10.1016/0306-4573(78)90006-7 COOVER RW, 1969, SPEC LIBR, V60, P446 Cornelius Ian, 2002, ANN REV INFORM SCI T, V36 COWLING D, 1982, IDENTIFYING MARKET C CRAIG GM, 1979, 5469 BRIT LIB R D DE Crane D., 1972, INVISIBLE COLL DIFFU Cranfield Geoffrey A., 1978, PRESS SOC CRICKMAN RD, 1979, LIBR TRENDS, V28, P311 CROFT D, 1983, APPL STAT MANAGEMENT Croft W. Bruce, 1995, D LIB MAGAZINE CROFT WB, 1993, INFORM PROCESS MANAG, V29, P313, DOI 10.1016/0306-4573(93)90058-L CRONIN B, 1983, J INFORM SCI, V7, P1, DOI 10.1177/016555158300700101 CRONIN B, 1982, ASLIB PROC, V34, P227, DOI 10.1108/eb050844 Curran James, 1977, MASS COMMUNICATION S CURWEN PJ, 1981, UK PUBLISHING IND Cutlip SM, 1954, JOURNALISM QUART, V31, P434 DANIELS PJ, 1986, J DOC, V42, P272, DOI 10.1108/eb026797 DAVIES R, 1985, J DOC, V41, P221, DOI 10.1108/eb026782 DAVIES R, 1986, INTELLIGENT INFORM S DAVIS CH, 1979, GUIDE INFORM SCI DAVIS R, 1977, P 5 INT JOINT C ART, P920 de Beaugrande R., 1980, TEXT DISCOURSE PROCE De Sola Pool I., 1973, HDB COMMUNICATION DEGENNARO R, 1982, LIB J, P1045 DEMBOWSKA M, 1968, DOCUMENTATION SCI IN Dempsey L, 1998, J DOC, V54, P145, DOI 10.1108/EUM0000000007164 DEMPSEY L, 1999, J DOC, V1, P100 *DEP ED SCI, 1973, PUBL LIB THEIR US *DEP ENV, 1972, STRUCT PROJ INF *DEP ENV, 1978, ED CONSTR IND INF *DEP ENV, 1971, INF SYST CONSTR IND DEUTSCHMANN PJ, 1960, JOURNALISM QUART, V37, P345 Dicken P, 1981, MODERN W SOC Ding Y, 2001, J INFORM SCI, V27, P377, DOI 10.1177/016555150102700603 DING Y, 2002, J INF SCI, V123, P1 Doerr M., 2001, J DIGITAL INFORM, V1 DOLIER JH, 1975, PLANNING NATL INFRAS DOUDS CF, 1971, R&D MANAGE, V1, P125, DOI 10.1111/j.1467-9310.1971.tb00070.x Doyle L, 1975, INFORM RETRIEVAL PRO DROTT MC, 1979, ASLIB PROC, V31, P296, DOI 10.1108/eb050687 DROTT MC, 1981, LIBR TRENDS, V30, P41 DRUCKER PF, 1971, AGE CONTINUITY DUNCAN CJ, 1969, MEDIA METHODS DUNN SC, 1965, ASLIB PROC, V17, P286, DOI 10.1108/eb050036 EARLE P, 1969, J DOC, V25, P123, DOI 10.1108/eb026468 EAST H, 1984, UK LIB INFORM SERVIC EAST H, 1983, J DOC, V39, P247, DOI 10.1108/eb026751 EAST H, 1984, ASLIB PROC, V36, P61, DOI 10.1108/eb050912 EAST H, 1979, 5488 BRIT LIB RES DE EFTHIMIADIS EN, 1996, ANN REV INFORM SCI T, V31 EFTHIMIADIS EN, 1990, J DOC, V46, P218, DOI 10.1108/eb026861 Egghe L., 1990, INTRO INFORMETRICS Ellis D, 1998, J DOC, V54, P28, DOI 10.1108/EUM0000000007160 Ellis D, 1999, ASLIB PROC, V51, P3, DOI 10.1108/EUM0000000006956 ELLIS D, 2004, ANN REV INF IN PRESS, V38 ELTON M, 1973, ASLIB PROC, V25, P305, DOI 10.1108/eb050417 ELTON MJC, 1973, DOCUMENT DELIVERY SE Enser P, 2000, J INFORM SCI, V26, P199, DOI 10.1177/0165551004233212 ENSER PGB, 1995, J DOC, V51, P126, DOI 10.1108/eb026946 ESCARPIT R, 1973, ECRIT COMMUNICATION EVANS E, 1972, B MED LIB ASS, V5, P102 Fahlman S.E., 1979, NETL SYSTEM REPRESEN FAIBISOFF SG, 1976, INFORM REPORTS BIBLI, V5 Fairthorne R. A, 1961, INFORM RETRIEVAL FAIRTHOR.RA, 1969, J DOC, V25, P319, DOI 10.1108/eb026481 FAIRTHOR.RA, 1967, J ACM, V14, P710, DOI 10.1145/321420.321430 FARACE RV, 1973, ANAL HUMAN COMMUNICA *FED INT DOC, 1978, BROAD SYST ORD FELDMAN S, 1999, SEARCHER, V7, P42 Fellbaum C, 1998, WORDNET ELECT LEXICA FENSEL D, 2001, ONTOLOGIES Findler N. V., 1979, ASS NETWORKS REPRESE FISHENDEN RM, 1959, INT C SCI INF P WASH, V1, P163 FLOWERDEW ADJ, 1974, 5206 BRIT LIB RES DE FLYNN RR, 1979, SERIALS LIB, V4, P23 FORD N, 1991, EXPERT SYSTEMS ARTIF Forrester JW, 1961, IND DYNAMICS FOSKETT AC, 1983, SUBJECT APPROACH INF Foskett D. J., 1970, Journal of Documentation, V26, DOI 10.1108/eb026504 FOSTER I, 2000, NATURE WEB MATT 1207 Fox EA, 2002, ANNU REV INFORM SCI, V36, P503 FREITAG J, 1981, INFORM REPORTS BIBLI, V10, P3 Fugmann R., 1993, SUBJECT ANAL INDEXIN Gaizauskas R, 1998, J DOC, V54, P70, DOI 10.1108/EUM0000000007162 Galtung Johan, 1965, J PEACE RES, V2, P64, DOI DOI 10.1177/002234336500200104 GAPEN DK, 1981, LIBR TRENDS, V30, P107 GARDIN JC, 1973, J DOC, V29, P137, DOI 10.1108/eb026553 Gardner G., 1978, SOCIAL SURVEYS SOCIA Garfield E., 1979, CITATION INDEXING GARVEY WD, 1972, INFORM STORAGE RET, V8, P159, DOI 10.1016/0020-0271(72)90001-0 Garvey W. D., 1979, COMMUNICATION ESSENC GARVEY WD, 1972, INFORM STORAGE RET, V8, P111, DOI 10.1016/0020-0271(72)90040-X GARVEY WD, 1976, INFORM PROCESS MANAG, V12, P165, DOI 10.1016/0306-4573(76)90003-0 GARVEY WD, 1972, INFORM STORAGE RET, V8, P265, DOI 10.1016/0020-0271(72)90017-4 GARVEY WD, 1972, INFORM STORAGE RET, V8, P207, DOI 10.1016/0020-0271(72)90031-9 GARVEY WD, 1974, INFORM STORAGE RET, V10, P115, DOI 10.1016/0020-0271(74)90014-X GARVEY WD, 1972, INFORM STORAGE RET, V8, P123, DOI 10.1016/0020-0271(72)90041-1 GERBNER G, 1956, AV COMMUN REV, V4, P171 Gilchrist A, 1994, 50 YEARS INFORMATION, P85 GILCHRIST A, 1971, THESAURUS RETRIEVAL Gilchrist A, 2002, J DOC, V59, P7 GILCHRIST AD, 2002, LIB ASS RECORD JAN GILMORE JS, 1967, CHANNELS TECHNOLOGY Glazier J., 1992, QUALITATIVE RES INFO GODDARD HC, 1971, LIBR QUART, V41, P244 Goffman W., 1980, SCI INFORM SYSTEMS P GOFFMAN W, 1966, NATURE, V212, P449, DOI 10.1038/212449a0 Goldfarb Charles F., 1990, SGML HDB Goldhaber G., 1978, HUMAN COMMUNICATION, V5, P76, DOI 10.1111/j.1468-2958.1978.tb00624.x GOWERS E, 1973, COMPLETE PLAIN WORDS GRALEWSKA A, 1970, EVALUATION STUDY ROC GRALEWSKAVICKER. A, 1975, 5229 OSTI GRALEWSKAVICKERY A, 1976, INFORM PROCESS MANAG, V12, P251, DOI 10.1016/0306-4573(76)90065-0 GREENBERG BS, 1964, PUBLIC OPIN QUART, V28, P225, DOI 10.1086/267239 Grefenstette G., 1998, CROSS LANGUAGE INFOR Gregory R.L., 1984, MIND SCI GRICE HP, 1978, UNDERSTANDING SPOKEN, P229 GRIFFITH BC, 1980, KEY PAPERS INFORM SC GRIFFITH BC, 1979, J DOC, V35, P179, DOI 10.1108/eb026679 Grolier E. de, 1962, STUDY GEN CATEGORIES GROSE D, 1974, ASLIB PROC, V26, P9, DOI 10.1108/eb050443 HAAS SW, 1996, ANN REV INFORM SCI T, V31 Hagstrom W., 1965, SCI COMMUNITY HALBERT MH, 1959, INT C SCI INF P WASH, V1, P97 HALLORAN JD, 1983, J INFORM SCI, V7, P159, DOI 10.1177/016555158300700404 Halsey A. H., 1972, TRENDS BRIT SOC 1900 HAMBURG M, 1974, LIB PLANNING DECISIO HARMON P, 1985, EXPERT SYSTEMS Harrah D., 1963, COMMUNICATION LOGICA HARRAH D, 1961, PHILOS SCI, V28, P40, DOI 10.1086/287779 HARRAH D, 1973, INSTR SCI, V1, P447, DOI 10.1007/BF00054977 Harter SP, 1998, J AM SOC INFORM SCI, V49, P507, DOI 10.1002/(SICI)1097-4571(19980501)49:6<507::AID-ASI3>3.0.CO;2-X Harter SP, 1997, ANNU REV INFORM SCI, V32, P3 Havelock R., 1969, COMP STUDY LIT DISSE Haverkamp DS, 1998, J AM SOC INFORM SCI, V49, P304, DOI 10.1002/(SICI)1097-4571(19980401)49:4<304::AID-ASI2>3.3.CO;2-C HAWKING D, 2002, OVERVIEW TREC 2001 W, P2001 Hayes-Roth F., 1983, BUILDING EXPERT SYST Heaps H. S., 1978, INFORM RETRIEVAL COM Hearst MA, 1999, P 37 ANN M ASS COMP Hendler J, 2003, SCIENCE, V299, P520, DOI 10.1126/science.1078874 HENDLEY T, 2000, DOCUMENT MANAGEMENT HENRY WM, 1980, ONLINE SEARCHING HERRING C, 1973, PHYSICS PERSPECTIV B, V2, P1265 Herring SC, 2002, ANNU REV INFORM SCI, V36, P109, DOI 10.1002/aris.1440360104 Hertzler J., 1965, SOCIOLOGY LANGUAGE HESELTINE RG, 1982, J LIBR, V14, P247, DOI 10.1177/096100068201400402 Hetman F, 1973, SOC ASSESSMENT TECHN HINDLE A, 1980, J DOC, V36, P209 HJERPPE R, 1978, OUTLINE BIBLIOMETRIC Hjorland B, 2002, J DOC, V58, P422, DOI 10.1108/00220410210431136 HOFFMAN M, 1977, USING ABUSING LITERA HOLLNAGEL E, 1983, INT J MAN MACH STUD, V18, P583, DOI 10.1016/S0020-7373(83)80034-0 HOLLNAGEL E, 1978, QUALITATIVE ASPECTS HOLMSTROM JE, 1956, RECORDS RES ENG IND HOLTON G, 1962, DAEDALUS, V91, P362 Homans G. C., 1951, HUMAN GROUP HOUGH RW, 1980, AIAA 3 COMM SAT SYST HUBEL DH, 1968, J PHYSIOL-LONDON, V195, P215 HUBERT JJ, 1981, LIBR TRENDS, V30, P65 HUTCHINS WJ, 1978, J DOC, V34, P119, DOI 10.1108/eb026657 HUTCHINS WJ, 1975, LANGUAGE INDEXING CL *I INF SCI, 1982, CRIT INF SCI Ingwersen P., 1992, INFORM RETRIEVAL INT INGWERSEN P, 1995, J DOC, V52, P3 IWINSKI MB, 1911, B I INT BIBLIO, V16, P1 Iyer H, 1995, CLASSIFICATORY STRUC Jackson S. L., 1974, LIB LIBRARIANSHIP W Johnson-Laird P. N., 1983, MENTAL MODELS Jones K., 1971, AUTOMATIC KEYWORD CL Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES K. S., 1973, LINGUISTICS INFORM S JONES KS, 1974, J DOC, V30, P154 JONES KS, 2000, INFORM PROCESS MANAG, V36, P37 KANTOR P, 1979, LIB RES, V1, P295 KANTOR PB, 1976, J AM SOC INFORM SCI, V27, P311, DOI 10.1002/asi.4630270507 KANTOR PB, 1982, ANNU REV INFORM SCI, V17, pCH4 KANTOR PB, 1981, RQ, V21, P43 Katz D., 1966, SOCIAL PSYCHOL ORG KATZ E, 1963, AM SOCIOL REV, V28, P237, DOI 10.2307/2090611 KATZ E, 1957, PUBLIC OPIN QUART, V21, P61, DOI 10.1086/266687 KEARSLEY GP, 1976, J PSYCHOLINGUIST RES, V5, P355, DOI 10.1007/BF01079934 KEEN EM, 1973, J DOC, V29, P1, DOI 10.1108/eb026547 KEHOE CA, 1985, ONLINE REV, V9, P489, DOI 10.1108/eb024201 Kelly B, 1999, J DOC, V55, P71, DOI 10.1108/EUM0000000007138 KENT A, 1978, COST BENEFI T STUDIE Kiewitt E. L., 1979, EVALUATING INFORM RE KING DW, 1980, ASLIB IIS LA JOINT C, P68 KING DW, 1981, TELECOMMUNICATIONS L KING DW, 1976, STAT INDICATORS SCI KING GB, 1972, RQ REFERENCE ADULT S, V12, P157 KING R, 1976, J AM SOC INFORM SCI, V27, P118, DOI 10.1002/asi.4630270207 Kintsch W, 1977, MEMORY COGNITION KLING R, 2003, ANN REV INFORM SCI T, V37 Klusch M., 1999, INTELLIGENT INFORM A Klusch M., 2001, J DATA KNOWLEDGE ENG, V36 KOBOYASHI M, 2000, ACM COMPUT SURV, V32, P144 KOCHEN M, 1977, INTEGRATIVE MECH LIT KOCHEN M, 1967, GROWTH KNOWLEDGE KOCHEN M, 1972, J DOC, V28, P322, DOI 10.1108/eb026546 Kochen M., 1974, PRINCIPLES INFORM RE KOCHEN M, 1976, INFORM COMMUNITY KOCHEN M, 1974, AM J PSYCHOL, V87, P369, DOI 10.2307/1421379 Korfhage R., 1997, INFORM STORAGE RETRI KRAFT DH, 1979, COLLECTION MANAGEMEN, V3, P163 Krech D., 1962, INDIVIDUAL SOC KUHLTHAU CC, 1993, J DOC, V49, P339, DOI 10.1108/eb026918 KUHN A, 1966, STUDY SOC Lachman R., 1979, COGNITIVE PSYCHOL IN Lamberton Donald M., 1971, EC INFORM KNOWLEDGE Lancaster F. W., 1968, EVALUATION MEDLARS D Lancaster F. W., 1979, INFORM RETRIEVAL SYS Lancaster F. W., 1972, VOCABULARY CONTROL I Lancaster F. W., 2003, INDEXING ABSTRACTING Lancaster F. Wilfred, 1977, MEASUREMENT EVALUATI LANCASTER FW, 1969, AM DOC, V20, P119, DOI 10.1002/asi.4630200204 LANCASTER FW, 1978, GUIDELINES EVALUATIO LANGRIDGE DW, 1992, CLASSIFI CATION LANGRISH J, 1971, R&D MANAGE, V1, P133, DOI 10.1111/j.1467-9310.1971.tb00071.x Large A., 1999, INFORM SEEKING ONLIN Lassila O., 2001, SCI AM MAY Lasswell H. D., 1948, COMMUNICATION IDEAS LAWANI SM, 1973, INT LIBR REV, V5, P341, DOI 10.1016/S0020-7837(73)80037-8 Lazarsfeld Paul F., 1955, LANGUAGE SOCIAL RES Lazarsfeld PF, 1972, CONTINUITIES LANGUAG LEGGATE P, 1973, BA PREVIEWS PROJECT LEGGATE P, 1971, EVALUATION OPERATION LEIMKUHLER FF, 1977, INFORM PROCESS MANAG, V13, P79, DOI 10.1016/0306-4573(77)90042-5 Lenat D. B., 1990, BUILDING LARGE KNOWL LENSKI G, 1978, HUMAN SOC, P3 Lesk M., 1997, PRACTICAL DIGITAL LI LEVITAN KB, 1982, J AM SOC INFORM SCI, V33, P44, DOI 10.1002/asi.4630330108 LEVITAN KB, 1982, ANN REV INFORM SCI T, V17 LEWIS DA, 1980, J INFORM SCI, V2, P59, DOI 10.1177/016555158000200202 LEWIS PR, 1994, 50 YEARS INFORM PROG, P53 Leydesdorff L., 2001, CHALLENGE SCIENTOMET LIEVROUW LA, 1994, J AM SOC INFORM SCI, V44, P350 LINDQUIST MG, 1978, J AM SOC INFORM SCI, V29, P67, DOI 10.1002/asi.4630290205 LINDQUIST MG, 1978, DYNAMICS INFORM SEAR LINDSAY PH, 1977, HUMAN INFORM PROCESS, P2 LINDSTROM TD, 1977, TOXICOL LETT, V1, P109, DOI 10.1016/0378-4274(77)90030-3 LINE MB, 1971, ASLIB PROC, V23, P412, DOI 10.1108/eb050298 Line M., 1974, ASLIB P, V26, P87, DOI 10.1108/eb050451 LINE MB, 1970, J DOC, V26, P46, DOI 10.1108/eb026486 LINE MB, 1974, J DOC, V30, P283, DOI 10.1108/eb026583 LINE MB, 1973, J LIBR, V5, P37, DOI 10.1177/096100067300500105 LINE MB, 1981, J LIBR AUTOMAT, V14, P252 LINE MB, 1994, 50YEARS INFORM PROGR, P189 LIU MX, 1993, J DOC, V49, P370, DOI 10.1108/eb026920 Lofland J., 1971, ANAL SOCIAL SETTINGS LOFTUS GR, 1976, HUMAN MEMORY Losee R.M., 1998, TEXT RETRIEVAL FILTE LOTKA AJ, 1926, WASHINGTON ACADEMY S, V16, P317 Lyman P., 2000, MUCH INFORM LYNCH MJ, 1978, LIBR QUART, V48, P119 LYNCH MJ, 1983, LIBR TRENDS, V31, P401 MacBride S., 1980, MANY VOICES ONE WORL Machlup F., 1983, STUDY INFORM Machlup Fritz, 1978, INFORM PRINTED WORD, V1 MACSHANE D, 1979, USING MEDIA Madge John, 1953, TOOLS SOCIAL SCI MAGSON MS, 1973, ASLIB PROC, V25, P164, DOI 10.1108/eb050402 MANSBRIDGE J, 1986, LIBR INFORM SCI RES, V8, P299 Manzer Bruce M., 1977, ABSTRACT J 1790 1920 Marcella R, 1999, J DOC, V55, P159, DOI 10.1108/EUM0000000007142 March J. G., 1958, ORGANIZATIONS Marchionini G., 1995, INFORM SEEKING ELECT Marchionini G, 1998, ANNU REV INFORM SCI, V33, P89 MARKEY K, 1981, INFORM PROCESS MANAG, V17, P215, DOI 10.1016/0306-4573(81)90016-9 MARTIN J, 1977, FUTURE DEV TELECOMMU, P2 Martin J., 1978, WIRED SOC MARTYN J, 1964, J DOC, V20, P212 MARTYN J, 1975, J DOC, V31, P290, DOI 10.1108/eb026610 MARTYN J, 1981, INVESTIGATIVE METHOD MARTYN J, 1984, ASLIB PROC, V36, P253, DOI 10.1108/eb050931 MARTYN J, 1967, J DOC, V23, P45, DOI 10.1108/eb026420 MASON D, 1973, ASLIB PROC, V25, P100, DOI 10.1108/eb050397 MASON D, 1972, J LIBR, V4, P95 McCall G., 1969, ISSUES PARTICIPANT O MCCALLUM SH, 1981, J LIBR AUTOMAT, V14, P194 MCCLURE CR, 1978, INFORM PROCESS MANAG, V14, P381, DOI 10.1016/0306-4573(78)90003-1 MCCLURE CR, 1984, SPEC LIBR, V75, P193 MCELROY AR, 1982, ASLIB PROC, V34, P249, DOI 10.1108/eb050846 MCFADYEN D, 1975, J LIBR, V7, P2, DOI 10.1177/096100067500700101 McGraw K. L., 1989, KNOWLEDGE ACQUISITIO MCKIERNAN G, 1999, MORNING BECOMES ELEC McLuhan M., 1967, UNDERSTANDING MEDIA MCNELLY FT, 1959, JOURNALISM QUART, V36, P23 McQuail D, 2000, MASS COMMUNICATION T McQuail D., 1975, COMMUNICATION McQuail D., 1981, COMMUNICATION MODELS MEADOW CT, 1981, BASICS ONLINE SEARCH MEADOW CT, 1970, MAN MACHINE COMMUNIC MEADOW CT, 1973, ANAL INFORM SYSTEMS Meadow C.T., 1992, TEXT INFORM RETRIEVA Meadows A. J., 1974, COMMUNICATION SCI Meadows A. J., 1998, COMMUNICATING RES MEADOWS AJ, 2001, UNDERSTANDING INFORM MEIER RL, 1963, LIBRI, V13, P1, DOI 10.1515/libr.1963.13.1.1 MEIER RL, 1961, LIBR QUART, V31, P215 Menard H. W., 1971, SCI GROWTH CHANGE Menzel H., 1960, REV STUDIES FLOW INF METOYERDURAN C, 1993, ANNU REV INFORM SCI, V28, P111 MIKHAILOV A.I., 1984, SCI COMMUNICATIONS I MILLER DC, 1945, AM SOCIOL REV, V10, P691 Miller GA, 1968, PSYCHOL COMMUNICATIO MILSTEAD JL, 1990, METHODOLOGIES SUBJEC MISHLER EG, 1975, J PSYCHOLINGUIST RES, V4, P99, DOI 10.1007/BF01077031 Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U MOLES A, 1971, COMMUNICATION MASS M MONTGOMERY KL, 1976, J AM SOC INFORM SCI, V27, P73 Montroll E. W., 1974, INTRO QUANTITATIVE A MORAVCSIK MJ, 1988, RES POLICY, V17, P293, DOI 10.1016/0048-7333(88)90009-1 MORSE PM, 1968, LIB EFFECTIVENESS MORSE PM, 1972, LIBR QUART, V42, P15 Moser C.A., 1971, SURVEY METHODS SOCIA MOTE LJB, 1962, J DOC, V18, P169, DOI 10.1108/eb026319 MOTE LJB, 1962, J DOC, V18, P6, DOI 10.1108/eb026311 Mumford L., 1966, CITY HIST MYLOPOULOS J, 1983, CONCEPTUAL MODELLING NARANAN S, 1971, J DOC, V27, P83, DOI 10.1108/eb026510 NARIN F, 1977, ANNU REV INFORM SCI, V12, P35 NARIN F, 1976, J AM SOC INFORM SCI, V27, P25 *NAT AC SCI NAT RE, 1959, INT C SCI INF P NEAL HE, 1963, COMMUNICATION STONE NELSON CE, 1970, COMMUNICATION SCI EN NEWCOMB TM, 1953, PSYCHOL REV, V60, P393, DOI 10.1037/h0063098 NEWELL A, 1982, ARTIF INTELL, V18, P87, DOI 10.1016/0004-3702(82)90012-1 NORMAN OG, 1979, RSR REFERENCE SERVIC, V7, P71, DOI 10.1108/eb048646 Norton M. J, 2000, INTRO CONCEPTS INFOR ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 Oddy R. N., 1977, Journal of Informatics, V1 Odlyzko A., 1998, J ELECT PUBLISHING, V4 Odlyzko A, 1997, LIBR TRENDS, V46, P152 ODLYZKO AM, 1995, INT J HUM-COMPUT ST, V42, P71, DOI 10.1006/ijhc.1995.1004 ODLYZKO AM, 1996, EUROMATH B, V2, P49 Ogden C. K., 1949, MEANING MEANING OLDMAN CM, 1976, MANAGEMENT BIBLIOGRA, V2, P211 OLDMAN CM, 1978, THESIS CRANFIELD I T Oppenheim A. Leo, 1964, ANCIENT MESOPOTAMIA ORR RH, 1973, J DOC, V29, P315, DOI 10.1108/eb026561 OSWITCH PA, 1983, LIB RES, V5, P129 OVERHAGE CF, 1974, INFORM STORAGE RET, V10, P157, DOI 10.1016/0020-0271(74)90057-6 PAISLEY W, 1980, PROGR COMMUNICATION, V2, P113 PAISLEY WJ, 1965, FLOW BEHAV SCI INFOR Palmer ES, 1981, LIBR RES, V3, P315 PAO ML, 1986, J AM SOC INFORM SCI, V37, P26, DOI 10.1002/asi.4630370105 PARK R, 1967, SOCIAL CONTROL COLLE, P32 PARSAYE K, 1989, INTELLIGENT DATABASE Pask G., 1961, APPROACH CYBERNETICS PENNER RJ, 1972, J EDUCATION LIBRARIA, V12, P17 PENNIMAN WD, 1980, INFORM PROCESSING MA, V35, P16 PETTIGREW K, 2000, INTERNET RES STATE I Pinto M, 1999, J INFORM SCI, V25, P365, DOI 10.1177/016555159902500503 PITERNICK AB, 1989, ONLINE REV, V13, P457, DOI 10.1108/eb024323 POOL ID, 1983, SCIENCE, V227, P609 Pool I.deS., 1984, COMMUNICATION FLOWS Popper K., 1976, UNENDED QUEST PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 POTTER WG, 1980, LIBR RESOUR TECH SER, V24, P3 POTTER WG, 1981, LIBR TRENDS, V30, P21 Poulter A, 1997, PROGRAM-ELECTRON LIB, V31, P131, DOI 10.1108/EUM0000000006884 POWELL R, 1984, BASIC RES METHODS LI POWELL RR, 1984, LIBR INFORM SCI RES, V6, P3 PRAUNLICH P, 1978, J AM SOC INFORM SCI, V29, P51, DOI 10.1002/asi.4630290203 PRICE D, 1976, J AM SOC INFORM SCI, V27, P92 PRICE DS, 1974, SPEC LIBR, V65, P49 Propper C., 1976, EC SOCIAL PROBLEMS QIN J, 1999, LIB TRENDS, V48 R H, 1968, B MED LIB ASS, V56, P241 RAITT D, 1984, INTRO ONLINE INFORM RAMSEY HR, 1983, ANNU REV INFORM SCI, V18, P29 Ranganathan S. R., 1967, PROLEGOMENA LIB CLAS, V3rd Ranganathan S. R., 1957, 5 LAWS LIB SCI RASMUSSEN E, 2003, ANN REV INFORM SCI T, V37 Rasmussen EM, 1997, ANNU REV INFORM SCI, V32, P169 RAVETZ JR, 1971, SCIENTIFIC KNOWLEDGE RAWSKI C., 1973, THEORY LIBRARIANSHIP Reid Ivan, 1981, SOCIAL CLASS DIFFERE REINECKE I, 1984, ELECT ILLUSIONS RESNIKOFF HL, 1971, ACCESS INFORM *ROYAL SOC, 1948, 5626 ROYAL SOC BRIT DEARBORN DC, 1958, SOCIOMETRY, V21, P140, DOI 10.2307/2785898 *SITPRO, 1978, FUT TRENDS COMP COMM Sparck Jones K., 1995, INFORM PROCESS MANAG, V31, P291 Sparck Jones K., 1981, INFORM RETRIEVAL EXP Steel T., 1976, LOGIC QUESTIONS ANSW *SYB, 2001, XML COMPL *UN, 1971, STUD REP FEAS WORLD Winston P, 1975, PSYCHOL COMPUTER VIS *WORLD WID WEB CON, 2003, RDF PRIM 1977, INFORM HOTLINE APR Spink A, 1996, ANNU REV INFORM SCI, V31, P33 GRIFFITH BC, 1974, SCI STUD, V4, P339, DOI 10.1177/030631277400400402 Moran James, 1974, 500 YEARS PRINTING Norvig P., 1995, ARTIFICIAL INTELLIGE References Scott A.C., 1991, PRACTICAL GUIDE KNOW REVANS RW, 1969, SURVEY CYBERNETICS, pCH17 RICH E, 1983, ARTIFI CIAL INTELLIG RICH E, 1991, ARTIFI CIAL INTELLIG ROBERTS N, 1984, J DOC, V40, P271, DOI 10.1108/eb026769 ROBERTSON A, 1974, ASLIB PROC, V26, P384, DOI 10.1108/eb050473 ROBERTSO.A, 1973, ASLIB PROC, V25, P130, DOI 10.1108/eb050399 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1997, J DOC, V53, P1 Robertson SE, 1994, 50 YEARS INFORMATION, P119 ROBERTSON SE, 1981, INFORM RETRIEVAL EXP ROBERTSON SE, 1975, J DOC, V31, P273, DOI 10.1108/eb026607 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTSON WP, 1972, QUESTION ANSWERS Rogers E. M., 1971, COMMUNICATION INNOVA Rogers E.M., 1976, COMMUNICATION ORG ROGERS JV, 1984, LIBR INFORM SCI RES, V6, P111 ROSENBLOOM RS, 1967, TECHNOLOGY INFORM OR ROTHENBERG DH, 1977, INFORM PROCESS MANAG, V13, P317, DOI 10.1016/0306-4573(77)90052-8 ROUSE WB, 1979, J AM SOC INFORM SCI, V30, P181, DOI 10.1002/asi.4630300403 ROUSE WB, 1976, J AM SOC INFORM SCI, V27, P88, DOI 10.1002/asi.4630270204 Rouse W. B., 1975, Journal of the American Society for Information Science, V26, DOI 10.1002/asi.4630260304 Rumelhart D E, 1977, INTRO HUMAN INFORM P RYDER J, 1970, MODERN ENGLISH SOC SAGER N, 1978, ADV COMPUT, V17, P89, DOI 10.1016/S0065-2458(08)60391-5 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 Salton G., 1975, DYNAMIC INFORM LIB P Salton G., 1983, INTRO MODERN INFORM SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 SARACEVIC T, 1970, INTRO INFORM SCI SARACEVIC T, 1976, ADV LIBRARIANSHIP, V6, P79 Saracevic T, 1999, J AM SOC INFORM SCI, V50, P1051, DOI 10.1002/(SICI)1097-4571(1999)50:12<1051::AID-ASI2>3.0.CO;2-Z Saracevic T., 1996, P 2 C CONC LIB INF S, P201 SARACEVIC T, 1968, ENQUIRY TESTING INFO Saracevic T., 1970, INTRO INFORMATION SC, P111 SAYERS WCB, 1967, MANUAL CLASSIFICATIO SCHANK RC, 1975, CONCEPTAL INFORM PRO Schank R.C., 1977, SCRIPT PLANS GOALS U Schramm W., 1964, MASS MEDIA NATL DEV SCHULTZ CK, 1968, HP LUHN SELECTED WOR SEATON J, 1975, J LIBR, V7, P69, DOI 10.1177/096100067500700201 SERAFINE S, 1981, INFORM REVOLUTION IT Shera J., 1972, FDN ED LIBRARIANSHIP SHERA JH, 1977, ANNU REV INFORM SCI, V12, P249 Sherif M., 1969, SOCIAL PSYCHOL Shibutani T, 1955, AM J SOCIOL, V60, P562, DOI 10.1086/221630 Simon H., 1969, SCI ARTIFICIAL Simon J. L., 1978, BASIC RES METHODS SO SINGLETON A, 1976, J DOC, V32, P258, DOI 10.1108/eb026628 SINHA BK, 1975, MANAGE SCI, V22, P547 SLATER M, 1972, DATA CHEM Sloman A., 1978, COMPUTER REVOLUTION SMALL H, 1974, SCI STUD, V4, P17, DOI 10.1177/030631277400400102 SMALL H, 1980, J DOC, V36, P183, DOI 10.1108/eb026695 SMITH A, 1980, GOODBYE GUTENBERG SMITH AG, 1973, COMMUNICATIONS ETHIC SMITH LC, 1981, LIBR TRENDS, V30, P83 SMITH LC, 1976, INFORM PROCESS MANAG, V12, P189, DOI 10.1016/0306-4573(76)90005-4 SMITH LC, 1984, J INFORM SCI, V8, P113, DOI 10.1177/016555158400800303 SMITH LC, 1980, ANNU REV INFORM SCI, V15, P67 SOERGEL D, 1967, INFORM STORAGE RET, V3, P219, DOI 10.1016/0020-0271(67)90012-5 SOMERVILLE AN, 1977, ONLINE, V1, P14 SORMUNEN E, 1989, 630 TECHN RES CTR FI Sowa J. F., 1984, CONCEPTUAL STRUCTURE Sowa J.F., 2000, KNOWLEDGE REPRESENTA STAUD JL, 1988, J INFORM SCI, V14, P141, DOI 10.1177/016555158801400303 STEINKE C, 1991, INFORM SEEKING COMMU STOKOE CM, 2002, AUTOMATED WORD SENSE STONE L, 1969, PAST PRESENT, V42, P120 STONE S, 1984, CRUS GUIDES, V1 Wilson T. D., 1981, Social Science Information Studies, V1, DOI 10.1016/0143-6236(81)90032-6 Strzalkowski T., 1999, NATURAL LANGUAGE INF Suchman E. A., 1967, EVALUATION RES PRINC Svenonius E., 2000, INTELLECTUAL FDN INF SWANSON RW, 1975, J AM SOC INFORM SCI, V26, P140, DOI 10.1002/asi.4630260303 SWARTZ A, 2001, SEMANTIC WEB BREADTH SWIGGER K, 1985, LIBR INFORM SCI RES, V7, P369 Tabah AN, 1999, ANNU REV INFORM SCI, V34, P249 TAGUE J, 1981, LIBR TRENDS, V30, P125 TAGUESUTCLIFFE J, 1994, 50 YEARS INFORM PROG, P147 Tague-Sutcliffe J., 1995, MEASURING INFORM INF Taylor Arlene G., 1999, ORG INFORM TAYLOR RS, 1968, COLL RES LIBR, V29, P178 Taylor R. S., 1967, QUESTION NEGOTIATION TAYLOR RS, 1973, EC INFORM DISSEMINAT TENOPIR C, 2000, TOWARDS ELECT J Thayer L., 1968, COMMUNICATION COMMUN THOMAS PA, 1975, J DOC, V31, P1, DOI 10.1108/eb026589 THORNTON S, 2003, MANAGING INFORM, V10, P33 Toffler A., 1971, FUTURE SHOCK TRYBULA W, 1999, ANNU REV INFORM SCI, V34, P3 Trybula WJ, 1997, ANNU REV INFORM SCI, V32, P197 UNWIN D, 1978, ENCY ED MEDIA COMMUN Urquhart D. J, 1958, P INT C SCI INF WASH, V1, P287 URQUHART DJ, 1959, J DOC, V15, P21 URQUHART JA, 1972, J DOC, V28, P233, DOI 10.1108/eb026541 URQUHART JA, 1971, J DOC, V27, P273, DOI 10.1108/eb026522 UZANNE O, 1950, ANATOMY BIBLIOMANIA VAN RUSBERGEN C. J. KEITH, 1979, INFORM RETRIEVAL VANRUSBERGEN CJ, 1976, J DOC, V32, P299 VANRUSBERGEN CJ, 1984, RES DEV INFORM RETRI VANSLYPE G, 1979, CONCEPTION GESTION S Veal DC, 2001, J DOC, V57, P192 Vellucci SL, 1998, ANNU REV INFORM SCI, V33, P187 VICKERS PH, 1983, J DOC, V39, P217, DOI 10.1108/eb026749 VICKERS PH, 1973, J DOC, V29, P258, DOI 10.1108/eb026558 VICKERY A, 1978, LARGE SCALE EVALUATI VICKERY A, 1984, J INFORM SCI, V9, P7, DOI 10.1177/016555158400900102 VICKERY A, 1987, J DOCUMENTATION, V43 VICKERY A, 1986, EXPERT SYSTEM REFERR VICKERY A, 1987, INFORM PROCESS MANAG, V23, P99, DOI 10.1016/0306-4573(87)90052-5 VICKERY A, 1978, ORG IMPACT TRAVELLIN VICKERY A, 1988, ONLINE INFORM, V88, P301 Vickery B, 1997, J DOC, V53, P457, DOI 10.1108/EUM0000000007206 Vickery B, 1997, J DOC, V53, P107, DOI 10.1108/EUM0000000007195 Vickery B, 1996, J DOC, V52, P198 VICKERY B, 1984, J DOC, V40, P36, DOI 10.1108/eb026756 VICKERY B, 1993, J DOC, V49, P103, DOI 10.1108/eb026912 VICKERY B, 1990, J INFORM SCI, V16, P65, DOI 10.1177/016555159001600111 Vickery B, 1999, J DOC, V55, P476, DOI 10.1108/EUM0000000007155 Vickery B. C., 1953, LIBRI, V2, P326, DOI 10.1515/libr.1953.2.1.326 Vickery B. C., 1960, FACETED CLASSIFICATI Vickery B. C., 2000, SCI COMMUNICATION HI Vickery B. C., 1975, CLASSIFICATION INDEX VICKERY BC, 1971, J DOC, V27, P69, DOI 10.1108/eb026509 VICKERY BC, 2003, P 2002 C HI IN PRESS Vickery BC, 1997, J INF SCI, V23, P277 VICKERY BC, 1978, J DOC, V34, P279, DOI 10.1108/eb026664 VICKERY BC, 1992, J DOCUMENTATION SEP VICKERY BC, 1968, J DOC, V24, P192, DOI 10.1108/eb026454 VICKERY BC, 1969, J LIBR, V1, P170, DOI 10.1177/096100066900100303 VICKERY BC, 1970, J DOC, V26, P30 VICKERY BC, 1964, ASLIB PROC, V16, P79, DOI 10.1108/eb049953 VICKERY BC, 1965, RETRIEVAL SYSTEM THE, P2 VICKERY BC, 1970, TECHNIQUES INFORM RE VICKERY BC, 1985, OCCASIONAL PUBLICATI, V4 VICKERY BC, 1969, ASLIB P, V21, P237 VICKERY BC, 1948, J DOC, V4, P198, DOI 10.1108/eb026133 VICKERY BC, 1971, COMPUTER SUPPORT PAR VICKERY BC, 1963, MINERVA, V2, P21, DOI 10.1007/BF01100778 VICKERY BC, 1989, INTELLIGENT INTERFAC VICKERY BC, 1986, J DOC, V42, P145, DOI 10.1108/eb026790 VICKERY BC, 1961, LIBR ASSOC REC, V63, P263 VICKERY BC, 1970, ASLIB PROC, V22, P597, DOI 10.1108/eb050269 VICKERY BC, 1969, METALS INFORM BRITAI Vickery BC, 1973, INFORM SYSTEMS VICKERY BC, 1994, 50 YEARS INFORM PROG von Bertalanffy L., 1971, GEN SYSTEM THEORY VOORHEES EM, 2002, OVERVIEW TREC 2001 VORESS HE, 1962, LIT NUCL SCI, P284 Waddington C., 1977, TOOLS FOR THOUGHT WALL T, 1980, LIB RES, V2, P177 Walsh J. P., 1996, Information Society, V12, DOI 10.1080/019722496129341 WANG P, 1999, ANN REV INFORM SCI T, V34 Waples D, 1932, LIBR QUART, V2, P42 WARD J, 1981, ASLIB IIS LA JOINT C Wardhaugh Ronald, 1985, CONVERSATION WORKS Waterman D. A., 1986, GUIDE EXPERT SYSTEMS WEECH TL, 1974, LIBR TRENDS, V22, P315 WEISGERBER DW, 1993, J DOC, V49, P231, DOI 10.1108/eb026914 Weisman H. M., 1972, INFORM SYSTEMS SERVI WESSEL CJ, 1968, ASLIB PROC, V20, P455 WESSEL MR, 1982, DATAMATION, V28, P234 White DM, 1950, JOURNALISM QUART, V27, P383 White HD, 2003, J AM SOC INF SCI TEC, V54, P423, DOI 10.1002/asi.10228 WHITE HD, 1998, J AM SOC INFORM SCI, V327, P1972 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 WHITE MD, 1981, RQ, V20, P373 WHITEHALL T, 1980, ASLIB PROC, V32, P87, DOI 10.1108/eb050720 WHITLEY R, 1973, HUM RELAT, V25, P537 Whyte LL, 1969, HIERARCHICAL STRUCTU WILKIN A, 1977, ADV LIB, V7, P257 Wilks Y., 1972, GRAMMAR MEANING MACH WILLETTS M, 1975, J DOC, V31, P158, DOI 10.1108/eb026599 WILLIAMS A, 1976, READING CONSUMER WILLIAMS M, 1998, GALES DIRECTORY DATA Williams Raymond, 1965, THE LONG REVOLUTION WILLS G, 1970, UNESCO BULL LIBR, V24, P9 WILLS G, 1972, TECHNOLOGICAL FORECA WILSON CS, 1999, ANNU REV INFORM SCI, V34, P3 WILSON TD, 1975, J LIBR, V7, P143, DOI 10.1177/096100067500700301 Wilson T. D., 1981, SOCIAL SCI INFORM ST, V1, P241, DOI 10.1016/0143-6236(81)90015-6 WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702 WILSON TD, 1977, J DOC, V33, P277, DOI 10.1108/eb026646 WILSON TD, 1979, J DOC, V35, P120, DOI 10.1108/eb026675 Wilson T.D., 1994, 50 YEARS INFORM PROG, P15 Winston P. H, 1984, ARTIFICIAL INTELLIGE, V2nd WOLEK FW, 1970, COMMUNICATION SCI EN WOLFE JN, 1974, EC TECHNICAL INFORM WOOD LJ, 1980, AREA, V12, P217 WOODBURN I, 1970, MATH MODEL HIERARCHI WORTHEN DB, 1973, J AM SOC INFORM SCI, V24, P343, DOI 10.1002/asi.4630240505 WYLLYS RE, 1981, LIBR TRENDS, V30, P53 YOKOI T, 1995, COMMUN ACM, V38, P42, DOI 10.1145/219717.219752 Young John Z., 1971, INTRO STUDY MAN Young JZ, 1978, PROGRAMS BRAIN Young P., 1966, SCI SOCIAL SURVEYS R YU C, 1998, PRINCIPLES QUERY PRO Zainab AN, 1998, J INF SCI, V24, P313, DOI 10.1177/016555159802400505 Zamir O., 1998, P 21 INT ACM SIGIR C ZELLDITCH M, 1962, AM J SOCIOL, V67, P566 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL ZUCKERMAN H, 1996, SCI ELITE NR 707 TC 10 Z9 10 PU WALTER DE GRUYTER & CO PI BERLIN 30 PA GENTHINER STRASSE 13, D-10785 BERLIN 30, GERMANY BN 978-3-598-44008-3 PY 2004 BP 1 EP 400 DI 10.1515/9783598440083 PG 400 WC Information Science & Library Science SC Information Science & Library Science GA BOJ85 UT WOS:000276867600015 ER PT J AU Meziane, F Rezgui, Y AF Meziane, F Rezgui, Y TI A document management methodology based on similarity contents SO INFORMATION SCIENCES LA English DT Article; Proceedings Paper CT 7th International Conference on Applications of Natural Language to Information Systems CY JUN 27-28, 2002 CL STOCKHOLM, SWEDEN HO ROYAL INST TECHNOL DE document consistency; ontology; similarity content ID INFORMATION-RETRIEVAL; SYSTEMS AB The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain. (C) 2003 Elsevier Inc. All rights reserved. C1 Univ Salford, Sch Engn & Comp Sci, Salford M5 4WT, Lancs, England. Univ Salford, Inst Informat Syst, Salford M5 4WT, Lancs, England. RP Meziane, F (reprint author), Univ Salford, Sch Engn & Comp Sci, Salford M5 4WT, Lancs, England. CR Backer A., 1996, Proceedings of the Twenty-Ninth Hawaii International Conference on System Sciences, DOI 10.1109/HICSS.1996.493179 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BERTHIER AN, 1996, P 19 ACM SIGIR C, P253 CELENTANO A, 1992, P 10 ANN INT C SYST, P63, DOI 10.1145/147001.147009 Dourish P, 2000, ACM T INFORM SYST, V18, P140, DOI 10.1145/348751.348758 Dourish P., 1999, ACM Transactions on Computer-Human Interaction, V6, DOI 10.1145/319091.319099 FLYNN P, 2003, XML FAQ VERSION 3 01 Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 Gudivada V. N., 1997, IEEE Internet Computing, V1, DOI 10.1109/4236.623969 LIMA C, 2002, P ESM RT EUR C INF C, P254 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Rijsbergen CJV, 1979, INFORMATION RETRIEVA, V2nd ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VERHOEFF J, 1961, COMMUN ACM, V4, P557, DOI 10.1145/366853.366861 Wartick S, 1992, INFORMATION RETRIEVA, P264 Wilkinson R., 1991, P ACM SIGIR C RES DE, P202, DOI 10.1145/122860.122880 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 YONGGANG Q, 1993, P 16 ANN INT C ACM S, P160 NR 23 TC 19 Z9 21 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD JAN PY 2004 VL 158 BP 15 EP 36 DI 10.1016/j.ins.2003.08.009 PG 22 WC Computer Science, Information Systems SC Computer Science GA 758JC UT WOS:000187629900002 ER PT J AU Lertnattee, V Theeramunkong, T AF Lertnattee, V Theeramunkong, T TI Effect of term distributions on centroid-based text categorization SO INFORMATION SCIENCES LA English DT Article DE centroid-based classifier; text categorization; term distribution ID RETRIEVAL; CLASSIFICATION AB Most of traditional text categorization approaches utilize term frequency (tf) and inverse document frequency (idf) for representing importance of words and/or terms in classifying a text document. This paper describes an approach to apply term distributions, in addition to tf and idf, to improve performance of centroid-based text categorization. Three types of term distributions, called inter-class, intra-class and in-collection distributions, are introduced. These distributions are useful to increase classification accuracy by exploiting information of (1) term distribution among classes, (2) term distribution within a class and (3) term distribution in the whole collection of training data. In addition, this paper investigates how these term distributions contribute to weight each term in documents, e.g., a high term distribution of a word promotes or demotes importance or classification power of that word. To this end, several centroid-based classifiers are constructed with different term weightings. Using various data sets, their performances are investigated and compared to a standard centroid-based classifier (TDIDF) and a centroid-based classifier modified with information gain. Moreover, we also compare them to two well-known methods: k-NN and naive Bayes. In addition to a unigram model of document representation, a bigram model is also explored. Finally, the effectiveness of term distributions to improve classification accuracy is explored with regard to the training set size and the number of classes. (C) 2003 Elsevier Inc. All rights reserved. C1 Sirindhorn Int Inst Technol, Informat Technol Program, Muang 12000, Pathumthani, Thailand. RP Theeramunkong, T (reprint author), Sirindhorn Int Inst Technol, Informat Technol Program, Bangkadi Campus,131 Moo 5 Tiwanont Rd, Muang 12000, Pathumthani, Thailand. RI Theeramunkong, Thanaruk/A-3459-2009 OI Theeramunkong, Thanaruk/0000-0003-0744-4213 CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Chuang WT, 2000, LECT NOTES COMPUT SC, V1874, P409 Cohen W., 1995, ADV INDUCTIVE LOGIC, P124 Craven M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence DEBOLE F, P SAC 03 18 ACM S AP DENG ZH, 2002, P ICADL 02 5 INT C A, P88 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Han E., 2000, PRINCIPLES DATA MINI, P424 Han E.H., 2001, P 5 PAC AS C KNOWL D, P53 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Ittner David J., 1995, P SDAIR 95 4 ANN S D, P301 Joachims T., 1998, LNCS, V1398, P137 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 Lee K.-H., 2002, LECT NOTES COMPUTER, V2417, P414 Lertnattee V, 2001, P INTECH 01 2 INT C, P349 McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P359 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SINGHAL A, 1995, TR951507 SINGHAL A, 1996, RES DEV INFORM RETRI, P21 Siolas G., 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, DOI 10.1109/IJCNN.2000.861458 Skalak D. B., 1994, INT C MACH LEARN, P293 SORENSEN H, 1995, P CIKM 95 INT INF AG THEERAMUNKONG T, 2001, P ISCIT 01 2 INT S C, P1167 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, J INFORMATION RETRIE, V1, P69 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 35 TC 26 Z9 33 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD JAN PY 2004 VL 158 BP 89 EP 115 DI 10.1016/j.ins.2003.07.007 PG 27 WC Computer Science, Information Systems SC Computer Science GA 758JC UT WOS:000187629900005 ER PT J AU Robertson, S AF Robertson, S TI Understanding inverse document frequency: on theoretical arguments for IDF SO JOURNAL OF DOCUMENTATION LA English DT Article DE information theory; probabilistic analysis; modelling; text retrieval ID PROBABILISTIC MODELS; RETRIEVAL; SPECIFICITY; INFORMATION AB The term-weighting function known as IDF was proposed in 1972, and has since been extremely widely used, usually as part of a TF*IDF function. It is often described as a heuristic, and many papers have been written (some based on Shannon's Information Theory) seeking to establish some theoretical basis for it Some of these attempts are reviewed, and it is shown that the Information Theory approaches are problematic, but that there are good theoretical justifications of both IDF and TF*IDF in the traditional probabilistic model of information retrieval. C1 Microsoft Res, Cambridge, England. City Univ London, London EC1V 0HB, England. RP Robertson, S (reprint author), Microsoft Res, Cambridge, England. CR Aizawa A, 2003, INFORM PROCESS MANAG, V39, P45, DOI 10.1016/S0306-4573(02)00021-3 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 CHURCH, 1995, 3 WORKSHOP VERY LARG, P121 Croft W.B, 2003, LANGUAGE MODELLING I CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 GRAY RM, 1990, ENTROPY INFORMATION HARMAN D, IN PRESS HIST IDF IN HARTER SP, 1975, J AM SOC INFORM SCI, V197, P280 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Lavrenko V., 2001, SIGIR Forum Papineni K., 2001, P N AM ASS COMP LING, P25 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTSON S, 2004, UNPUB INFORMATION RE Robertson S. E., 1994, P 17 ANN INT ACM SIG, P345 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSO.SE, 1972, J DOC, V28, P164 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTSO.SE, 1974, J DOC, V30, P41 Roelleke T, 2003, P 26 ANN INT ACM SIG, P227 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SHANNON CE, 1948, AT&T TECH J, V27, P379 SIEGLER M, 1999, P IEEE INT C AC SPEE, P505 Sparck J. K., 2000, INFORMATION PROCESSI, V36, P779 SPARCKJONES K, 1972, J DOC, V28, P11 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 29 TC 139 Z9 140 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PY 2004 VL 60 IS 5 BP 503 EP 520 DI 10.1108/00220410560582 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 867VY UT WOS:000224871900003 ER PT J AU Sparck-Jones, K AF Sparck-Jones, K TI IDF term weighting and IR research lessons SO JOURNAL OF DOCUMENTATION LA English DT Article DE information research; information retrieval; information science and documentation AB Robertson comments on the theoretical status of IDF term weighting. Its history illustrates how ideas develop in a specific research context, in theory/experiment interaction, and in operational practice. C1 Univ Cambridge, Comp Lab, Cambridge CB2 3QG, England. RP Sparck-Jones, K (reprint author), Univ Cambridge, Comp Lab, Pembroke St, Cambridge CB2 3QG, England. CR CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 ROBERSTON SE, 2004, J DOCUMENTATION, V60 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1975, THEORY INDEXING SCHULTZ CK, 1968, HP LUHN PIONEER INFO SVENONIU.E, 1972, J AM SOC INFORM SCI, V23, P109, DOI 10.1002/asi.4630230205 VOORHEES EM, TREC EXPT EVALUATION NR 9 TC 6 Z9 6 PU EMERALD GROUP PUBLISHING LIMITED PI BRADFORD PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PY 2004 VL 60 IS 5 BP 521 EP 523 DI 10.1108/00220410410560591 PG 3 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 867VY UT WOS:000224871900004 ER PT J AU Khan, MS Khor, S AF Khan, MS Khor, S TI Enhanced Web document retrieval using automatic query expansion SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL AB The ever growing popularity of the Internet as a source of information, coupled with the accompanying growth in the number of documents made available through the World Wide Web, is leading to an increasing demand for more efficient and accurate information retrieval tools. Numerous techniques have been proposed and tried for improving the effectiveness of searching the World Wide Web for documents relevant to a given topic of interest. The specification of appropriate keywords and phrases by the user is crucial for the successful execution of a query as measured by the relevance of documents retrieved. Lack of users' knowledge on the search topic and their changing information needs often make it difficult for them to find suitable keywords or phrases for a query. This results in searches that fail to cover all likely aspects of the topic of interest. We describe a scheme that attempts to remedy this situation by automatically expanding the user query through the analysis of initially retrieved documents. Experimental results to demonstrate the effectiveness of the query expansion scheme are presented. C1 Murdoch Univ, Sch Informat Technol, Perth, WA 6150, Australia. RP Khan, MS (reprint author), Murdoch Univ, Sch Informat Technol, Perth, WA 6150, Australia. CR Anick PG, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P314, DOI 10.1145/258525.258601 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley Chris, 1995, P 3 TEXT RETR C TREC, P69 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 Croft W. B., 1990, P 13 INT C RES DEV I, P349, DOI 10.1145/96749.98240 DEVADASON FJ, 1996, P 62 IFLA GEN C BEIJ Fagan J., 1987, 10TH P ASS COMP MACH, P91 Frank E, 1999, P 16 INT JOINT C ART, P668 Hansen P., 2000, P ACM SIGIR 2000 WOR, P43 Harman D, 1988, P 11 ANN INT ACM SIG, P321, DOI 10.1145/62437.62469 HEARST MA, 1998, P WORDN EL LE DAT, P129 Huberman BA, 1998, SCIENCE, V280, P95, DOI 10.1126/science.280.5360.95 Jing Y., 1994, P RIAO 94, P146 Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 KUHLTHAU CC, 1996, P ASIS 1996 ANN C AM Campos JL, 2001, APPL BIOCHEM BIOTECH, V95, P1, DOI 10.1385/ABAB:95:1:01 Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 NARITA M, 2000, P 23 ANN INT ACM SIG, P318, DOI 10.1145/345508.345621 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 1994, P 3 TEXT RETR C TREC, P109 RUNKLER TA, 2000, IEEE, V2, P636 Joho H., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354817 TURNEY P, 1997, EXTR KEYPH TEXT EV 4, P1 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 van Rijsbergen CJ, 1986, P 1986 ACM C RES DEV, P194, DOI 10.1145/253168.253208 WALLIS P, 1995, RELEVANCE JUDGMENTS, P1 Wilson T.D., 1999, P MIRA 99 EV FRAM MU, P75 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 30 TC 19 Z9 20 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JAN 1 PY 2004 VL 55 IS 1 BP 29 EP 40 DI 10.1002/asi.10341 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 753ZW UT WOS:000187279100004 ER PT S AU Nanas, N Uren, V de Roeck, A Domingue, J AF Nanas, N Uren, V de Roeck, A Domingue, J BE Vouros, GA Panayiotopoulos, T TI Multi-topic information filtering with a single user profile SO METHODS AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd Hellenic Conference on Artificial Intelligence CY MAY 05-08, 2004 CL Samos, GREECE SP Hellen Soc Artificial Intelligence, Artificial Intelligence Lab, Dept Informat & Commun Syst Engn, Univ Aegean ID RELEVANCE AB In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results. C1 Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England. Open Univ, Dept Comp, Milton Keynes MK7 6AA, Bucks, England. RP Nanas, N (reprint author), Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, Bucks, England. EM N.Nanas@open.ac.uk; V.S.Uren@open.ac.uk; A.DeRoeck@open.ac.uk; J.B.Domingue@open.ac.uk CR Amati Gianni, 1997, J UNIVERS COMPUT SCI, V3, P1007 ANICK P, 1999, 22 ANN INT ACM SIGIR, P153 BILLSUS D, 1999, 3 INT C AUT AG SEATT Crestani F, 1997, ARTIF INTELL REV, V11, P453, DOI 10.1023/A:1006569829653 DOYLE LB, 1961, J ACM, V8, P553, DOI 10.1145/321088.321095 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Krulwich B, 1997, IEEE EXPERT, V12, P22, DOI 10.1109/64.621224 Lang K., 1995, 12 INT C MACH LEARN Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 MCELLIGOTT M, 1994, 4 IR NEUR NETW C 94 Mostafa J, 1997, ACM T INFORM SYST, V15, P368, DOI 10.1145/263479.263481 Moukas A., 1998, AUTON AGENT MULTI-AG, V1, P59, DOI 10.1023/A:1010094506174 Nanas N., 2003, 26 ANN INT ACM SIGIR, P198 Nevill-Manning C., 1999, INT J DIGITAL LIB, V2, P111, DOI 10.1007/s007990050041 PAZZANI M, 1996, 13 NAT C ART INT POR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE T, 2002, 3 INT C LANG RES EV SALTON G, 1983, INTRO MODERN INFORMA Sanderson M., 1999, 22 ANN INT ACM SIGIR, P206 Sebastiani F., 2002, ACM COMPUTING SURVEY, V34 Sheth B. D., 1994, THESIS MIT SORENSEN H, 1997, J UNIVERS COMPUT SCI, V3, P988 WILKINSON R, 1991, 14 ANN INT ACM SIGIR, P202 NR 23 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-21937-4 J9 LECT NOTES COMPUT SC PY 2004 VL 3025 BP 400 EP 409 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BAC99 UT WOS:000221610800042 ER PT B AU Hu, XH Yoo, IH Song, IY Song, M Han, JC Lechner, M AF Hu, XH Yoo, IH Song, IY Song, M Han, JC Lechner, M GP ieee TI Extracting and mining protein-protein interaction network from biomedical literature SO PROCEEDINGS OF THE 2004 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY LA English DT Proceedings Paper CT IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology CY OCT 07-08, 2004 CL La Jolla, CA SP IEEE Computat Intelligence Soc DE graph; protein-protein interaction; text mining ID INFORMATION; GRAPH AB In this paper we present a biomedical literature data mining system SPIE-DM (Scalable and Portable Information Extraction and Data Mining) to extract and mine the protein-protein interaction network from biomedical literature such as MedLine. SPIE-DM consists of two phases: in Phase 1, we develop a Scalable and Portable IE method (SPIE) to extract the protein-protein interaction from the biomedical literature. These extracted protein-protein interactions form a scale-free network graph. In Phase 2, we apply a novel clustering method SFCluster to mine the protein-protein interaction network. The clusters in the network graph represent some potential protein complexes, which are very important for biologist to study the protein functionality. The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to rind more meaningful clusters at different density levels. The experiments of SPIE-DM on around 1600 chromatin proteins indicate that our system is very promising for extracting and mining from biomedical literature databases. C1 Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. RP Hu, XH (reprint author), Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. CR Agichtein E., 2000, P 5 ACM INT C DIG LI, P85, DOI 10.1145/336597.336644 Albert R, 2000, NATURE, V406, P378, DOI 10.1038/35019019 Bader GD, 2003, BMC BIOINFORMATICS, V4, DOI 10.1186/1471-2105-4-2 BARABASI AL, 1999, SCIENCE, V286, P209 Broder A., 2000, P 9 INT WORLD WID WE, P309 Cohen WW, P 12 INT C MACH LEAR, P115 Erdos P., 1960, PUBL MATH I HUNG, V5, P17 Fukuda K, 1998, Pac Symp Biocomput, P707 Hirschman L, 2002, BIOINFORMATICS, V18, P1553, DOI 10.1093/bioinformatics/18.12.1553 HU X, 2003, P COMPSAC 2003 DALL HU X, 2003, ACM SIGKDD 2003 2 WO Ito T, 2001, P NATL ACAD SCI USA, V98, P4569, DOI 10.1073/pnas.061034498 Kumar R., 2000, P 19 ACM SIGMOD SIGA, P1, DOI 10.1145/335168.335170 Matsuda H, 1999, THEOR COMPUT SCI, V210, P305, DOI 10.1016/S0304-3975(98)00091-7 Ono T, 2001, BIOINFORMATICS, V17, P155, DOI 10.1093/bioinformatics/17.2.155 Palmer C. R., 2002, P 8 ACM SIGKDD INT C, P81 Riloff E, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P1044 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rougemont J, 2003, BMC BIOINFORMATICS, V4, DOI 10.1186/1471-2105-4-15 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 URQUHART R, 1982, PATTERN RECOGN, V15, P173, DOI 10.1016/0031-3203(82)90069-3 WEIZENBA.J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/365153.365168 Xenarios I, 2001, NUCLEIC ACIDS RES, V29, P239, DOI 10.1093/nar/29.1.239 NR 23 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-8728-7 PY 2004 BP 244 EP 251 PG 8 WC Biochemical Research Methods; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Biochemistry & Molecular Biology; Computer Science GA BBJ29 UT WOS:000225765700036 ER PT S AU Hust, A AF Hust, A BE Dengel, A Junker, M Weisbecker, A TI Introducing query expansion methods for collaborative information retrieval SO READING AND LEARNING: ADAPTIVE CONTENT RECOGNITION SE Lecture Notes in Computer Science LA English DT Article ID TERM-BASED CONCEPTS; DOCUMENT-RETRIEVAL AB The accuracy of ad-hoc document retrieval systems has plateaued in the last few years. At DFKI, we are working on so-called collaborative information retrieval (CIR) systems which unobtrusively learn from their users' search processes. We focus on a restricted setting in CIR in which only old queries and correct answer documents to these queries are available for improving a new query. For this restricted setting we propose new approaches for query expansion procedures. This paper describes query expansion methods to be used in collaborative information retrieval. We define collaborative information retrieval as a task, where an information retrieval system uses information gathered from previous search processes from one or several users to improve retrieval performance for the current user searching for information. We show how collaboration of individual users can improve overall information retrieval performance. Performance in this case is expressed in terms of quality and utility of the retrieved information regardless of specific user groups. C1 DFKI GmbH, German Res Ctr Artificial Intelligence, D-67608 Kaiserslautern, Germany. RP Hust, A (reprint author), DFKI GmbH, German Res Ctr Artificial Intelligence, POB 2080, D-67608 Kaiserslautern, Germany. EM armin.hust@dfki.de CR Agichtein E., 2001, P 10 INT WORLD WID W, P169, DOI 10.1145/371920.371976 ALSAFFAR AH, 2000, LECT NOTES ARTIF INT, V1932, P435 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley Chris, 1995, P 3 TEXT RETR C TREC, P69 Crestani F, 1998, ACM T INFORM SYST, V16, P225, DOI 10.1145/290159.290161 CRESTANI F, 1995, P ACM SIGIR C RES DE, P291, DOI 10.1145/215206.215373 CRESTANI F, 1995, J DOC, V51, P1 CRISTIANINI N, 2000, LATENT SEMANTIC KERN CUI H, 2002, 11 INT WORLD WID WEB DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 FERBER R, 2003, INFORMATION RETRIEVA FUHR N, 1996, GOALS TASKS IR GROUP Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 Harman D., 1992, INFORMATION RETRIEVA, P241 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 HUST A, 2002, LECT NOTES INFORMATI, V19, P176 HUST A, 2003, TEXT MINING THEORETI Hust A., 2002, Proceedings of the IASTED International Conference Information and Knowledge Sharing HUST A, 2003, IN PRESS MATH MODEL JOACHIMS T, 2002, UNBIASED EVALUATION KISE K, 2001, P DISC SCI 4 INT C D, V2226, P155 Kise K., 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition, DOI 10.1109/ICDAR.2001.953858 Klink S, 2002, LECT NOTES COMPUT SC, V2423, P376 Klink S, 2002, LECT NOTES ARTIF INT, V2430, P195 Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 KOLDA TG, 1997, CSTR3806 U MAR LANCASTER FW, 1968, INFORMATION RETRIEVA Manning C. D., 1999, FDN NATURAL LANGUAGE MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 OLSSON T, 1998, THESIS ROYAL I TECHN Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 RAGHAVAN V. V., 1995, P 18 ANN INT ACM SIG, P344, DOI 10.1145/215206.215381 ROBERTSON S, 1998, P 7 TEXT RETR C TREC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SEVER H, 1995, THESIS U LOUISIANA L SPARCK-JONES K, 1968, INFORM STORAGE RET, V4, P91 THORNTON J, 2003, COLLABORATIVE FILTER Tian LF, 2003, LECT NOTES COMPUT SC, V2633, P135 VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VONNEUMANN J, 1944, THEORY GAMES EC BEH Voorhees E. M., 2000, P 8 TEXT RETR C TREC VOORHEES EM, 2001, P 9 TEXT RETR C TREC VOORHEES EM, 2002, P 10 TEXT RETR C TRE Wen J. R., 2001, P 10 INT C WORLD WID, P162, DOI 10.1145/371920.371974 Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 WHITE RW, 2002, ADV INFORMATION RETR, V2291, P93, DOI 10.1007/3-540-45886-7_7 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 58 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-21904-8 J9 LECT NOTES COMPUT SC PY 2004 VL 2956 BP 252 EP 280 PG 29 WC Computer Science, Artificial Intelligence SC Computer Science GA BAC11 UT WOS:000221517400015 ER PT S AU Komarjaya, J Poo, DCC Kan, MY AF Komarjaya, J Poo, DCC Kan, MY BE Heery, R Lyon, L TI Corpus-based query expansion in Online public access catalogs SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 8th International European Conference on Research and Advanced Technology for Digital Libraries(ECDL 2004) CY SEP 12-17, 2004 CL Bath, ENGLAND SP Informat Syst Comm HO Univ Bath ID INFORMATION-RETRIEVAL AB We propose a probabilistic method for query expansion in online public access catalogs that utilizes both historical query logs and the subject headings in the library catalog. Our method creates correlations between query and document terms, allowing relevant subject headings from the corpus to be retrieved and added to a query. Experiments demonstrate an average of 31.1% performance increase over currently fielded baselines. C1 Natl Univ Singapore, Sch Comp, Singapore 117543, Singapore. RP Komarjaya, J (reprint author), Natl Univ Singapore, Sch Comp, 3 Sci Dr 2, Singapore 117543, Singapore. EM jeffry.komarjaya@nus.edu.sg; dpoo@comp.nus.edu.sg; kanmy@comp.nus.edu.sg CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CARPINETO C, 2001, ACM T INFORM SYST, V20, P259 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 DAVIS E, 2002, WEB SEARCH ENGINES Eguchi K., 1999, Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, DOI 10.1109/DASFAA.1999.765733 Fitzpatrick L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P306, DOI 10.1145/258525.258597 Jing Y., 1994, P RIAO 94, P146 JONES KS, 1971, AUTOMATIC KEYWORD CL Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 MARKEY K, 1984, SUBJECT SEARCHING LI MITRA M, 1998, P 21 ANN INT ACM SIG, P275 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 RADECKI T, 1982, P RES DEV INF RETR, P133 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 VOORHEES EM, 1998, P 6 TEXT RETR C TREC Wu H., 1981, P 4 ANN INT ACM SIGI, P30, DOI 10.1145/511754.511759 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 23 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-23013-0 J9 LECT NOTES COMPUT SC PY 2004 VL 3232 BP 221 EP 231 PG 11 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BAX48 UT WOS:000224092600021 ER PT J AU Lamirel, JC Al Shehabi, S Francois, C Polanco, X AF Lamirel, JC Al Shehabi, S Francois, C Polanco, X TI Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project SO SCIENTOMETRICS LA English DT Article ID INFORMATION; MAP AB This paper present a compound approach for Webometrics based on an extension the self-organizing multimap MultiSOM model. The goal of this new approach is to combine link and domain clustering in order to increase the reliability and the precision of Webometrics studies. The extension proposed for the MultiSOM model is based on a Bayesian network-oriented approach. A first experiment shows that the behaviour of such an extension is coherent with its expected properties for Webometrics. A second experiment is carried out on a representative Web dataset issued from the EISCTES IST project context. In this latter experiment each map represents a particular viewpoint extracted from the Web data description. The obtained maps represented either thematic or link classifications. The experiment shows empirically that the communication between these classifications provides Webometrics with new explaining capabilities. C1 LORIA, F-54506 Vandoeuvre Les Nancy, France. CNRS, URI INIST, Vandoeuvre Les Nancy, France. RP Lamirel, JC (reprint author), LORIA, BP 239, F-54506 Vandoeuvre Les Nancy, France. EM lamirel@loria.fr CR DECAMPOS LM, 2000, ECIR, P169 DECAMPOS LM, DEXA WORKSH 2000, P543 INGWERSEN P, 2003, ISSI 2003 *IST, IST199920350 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 Kohonen T., 1997, SELF ORG MAPS Kohonen T., 1984, SELF ORG ASS MEMORY Kopcsa A, 1998, J AM SOC INFORM SCI, V49, P7 LAMIREL JC, 1995, THESIS U NANCY 1 H P Lamirel JC, 2004, SCIENTOMETRICS, V60, P445, DOI 10.1023/B:SCIE.0000034386.05278.e8 LAMIREL JC, 2001, P ISSI 2001 SYDN AUS LAMIREL JC, 2003, P ACL 2003 SAPP JAP Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST *SOM, SOM PAP NR 18 TC 7 Z9 7 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PY 2004 VL 61 IS 3 BP 427 EP 441 DI 10.1023/B:SCIE.0000045119.88828.ce PG 15 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 862AA UT WOS:000224460100009 ER PT J AU Lamirel, JC Francois, C AL Shehabi, S Hoffmann, M AF Lamirel, JC Francois, C AL Shehabi, S Hoffmann, M TI New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping SO SCIENTOMETRICS LA English DT Article; Proceedings Paper CT 9th International Conference on Scientometrics and Informatics CY AUG, 2003 CL Beijing, PEOPLES R CHINA ID SELF-ORGANIZING-MAPS AB The information analysis process includes a cluster analysis or classification step associated with an expert validation of the results. In this paper, we propose new measures of Recall/Precision for estimating the quality of cluster analysis. These measures derive both from the Galois lattice theory and from the Information Retrieval (IR) domain. As opposed to classical measures of inertia, they present the main advantages to be both independent of the classification method and of the difference between the intrinsic dimension of the data and those of the clusters. We present two experiments on the basis of the MultiSOM model, which is an extension of Kohonen's SOM model, as a cluster analysis method. Our first experiment on patent data shows how our measures can be used to compare viewpoint-oriented classification methods, such as MultiSOM, with global cluster analysis method, such as WebSOM Our second experiment, which takes part in the EICSTES EEC project, is an original Webometrics experiment that combines content and links classification starting from a large non-homogeneous set of web pages. This experiment highlights the fact that break-even points between our different measures of Recall/Precision can be used to determine an optimal number of clusters for web data classification. The content of the clusters obtained when using different break-even points are compared for determining the quality of the resulting maps. C1 LORIA, F-54506 Vandoeuvre Les Nancy, France. URI INIST, CNRS, Vandoeuvre Les Nancy, France. RP Lamirel, JC (reprint author), LORIA, BP 239, F-54506 Vandoeuvre Les Nancy, France. EM lamirel@loria.fr CR Barbut M., 1970, ORDRE CLASSIFICATION Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kohonen T., 1997, SELF ORG MAPS LAMIREL JC, 1995, THESIS U NANCY 1 H P LAMIREL JC, 2000, P 1 DELOS NETW EXC W LAMIREL JC, 2001, P ISSI 2001 SYDN AUS, P339 Lebart L, 1982, TRAITEMENT DONNEES S LELU A, 1990, P IJCNN PAR FRANC, P829 Mather LA, 2000, J AM SOC INFORM SCI, V51, P602, DOI 10.1002/(SICI)1097-4571(2000)51:7<602::AID-ASI3>3.3.CO;2-T Polanco X, 2001, SCIENTOMETRICS, V51, P267, DOI 10.1023/A:1010537316758 RHAM C, 1980, CAHIERS ANAL DONNEES, V5, P135 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA YAHYA MAO, 1997, MEMOIRE STAGE DESS I NR 15 TC 14 Z9 14 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PY 2004 VL 60 IS 3 BP 445 EP 462 DI 10.1023/B:SCIE.0000034386.05278.e8 PG 18 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 835QY UT WOS:000222501800015 ER PT S AU Vinay, V Cox, IJ Milic-Frayling, N Wood, K AF Vinay, V Cox, IJ Milic-Frayling, N Wood, K BE Apostolico, A Melucci, M TI Evaluating relevance feedback and display strategies for searching on small displays SO STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Meeting Abstract CT 11th International Conference on String Processing and Information Retrieval (SPIRE) CY OCT 05-08, 2004 CL Padua, ITALY SP Natl Res Council, Inst High Performance Comp & Networking, Italian Assoc Informat & Automat Computat, Elsevier HO Univ Padova, Dept Informat Engn ID INFORMATION-RETRIEVAL; PROBABILISTIC MODEL C1 UCL, Dept Comp Sci, London WC1E 6BT, England. Microsoft Res, Cambridge, England. RP Vinay, V (reprint author), UCL, Dept Comp Sci, London WC1E 6BT, England. EM v.vinay@cs.ucl.ac.uk; ingemar@ieee.org; natasamf@microsoft.com; krw@microsoft.com CR Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 HARMAN D, 1992, P 15 ANN INT ACM SIG Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 NR 6 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-23210-9 J9 LECT NOTES COMPUT SC PY 2004 VL 3246 BP 131 EP 133 PG 3 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BBA15 UT WOS:000224377200019 ER PT J AU Atlam, ES Fuketa, M Morita, K Aoe, J AF Atlam, ES Fuketa, M Morita, K Aoe, J TI Documents similarity measurement using field association terms SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; FA terms; FA-Sim; recall; precision ID AUTOMATIC TEXT-RETRIEVAL; CLASSIFICATION; INFORMATION; MODELS; WORDS AB Conventional approaches to text analysis and information retrieval which measured document similarity by using considering all of the information in texts are a relatively inefficiency for processing large text collections in heterogeneous subject areas. This paper outlined a new text manipulation system FA-Sim that is useful for retrieving information in large heterogeneous texts and for recognizing content similarity in text excerpts. FA-Sim is based on flexible text matching procedures carried out in various contexts and various field ranks. FA-Sim measures texts similarity by using specific field association (FA) terms instead of by comparing all text information. Similarity between texts is faster and higher by using FA-Sim than other two analysis methods. Therefore, Recall and Precision significantly improved by 39% and 37% over these two traditional methods. (C) 2003 Elsevier Ltd. All rights reserved. C1 Univ Tokushima, Dept Informat Sci & Intelligent Syst, Tokushima 7708506, Japan. RP Atlam, ES (reprint author), Univ Tokushima, Dept Informat Sci & Intelligent Syst, Tokushima 7708506, Japan. CR AOE J, 1989, T IPSJ, V39, P2563 Atlam E, 2002, INFORM PROCESS MANAG, V38, P807, DOI 10.1016/S0306-4573(01)00062-0 Atlam ES, 2000, INFORM PROCESS MANAG, V36, P717, DOI 10.1016/S0306-4573(00)00009-1 BLAIR DC, 1984, COMMUNICATIONS MCA, V28, P289 Breiman L., 1984, CLASSIFICATION REGRE CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1984, P ACM C RES DEV INF, P201 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 DOZAWA T, 1999, ANN SERIES FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Fuketa M, 2000, INFORM SCIENCES, V126, P57, DOI 10.1016/S0020-0255(00)00042-6 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 JIN Y, 1995, NLPRS, V32, P357 LANCASTER FW, 1979, INFORMATION RETRIEVA LUHN HP, 1957, IBM J RES DEV, V1, P309 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAFAVIAN SR, 1991, IEEE T SYST MAN CYB, V21, P660, DOI 10.1109/21.97458 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1989, AUTOMATIV TEXT PROCE SPARCKJONES K, 1972, J DOC, V28, P11 SPARK KJ, 1971, AUTOMATIC KEYWORDS C TAKENOBU T, 1994, CATEGORIZATION BASED, P33 Tom M.M., 1997, MACHINE LEARNING TSUJI T, 1999, P 18 INT C COMP PROC, P301 *U PENNS, 1995, TREEB PROJ REL 2 1 M UTSUMI A, 1999, J NATURAL LANGUAGE P, V5, P30 VANRIJSBERGEN CJ, 1979, INFROMATION RETRIEVA Yang Y., 1997, ICML 97, P412 Zhang J, 2001, INFORM PROCESS MANAG, V37, P279, DOI 10.1016/S0306-4573(00)00027-3 NR 33 TC 32 Z9 32 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2003 VL 39 IS 6 BP 809 EP 824 DI 10.1016/S0306-4573(03)00019-0 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 723HW UT WOS:000185427800001 ER PT J AU Cordon, O Herrera-Viedma, E Lopez-Pujalte, C Luque, M Zarco, C AF Cordon, O Herrera-Viedma, E Lopez-Pujalte, C Luque, M Zarco, C TI A review on the application of evolutionary computation to information retrieval SO INTERNATIONAL JOURNAL OF APPROXIMATE REASONING LA English DT Article; Proceedings Paper CT 7th International ISKO Conference (ISKO 02) CY FEB 07, 2002 CL GRANADA, SPAIN SP ISKO DE information retrieval; evolutionary algorithms; automatic indexing; document clustering; query definition; user profiles; Internet search agents; image retrieval; similarity functions; web pages ID GENETIC ALGORITHMS; RELEVANCE FEEDBACK; DOCUMENT-RETRIEVAL; QUERY; AGENT; OPTIMIZATION; MODELS; TERMS AB In this contribution, different proposals found in the specialized literature for the application of evolutionary computation to the field of information retrieval will be reviewed. To do so, different kinds of IR problems that have been solved by evolutionary algorithms are analyzed. Some of the specific existing approaches will be specifically described for some of these problems and the obtained results will be critically evaluated in order to give a clear view of the topic to the reader. (C) 2003 Elsevier Inc. All rights reserved. C1 Univ Granada, ETSI Informat, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain. Univ Extremadura, Lib Sci Studies Fac, E-06071 Badajoz, Spain. PULEVA Food SA, Granada 18004, Spain. RP Cordon, O (reprint author), Univ Granada, ETSI Informat, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain. RI HERRERA-VIEDMA, ENRIQUE/C-2704-2008; Cordon, Oscar/F-6672-2011; Herrera-Viedma, Enrique/ OI Cordon, Oscar/0000-0001-5112-5629; Herrera-Viedma, Enrique/0000-0002-7922-4984 CR Back T., 1997, HDB EVOLUTIONARY COM Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Bergstrom A., 2000, P 2000 INT C INT US, P29, DOI 10.1145/325737.325763 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 Bordogna G., 1995, FUZZINESS DATABASE M, P231 Boughanem M., 1999, Information Retrieval, V1, DOI 10.1023/A:1009931404333 BOUGHANEM M, 2003, INFORM PROCESSING MA, V39, P215 Boughanem M, 2002, J AM SOC INF SCI TEC, V53, P934, DOI 10.1002/asi.10119 Chang C. H., 1999, THESIS NATL TAIWAN U CHANG YK, 1971, SMART RETRIEVAL SYST, P335 Chen HC, 1998, J AM SOC INFORM SCI, V49, P693 Chen HC, 1998, DECIS SUPPORT SYST, V23, P41, DOI 10.1016/S0167-9236(98)00035-9 CHEN Y, 2001, P INT C ART INT SOFT Cho SB, 2002, IEEE T SYST MAN CY A, V32, P452, DOI 10.1109/TSMCA.2002.802812 CORDON O, 2003, LNAI, V2715 CORDON O, 2000, MATHWARE SOFT COMPUT, V7, P309 CORDON O, 2003, FUZZY LOGIC INTERNET CORDON O, 2002, LNCS, V2439, P710 Cordon O., 2002, Soft Computing, V6, DOI 10.1007/s00500-002-0184-8 FAN W, 2000, P 2000 INT C INF SYS Fan W., 1999, AM C INF SYST MILW U FAN W, IN PRESS IEEE T KNOW Fernandez-Villacanas Martin J.-L., 2003, Future Generation Computer Systems, V19, DOI 10.1016/S0167-739X(02)00108-5 Fogel D. B., 1991, SYSTEM IDENTIFICATIO FONSECA CM, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P416 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GENERSCHWEFEL HP, 1995, GENERATION COMPUTER, V6 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P311, DOI 10.1002/(SICI)1097-4571(199106)42:5<311::AID-ASI1>3.0.CO;2-J Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X Kato S., 1998, Proceedings. Twelfth International Conference on Information Networking (ICOIN-12) (Cat. No.98EX104), DOI 10.1109/ICOIN.1998.648404 Koza J, 1992, GENETIC PROGRAMMING Kraft D.H., 1997, GENETIC ALGORITHMS F, P155 KWOK KL, 1997, ACM SIGIR 97, P34 LARSEN H, 2000, MATHWARE SOFT COMPUT, V7, P275 LOIA V, 2001, 1 AS PAC C MAEB CIT, P292 Lopez-Pujalte C, 2003, INFORM PROCESS MANAG, V39, P669, DOI 10.1016/S0306-4573(02)00044-4 Lopez-Pujalte C, 2002, INFORM PROCESS MANAG, V38, P793, DOI 10.1016/S0306-4573(01)00061-9 Lopez-Pujalte C, 2003, J AM SOC INF SCI TEC, V54, P152, DOI 10.1002/asi.10179 Martin-Bautista MJ, 1999, J AM SOC INFORM SCI, V50, P760, DOI 10.1002/(SICI)1097-4571(1999)50:9<760::AID-ASI4>3.3.CO;2-F Michalewicz Z., 1996, GENETIC ALGORITHMS D Mitchell T.M., 1997, MACHINE LEARNING Pathak P., 2000, P 33 HAW INT C SCI H Robertson AM, 1996, J DOC, V52, P405, DOI 10.1108/eb026973 ROBERTSON AM, 1994, J DOC, V50, P213, DOI 10.1108/eb026931 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SANCHEZ E, 1995, P 6 IFSA C SAO PAUL, P293 SANCHEZ L, 2000, MATHWARE SOFT COMPUT, V7, P337 Shih TK, 2001, INFORM SCIENCES, V137, P53, DOI 10.1016/S0020-0255(01)00109-8 Smith MP, 1997, J INFORM SCI, V23, P423 Stejic Z, 2003, INFORM PROCESS MANAG, V39, P1, DOI 10.1016/S0306-4573(02)00024-9 VANRIJSBERGEN CJ, 1979, INFORMATION RETREIVA Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 WALKER RL, 1999, P GECCO 99, P1750 Walker RL, 2001, PARALLEL COMPUT, V27, P71, DOI 10.1016/S0167-8191(00)00089-2 YANG JJ, 1994, INT J EXPERT SYST, V7, P165 NR 57 TC 34 Z9 34 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0888-613X J9 INT J APPROX REASON JI Int. J. Approx. Reasoning PD NOV PY 2003 VL 34 IS 2-3 BP 241 EP 264 DI 10.1016/j.ijar.2003.07.010 PG 24 WC Computer Science, Artificial Intelligence SC Computer Science GA 743LH UT WOS:000186572900010 ER PT J AU Crestani, F Melucci, M AF Crestani, F Melucci, M TI Automatic construction of hypertexts for self-referencing: the Hyper-TextBook project SO INFORMATION SYSTEMS LA English DT Article ID INFORMATION-RETRIEVAL; BOOKS; LINKS AB We present the results of the Hyper-TextBook project. The aim of the project was to design, develop and test a methodology and a tool for the fully automatic authoring of hypertexts from full-text documents. The target documents were textbooks because of their specific characteristics and usage, and the project aimed at automatically creating hypertextual versions of textbooks, i.e. hyper-textbooks. In this first phase of the project hyper-textbooks have been designed and implemented to be used mostly as self-reference sources. The results of a formative design evaluation of a hyper-textbook support the conclusion that our conceptual structure and navigation enhance the usability of the textbook with respect to both paper and online versions of the same textbook. Yet, this evaluation indicates a number of possible ways to improve it. (C) 2002 Elsevier Science Ltd. All rights reserved. C1 Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. Univ Padua, Dept Informat Engn, Padua, Italy. RP Crestani, F (reprint author), Univ Strathclyde, Dept Comp & Informat Sci, 26 Richmond St, Glasgow G1 1XH, Lanark, Scotland. CR AGOSTI M, 1993, P ACM S APPL COMP IN, P745, DOI 10.1145/162754.167175 AGOSTI M, 1995, MULTIMEDIA SYST, V3, P15, DOI 10.1007/BF01236576 Agosti M., 1991, P 14 ANN INT ACM SIG, P316, DOI 10.1145/122860.122892 Agosti M, 1998, P IEEE INT FORUM RES, P137, DOI 10.1109/ADL.1998.670388 Agosti M, 1997, INFORM PROCESS MANAG, V33, P133, DOI 10.1016/S0306-4573(96)00058-1 BELEW R, 2000, FINDING OUT ABOUT CO BOTAFOGO RA, 1992, ACM T INFORM SYST, V10, P142, DOI 10.1145/146802.146826 Botafogo R.A., 1993, P 16 ANN INT ACM SIG, P116, DOI 10.1145/160688.160704 BRUZA PD, 1992, COMPUT J, V35, P208, DOI 10.1093/comjnl/35.3.208 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Crestani F, 1998, P IEEE INT FORUM RES, P157, DOI 10.1109/ADL.1998.670390 Crestani F, 1998, DATA KNOWL ENG, V27, P1, DOI 10.1016/S0169-023X(97)00043-8 Crestani F., 2002, Journal of Educational Multimedia and Hypermedia, V11 CRIVELLARI F, 2001, P ACM S APPL COMP SA DUNLOP MD, 1993, INFORM PROCESS MANAG, V29, P287, DOI 10.1016/0306-4573(93)90056-J EGAN DE, 1989, ACM T INFORM SYST, V7, P30, DOI 10.1145/64789.64790 Egan D. E., 1989, Machine-Mediated Learning, V3 FRISSE ME, 1988, COMMUN ACM, V31, P880, DOI 10.1145/48511.48518 Furuta R., 1989, Electronic Publishing: Origination, Dissemination and Design, V2 LANDONI M, 1997, THESIS U STRATHCLYDE LANDONI M, 1993, ELECTRON LIBR, V11, P175, DOI 10.1108/eb045228 LANDONI M, 2000, P RIAO 2000 C PAR FR, P247 MAYFIELD J, 1997, LECT NOTES COMPUTER, P91 MILLER SH, 1984, EXPT DESIGN STAT O'Hara K., 1997, P CHI 97, P335, DOI DOI 10.1145/258549.258787 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Press L, 2000, COMMUN ACM, V43, P17, DOI 10.1145/332833.332849 RADA R, 1992, ACM T INFORM SYST, V10, P294, DOI 10.1145/146760.146791 ROBERTSON J, 1994, P ACM INT C ECHT, P177, DOI 10.1145/192757.192796 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1988, P ACM SIGIR GREN FRA SALTON G, 1996, INFORMATION RETRIEVA, P51 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 SMEATON A, 1995, CA0295 SCH COMP APPL SMEATON A, 1995, P IWHD C MONTP FRANC Tebbutt J, 1999, INFORM PROCESS MANAG, V35, P1, DOI 10.1016/S0306-4573(98)00039-9 THOMPSON R, 1989, DESIGN IMPLEMENTATIO VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 39 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD OCT PY 2003 VL 28 IS 7 BP 769 EP 790 DI 10.1016/S0306-4379(02)00082-0 PG 22 WC Computer Science, Information Systems SC Computer Science GA 718PY UT WOS:000185156700004 ER PT J AU Leroy, G Lally, AM Chen, H AF Leroy, G Lally, AM Chen, H TI The use of dynamic contexts to improve casual Internet searching SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE algorithms; human Factors; performance; information retrieval; personalization; Internet; genetic algorithm; relevance feedback; automatic query expansion; implicit user feedback ID RELEVANCE FEEDBACK; WEB; RETRIEVAL; QUERIES AB Research has shown that most users' online information searches are suboptimal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links and text in a search engine result set to expand user queries with a variable number of keywords in two manners. Positive expansion adds terms to a user's keywords with a Boolean "and," negative expansion adds terms to the user's keywords with a Boolean "not." Each algorithm was examined for three user groups, high, middle, and low achievers, who were classified according to their overall performance. The interactions of users with different levels of expertise with different expansion types or algorithms were evaluated. The genetic algorithm with negative expansion tripled recall and doubled precision for low achievers, but high achievers displayed an opposed trend and seemed to be hindered in this condition. The effect of other conditions was less substantial. C1 Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. RP Leroy, G (reprint author), Claremont Grad Univ, Claremont, CA 91711 USA. CR ANATI G, 2001, P 10 TEXT RETR C TRE, P182 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BELKIN NJ, 1999, P 8 TEXT RETRIEVAL C, P565 BODNER RC, 1998, P 7 TEXT RETR C TREC, P573 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Budzik J., 2000, P 5 INT C INT US INT, P44, DOI 10.1145/325737.325776 CHEN CC, 2001, P 7 ACM SIGKDD INT C, P257, DOI 10.1145/502512.502548 Chen HC, 1998, J AM SOC INFORM SCI, V49, P604 Chen HC, 1998, J AM SOC INFORM SCI, V49, P693 Claypool M., 2001, P 6 INT C INT US INT, P33, DOI DOI 10.1145/359784.359836 de Lima E. F., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312669 Fan Weiguo, 2000, P 2000 INT C INF SYS, P20 Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 FULLER R, 1996, P C DES WEB EMP STUD Harman D, 1988, P 11 ANN INT ACM SIG, P321, DOI 10.1145/62437.62469 HARMAN D, 1992, P 15 INT ACM SIGIR C HAWKING D, 2001, P 10 TEXT RETR C TRE, P61 HERSH W, 2001, P 10 TEXT RETR C TRE, P434 HERSH W, 2000, P 9 TEXT RETR C TREC, P407 HERSH W, 2000, P 23 ANN INT ACM SIG Ide E., 1971, SMART RETRIEVAL SYST, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Kracker J, 2002, J AM SOC INF SCI TEC, V53, P295, DOI 10.1002/asi.10041 Kraft D. H., 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence (Cat. No.94TH0650-2), DOI 10.1109/ICEC.1994.349905 KROENEMANN J, 1996, P C HUMAN FACT COMP Lai HC, 2000, DECIS SUPPORT SYST, V28, P219, DOI 10.1016/S0167-9236(99)00087-1 MAGENNIS M, 1997, P 29 ANN INT ACM SIG, P342 MEYER B, 1997, P C HUM FACT COMP SY, P295 MICHALEWICZ Z, 1992, GENETIC ALGORITHMS P Nick ZZ, 2001, IEEE INTERNET COMPUT, V5, P18, DOI 10.1109/4236.914644 Nordlie R., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312618 PATHAK P, 2000, P 33 ANN HAW INT C S, P533 Pitkow JE, 1996, COMMUN ACM, V39, P106, DOI 10.1145/228503.228525 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J. J., SMART RETRIEVAL SYST, P313 Ross NCM, 2000, J AM SOC INFORM SCI, V51, P949, DOI 10.1002/1097-4571(2000)51:10<949::AID-ASI70>3.0.CO;2-5 Specht M., 1999, P 2 WORKSH AD SYST U, P89 Spink A, 1996, J AM SOC INFORM SCI, V47, P603, DOI 10.1002/(SICI)1097-4571(199608)47:8<603::AID-ASI4>3.0.CO;2-X Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R SULLIVAN D, 2000, NPD SEARCH PORTAL SI THURY EM, 1998, P 16 ANN INT C COMP, P265, DOI 10.1145/296336.296393 TOMS EG, 2001, P 10 TEXT RETR C TRE VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOGT CC, 2000, P 9 TEXT RETR C TREC, P141 WHITE R, 2001, P 10 TEXT RETR C TRE White R. W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 YANG JJ, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P603 YANG K, 1998, P 7 TEXT RETR C TREC, P555 YANG K, 1999, P 8 TEXT RETR C TREC, P645 NR 52 TC 18 Z9 22 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 2003 VL 21 IS 3 BP 229 EP 253 DI 10.1145/858476.858477 PG 25 WC Computer Science, Information Systems SC Computer Science GA 702BV UT WOS:000184205500001 ER PT J AU Cui, H Wen, JR Nie, JY Ma, WY AF Cui, H Wen, JR Nie, JY Ma, WY TI Query expansion by mining user logs SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article; Proceedings Paper CT 11th International World Wide Web Conference CY MAY 07-11, 2002 CL HONOLULU, HAWAII DE query expansion; user log; probabilistic model; information retrieval; search engine ID INFORMATION-RETRIEVAL AB Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods. C1 Natl Univ Singapore, Dept Comp Sci, Sch Comp, Singapore 117543, Singapore. Microsoft Res Asia, Beijing 100080, Peoples R China. Univ Montreal, Dept Informat & Rech Operat, CP 6128,Succursale Ctr Ville, Montreal, PQ H3C 3J7, Canada. RP Cui, H (reprint author), Natl Univ Singapore, Dept Comp Sci, Sch Comp, Singapore 117543, Singapore. CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BATES MJ, 1981, ANNU REV INFORM SCI, V16, P139 Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 BRAJNIK G, 1996, P 19 ANN INT ACM SIG, P128, DOI 10.1145/243199.243249 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 1998, P 6 TEXT RETR C TREC, P107 Buckley C., 1992, P 1 TEXT RETR C TREC, P59 BUCKLEY GJ, 1994, TECHNOL DISABIL, V3, P69 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Cooper J., 1997, P 2 ACM INT C DIG LI, P237, DOI 10.1145/263690.263828 CROFT WB, 1995, P 2 INT C THEOR PRAC, P19 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 EFTHIMIADIS E, 1994, P 2 TEXT RETR C TREC EVANS D, 1994, P 2 TEXT RETR C TREC FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Harter S. P., 1986, ONLINE INFORMATION R Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Jing Y., 1994, P RIAO 94, P146 JONES KS, 1971, AUTOMATIC KEYWORD CL LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 Lu A., 1997, P TEXT RETR C TREC 5, P229 Miller G., 1990, INT J LEXICOGRAPHY, V3 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Sakai T., 2001, P BCS IRSG ECIR, VXVI, P3 Salton G., 1971, SMART RETRIEVAL SYST Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 WONG SKM, 1993, J AM SOC INFORM SCI, V44, P431, DOI 10.1002/(SICI)1097-4571(199309)44:8<431::AID-ASI1>3.0.CO;2-V WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 38 TC 94 Z9 104 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JUL-AUG PY 2003 VL 15 IS 4 BP 829 EP 839 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 696RY UT WOS:000183902100006 ER PT J AU Cho, BH Lee, C Lee, GG AF Cho, BH Lee, C Lee, GG TI Exploring term dependences in probabilistic information retrieval model SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; term dependence; Bahadur-Lazarsfeld expansion; probabilistic model; 2-Poisson model ID RELEVANCE INFORMATION; BOOLEAN QUERIES; PERFORMANCE AB Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each another. However, this kind of conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence in probabilistic retrieval model by adapting Bahadur-Lazarsfeld expansion (BLE) to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply BLE to the general probabilistic models and the state-of-the-art 2-Poisson model. Through the experiments on two standard document collections, HANTEC2.0 in Korean and WT10g in English, we demonstrate that incorporation of term dependences using the BLE significantly contribute to the improvement of performance in at least two different language IR systems. (C) 2002 Elsevier Science Ltd. All rights reserved. C1 Pohang Univ Sci & Technol, Dept Comp Sci & Engn, Nam Gu, Pohang 790784, South Korea. Voiceware Co Ltd, R&D Ctr, Gangnam Gu, Seoul 135280, South Korea. RP Lee, C (reprint author), Pohang Univ Sci & Technol, Dept Comp Sci & Engn, Nam Gu, San 31 Hyoja Dong, Pohang 790784, South Korea. CR Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHO B, 2001, 13 KOR LANG INF PROC, P183 COOPER RJ, 1982, J APPL TOXICOL, V2, P99, DOI 10.1002/jat.2550020208 COOPER WS, 1991, 14 ANN INT ACM SIGIR, P57 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, 10 ANN INT ACM SIGIR, P26 Duda R.O., 1973, PATTERN CLASSIFICATI, P111 Harman D., 1992, 15TH P ANN INT ACM S, P1 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 Kim JH, 2001, INFORM RETRIEVAL, V4, P115, DOI 10.1023/A:1011466928139 LEE G, 2001, 10 TEXT RETR C TREC, P437 Lee GG, 2002, COMPUT LINGUIST, V28, P53, DOI 10.1162/089120102317341774 Lee WH, 2000, J ELECTROCERAM, V5, P31, DOI 10.1023/A:1009937310444 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X Losee RM, 2001, J AM SOC INF SCI TEC, V52, P1019, DOI 10.1002/asi.1155 LOSEE RM, 1988, INFORM PROCESS MANAG, V24, P315, DOI 10.1016/0306-4573(88)90097-0 LOSEE RM, 1991, INFORM PROCESS MANAG, V27, P1, DOI 10.1016/0306-4573(91)90027-J LUHN HP, 1958, IBM J RES DEV, V2, P159 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MITRA M, 1998, 21 ANN INT ACM SIGIR, P206 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1997, 20 ANN INT ACM SIGIR, P16 ROBERTSON SE, 1994, 17 ANN INT ACM SIGIR, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 *TREC EV, 1992, TREC EV PROGR AV VIA VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 34 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2003 VL 39 IS 4 BP 505 EP 519 DI 10.1016/S0306-4573(02)00078-X PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 686VV UT WOS:000183342400001 ER PT J AU Joseph, S Hoshiai, T AF Joseph, S Hoshiai, T TI Decentralized meta-data strategies: Effective peer-to-peer search SO IEICE TRANSACTIONS ON COMMUNICATIONS LA English DT Article DE P2P; meta-data; discovery; decentralized AB Gnutella's service announcement in March 2000 stirred worldwide interest by referring to P2P model. Basically, the P2P model needs not the broker "the centralized management server" that until now has figured so importantly in prevailing business models, and offers a new approach that enables peers such as end terminals to discover out and locate other suitable peers on their own without going through an intermediary server. It seems clear that the wealth of content made available by peer-to-peer systems like Gnutella and Freenet have spurred many authors into considering how meta-data might be used to support more effective search in a distributed environment. This paper has reviewed a number of these systems and attempted to identify some common themes. At this time the major division between the different approaches is the use of a hash-based routing scheme. C1 Univ Tokyo, Strateg Software Div, Tokyo 1138656, Japan. NTT Corp, NTT Network Serv Syst Lab, Musashino, Tokyo 1808585, Japan. RP Joseph, S (reprint author), Univ Tokyo, Strateg Software Div, Tokyo 1138656, Japan. EM hoshiai.takashige@lab.ntt.co.jp CR BABAOGLU O, 2001, UBLCS200109 BAWA M, 2003, MAKE IT FRESH MAKE I BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BONSMA E, 2002, P SYST CYB INF SCI BOWMAN CM, 1995, COMPUT NETWORKS ISDN, V28, P119, DOI 10.1016/0169-7552(95)00098-5 Burton K., 2002, DESIGN OPENPRIVACY D CHAPEWESKE J, 2002, HTTP EXTENSIONS CONT CLARKE I, 2000, P WORKSH DES ISS AN COHEN E, 2002, CASE ASSOCIATIVE PEE Crespo A., 2002, 22 INT C DISTR COMP CUENCAACUNA FM, 2002, INT WORKSH PEER PEER DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEUTSCH P, 1995, 1835 RFC DORNFEST R, 2001, PEER PEER HARNESSING, P191 Druschel P., 2001, P 18 IFIP ACM INT C HOSHIAI T, 2002, HPSR2002 TUTORIAL, P1 JOSEPH S, 2002, INT WORKSH PEER PEER JOSEPH SRH, 2001, AGENT ENG, P7 KAN G, 2001, PEER PEER HARNESSING, P94 KRISHNAMURTHY B, 2001, ACM SIGCOMM INT MAN Kronfol AZ, 2002, FASD FAULT TOLERANT LEVIEN R, 2002, THESIS U CALIFORNIA NEJDL W, 2001, EDUTELLA P2P NETWORK Page L., 1998, PAGERANK CITATION RA PLAXTON C, 1997, P ACM SPAA JUN Ratnasamy S., 2001, P ACM SIGCOMM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROHRS C, 2002, QUERY ROUTING GNUTEL SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SCHLEGEL G, 1985, EC REV Q, V2, P2 SHELDON MA, 1995, P 3 INT WORLD WID WE STOICA I, 2001, P ACM SIGCOMM 01 C A SUGIURA A, 2000, P 9 INT WORLD WID WE TANG C, 2002, PSEARCH INFORMATION Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 WILEY B, 2002, TRISTERO XU ZC, 2003, 23 INT C DISTR COMP Zhao B., 2001, CSD011141 UC BERK NR 38 TC 15 Z9 15 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN SN 0916-8516 EI 1745-1345 J9 IEICE T COMMUN JI IEICE Trans. Commun. PD JUN PY 2003 VL E86B IS 6 BP 1740 EP 1753 PG 14 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA 687ED UT WOS:000183362300003 ER PT J AU Ruthven, I Lalmas, M AF Ruthven, I Lalmas, M TI A survey on the use of relevance feedback for information access systems SO KNOWLEDGE ENGINEERING REVIEW LA English DT Review ID QUERY EXPANSION; DOCUMENT-RETRIEVAL; SEARCH TERMS; PROBABILISTIC MODEL; SELECTION; PRINCIPLE; RANKING; CATALOG; REPRESENTATIONS; JUDGMENTS AB Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems. C1 Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. Univ London, Dept Comp Sci, London E1 4NS, England. RP Ruthven, I (reprint author), Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. EM Ian.Ruthven@cis-strath.ac.uk; mounia@dcs.qmul.ac.uk CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2 BATES MJ, 1990, INFORM PROCESS MANAG, V26, P575, DOI 10.1016/0306-4573(90)90103-9 Bayes T., 1763, PHILOS T ROY SOC LON, V53, P370, DOI DOI 10.1098/RSTL.1763.0053 Beaulieu M, 1997, J DOC, V53, P8, DOI 10.1108/EUM0000000007187 Beaulieu M, 1998, INTERACT COMPUT, V10, P237, DOI 10.1016/S0953-5438(98)00008-3 BELEW RK, 2000, FINDING ABOUT BELKIN NJ, 1997, P 6 TEXT RETR C TREC, P257 BELKIN NJ, 1999, P 7 TEXT RETR C TREC, P275 BELKIN NJ, 1997, P 34 ANN CLIN LIB AP, P45 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A BELKIN NJ, 1998, P 6 TEXT RETR C TREC, P597 BERGER F, 1997, INFORMATION PROCESSI, V33, P45 BHATIA SK, 1992, P 1992 ACM SIGAPP S, V1, P224 Borgman CL, 1996, J AM SOC INFORM SCI, V47, P493, DOI 10.1002/(SICI)1097-4571(199607)47:7<493::AID-ASI3>3.0.CO;2-P BORGMAN CL, 1989, INFORM PROCESS MANAG, V25, P237, DOI 10.1016/0306-4573(89)90042-3 BORLAND P, 1999, EL WORKSH COMP BRIT Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198 BUCKLAND M, 1994, J AM SOC INFORM SCI, V45, P12, DOI 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley Chris, 1995, P 3 TEXT RETR C TREC, P69 Callan J. P., 1992, DEXA 92. Database and Expert Systems Applications. Proceedings of the International Conference Campbell I., 1996, P 2 INT C CONC LIB I, P251 CAMPBELL I, 1995, MIRO 95 EL WORKSH CO CAMPBELL I, 1999, J INFORMATION RETRIE, V2, P89 CHANG YK, 1971, SMART RETRIEVAL SYST Cool C., 1996, P 2 INT C CONC LIB I, P403 Crestani F., 1998, ADV MODELS REPRESENT Crestani F., 1995, P 4 TEXT RETR C TREC, P509 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DENNIS S, 1998, P 3 AUSTR DOC COMP S Denos N., 1997, Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 EFTHIMIADIS E, 1994, P 2 TEXT RETR C TREC, P279 EFTHIMIADIS E. N., 1993, P 16 ANN INT ACM SIG, P146, DOI 10.1145/160688.160710 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W EISENBERG M, 1988, J AM SOC INFORM SCI, V39, P293, DOI 10.1002/(SICI)1097-4571(198809)39:5<293::AID-ASI1>3.0.CO;2-I ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 FANTS VI, 1999, J AM SOC INFORM SCI, V50, P86 FIDEL R, 1991, J AM SOC INFORMATION, V34 Florance V., 1995, P 18 ANN INT ACM SIG, P158, DOI 10.1145/215206.215353 FOWKES H, 2000, IRSG 2000 C IR RES FREI HP, 1991, INFORMATION RETRIEVA FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 HARMAN D, 1992, INFORMATION RETRIEVA Harman D, 1988, P 11 ANN INT ACM SIG, P321, DOI 10.1145/62437.62469 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Harman D. K., 1993, P 3 TEXT RETRIEVAL C, P1 HEARST MA, 1999, MODERN INFORMATION R Heuer R. J., 1999, PSYCHOL INTELLIGENCE HSIEHYEE I, 1993, J AM SOC INFORM SCI, V44, P161, DOI 10.1002/(SICI)1097-4571(199304)44:3<161::AID-ASI5>3.0.CO;2-8 HUIBERS TWC, 1996, THESIS UTRECHT U Ide E., 1971, SMART RETRIEVAL SYST INGWERSEN P, 1992, INFORMATION RETRIEVA Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 IWAYAMA M., 2000, P 23 ANN INT ACM SIG, P10, DOI 10.1145/345508.345538 JANES JW, 1991, INFORM PROCESS MANAG, V27, P629, DOI 10.1016/0306-4573(91)90004-6 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 1997, READINGS INFORMATION KHOO CSG, 1994, INFORM PROCESS MANAG, V30, P223, DOI 10.1016/0306-4573(94)90066-3 Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 KOENEMANNBELLIV.J, 1995, P 3 TEXT RETR C TREC, P145 KUHLTHAU CC, 1993, J DOC, V49, P339, DOI 10.1108/eb026918 KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-# LALMAS M, 1998, KNOWL ENG REV, V13, P1 LALMAS M, 1996, ECAI WORKSH UNC INF, P29 Lee JH, 1998, INFORM PROCESS MANAG, V34, P681, DOI 10.1016/S0306-4573(98)00034-X Lundquist C., 1997, Proceedings of the Sixth International Conference on Information and Knowledge Management. CIKM'97, DOI 10.1145/266714.266844 Magennis M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P324, DOI 10.1145/258525.258603 MARON ME, 1960, J ASSOC COMPUT MACH, V15, P8 MARON ME, 1964, STAT ASS METHODS MEC, P269 MARTIN WA, 1982, P 6 INT ONL INF M 19, P67 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 MULLER A, 1998, INFORMATION RETRIEVA Ng K.B., 1996, P 4 TEXT RETR C TREC, P181 NIE J, 1989, INFORMATION PROCESSI, V25, P471 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PETERS TA, 1989, J ACAD LIBR, V15, P267 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON S, 1993, P TREC C TREC 1 GAIT, P21 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST RUTHVEN I, 2003, J AM SOC INFORM SCI, V54, P528 RUTHVEN I, 2001, P 24 ANN INT ACM SIG, P181 RUTHVEN I, 2002, P 6 EUR C DIG LIB, P338 Ruthven I, 2002, J AM SOC INF SCI TEC, V53, P378, DOI 10.1002/asi.10046 RUTHVEN I, 2002, P 4 INT C CONC LIB I, V4, P199 RUTHVEN I, 2001, 23 BCS EUR ANN C INF Salton G., 1971, SMART RETRIEVAL SYST Sebastiani F., 1994, P 17 ANN INT ACM SIG, P122 SHENOUDA W, 1991, P 53 ANN M AM SOC IN, V26, P3 SIMONNOT B, 1996, THESIS H POINCARE U SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SMEATON AF, 1998, P 20 BCS IRSG C SPARCKJONES K, 1972, J DOC, V28, P11 SPIEGEL J, 1964, NATL BUREAU STANDARD, P269 Spink A, 1997, J AM SOC INFORM SCI, V48, P382 Spink A, 1996, J AM SOC INFORM SCI, V47, P603, DOI 10.1002/(SICI)1097-4571(199608)47:8<603::AID-ASI4>3.0.CO;2-X Spink A, 1998, INFORM PROCESS MANAG, V34, P599, DOI 10.1016/S0306-4573(98)00025-9 Spink A, 1997, J AM SOC INFORM SCI, V48, P741, DOI 10.1002/(SICI)1097-4571(199708)48:8<741::AID-ASI7>3.0.CO;2-S SPINK A, 1999, MIRA 99 EVALUATING I SPINK A, 1993, P ASIS ANNU MEET, V30, P63 SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 SUMNER RG, 1998, P 6 TEXT RETR C TREC, P711 TIAMIYU MA, 1988, INFORM PROCESS MANAG, V24, P391, DOI 10.1016/0306-4573(88)90043-X Vakkari P., 2000, P 23 ANN INT ACM SIG, P2, DOI 10.1145/345508.345512 VAKKARI P, 2001, P RIAO C CONT BAS MU VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P48 VOORHEES EM, 1996, P 5 TEXT RETR C TREC, P1 Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P3, DOI 10.1016/S0306-4573(99)00043-6 White RW, 2003, INFORM PROCESS MANAG, V39, P707, DOI 10.1016/S0306-4573(02)00033-X WILLIE S, 1995, P 18 ANN INT ACM SIG, P205, DOI 10.1145/215206.215361 NR 126 TC 98 Z9 102 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH ST, NEW YORK, NY 10011-4211 USA SN 0269-8889 J9 KNOWL ENG REV JI Knowl. Eng. Rev. PD JUN PY 2003 VL 18 IS 2 BP 95 EP 145 DI 10.1017/S0269888903000638 PG 51 WC Computer Science, Artificial Intelligence SC Computer Science GA 764TD UT WOS:000188221400001 ER PT J AU Vechtomova, O Robertson, S Jones, S AF Vechtomova, O Robertson, S Jones, S TI Query expansion with long-span collocates SO INFORMATION RETRIEVAL LA English DT Article DE long-span word collocation; automatic query expansion; relevance feedback; probabilistic information retrieval ID CO-OCCURRENCE DATA; INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; FEEDBACK AB The paper presents two novel approaches to query expansion with long-span collocates-words, significantly co-occurring in topic-size windows with query terms. In the first approach-global collocation analysis-collocates of query terms are extracted from the entire collection, in the second-local collocation analysis-from a subset of retrieved documents. The significance of association between collocates was estimated using modified Mutual Information and Z score. The techniques were tested using the Okapi IR system. The effect of different parameters on performance was evaluated: window size, number of expansion terms, measures of collocation significance and types of expansion terms. We present performance results of these techniques and provide comparison with related approaches. C1 Univ Waterloo, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. City Univ London, Ctr Interact Syst Res, London EC1V 0HB, England. Microsoft Res Cambridge, Cambridge, England. RP Vechtomova, O (reprint author), Univ Waterloo, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. CR Beeferman D, 1997, 35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P373 BUCKLEY C, 2000, 8 TEXT RETR C TREC 8, P577 Church K., 1994, COMPUTATIONAL APPROA, P153 Church K., 1991, LEXICAL ACQUISITION, P115 CORMACK GV, 2000, 8 TEXT RETR C TREC 8, P735 CROFT WB, 1991, 14TH P INT C RES DEV, P32 Edmonds P, 1997, 35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P507 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Fano RM, 1961, TRANSMISSION INFORMA Halliday M. A. K., 1976, COHESION ENGLISH Church K. W., 1990, Computational Linguistics, V16 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Hawking D., 1998, Sixth Text REtrieval Conference (TREC-6) (NIST SP 500-240) HEARST MA, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P9 HOEY M, 1991, PATTERNS LEXIS Ishikawa K., 1998, Sixth Text REtrieval Conference (TREC-6) (NIST SP 500-240) JELINEK F, 1990, READINGS SPEECH RECO JING YF, 1994, RIAO 94 CONFERENCE PROCEEDINGS - INTELLIGENT MULTIMEDIA INFORMATION RETRIEVAL SYSTEMS AND MANAGEMENT, P146 Jones K. S., 1970, Information Storage and Retrieval, V5, DOI 10.1016/0020-0271(70)90046-X Jones K. S., 2000, Information Processing & Management, V36 Manning C. D., 1999, FDN STAT NATURAL LAN MCENERGY T, 1996, CORPUS LINGUISTICS Palmer F. R., 1981, SEMANTICS Qiu Y, 1993, 16 ACM SIGIR INT C R, P160 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SINCLAIR JM, 1974, ENGLISH LEXICAL COLL SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 STRZALKOWSKI T, 2000, 8 TEXT RETRIEVAL C G, P381 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VECHTOMOVA O, 2000, 22 BCS IRSG P C CAMB, P165 Xu J., 1996, 19 ANN INT ACM SIGIR, P4 YAROWSKY D, 1992, COLING 92, P454 NR 32 TC 17 Z9 17 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2003 VL 6 IS 2 BP 251 EP 273 DI 10.1023/A:1023936321956 PG 23 WC Computer Science, Information Systems SC Computer Science GA 682VW UT WOS:000183113900004 ER PT J AU Ruthven, I Lalmas, M van Rijsbergen, K AF Ruthven, I Lalmas, M van Rijsbergen, K TI Incorporating user search behavior into relevance feedback SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID QUERY EXPANSION; INFORMATION-SEEKING AB In this paper, we present five user experiments on incorporating behavioral information into the relevance feedback process. In particular, we concentrate on ranking terms for query expansion and selecting new terms to add to the user's query. Our experiments are an attempt to widen the evidence used for relevance feedback from simply the relevant documents to include information on how users are searching. We show that this information can lead to more successful relevance feedback techniques. We also show that the presentation of relevance feedback to the user is important in the success of relevance feedback. C1 Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. Univ London Queen Mary Coll, Dept Comp Sci, London E1 4NS, England. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Ruthven, I (reprint author), Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. CR Beaulieu M, 1997, J DOC, V53, P8, DOI 10.1108/EUM0000000007187 BORLUND P, 2000, THESIS ABO AKAD U BORLUND P, 1999, EL WORKSH COMP BRIT Borlund P, 2000, J DOC, V56, P71, DOI 10.1108/EUM0000000007110 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Campbell I., 1996, P 2 INT C CONC LIB I, P251 Campbell Iain, 2000, J INFORMATION RETRIE, V2, P89 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 Florance V., 1995, P 18 ANN INT ACM SIG, P158, DOI 10.1145/215206.215353 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 Harman D., 1992, INFORMATION RETRIEVA, P241 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 KUHLTHAU CC, 1993, J DOC, V49, P339, DOI 10.1108/eb026918 KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-# OVER P, 1998, NIST SPECIAL PUBLICA, V500, P73 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUTHVEN I, 2001, J AM SOC INFORM SCI, V53, P378 RUTHVEN I, 2001, P 24 ANN INT ACM SIG, P181 RUTHVEN I, 2001, THESIS U GLASGOW RUTHVEN I, 2001, 23 BCS EUR ANN C INF RUTHVEN I, 2002, P 6 EUR C DIG LIB EC Spink A, 1998, INFORM PROCESS MANAG, V34, P599, DOI 10.1016/S0306-4573(98)00025-9 VAKKARI P, 2001, P RIAO C CONT BAS MU, P894 Vakkari P., 2000, P 23 ANN INT ACM SIG, P2, DOI 10.1145/345508.345512 Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P3, DOI 10.1016/S0306-4573(99)00043-6 NR 28 TC 16 Z9 17 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD APR PY 2003 VL 54 IS 6 BP 529 EP 549 DI 10.1002/asi.10240 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 662BF UT WOS:000181925300006 ER PT J AU de Campos, LM Fernandez-Luna, JM Huete, JF AF de Campos, LM Fernandez-Luna, JM Huete, JF TI Implementing Relevance Feedback in the Bayesian network retrieval model SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; PROBABILISTIC MODEL AB Relevance Feedback consists in automatically formulating a new query according to the relevance judgments provided by the user after evaluating a set of retrieved documents. In this article, we introduce several relevance feedback methods for the Bayesian Network Retrieval Model. The theoretical frame on which our methods are based uses the concept of partial evidences, which summarize the new pieces of information gathered after evaluating the results obtained by the original query. These partial evidences are inserted into the underlying Bayesian network and a new inference process (probabilities propagation) is run to compute the posterior relevance probabilities of the documents in the collection given the new query. The quality of the proposed methods is tested using a preliminary experimentation with different standard document collections. C1 Univ Granada, ETSI Informat, Dept Ciencias Computac & IA, E-18071 Granada, Spain. Univ Jaen, Escuela Politecn Super, Dept Informat, Jaen 23071, Spain. RP de Campos, LM (reprint author), Univ Granada, ETSI Informat, Dept Ciencias Computac & IA, E-18071 Granada, Spain. RI de Campos, Luis/B-7436-2012; Huete, Juan/C-2428-2012; Fernandez Luna, Juan Manuel/C-6845-2012 OI de Campos, Luis/0000-0001-9125-1195; CR Spink A, 1996, ANNU REV INFORM SCI, V31, P33 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cestnik B., 1990, P EUR C ART INT, P147 Chang Y, 1971, SMART RETRIEVAL SYST, P355 de Campos L. M., 1998, P 14 UNC ART INT C, P53 de Campos LM, 2002, LECT NOTES COMPUT SC, V2291, P169 DECAMPOS LM, 2002, UNPUB INT J APPROXIM de Campos LM, 2000, 11TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATION, PROCEEDINGS, P543 FERNANDEZLUNA JM, 2001, THESIS U GRANADA FUNG R, 1995, COMMUN ACM, V38, P42, DOI 10.1145/203330.203340 Ghazfan D., 1996, P IPMU 96 C, P841 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Jensen F. V., 1996, INTRO BAYESIAN NETWO Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Pearl J., 1988, PROBABILISTIC REASON RIBEIRONETO BA, 1996, 19 ACM SIGIR C, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SILVA I, 2000, P 23 ANN INT ACM SIG, P96, DOI 10.1145/345508.345554 Silva I. Reis, 2000, THESIS U FEDERAL MIN Turtle H., 1991, P RIA0 91 C, P644 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Turtle H. R., 1990, THESIS U MASSACHUSET NR 25 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB 15 PY 2003 VL 54 IS 4 BP 302 EP 313 DI 10.1002/asi.10210 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 649EW UT WOS:000181194800003 ER PT J AU Lam, W Yu, KL AF Lam, W Yu, KL TI High-dimensional learning framework for adaptive document filtering SO COMPUTATIONAL INTELLIGENCE LA English DT Article DE information filtering; information retrieval; machine learning ID PROBABILISTIC INFORMATION-RETRIEVAL; RELEVANCE; MODEL; TERMS AB We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high-dimensional on-line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on-line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them. C1 Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. RP Lam, W (reprint author), Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. CR Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BLUM A, 1992, MACH LEARN, V9, P373, DOI 10.1007/BF00994112 Callan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290998 Callan James, 1992, P 3 INT C DAT EXP SY, P78 Callan J.P., 1996, P 19 ANN INT ACM SIG, P262, DOI 10.1145/243199.243273 Cohen W, 1996, P 19 ANN INT ACM SIG, P307, DOI 10.1145/243199.243278 EICHMANN D, 1998, P 7 TEXT RETR C TREC Evans D. A., 1991, Intelligent Text and Image Handling. Proceedings of a Conference. RIAO '91 EVANS DA, 1995, INFORM PROCESS MANAG, V31, P385, DOI 10.1016/0306-4573(94)00054-7 HELMBOLD L, 1992, P 33 ANN S FDN COMP HULL DA, 1998, P 7 TEXT RETR C TREC HULL DA, 1997, P 6 TEXT RETR C TREC KIVINEN J, 1994, UCSCCRL9416 U CAL BA Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 KWOK K, 1998, P 7 TEXT RETR C TREC KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 Lam W, 1999, IEEE T KNOWL DATA EN, V11, P865 Lam W., 1998, P 21 ANN INT ACM SIG, P81, DOI 10.1145/290941.290961 LAM W, 1999, P 22 ANN INT ACM SIG, P287, DOI 10.1145/312624.312711 Lang K., 1995, P 12 INT C MACH LEAR, P331 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Lewis LF, 1996, GROUP DECIS NEGOT, V5, P5, DOI 10.1007/BF02404173 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MILICFRAYLING N, 1997, P 6 TEXT RETR C TREC, P415 Mostafa J, 1997, ACM T INFORM SYST, V15, P368, DOI 10.1145/263479.263481 Mostafa J, 2000, INFORM PROCESS MANAG, V36, P415, DOI 10.1016/S0306-4573(99)00033-3 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 Resnick P., 1994, P 1994 COMP SUPP COO ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1983, INTRO MODERN INFORMA SHETH B, 1993, P 9 C ART INT APPL Sheth B. D., 1994, THESIS MIT TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VOORHEES EM, 1996, P 5 TEXT RETR C TREC, P1 YAN YW, 1995, P 1995 USENIX TECHN, P177 Yu KL, 1998, P CIKM 98 7 ACM INT, P156, DOI 10.1145/288627.288652 ZHAI CX, 1998, P 7 TEXT RETR C TREC NR 40 TC 2 Z9 2 PU BLACKWELL PUBLISHERS PI MALDEN PA 350 MAIN STREET, STE 6, MALDEN, MA 02148 USA SN 0824-7935 J9 COMPUT INTELL JI Comput. Intell. PD FEB PY 2003 VL 19 IS 1 BP 42 EP 63 DI 10.1111/1467-8640.00213 PG 22 WC Computer Science, Artificial Intelligence SC Computer Science GA 698BN UT WOS:000183977900003 ER PT J AU An, A AF An, A TI Learning classification rules from data SO COMPUTERS & MATHEMATICS WITH APPLICATIONS LA English DT Article DE machine learning; rule induction; classification; data mining; artificial intelligence AB We present ELEM2, a machine learning system that induces classification rules from a set of data based on a heuristic search over a hypothesis space. ELEM2 is distinguished from other rule induction systems in three aspects. First, it uses a new heuristic function to guide the heuristic search. The function reflects the degree of relevance of an attribute-value pair to a target concept and leads to selection of the most relevant pairs for formulating rules. Second, ELEM2 handles inconsistent training examples by defining an unlearnable region of a concept based on the probability distribution of that concept in the training data. The unlearnable region is used as a stopping criterion for the concept learning process, which resolves conflicts without removing inconsistent examples. Third, ELEM2 employs a new rule quality measure in its post-pruning process to prevent rules from overfitting the data. The rule quality formula measures the extent to which a rule can discriminate between the positive and negative examples of a class. We describe features of ELEM2, its rule induction algorithm and its classification procedure. We report experimental results that compare ELEM2 with C4.5 and CN2 on a number of datasets. (C) 2003 Elsevier Science Ltd. All rights reserved. C1 York Univ, Dept Comp Sci, Toronto, ON M3J 1P3, Canada. RP An, A (reprint author), York Univ, Dept Comp Sci, Toronto, ON M3J 1P3, Canada. CR AN A, 1997, THESIS U REGINA REGI AN A, 1999, P 3 PAC AS C KNOWL D Cendrowska J., 1988, KNOWLEDGE ACQUISITIO CLARK AJL, 1989, J MOL ENDOCRINOL, V2, P3 Grzymala- Busse J. W., 1992, INTELLIGENT DECISION, P3 HAMILTON HJ, 1996, CS9606 U REGINA Holte R., 1989, P 11 INT JOINT C ART Michalski R. S., 1986, Proceedings AAAI-86: Fifth National Conference on Artificial Intelligence Murphy P.M., UCI REPOSITORY MACHI Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN QUINLAN JR, 1983, MACHINE LEARNING ART, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 12 TC 14 Z9 14 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0898-1221 J9 COMPUT MATH APPL JI Comput. Math. Appl. PD FEB-MAR PY 2003 VL 45 IS 4-5 BP 737 EP 748 AR PII S0898-1221(03)00034-9 DI 10.1016/S0898-1221(03)00034-8 PG 12 WC Mathematics, Applied SC Mathematics GA 661BJ UT WOS:000181871100019 ER PT J AU Acid, S de Campos, LM Fernandez-Luna, JM Huete, JF AF Acid, S de Campos, LM Fernandez-Luna, JM Huete, JF TI An information retrieval model based on simple Bayesian networks SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS LA English DT Article; Proceedings Paper CT Conference of the Spanish-Association-for-Artificial-Intelligence CY NOV 14-16, 2001 CL GIJON, SPAIN SP Spanish Assoc Artificial Intelligence AB In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two versions of an extended model, which also represents direct relationships between documents. In either case the BNs are used to compute efficiently, by means of a new and exact propagation algorithm, the posterior probabilities of relevance of the documents in the collection given a query. The performance of the proposed retrieval models is tested through a series of experiments with several standard document collections. (C) 2003 Wiley Periodicals, Inc. C1 Univ Granada, ETSI Informat, Dept Ciencias Computac & Inteligencia Artificial, E-18071 Granada, Spain. Univ Jaen, EPS, Dept Informat, E-23071 Jaen, Spain. RP de Campos, LM (reprint author), Univ Granada, ETSI Informat, Dept Ciencias Computac & Inteligencia Artificial, E-18071 Granada, Spain. RI de Campos, Luis/B-7436-2012; Huete, Juan/C-2428-2012; Fernandez Luna, Juan Manuel/C-6845-2012 OI de Campos, Luis/0000-0001-9125-1195; CR ACID S, 2002, 020201 DECSAI deCampos LM, 1997, INT J INTELL SYST, V12, P495, DOI 10.1002/(SICI)1098-111X(199707)12:7<495::AID-INT2>3.0.CO;2-G DECAMPOS LM, 2000, 2 WORKSH LOG UNC MOD, p543P FERNANDEZLUNA JM, 2001, THESIS U GRANADA FRAKES WR, 1992, INFORMATION RETRIEVA FUNG R, 1995, COMMUN ACM, V38, P42, DOI 10.1145/203330.203340 GEIGER D, 1993, INT J INTELL SYST, V8, P231 Jones K. S., 2000, Information Processing & Management, V36 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Pearl J., 1988, PROBABILISTIC REASON ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Silva I. Reis, 2000, THESIS U FEDERAL MIN Turtle H. R., 1990, THESIS U MASSACHUSET VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, 2000, NIST SPECIAL PUBLICA, P1 NR 17 TC 23 Z9 28 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0884-8173 J9 INT J INTELL SYST JI Int. J. Intell. Syst. PD FEB PY 2003 VL 18 IS 2 BP 251 EP 265 DI 10.1002/int.10088 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA 641WK UT WOS:000180769900009 ER PT S AU Chen, AT AF Chen, AT BE Peters, C Braschler, M Gonzalo, J Kluck, M TI Cross-language retrieval experiments at CLEF 2002 SO ADVANCES IN CROSS-LANGUAGE INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd Workshop of the Cross-Language Evaluation Forum (CLEF 2002) CY SEP 19-20, 2002 CL ROME, ITALY SP US Natl Inst Stand & Technol, DELOS Network Excellence Digital Libs AB This paper describes monolingual, bilingual, and multilingual retrieval experiments using CLEF 2002 test collection. The paper presents a technique for incorporating blind relevance feedback into a document ranking formula based on logistic regression analysis. Both blind relevance feedback and decompounding in German or Dutch are shown to be effective in monolingual and bilingual retrieval. The amount of improvement of performance by decompounding varies from one set of topics to another. The simple raw-score merging strategy in multilingual retrieval can be effective if the individual ranked lists of documents, one for each document language, axe produced using the same retrieval system under similar conditions. The performance of English to French bilingual retrieval using a large parallel corpus as the translation resource is comparable to that using machine translation systems. C1 Univ Calif Berkeley, Sch Informat Management & Syst, Berkeley, CA 94720 USA. RP Chen, AT (reprint author), Univ Calif Berkeley, Sch Informat Management & Syst, Berkeley, CA 94720 USA. EM aitao@sims.berkeley.edu CR Chen AT, 2002, LECT NOTES COMPUT SC, V2406, P44 Cooper W. S., 1994, Second Text REtrieval Conference (TREC-2) (NIST-SP 500-215) Fox A., 1990, STRUCTURE GERMAN Gale W. A., 1993, Computational Linguistics, V19 Grefenstette G., 1998, CROSS LANGUAGE INFOR Harman D., 1992, INFORMATION RETRIEVA, P241 KARP D, 1992, P COLING Och F. J., 2000, ACL00, P440 PETERS C, 2002, LECT NOTES COMPUTER, V2406 PETERS C., 2001, LECT NOTES COMPUTER, V2069 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Voorhees E., 1998, 7 TEXT RETR C TREC 7 Voorhees E. M., 1999, 8 TEXT RETR C TREC 8 NR 13 TC 16 Z9 16 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40830-4 J9 LECT NOTES COMPUT SC PY 2003 VL 2785 BP 28 EP 48 PG 21 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BY17E UT WOS:000188037000002 ER PT S AU Lam-Adesina, AM Jones, GJF AF Lam-Adesina, AM Jones, GJF BE Peters, C Braschler, M Gonzalo, J Kluck, M TI Exeter at CLEF 2002: Experiments with machine translation for monolingual and bilingual retrieval SO ADVANCES IN CROSS-LANGUAGE INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd Workshop of the Cross-Language Evaluation Forum (CLEF 2002) CY SEP 19-20, 2002 CL ROME, ITALY SP US Natl Inst Stand & Technol, DELOS Network Excellence Digital Libs AB The University of Exeter participated in the CLEF 2002 monolingual and bilingual tasks for two languages; Italian and Spanish. Our approach for CLEF 2002 was translating the documents and topics into English, and applying our standard retrieval methods. Four ranked results were submitted for each monolingual task and five for each bilingual task. We explored the potential of term weight estimation from a contemporaneous English text collection and show that although this method has been shown to be effective in retrieval from English collections, it cannot be applied effectively to translated document collections in a simple way. This paper also reports our unofficial results for monolingual and bilingual English runs. C1 Univ Exeter, Dept Comp Sci, Exeter EX4 4QF, Devon, England. RP Lam-Adesina, AM (reprint author), Univ Exeter, Dept Comp Sci, Exeter EX4 4QF, Devon, England. EM a.m.lam-adesina@ex.ac.uk; g.j.f.jones@ex.ac.uk CR Ballesteros L., 1997, P 20 ANN INT ACM SIG BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 Jones G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312690 JONES GJF, 2002, P 13 IR C ART INT CO, P190 JONES GJF, 2002, P CLEF 2001 WORKSH C, P59 Lam-Adesina AM, 2001, P 24 ANN INT ACM SIG, P1, DOI 10.114/383952.383953 LAMADESINA AM, 2002, P WORKSH INF RETR OC LUHN HP, 1958, IBM J RES DEV, V2, P159 PORTER MF, 1980, PROGRAM, V14, P10 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 TOMBROS, 1998, P 2U ANN INT ACM SIG, P2 NR 16 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40830-4 J9 LECT NOTES COMPUT SC PY 2003 VL 2785 BP 127 EP 146 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BY17E UT WOS:000188037000009 ER PT S AU Jones, KS AF Jones, KS BE Sebastiani, F TI Document retrieval: Shallow data, deep theories; Historical reflections, potential directions SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 25th European Conference on Information Retrieval Research (ECIR 2003) CY APR 14-16, 2003 CL PISA, ITALY SP Elsevier, Assoc Italiana Informat Calcolo Automat, CEPIS, LIBERO, Microsoft Res, CRE, fast, Sharp, IBM, Data Port ID INFORMATION-RETRIEVAL; MODEL; RELEVANCE AB This paper reviews the development of statistically-based retrieval. Since the 1950s statistical techniques have clearly demonstrated their practical worth and statistical theories their staying power, for document or text retrieval. In the last decade the TREC programme, and the Web, have offered new retrieval challenges to which these methods have successfully risen. They are now one element in the much wider and very productive spread of statistical methods to all areas of information and language processing, in which innovative approaches to modelling their data and tasks are being applied. C1 Univ Cambridge, Comp Lab, Cambridge CB3 0FD, England. RP Jones, KS (reprint author), Univ Cambridge, Comp Lab, William Gates Bldg,JJ Thomson Ave, Cambridge CB3 0FD, England. CR ATHERTON P, 1965, CLASSIFICATION RES Banko M., 2000, ACL 2000, P318 BELKIN NJ, 1983, J INFORM SCI, V5, P153 BELY N, 1970, PROCEDURES ANAL SEMA Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 BIEBRICHER B, 1988, P 11 ANN INT ACM SIG, P333 Brown P. F., 1992, Computational Linguistics, V18 Classification Research Group, 1969, CLASS INF CONTR PAP CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 CROFT WB, IN PRESS LANGUAGE MO CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 Damianos L, 2002, AI MAG, V23, P13 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 Dumais S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 HAWKING D, 2002, P 10 TEXT RETR C TRE Hiemstra D., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1971, J AM SOC INFORM SCI, V22, P66 Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 1980, J INFORM SCI, V1, P325, DOI 10.1177/016555157900100603 Knight K, 2002, ARTIF INTELL, V139, P91, DOI 10.1016/S0004-3702(02)00222-9 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 LUHN HP, 1957, IBM J RES DEV, V1, P309 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Mooers C. N., 1951, AM DOC, V2, P20, DOI 10.1002/asi.5090020107 NEEDHAM RM, 1964, J DOC, V20, P5 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1981, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1988, INFORM PROCESS MANAG, V24, P269, DOI 10.1016/0306-4573(88)90094-5 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1975, THEORY INDEXING Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1995, M 30 YEARS INF RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1990, INFORM PROCESS MANAG, V26, P73, DOI 10.1016/0306-4573(90)90010-Y SCHULTZ CK, 1968, HP LUHN PIONEER INFO STEVENS ME, 1965, MISCELLANEOUS PUBLIC, V269 STEVENS ME, 1965, MONOGRAPH NATL BUR S, V91 Taube M., 1952, AM DOC, V3, P213, DOI 10.1002/asi.5090030404 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, IN PRESS TREC EXPT E WILKINSON R, 1998, SPECIAL PUBLICATION, P25 Young SJ, 1998, COMPUT SPEECH LANG, V12, P263, DOI 10.1006/csla.1998.0101 1995, INFORMATION PROCESSI, V31, P269 2000, INFORMATION PROCESSI, V36, P1 NR 53 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-01274-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2633 BP 1 EP 11 PG 11 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BX27M UT WOS:000184816000001 ER PT S AU Rasolofo, Y Savoy, J AF Rasolofo, Y Savoy, J BE Sebastiani, F TI Term proximity scoring for keyword-based retrieval systems SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 25th European Conference on Information Retrieval Research (ECIR 2003) CY APR 14-16, 2003 CL PISA, ITALY SP Elsevier, Assoc Italiana Informat Calcolo Automat, CEPIS, LIBERO, Microsoft Res, CRE, fast, Sharp, IBM, Data Port ID QUERIES; LIFE; WEB AB This paper suggests the use of proximity measurement in combination with the Okapi probabilistic model. First, using the Okapi system, our investigation was carried out in a distributed retrieval framework to calculate the same relevance score as that achieved by a single centralized index. Second, by applying a term-proximity scoring heuristic to the top documents returned by a keyword-based system, our aim is to enhance retrieval performance. Our experiments were conducted using the TREC8, TREC9 and TREC10 test collections, and show that the suggested approach is stable and generally tends to improve retrieval effectiveness especially at the top documents retrieved. C1 Univ Neuchatel, CH-2000 Neuchatel, Switzerland. RP Rasolofo, Y (reprint author), Univ Neuchatel, CH-2000 Neuchatel, Switzerland. CR ALLAN J, 1996, NIST SPECIAL PUBLICA, P49 ARAMPATZIS A, 2000, ENCY LIB INFORMATION, V39 BUCKLEY C, 1997, NIST SPEICAL PUBLICA, P105 CARMEL D, 2002, NIST SPECIAL PUBLICA, P228 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 Craswell N., 2001, P 24 ANN INT ACM SIG, P250, DOI 10.1145/383952.383999 Croft WB, 2000, ADV INFORMATION RETR, P1 Dumais S., 1994, NATL I STANDARDS TEC, V500-215, P105 EVANS DA, 1996, NIST SPECIAL PUBLICA, P305 FAGAN J, 1987, THESIS CORNELL U FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B HAWKING D, 2002, NIST SPECIAL PUBLICA, P61 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 HAWKINGS D, 1996, NIST SPECIAL PUBLICA, P131 HULL DA, 1997, NIST SPECIAL PUBLICA, P167 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 MITRA M, 1997, P RIAO 97 Papka R., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288648 Rasolofo Y., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management RASOLOFO Y, 2003, IN PRESS INFORMATION Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SAVOY J, 2002, NIST SPECIAL PUBLICA, P586 Silverstein C., 1999, SIGIR Forum, V33 Singhal A., 2001, P 10 INT WORLD WID W, P708, DOI 10.1145/371920.372186 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R STRZALKOWSKI T, 1997, NATL I STANDARDS TEC, P291 VOORHEES EM, 2002, NIST SPECIAL PUBLICA, P42 NR 29 TC 27 Z9 27 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-01274-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2633 BP 207 EP 218 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BX27M UT WOS:000184816000015 ER PT S AU Peng, FC Schuurmans, D AF Peng, FC Schuurmans, D BE Sebastiani, F TI Combining naive Bayes and n-gram language models for text classification SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 25th European Conference on Information Retrieval Research (ECIR 2003) CY APR 14-16, 2003 CL PISA, ITALY SP Elsevier, Assoc Italiana Informat Calcolo Automat, CEPIS, LIBERO, Microsoft Res, CRE, fast, Sharp, IBM, Data Port ID CATEGORIZATION; TERMS AB We augment the naive Bayes model with an n-gram. language model to address two shortcomings of naive Bayes text classifiers. The chain augmented naive Bayes classifiers we propose have two advantages over standard naive Bayes classifiers. First, a chain augmented naive Bayes model relaxes some of the independence assumptions of naive Bayes-allowing a local Markov chain dependence in the observed variables-while still permitting efficient inference and learning. Second, smoothing techniques from statistical language modeling can be used to recover better estimates than the Laplace smoothing techniques usually used in naive Bayes classification. Our experimental results on three real world data sets show that we achieve substantial improvements over standard naive Bayes classification, while also achieving state of the art performance that competes with the best known methods in these cases. C1 Univ Waterloo, Sch Comp Sci, Waterloo, ON N2L 3G1, Canada. RP Peng, FC (reprint author), Univ Waterloo, Sch Comp Sci, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada. CR Bell T.C., 1990, TEXT COMPRESSION CAVNER W, 1994, P SDAIR 94 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Duda R., 1973, PATTERN CLASSIFICATI EYHERAMENDY S, 2003, IN PRESS ARTIFICAL I Friedman N, 1997, MACH LEARN, V29, P131, DOI 10.1023/A:1007465528199 Goodman J., 1998, TR1098 HARV U He J., 2000, P PRICAI 2000 INT WO, P24 Hiemstra D., 2001, THESIS U TWENTE KEOGH E, 1999, ARTIFICIAL INTELLIGE Kwok KL, 1999, J AM SOC INFORM SCI, V50, P709, DOI 10.1002/(SICI)1097-4571(1999)50:8<709::AID-ASI8>3.0.CO;2-V LEWIS D, 1998, P ECML 98 Manning C. D., 1999, FDN STAT NATURAL LAN McCallum A, 1998, P AAAI 98 WORKSH LEA NEY H, 1994, COMPUT SPEECH LANG, V8, P1, DOI 10.1006/csla.1994.1001 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 RENNIE J, 2001, AITR2001004 Rish I., 2001, P IJCAI 01 WORKSH EM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Scott S., 1999, P 16 INT C MACH LEAR, P379 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Stamatatos E, 2000, COMPUT LINGUIST, V26, P471, DOI 10.1162/089120100750105920 TEAHAN W, 2001, P WORKSH LMIR TURPIN A, 2000, P SIGIR 1999, P309 Yang Y, 1999, INFORMATION RETRIEVA, V1, P67 NR 26 TC 14 Z9 15 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-01274-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2633 BP 335 EP 350 PG 16 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BX27M UT WOS:000184816000024 ER PT S AU Goker, A He, DQ AF Goker, A He, DQ BE Bryans, JB TI Personalization via collaboration in web retrieval systems: A context based approach SO ASIST 2003: PROCEEDINGS OF THE 66TH ASIST ANNUAL MEETING, VOL 40, 2003: HUMANIZING INFORMATION TECHNOLOGY: FROM IDEAS TO BITS AND BACK SE PROCEEDINGS OF THE ASIST ANNUAL MEETING LA English DT Article; Proceedings Paper CT 66th Annual Meeting of the American-Society-for-Information-Science-and-Technology CY OCT 19-22, 2003 CL Long Beach, CA SP Amer Soc Informat Sci & Technol ID IDENTIFICATION; OKAPI AB World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization. C1 Robert Gordon Univ, Sch Comp, Aberdeen AB25 1HG, Scotland. Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA. RP Goker, A (reprint author), Robert Gordon Univ, Sch Comp, St Andrew St, Aberdeen AB25 1HG, Scotland. EM asg@comp.rgu.ac.uk; daqing@umiacs.umd.edu RI Goker, Ayse/ OI Goker, Ayse/0000-0002-4462-5489 CR BLOEDORN E, 1996, AAAI SPR S MACH LEAR, P25 COOL C, 2002, ISSUES CONTEXT INFOR, V38 COON D, 1992, INTRO PSYCHOL Dix AJ, 1993, HUMAN COMPUTER INTER EDWARDS P, 1996, SS9605 AAAI SPR S MA, P31 GOKER A, 1991, 6 INT S ISMIS 91, V2, P348 GOKER A, 1999, P 16 INT JOINT C ART, P21 Goker A., 2000, P AH2000 INT C AD HY, P319 Goker A, 1997, J DOC, V53, P80, DOI 10.1108/EUM0000000007194 HAN S, 2001, 8 INT C US MOD He D., 2000, P BCS IRSG 22 ANN C, P57 He DQ, 2002, INFORM PROCESS MANAG, V38, P727, DOI 10.1016/S0306-4573(01)00060-7 Hersh W., 2000, SIGIR FORUM, V34 JANSEN MBJ, 1998, SIGIR FORUM, V32, P5 MATWIN S, 1996, P ICML 96 WORKSH LEA, P1 Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 RESNICK P, 1997, COMMUN ACM, V40, P3 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Spink A, 1998, MODELING USERS SUCCE WALKER S, 1991, 6056 BRIT LIB R D CO NR 22 TC 0 Z9 0 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055 USA SN 0044-7870 BN 1-57387-197-4 J9 P ASIST ANNU PY 2003 VL 40 BP 357 EP 365 DI 10.1002/meet.1450400144 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BBZ20 UT WOS:000228354100044 ER PT J AU MacFarlane, A AF MacFarlane, A TI On open source IR SO ASLIB PROCEEDINGS LA English DT Article DE information retrieval; open systems; software engineering; worldwide web AB Open source software development is becoming increasingly popular as a way of producing software, due to a number of factors. It is argued in this paper that these factors may have a significant impact on the future of information retrieval (IR) systems, and that it is desirable that these systems are made open to all. Some problems are outlined that may prevent the uptake of open source IR systems and a number of open source IR systems are described. C1 City Univ London, Ctr Interact Syst Res, London EC1V 0HB, England. RP MacFarlane, A (reprint author), City Univ London, Ctr Interact Syst Res, London EC1V 0HB, England. CR BROOKS FP, 1982, MYTHICAL MAN MONTYH Feller J., 2002, UNDERSTANDING OPEN S Kluev V., 2000, SIGIR Forum, V34 McConnell S, 1999, IEEE SOFTWARE, V16, P4 Moody G, 2001, REBEL CODE LINUX OPE NASSAR N, 1997, SEARCHING ISEARCH NICHOLS DM, 2001, USABILITY OPEN SOURC PORTER MF, 2000, SIGIR FORUM, V34, P16 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VOORHEES EM, 1999, NIST SPECIAL PUBLICA Witten Ian H., 1999, MANAGING GIGABYTES, V2nd NR 12 TC 1 Z9 1 PU EMERALD PI BRADFORD PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND SN 0001-253X J9 ASLIB PROC JI Aslib Proc. PY 2003 VL 55 IS 4 BP 217 EP 222 DI 10.1108/00012530310486575 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 730NA UT WOS:000185835800003 ER PT B AU Valsan, Z Emele, M AF Valsan, Z Emele, M GP IEEE TI Thematic text clustering for domain specific language model adaptation SO ASRU'03: 2003 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING ASRU '03 LA English DT Proceedings Paper CT IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU 03) CY NOV 30-DEC 03, 2003 CL St Thomas, VI SP IEEE AB We propose a new approach for thematic text clustering. The text clusters are used to generate domain specific language models in order to address the problem of language model adaptation. The method relies on a new discriminative n-gram based term selection process (n>1), which reduces the influence of the corpus inhomogeneity, and outputs only semantically focused n-grams as being the most representative key terms in the corpus. These key terms are then used to automatically cluster the whole document collection and generate LMs out of these text clusters. Different key term selection methods are evaluated using perplexity as a measure. Automatically computed clusters are compared with manually assigned labelling according to genre information. The results of these experimental studies are presented and discussed. Compared to the manually clustering a significant performance improvement between 21.87% and 53.12% is observed depending on the chosen key term selection method. C1 Sony Corp Labs Europe, Tangible User Interface Grp, D-70327 Stuttgart, Germany. RP Valsan, Z (reprint author), Sony Corp Labs Europe, Tangible User Interface Grp, Hedelfinger Str 61, D-70327 Stuttgart, Germany. CR BELLEGARDA JR, 1996, INT C AC SPEECH SIGN, V1, P172 Bellegarda JR, 2000, P IEEE, V88, P1279, DOI 10.1109/5.880084 CLARKSON PR, 1997, IEEE INT C SPEECH SI DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Goodman JT, 2001, COMPUT SPEECH LANG, V15, P403, DOI 10.1006/csla.2001.0174 IYER R, 1999, DARPA HLT, P82 Jelinek F., 1991, Computational Linguistics, V17 JELINK F, 1997, STAT METHODS SPEECH Kaski S., 1998, P IJCNN 98 INT JOINT, V1, P413 KUHN R, 1990, IEEE T PATTERN ANAL, V12, P570, DOI 10.1109/34.56193 Kurimo M, 2002, SPEECH COMMUN, V38, P29, DOI 10.1016/S0167-6393(01)00042-5 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 NEISLER TR, 1997, P IEEE ICASSP Rasmussen E., 1992, INFORMATION RETRIEVA, P419 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SEYMORE K, 1997, CMUCS97152 SRIHARI R, 1992, AAAI S PROB APPR NAT, P121 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7980-2 PY 2003 BP 513 EP 518 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BAM13 UT WOS:000222782800088 ER PT S AU Kojima, Y Itoh, H Mano, H Ogawa, Y AF Kojima, Y Itoh, H Mano, H Ogawa, Y BE Peters, C Gonzalo, J Braschler, M Kluck, M TI Ricoh at CLEF 2003 SO COMPARATIVE EVALUATION OF MULTILLINGUAL INFORMATION ACCESS SYSTEMS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 4th Workshop of the Cross-Language Evaluation Forum (CLEF 2003) CY AUG 21-22, 2003 CL Trondheim, NORWAY SP US, Natl Inst Stand & Technol, DELOS, Network Excellence Digit Lib AB This paper describes RICOH's participation in the Monolingual Information Retrieval tasks of the Cross-Laneuaae Evaluation Forum (CLEF) 2003. We applied our system using the same kind of stemmer. the same Options and different parameters to five European languages and compared the results for each langauge. Although the overall performance of the system was reasonable, there were two problems. The first was the lack of a compound splitter for German and the second was the failure of query expansion when there were few relevant documents. C1 RICOH Co Ltd, Software R&D Grp, Bunkyo Ku, Tokyo 1120002, Japan. RP Kojima, Y (reprint author), RICOH Co Ltd, Software R&D Grp, Bunkyo Ku, 1-1-17 Koishikawa, Tokyo 1120002, Japan. EM ykoji@src.ricoh.co.jp CR ITOH H, 2001, 10 TEXT RETR C TREC, P457 MACFARLANE A, 2002, WORK NOT CLEF 2002 W OGAWA Y, 2000, 8 TEXT RETR C TREC 8, P541 OGAWA Y, 2001, P 2 NTCIR WORKSH M, P121 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 TOYODA M, U TOKYO RICOH NTCIR NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24017-9 J9 LECT NOTES COMPUT SC PY 2003 VL 3237 BP 367 EP 372 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBM36 UT WOS:000226159600035 ER PT J AU Aizawa, A AF Aizawa, A TI An information-theoretic perspective of tf-idf measures SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT ACM SIGIR 2000 Meeting CY JUL 28, 2000 CL ATHENS, GREECE SP ACM DE tf-idf; term weighting theories; information theory; text categorization ID SEARCH TERMS; RETRIEVAL; MODELS; SELECTION AB This paper presents a mathematical definition of the "probability-weighted amount of information" (PWI), a measure of specificity of terms in documents that is based on an information-theoretic view of retrieval events. The proposed PWI is expressed as a product of the occurrence probabilities of terms and their amounts of information, and corresponds well with the conventional term frequency-inverse document frequency measures that are commonly used in today's information retrieval systems. The mathematical definition of the PWI is shown, together with some illustrative examples of the calculation. (C) 2002 Elsevier Science Ltd. All rights reserved. C1 Natl Inst Informat, Chiyoda Ku, Tokyo 1018430, Japan. RP Aizawa, A (reprint author), Natl Inst Informat, Chiyoda Ku, 2-1-2 Hitotsubashi, Tokyo 1018430, Japan. CR Aizawa A., 2001, P 6 NAT LANG PROC PA, P307 Aizawa A., 2000, P 23 ACM INT C RES D, P104, DOI 10.1145/345508.345556 AMATI G, 1998, INFORMATION RETRIEVA, P189 Baayen R. H., 2001, WORD FREQUENCY DISTR BAEZAYATES R, 1988, MODERN INFORMATION R BROOKES BC, 1972, J DOC, V28, P160, DOI 10.1108/eb026537 CHURCH KW, 1999, NATURAL LANGUAGE PRO, P283 CHURCH KW, 1990, COMPUTATIONAL LINGUI, V6, P22 Cover T. M., 1991, ELEMENTS INFORMATION CRESTANI F, 2000, J INFORMATION RETRIE, V2, P23 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DENNIS SF, 1964, MISCELLANEOUS PUBLIC, V269 Dunning T., 1993, Computational Linguistics, V19 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Fung P., 1997, Machine Translation, V12, DOI 10.1023/A:1007974605290 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Greiff W. R., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290948 Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Joachims T, 1997, P 14 INT C MACH LEAR, P143 KAGEURA K, 1999, P 1 NTCIR WORKSH RES, P42 Kageura K., 1996, Terminology, V3, DOI 10.1075/term.3.2.03kag KITA K, 1999, PROBABILISTIC LANGUA Koller D., 1997, P 14 INT C MACH LEAR, P170 Koller D., 1996, P 13 INT C MACH LEAR, P284 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 LUHN HP, 1957, IBM J RES DEV, V1, P309 Manning C. D., 1999, FDN STAT NATURAL LAN MATSUMOTO Y, 1999, NAISTISTR99012 McCallum A., 1998, WORKSH LEARN TEXT CA, P41 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 NAGAO M, 1976, T INFORMATION PROCES, V17, P110 *NAT CTR SCI INF S, 1999, NTCIR WORKSH 1 P 1 N ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1994, J DOC, V50, P233, DOI 10.1108/eb026932 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 Smadja F., 1993, Computational Linguistics, V19 SPARCKJONES K, 1972, J DOC, V28, P11 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 WONG SKM, 1992, J AM SOC INFORM SCI, V43, P54, DOI 10.1002/(SICI)1097-4571(199201)43:1<54::AID-ASI5>3.0.CO;2-A Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 45 TC 72 Z9 76 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2003 VL 39 IS 1 BP 45 EP 65 AR PII S0306-4573(02)00021-3 DI 10.1016/S0306-4573(02)00021-3 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 637DC UT WOS:000180495500003 ER PT J AU Savoy, J AF Savoy, J TI Cross-language information retrieval: experiments based on CLEF 2000 corpora SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE cross-language information retrieval; bilingual information retrieval; French, German Italian languages; database merging strategies; evaluation ID TEXT RETRIEVAL; SEARCH TERMS; ALGORITHM; SELECTION; QUERIES; LIFE; WEB AB Search engines play an essential role in the usability of Internet-based information systems and without them the Web would be much less accessible, and at the very least would develop at a much slower rate. Given that non-English users now tend to make up the majority in this environment, our main objective is to analyze and evaluate the retrieval effectiveness of various indexing and search strategies based on test-collections written in four different languages: English, French, German, and Italian. Our second objective is to describe and evaluate various approaches that might be implemented in order to effectively access document collections written in another language. As a third objective, we will explore the underlying problems involved in searching document collections written in the four different languages, and we will suggest and evaluate different database merging strategies capable of providing the user with a single unique result list. (C) 2002 Published by Elsevier Science Ltd. C1 Univ Neuchatel, Inst Interfac Informat, CH-2000 Neuchatel, Switzerland. RP Savoy, J (reprint author), Univ Neuchatel, Inst Interfac Informat, CH-2000 Neuchatel, Switzerland. CR ADRIANI M, 2001, RESULTS CLEF 2001 CR, P143 Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Baumgarten C., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312685 Bishop CM, 1995, NEURAL NETWORKS PATT BRASCHLER M, 2001, LNCS, V2069, P140 BROGLIO J, 1995, NIST SPECIAL PUBLICA, P29 Brown P. F., 1993, Computational Linguistics, V19 BUCKLEY C, 1996, NIST PUBLICATION Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 CHEN A, 2001, P CLEF 2001 SOPH ANT, P21 CHEN A, 2001, IN PRESS NIST PUBLIC Davis MW, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P92, DOI 10.1145/258525.258542 Dumais S., 1994, NATL I STANDARDS TEC, V500-215, P105 FLURY B, 1 COURSE MULTIVARIAT Fox C, 1989, ACM SIGIR FORUM, V24, P19 Fox E. A., 1994, NIST SPECIAL PUBLICA, P243 FRANZ M, 2000, NIST PUBLICATION, P391 Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 GACHOT DA, 1998, CROSS LANGUAGE INFOR, P105 GALEF D, 1993, VERBATIM, V19, P1 *GLOB RES, 2001, GLOB INT STAT LANG Grefenstette G., 1998, CROSS LANGUAGE INFOR HEDLUND T, 2001, INFORMATION PROCESSI, V33, P147 Hiemstra D., 2001, LECT NOTES COMPUTER, V2069, P102 Hosmer D, 1989, APPL LOGISTIC REGRES Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Hull D. A., 1996, P 19 ANN INT ACM SIG, P49, DOI 10.1145/243199.243212 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 JONES GJF, 2001, RESULTS CLEF 2001 CR, P105 JONES KS, 1977, RES AUTOMATIC INDEXI Kraaij W., 1996, P 19 ANN INT ACM SIG, P40, DOI 10.1145/243199.243209 KRAAIJ W, 2001, RESULTS CLEF 2001 CR, P29 KRAAIJ W, 2000, NIST PUBLICATION, P285 KWOK KL, 1995, NIST PUBLICATION, P247 KWOK KL, 2001, IN PRESS NIST PUBLIC Landauer T., 1990, P 6 ANN C UW CTR NEW, P31 Le Calve A, 2000, INFORM PROCESS MANAG, V36, P341, DOI 10.1016/S0306-4573(99)00036-9 Lee S, 1997, J GUID CONTROL DYNAM, V20, P97, DOI 10.2514/2.4000 LOVINS JB, 1968, MECH TRANSL, V11, P22 Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005 MAYFIELD J, 2000, NIST PUBLICATION, P445 MCNAMEE P, 2001, RESULTS CLEF 2001 CR, P121 MCNAMEE P, 2001, LNCS, V2069, P129 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 MOFFAT A, 1995, NIST PUBLICATION, P85 Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 Nie JY, 1999, INFORM PROCESS MANAG, V35, P443, DOI 10.1016/S0306-4573(98)00051-X NIE JY, 2001, RESULTS CLEF 2001 CR, P75 Oard D. W., 1996, SURVEY MULTILINGUAL Oard DW, 1999, INFORM PROCESS MANAG, V35, P363, DOI 10.1016/S0306-4573(98)00066-1 PETERS C., 2001, LECT NOTES COMPUTER, V2069 Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Powell A. L., 2000, P 23 ANN INT ACM SIG, P232, DOI 10.1145/345508.345584 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roukos S., 1999, NIST PUBLICATION, P157 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1971, SMART RETRIEVAL SYST, P206 SALTON G, 1983, INTRO MODERN INFORMA SAVOY J, 1996, NIST SPECIAL PUBLICA, P537 Savoy J, 1997, INFORM PROCESS MANAG, V33, P495, DOI 10.1016/S0306-4573(97)00027-7 SAVOY J, 2001, IN PRESS NIST PUBLIC Savoy J, 1999, J AM SOC INFORM SCI, V50, P944, DOI 10.1002/(SICI)1097-4571(1999)50:10<944::AID-ASI9>3.3.CO;2-H Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 SINGHAL A, 1999, NIST SPECIAL PUBLICA, P239 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Spink A, 1997, J AM SOC INFORM SCI, V48, P741, DOI 10.1002/(SICI)1097-4571(199708)48:8<741::AID-ASI7>3.0.CO;2-S Sproat Richard W., 1992, MORPHOLOGY COMPUTATI VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Venables WN, 1999, MODERN APPL STAT S P Voorhees Ellen M., 1995, P 18 ANN INT ACM SIG, P172, DOI 10.1145/215206.215357 VOORHEES EM, 1995, NIST PUBLICATION, P95 Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P1, DOI 10.1016/S0306-4573(99)00042-4 VOORHEES EM, 1996, NIST PUBLICATION, P121 XU J, 2001, IN PRESS NIST PUBLIC Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 2001, EMARKETER WORLDS LEA NR 79 TC 23 Z9 23 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2003 VL 39 IS 1 BP 75 EP 115 AR PII S0306-4573(02)00015-3 DI 10.1016/S0306-4573(02)00018-3 PG 41 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 637DC UT WOS:000180495500005 ER PT S AU Blanken, HM Grabs, T Schek, HJ Schenkel, R Weikum, G AF Blanken, HM Grabs, T Schek, HJ Schenkel, R Weikum, G BE Blanken, H Grabs, T Schek, HJ Schenkel, R Weikum, G TI Intelligent search on XML data - Preface SO INTELLIGENT SEARCH ON XML DATA: APPLICATIONS, LANGUAGES, MODELS IMPLEMENTATIONS AND BENCHMARKS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT Workshop on Intelligent Search in XML Data CY DEC, 2002 CL SCHLOSS DAGSTUHL, GERMANY ID INFORMATION-RETRIEVAL; SEMISTRUCTURED DATA; SEMANTIC SIMILARITY; RELEVANCE FEEDBACK; QUERY LANGUAGE; PROXIMAL NODES; WEB; TEXT; SYSTEMS; MODEL C1 Univ Twente, Fac EEMCS, Dept Comp Sci, NL-7500 AE Enschede, Netherlands. ETH, Database Res Grp, Inst Informat Syst, CH-8092 Zurich, Switzerland. Univ Saarland, Fachrichtung Informat 62, D-66041 Saarbrucken, Germany. RP Blanken, HM (reprint author), Univ Twente, Fac EEMCS, Dept Comp Sci, POB 217, NL-7500 AE Enschede, Netherlands. EM blanken@cs.utwente.nl; grabs@inf.ethz.ch; schek@inf.ethz.ch; schenkel@cs.uni-sb.de; weikum@cs.uni-sb.de; grabs@inf.ethz.ch; schek@inf.ethz.ch; schenkel@cs.uni-sb.de; weikum@cs.uni-sb.de CR Abiteboul S., 1997, International Journal on Digital Libraries, V1 ABITEBOUL S, 1999, P 8 ACM SIGACT SIGMO Abiteboul S, 2000, DATA WEB RELATIONS S Abiteboul S., 1997, P 6 INT C DAT THEOR, P1 ABOULNAGA A, 2001, VLDB, P591 Agirre E., 1996, P 16 INT C COMP LING, P16 AGRAWAL R, 2002, P 18 INT C DAT ENG I AHMEDI L, 2002, P SEM WEB WORKSH SEM AMATO G, 2000, P 4 EUR C ECDL, P328 AMERYAHIA S, 2003, WORLD WID WEB CONS Amsaleg L, 1998, DISTRIB PARALLEL DAT, V6, P217, DOI 10.1023/A:1008646115473 ANDERSON TL, 1990, LECT NOTES COMPUT SC, V416, P317 [Anonymous], INT STAT CLASSIFICAT [Anonymous], MED SUBJECT HEADINGS APERS PMG, 2001, VLDB 2001 P 27 INT C APPELT E, 1999, ITNRO INFORMATION EX ATKINSON MP, 1999, VLDB 99 P 25 INT C V Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BaezaYates R, 2002, J AM SOC INF SCI TEC, V53, P504, DOI 10.1002/asi.10061 BAEZAYATES R, 1996, ACM SIGMOD RECORD, V25, P67, DOI 10.1145/381854.381890 Baker C.F., 1998, P 36 ANN M ASS COMP BARYOSSEF Z, 1999, LECT NOTES COMPUTER, V1649, P2 BATTERYWALA Y, 2000, ACM SIGKDD WORKSH TE Baumgartner R., 2001, VLDB 2001 P 27 INT C, P715 BEERI C, ACM SIGMOD WORKSH WE, P37 BELKIN NJ, 1995, P 4 TEXT RETR C TREC, P181 BELKIN NJ, 1997, P 20 ANN INT ACM SIG BENCHCAPON TJM, 1999, P DEXA 99, P250 BERGER A, 1999, SIGIR 99 P 22 ANN IN Berglund A., 2002, XML PATH LANGUAGE XP BERNERSLEE T, 2001, SCI AM MAY BLOK HE, 2002, THESIS U TWENTE ENSC BLOK HE, 2001, P 2001 ACM CIKM INT Bohannon P, 2002, PROC INT CONF DATA, P64, DOI 10.1109/ICDE.2002.994698 Bohme T, 2001, P GERM DAT C BTW2001, P264 BONCZ P, 2002, THESIS CWI Boncz PA, 1999, VLDB J, V8, P101, DOI 10.1007/s007780050076 Bonifati A, 2000, SIGMOD RECORD, V29, P68 BOURETT R, XML DATABASE PRODUCT Bozsak E, 2002, LECT NOTES COMPUT SC, V2455, P304 Bradley N., 1998, XML COMPANION BRAY T, 2000, EXTENSIBLE MARKUP LA BREMER JM, 2002, P 5 ITN WORKSH WEB D BRESSAN S, 2001, P 10 ACM INT C INF K, P167 BRIGHT MW, 1994, ACM T DATABASE SYST, V19, P212, DOI 10.1145/176567.176569 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BRUDER I, 2000, P 5 INT C APPL NAT L, P227 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUDANITSKY A, 2001, P WORKSH WORDN LEX R Buneman P, 2000, VLDB J, V9, P76, DOI 10.1007/s007780050084 BUNEMAN P, 1994, SIGMOD RECORD Buneman P, 2001, SIGMOD RECORD, V30, P47 Buneman P., 1996, P ACM SIGMOD INT C M, P505, DOI 10.1145/233269.233368 Burges C., 1998, DATA MINING KNOWLEDG, V2 BUXTON S, 2003, WORLD WID WEB CONS F Califf M. E., 1998, THESIS U TEXAS AUSTI CAREY M, 1997, SIGMOD 1997, P135 Carey M. J., 1993, P ACM SIGMOD INT C M, P12, DOI 10.1145/170035.170041 CATTELL RGG, 1992, ACM T DATABASE SYST, V17, P1, DOI 10.1145/128765.128766 CHAKRABARTI S, 1999, 8 WWW C Chakrabarti Soumen, 2002, MINING WEB CHAMBERLIN D, 2002, XML QUERY USE CASES CHAMBERLIN D, XML QUERY LANGUAGE H, P53 CHAUDHURI S, 1995, PROC INT CONF DATA, P190, DOI 10.1109/ICDE.1995.380392 CHEN H, 2000, ACM CHI C HUM FACT C Chen JJ, 2002, PROC INT CONF DATA, P345 CHIARAMELLA Y, 1996, 8134 FERMI ESPRIT BR Chinenyanga T., 2001, P 24 ANN INT C RES D, P163, DOI 10.1145/383952.383982 CHUNG CW, APEX ADAPTIVE PATH I, P121 CIRAVEGNA F, 2000, ECAI WORKSH MACH LEA CLUET S, 1999, ACM SIGMOD WORKSH WE CLUET S, 2001, VLDB, P271 Consens M. P., 1995, Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1995, DOI 10.1145/212433.212437 Cooper B. F., 2001, VLDB, P341 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 Cormen T., 2001, INTRO ALGORITHMS CRESPO A, 1994, SURVEY SEMIAUTOMATIC CROFT W, 2001, P 24 ANN INT C RES D CSUHAJVARHU E, 1994, TOPICS COMPUTER MATH, V5 CUNNINGHAM H, 1999, INFORMATION EXTRACTI CUNNINGHAM H, 2002, P 49 ANN M ASS COMP DECKER S, 1998, IFIP C P, V138, P351 DELBIMBO A, 1999, VISUAL INFORMATION R DELIS A, 1999, SIGMOD 1999 DEUTSCH A, 1998, QL 98 QUER LANG WORK Deutsch A, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P431 DEVRIES A, 1999, INT C DAT SEM DS 8 R, P16 de Vries AP, 2001, J AM SOC INF SCI TEC, V52, P954, DOI 10.1002/asi.1144 DEVRIES AP, 1999, THESIS U RWENTE ENSC Dietz P., 1982, P 14 ANN ACM S THEOR, P122, DOI 10.1145/800070.802184 DING Y, 2003, SEM SEM WEB TECHN Duda R. O., 2000, PATTERN CLASSIFICATI, V2nd Dumais S.T., 2000, SIGIR 00, P256 *DVB, DVB CONS MULT HOM PL EGNOR D, 2000, ACM SIGIR 2000 WORKS *ENV, 1995, 1828 ENV CEN *EUR, EUR RETR ENG Fankhauser P., 1993, Electronic Publishing: Origination, Dissemination and Design, V6 FARQUHAR A, KSL9626 Fellbaum C, WORDNET ELECT LEXICA FERNANDEZ M, 2002, WORLD WID WEB CONS N Florescu D., 1999, IEEE DATA ENG B, V22, P27 Florescu D, 1999, 3680 INRIA FLORESCU D, 2000, P INT WWW C AMST MAY FOX EA, 1988, INFORM PROCESS MANAG, V24, P257, DOI 10.1016/0306-4573(88)90093-3 FRANKLIN MJ, 2002, P 2002 ACM SIGMOD IN FREI HP, 1991, INFORMATIK FACHBERIC, V289, P1 Freitag D., 1998, THESIS CARNEGIE MELL Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1997, T INFORMATION SYSTEM, V14, P32 FUHR N, 2002, P 1 INEX WORKSH DAGS Fuhr N., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291005 Fuhr N, 1999, INFORM PROCESS MANAG, V35, P101, DOI 10.1016/S0306-4573(99)00009-6 GEY F, 1999, SIGIR 99 P 22 ANN IN Goldman Roy, 1997, VLDB, P436 GRABS T, 2003, THESIS ETH GRABS T, 2003, P 1 INEX WORKSH DOGS, P35 GRABS T, IN PRESS KNOWLEDGE I GRABS T, 2000, 340 ETH DEP COMP SCI Grabs T., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Grabs T., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 GRABS T, 2002, XML INF RETR WORKSH GRAY J, 1993, DATABASES TRANSACTIO Grishman R., 1997, LECT NOTES ARTIF INT, V1299, P10 Grossman DA, 1997, J AM SOC INFORM SCI, V48, P122, DOI 10.1002/(SICI)1097-4571(199702)48:2<122::AID-ASI3>3.0.CO;2-# GRUBER TR, 1993, FORMAL ONTOLOGY CONC, P89 Grust T, 2002, P 2002 ACM SIGMOD IN, P109 Grust T., 2003, P 29 INT C VER LARG GUARNIZO LE, 1998, TRANSNATIONALISM BEL, V6, P3 GUPTA N, 2000, P 16 INT C DAT ENG 2, P84, DOI 10.1109/ICDE.2000.839392 Guttman A, 1984, P ACM SIGMOD INT C M, P47 HALEVY A, 2003, P 1 SEM C INN DAT SY HARMAN D, 1995, SCHRIFTEN INFORMATIO, V20 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 *HAV INC, 2000, HAVI HOM AUD VID INT Health_Level 7, HLTH LEVEL 7 Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 HEARST MA, 1999, MODERN INFORMATION R Hearst Marti A., 1995, P ACM SIGCHI C HUM F, P59, DOI 10.1145/223904.223912 HELMER S, 2001, MA0115 U MANNH HIEMSTRA D, 2003, P 1 INEX WORKSH DAGS Hiemstra D, 1999, LECT NOTES COMPUT SC, V1696, P274 HIEMSTRA D, 1999, NIST SPECIAL PUBLICA, P227 Hirst G., 1998, WORDNET ELECT LEXICA, P305 HITZ O, 1999, 1 INT C DOC AN REC, P374 Horrocks I, 2002, LECT NOTES COMPUT SC, V2287, P2 HUCK G, 1999, P OOPSLA WORKSH JAV Humphreys BL, 1998, J AM MED INFORM ASSN, V5, P1 Ide E., 1971, SMART RETRIEVAL SYST, P337 *ISO IEC, 2000, 1324922000 ISOIEC Ives ZG, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P299 Ives Z. G., 2000, UWCSE20000502 JAAKKOLA J, 1999, C1992 U HELS DEP COM JAMASZ M, 2003, TR200301 U OTT JAMASZ M, 2001, P NAACL WORKSH WORDN Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 JAOCHIMS T, 2002, THESIS JENSEN CS, 2002, LECT NOTES COMPTUER, V2287 Jiang Jay J., 1997, P INT C RES COMP LIN, P19 JOHNSON B, 1991, TR2657 U MAR COMP SC JONSSON B, 1998, SIGMOD 1999 P ACM SI, P118 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 KAZAI G, 2003, 25 EUR C INF RETR RE, P88 Keen M., 1966, FACTORS DETERMINING, V2 KEKALAINEN J, 2002, J AM SOC INFORMATION, V53 Kerschberg L., 2001, P 2 INT C WEB INF SY, P41 KILPELAINEN P, 1993, P 16 ANN INT ACM SIG, P214, DOI 10.1145/160688.160722 KIMOTO H, 1990, P 13 ANN INT ACM SIG, P227, DOI 10.1145/96749.98228 Kleinberg J., 1999, J ACM, V46 KLETTKE M, 2001, WORLD WID WEB DAT 3, P63 Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Kriegel H., 2000, P 26 INT C VER LARG, P407 KRIEWEL S, 2001, THESIS U DORTMOUND Kwon O.-W., 1994, P 3 INT C INF KNOWL, P140, DOI 10.1145/191246.191270 Lalmas M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P110, DOI 10.1145/258525.258546 LEACOCK C, 1998, WORDNET ELECT LEXICL, P264 Lenat D. B., 1990, BUILDING LARGE KNOWL LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 LEWIS WD, 2002, 12 U AR LI Q, 2000, AMIA AN S LOS ANG CA Li Q, 2001, VLDB, P361 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 LIST JA, 2003, IN EV XML RETR INEX Lundquist C., 1997, Proceedings of the Sixth International Conference on Information and Knowledge Management. CIKM'97, DOI 10.1145/266714.266844 MACLEOD IA, 1991, COMPUT J, V34, P254, DOI 10.1093/comjnl/34.3.254 MAEDCHE A, 2001, CEUR WORKS P, V40 Maedche A., 2000, P 12 INT C SOFTW KNO Manning C. D., 1999, FDN STAT NATURAL LAN Manolescu I., 2001, VLDB, P241 MCHALE ML, 1998, P WORKSH CONT VIS IN McHugh J., 1999, Very Large Data Bases. Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller DRH, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P214 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Milo T, 1999, LECT NOTES COMPUT SC, V1540, P277 Mitchell T., 1996, MACHINE LEARNING Mitra P, 2000, LECT NOTES COMPUT SC, V1777, P86, DOI 10.1007/3-540-46439-5_6 Mizzaro S., 1998, INTERACTING COMPUTER, V10, P305 Myaeng S. H., 1998, P 21 ANN INT ACM SIG, P138, DOI 10.1145/290941.290980 Nanopoulos A, 2002, VLDB J, V11, P138, DOI 10.1007/s00778-002-0068-7 Nascimento MA, 2002, DATA KNOWL ENG, V43, P57, DOI 10.1016/S0169-023X(02)00122-2 Nash B, 2003, BRIT MED J, V326, P674, DOI 10.1136/bmj.326.7391.674 Naughton JF, 2001, IEEE DATA ENG B, V24, P27 Navarro G, 1997, ACM T INFORM SYST, V15, P400, DOI 10.1145/263479.263482 Neumann G, 2002, COMPUT INTELL, V18, P451, DOI 10.1111/0824-7935.00197 Nilsson N. J., 1998, ARTIFICIAL INTELLIGE *NIST, 1998, P 7 MESS UND C MUC Noy NF, 2000, LECT NOTES ARTIF INT, V1937, P17 Ogilvie P., 2003, P 1 INEX WORKSH DAGS, P18 OLEARY DE, 1999, LECT NOTES ARTIF INT, V1621, P1 Papazoglou MP, 1998, LECT NOTES COMPUT SC, V1413, P251 PAQUES H, 2001, P 2001 ACM CIKM INT PIWOWARSKI B, 2003, P 1 INEX WORKSH DAGS, P7 PLASSARD MF, 1998, UBCIM PUBLICATIONS N, V19 QIU U, 1993, P SIGIR 93 16 ACM IN, P160 QIU Y, 1995, 255 SWISS FED I TECH Rabiner L.R., 1990, READINGS SPEECH RECO, P267 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 RECTOR AL, 1992, P 7 WORLD C MED INF, P1420 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resniks P., 1995, P 14 INT JOINT C ART, P448 RICHARDSON R, 1994, P ACIS C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 RUI Y, 1998, P IS T SPIE STOR RET, V6, P25 RUNAPONGSA K, 2002, P EEXTT02 EL VERS Russel S., 1995, ARTIFICIAL INTELLIGE SAHUGUET A, 1999, ACM SIGMOD WORKSH WE SALMINEN A, 1993, ACTA LINGUISTICA HUN, V41, P277 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracevic T., 1995, P 18 ANN INT ACM SIG, P138, DOI 10.1145/215206.215351 SARAWAGI S, 2002, VLDB 2002 TUR NOT 28, P1 SCHAUBLE P, 1997, MULTIMEDIA INFORMATI SCHLIEDER T, 2000, DELOS WORKSH INF SEE SCHMIDT A, 2002, XMARK QUERIES SCHMIDT A, 2001, LECT NOTES COMPUTER, V1997, P47 Schmidt A, 2001, INSR0103 CWI SCHMIDT A, 2002, VLDB 2002 P 28 INT C SCHMIDT AR, 2000, XML STORE BENCHMARK Schoning H, 2001, PROC INT CONF DATA, P149, DOI 10.1109/ICDE.2001.914823 SCHONING H, 2003, XML DATA MANAGEMENT Schoning H, 2000, LECT NOTES COMPUT SC, V1777, P383 Selinger P.G., 1979, P ACM SIGMOD INT C M, P23 SHANMUGASUNDARA.J, 2000, LECT NOTES COMPUTER, V1997, P17 Shanmugasundaram J., 1999, Very Large Data Bases. Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SIZOV S, 2003, P 1 SEM C INN DAT SY SIZOV S, 2002, P 3 INT C WEB INF SY, P323, DOI 10.1109/WISE.2002.1181668 Soderland S., 1999, MACHINE LEARNING SOUTHALL R, 1988, INT C EL PUBL DOC MA, P35 STAAB S, 2000, P 14 EUR C ART INT E Staab S, 2000, COMPUT NETW, V33, P473, DOI 10.1016/S1389-1286(00)00039-6 Staab S., 1999, P 3 WORKSH COOP INF, P113 Suciu D, 2002, ACM T DATABASE SYST, V27, P1, DOI 10.1145/507234.507235 SUCIU D, 2001, LECT NOTES COMPUTER, V1997 Sussna M., 1993, CIKM 93. Proceedings of the Second International Conference on Information and Knowledge Management TAGHVA K, 1995, P IS T SPIE 1995 INT, P317, DOI 10.1117/12.205833 TAN J, 1995, HLTH MANAGEMENT APR THEOBALD A, 2002, LECT NOTES COMPUTER, V2287 THEOBALD A, 2000, LECT NOTES COMPUTER, V1997, P105 THOMPSON A, 2001, XML SCHEMA 1 TIBERIO P, 1995, ENCY MICROCOMPUTERS, V16, P377 TUTTE K, 2002, MERGE LATTIC JOIN XM *UN CONS, UN STAND VERS 3 2 0 Valduriez Patrick, 1999, PRINCIPLES DISTRIBUT VANBEMMEL J, 1997, HD BMED INFORMATICES VANDOORN MGM, 2000, P 5 ACM DIG LIBR C D VANKEULEN M, 2002, TRCTIT0219 U TWENT VANZWOL R, 2002, P 19 BRIT NAT C DAT, P150 VANZWOL R, 2002, MODELLING SEARCHING Vapnik VN, 1998, STAT LEARNING THEORY Volz M, 1996, PROC INT CONF DATA, P10, DOI 10.1109/ICDE.1996.492084 VOORHEES EM, 2002, 10 TEXT RETR C TREC *W3C, W3C XML SCH *W3C DOM WORK GROU, 2002, DOC OBJ MOD DOM WACTLAR H, INFOMRATION DIGITAL WEBER R, 1998, VLDB 98 P 24 INT C V Weber R, 2000, LECT NOTES COMPUT SC, V1777, P21 WESTERVELD T, 2003, EURASIP J APPL SIG P, P186 White RW, 2002, LECT NOTES COMPUT SC, V2291, P93 WINDHOUWER MA, 2001, INSR0111 CWI Windhouwer M, 2003, WEB-ENABLED SYSTEMS INTEGRATION: PRACTICES AND CHALLENGES, P200 Windhouwer M.A., 1999, INT WORKSH INF INT W *WORLD WID WEB CON, SEM WEB ACT World Wide Web Consortium, RES DESCR FRAM WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133 XU J, 1994, SURVEY SEMIAUTOMATIC, P105 Xu Y, 2003, ARTIF INTELL MED, V27, P283, DOI 10.1016/S0933-3657(03)00007-1 YANG Y, 1999, J INFORMATION RETRIE, V1 Yang Y., 1997, INT C MACH LEARN ICM YAO BB, 2002, TRCS200239 U WAT Yoshikawa M, 2001, ACM T INTERNET TECHN, V1, P110, DOI 10.1145/383034.383038 ZANIOLO C, 2000, LECT NOTES COMPUTER, V1777 ZEZULA P, 2003, 2003TR04 ISTICNR Zhang C., 2001, P ACM SIGMOD INT C M, V5, P425, DOI [10.1145/375663.375722, DOI 10.1145/375663.375722] MEDPICS CERTIFICATIO GEN ARCHITECTURE LAN NR 305 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40768-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2818 BP V EP + PG 23 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BX78R UT WOS:000186428900001 ER PT J AU Ruch, P Baud, R Geissbuhler, A AF Ruch, P Baud, R Geissbuhler, A TI Evaluating and reducing the effect of data corruption when applying bag of words approaches to medical records SO INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS LA English DT Article; Proceedings Paper CT Symposium on Natural Language Processing for Biomedical Applications CY MAR, 2002 CL NICOSIA, CYPRUS DE corruption; information retrieval; medical records; spelling correction; natural language processing ID TEXT AB Unlike journal corpora, which are supposed to be carefully reviewed before being published, the quality of documents in a patient record are often corrupted by misspelled words and conventional graphies or abbreviations. After a survey of the domain, the paper focuses on evaluating the effect of such corruption on an information retrieval (IR) engine. The IR system uses a classical bag of words approach, with stems as representation items and term frequency-inverse document frequency (tf-idf) as weighting schema; we pay special attention to the normalization factor. First results shows that even low corruption levels (3%) do affect retrieval effectiveness (4-7%), whereas higher corruption levels can affect retrieval effectiveness by 25%. Then, we show that the use of an improved automatic spelling correction system, applied on the corrupted collection, can almost restore the retrieval effectiveness of the engine. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved. C1 Swiss Fed Inst Technol, Theoret Comp Sci Lab, CH-1015 Lausanne, Switzerland. Univ Hosp Geneva, Med Informat Div, Geneva, Switzerland. RP Ruch, P (reprint author), Swiss Fed Inst Technol, Theoret Comp Sci Lab, CH-1015 Lausanne, Switzerland. CR Aizawa A., 2001, P 6 NAT LANG PROC PA, P307 Carmel D., 2001, P 24 ANN INT ACM SIG, P43, DOI 10.1145/383952.383958 DEBRUIJN L, 1999, AUTOMATIC SNOMED CLA FRANZ P, 2000, AMIA S P Friedman C., 2001, BIOINFORMATICS S1, V17, P74 HAHN U, 2002, PAC S BIOCOMPUT, V7, P338 ILIOPOULOS I, 2001, PAC S BIOCOMPUT, V6, P384 Joachims T., 1998, ADV KERNEL METHODS S JURAFEKY D, 2000, SPEECH LANGUAGE PROC KANTOR P, 2000, INFORMATION RETRIEVA, P165 KLAVANS J, 2002, JCDL 01 KUKICH K, 1992, COMPUT SURV, V24, P377 MITTENDORF E, 1996, SDAIR P PETERSON JL, 1980, COMMUN ACM, V23, P12 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruch P, 2001, LECT NOTES ARTIF INT, V2101, P249 RUCH P, 2001, ACL WORKSH COMP CORP Ruch P, 2000, J AM MED INFORM ASSN, P729 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST Shatkay H, 2000, Proc Int Conf Intell Syst Mol Biol, V8, P317 SINGHAL A, 1995, TR951507 NEC TAGHVA K, 1994, ACM SIGIR C RES DEV, P202 YANG Y, 1998, ACM SIGIR1, P42 ZENG Q, 2001, MED 2001 P NR 26 TC 8 Z9 8 PU ELSEVIER SCI IRELAND LTD PI CLARE PA CUSTOMER RELATIONS MANAGER, BAY 15, SHANNON INDUSTRIAL ESTATE CO, CLARE, IRELAND SN 1386-5056 J9 INT J MED INFORM JI Int. J. Med. Inform. PD DEC 4 PY 2002 VL 67 IS 1-3 BP 75 EP 83 AR PII S1386-5056(02)00057-6 DI 10.1016/S1386-5056(02)00057-6 PG 9 WC Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics SC Computer Science; Health Care Sciences & Services; Medical Informatics GA 630JU UT WOS:000180104400007 PM 12460633 ER PT J AU Yao, YY Zhong, N Huang, JJ Ou, CX Liu, CN AF Yao, YY Zhong, N Huang, JJ Ou, CX Liu, CN TI Using market value functions for targeted marketing data mining SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT International Joint Conference on Web Intelligence and Intelligent Agent Technology CY OCT 23-26, 2001 CL MAEBASHI CITY, JAPAN DE data mining; targeted marketing; e-commerce; market value functions; business intelligence ID DISCOVERY; MODEL AB Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects of interest according to their market values, instead of classifying the objects. Both the theoretical and experimental results are reported in this paper. It establishes a basis on which further studies and experimental evaluation can be carried out. C1 Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada. Maebashi Inst Technol, Dept Informat Engn, Maebashi, Gumma 371, Japan. Beijing Polytech Univ, Coll Comp Sci, Beijing 100022, Peoples R China. RP Yao, YY (reprint author), Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 FISHBURN PC, 1974, J MATH PSYCHOL, V11, P294, DOI 10.1016/0022-2496(74)90024-8 HAN JW, 1993, IEEE T KNOWL DATA EN, V5, P29, DOI 10.1109/69.204089 JONES KS, 1997, READINGS INFORMATION KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Leake D. B., 1996, CASE BASED REASONING Lingjaerde O, 1998, ACTA PSYCHIAT SCAND, V98, P73, DOI 10.1111/j.1600-0447.1998.tb10045.x Pawlak Z, 1991, ROUGH SETS THEORETIC Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RAO CR, 1982, THEOR POPUL BIOL, V21, P24, DOI 10.1016/0040-5809(82)90004-1 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA WATANABE S, 1981, PATTERN RECOGN, V13, P381, DOI 10.1016/0031-3203(81)90094-7 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1992, J AM SOC INFORM SCI, V43, P54, DOI 10.1002/(SICI)1097-4571(199201)43:1<54::AID-ASI5>3.0.CO;2-A WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 Yao YY, 2001, P INT COMP SOFTW APP, P517, DOI 10.1109/CMPSAC.2001.960662 Yao Y Y., 1999, LECT NOTES ARTIF INT, V1574, P479 YAO YY, 2002, DATA MINING ROUGH SE, P102 Zhong N, 1999, LECT NOTES ARTIF INT, V1704, P136 Zhong N, 2001, NEUROCOMPUTING, V36, P171, DOI 10.1016/S0925-2312(00)00341-6 NR 22 TC 19 Z9 19 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA JOURNAL DEPT PO BOX 128 FARRER ROAD, SINGAPORE 912805, SINGAPORE SN 0218-0014 J9 INT J PATTERN RECOGN JI Int. J. Pattern Recognit. Artif. Intell. PD DEC PY 2002 VL 16 IS 8 BP 1117 EP 1131 DI 10.1142/S0218001402002180 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA 638AH UT WOS:000180545600009 ER PT J AU Ruthven, I Lalmas, M AF Ruthven, I Lalmas, M TI Using Dempster-Shafer's theory of evidence to combine aspects of information use SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE information retrieval; relevance feedback; uncertain evidence; Dempster-Shafer's theory of evidence ID RELEVANCE FEEDBACK; RETRIEVAL AB In this paper we propose a model for relevance feedback. Our model combines evidence from user's relevance assessments with algorithms describing how words are used within documents. We motivate the use of the Dempster-Shafer framework as an appropriate theory for modelling combination of evidence. This model also incorporates the uncertain nature of information retrieval and relevance feedback. We discuss the sources of uncertainty in combining evidence in information retrievel and the importance of combining evidence in relevance feedback. We also present results from a series of experiments that highlight various aspects of our approach and discuss our findings. C1 Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. Univ London Queen Mary Coll, Dept Comp Sci, London E1 4NS, England. RP Ruthven, I (reprint author), Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. CR Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CAMPBELL I, 1996, COLIS, V2, P251 Chang Y, 1971, SMART RETRIEVAL SYST, P355 DASILVA WT, 1993, J AM SOC INFORM SCI, V44, P10, DOI 10.1002/(SICI)1097-4571(199301)44:1<10::AID-ASI2>3.0.CO;2-V DEMPSTER AP, 1968, J ROY STAT SOC B, V30, P205 Denos N., 1997, Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 Harman D., 1992, INFORMATION RETRIEVA, P363 Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 Lalmas M, 1998, J DOC, V54, P529, DOI 10.1108/EUM0000000007180 Lee JH, 1998, INFORM PROCESS MANAG, V34, P681, DOI 10.1016/S0306-4573(98)00034-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RUTHVEN I, 1999, COLIS, V3 RUTHVEN I, 1999, 10 IR C ART INT COGN SAFFIOTTI A, 1987, KNOWL ENG REV, V2, P75 SCHOKEN SS, 1993, INT J MAN MACH STUD, V39, P1 Shafer G, 1976, MATH THEORY EVIDENCE SILVA I, 2000, P 23 ANN INT ACM SIG, P96, DOI 10.1145/345508.345554 SPARCKJONES K, 1972, J DOC, V28, P11 Vakkari P., 2000, P 23 ANN INT ACM SIG, P2, DOI 10.1145/345508.345512 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, 1996, NIST SPECIAL PUBLICA, P1 NR 28 TC 14 Z9 14 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD NOV PY 2002 VL 19 IS 3 BP 267 EP 301 DI 10.1023/A:1020114205638 PG 35 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 592QF UT WOS:000177947800001 ER PT J AU Amati, G Van Rijsbergen, CJ AF Amati, G Van Rijsbergen, CJ TI Probabilistic models of information retrieval based on measuring the divergence from randomness SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE algorithms; experimentation; theory; aftereffect model; BM25; binomial law; Bose-Einstein statistics; document length normalization; eliteness; idf; information retrieval; Laplace; Poisson; probabilistic models; randomness; succession law; term frequency normalization; term weighting ID TEXT RETRIEVAL; TERMS AB We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose-Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document-query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model. C1 Fdn Ugo Bordoni, I-00142 Rome, Italy. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Amati, G (reprint author), Fdn Ugo Bordoni, Via B Castiglione 59, I-00142 Rome, Italy. CR ALLAN J, 1996, NIST SPECIAL PUBLICA, P119 AMATI G, 2001, NIST SPECIAL PUBLICA BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 CARPINETO C, 2000, NIST SPECIAL PUBLICA, P377 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 COX RT, 1961, ALGEBRA PROBABLE INF CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DAMERAU FJ, 1965, AM DOC, V16, P283, DOI 10.1002/asi.5090160403 Feller W, 1968, INTRO PROBABILITY TH FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 GOOD IJ, 1968, ESTIMATION PROBABILI, V30 HARMAN D, 1993, NIST SPECIAL PUBLICA, P1 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 Harter SP, 1974, THESIS U CHICAGO HIEMSTRA D, 2000, TRCTIT0009 Hintikka J., 1970, INFORMATION INFERENC, P3 KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Margulis E., 1992, P ACM SIGIR COP JUN, P177, DOI 10.1145/133160.133195 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Popper K., 1959, LOGIC SCI DISCOVERY RENYI A, 1969, FDN PROBABILITY ROBERTSON S, 1996, NIST SPECIAL PUBLICA, P182 ROBERTSON S, 1986, J DOC, V42, P288 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 SOLOMONOFF RJ, 1964, INFORM CONTROL, V7, P224, DOI 10.1016/S0019-9958(64)90131-7 SOLOMONOFF RJ, 1964, INFORM CONTROL, V7, P1, DOI 10.1016/S0019-9958(64)90223-2 TITTERINGTON DM, 1985, STAT ANAL FINITE MIX TURTLE HR, 1992, COMPUT J, V35, P279, DOI 10.1093/comjnl/35.3.279 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 WILLIS DG, 1970, J ACM, V17, P241, DOI 10.1145/321574.321578 Witten Ian H., 1999, MANAGING GIGABYTES, V2nd Wong S., 1995, ACM T INFORM SYST, V16, P38 NR 38 TC 147 Z9 147 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD OCT PY 2002 VL 20 IS 4 BP 357 EP 389 DI 10.1145/582415.582416 PG 33 WC Computer Science, Information Systems SC Computer Science GA 610PE UT WOS:000178968500001 ER PT J AU Boughanem, M Chrisment, C Tamine, L AF Boughanem, M Chrisment, C Tamine, L TI On using genetic algorithms for multimodal relevance optimization in information retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID DOCUMENT-RETRIEVAL AB This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multi-modal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach. C1 Univ Toulouse 3, IRIT SIG, F-31062 Toulouse, France. Univ Toulouse 2, GRIMMISYCOM, F-31058 Toulouse, France. RP Boughanem, M (reprint author), Univ Toulouse 3, IRIT SIG, 118 Route de Narbonne, F-31062 Toulouse, France. CR Baker J. E., 1985, P 1 INT C GEN ALG TH, P101 Beasley D, 1993, EVOL COMPUT, V1, P101, DOI 10.1162/evco.1993.1.2.101 Belkin N. J., 1993, P 16 ANN INT ACM SIG, P339, DOI 10.1145/160688.160760 Boughanem M., 1999, Information Retrieval, V1, DOI 10.1023/A:1009931404333 BOUGHANEM M, 1997, P 5 INT C COMP ASS I, P469 Chang Y, 1971, SMART RETRIEVAL SYST, P355 De Jong K.A., 1975, DISS ABSTR INT, V36, p5140B DUMAIS S, 1994, P 3 C TEXT RETR C TR, P219 Fonseca C., 1995, IEEE INT C GEN ALG E, P45 Goldberg D., 1987, P 2 INT C GEN ALG, P41 Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GUTTMAN L, 1978, STATISTICIAN, V81, P107 HAINES D, 1993, RELEVANCE FEEDBACK I, P2 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Holland J.H., 1962, SELF ORG SYSTEMS, P215 Horn J., 1997, THESIS U ILLINOIS UR Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X Katzer J., 1982, Information Technology: Research and Development, V1 KRAFT DH, 1995, FUZZINESS DATABASE M, P330 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Mahfoud SW, 1995, THESIS U ILLINOIS UR MCGILL M, 1979, EVALUATION FACTORS A PETROWSKI A, 1997, P IEE INT C EV COMP ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1968, AUTOMATIC INFORMATIO TALBI EG, 1999, METAHEURISTIQUES OPT TAMINE L, 2001, J INFORMATION INTELL, V1 TAMINE L, 2000, THESIS U P SABATIER TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 YANG JJ, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P603 NR 35 TC 17 Z9 17 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD SEP PY 2002 VL 53 IS 11 BP 934 EP 942 DI 10.1002/asi.10119 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 587TE UT WOS:000177658400006 ER PT J AU Kurimo, M AF Kurimo, M TI Thematic indexing of spoken documents by using self-organizing maps SO SPEECH COMMUNICATION LA English DT Article DE latent semantic indexing; self-organizing maps; spoken document retrieval; broadcast news indexing AB A method is presented to provide a useful searchable index for spoken audio documents. The task differs from the traditional (text) document indexing, because large audio databases are decoded by automatic speech recognition and decoding errors occur frequently. The idea in this paper is to take advantage of the large size of the database and select the best index terms for each document with the help of the other documents close to it using a semantic vector space. First, the audio stream is converted into a text stream by a speech recognizer. Then the text of each story is represented in a vector space as a document vector which is the normalized sum of the word vectors in the story. A large collection of such document vectors is used to train a self-organizing map (SOM) to find latent semantic structures in the collection. As the stories in spoken news are short and will include speech recognition errors, smoothing of the document vectors using the semantic clusters determined by the SOM is introduced to enhance the indexing. The application in this paper is the indexing and retrieval of broadcast news on radio and television. Test results are given using the evaluation data from the text retrieval conference (TREC) spoken document retrieval (SDR) task. (C) 2002 Elsevier Science B.V. All rights reserved. C1 Aalto Univ, Neural Networks Res Ctr, Espoo 02150, Finland. RP Kurimo, M (reprint author), Aalto Univ, Neural Networks Res Ctr, POB 5400,Konemiehentie 2, Espoo 02150, Finland. EM mikko.kurimo@hut.fi RI Kurimo, Mikko/F-6647-2012 CR ABBERLEY D, 1999, ESCA ETRW WORKSH ACV, P14 ABBERLEY D, 1999, P 8 TEXT RETR C ALLAN J, 1998, P 6 TEXT RETR C TREC, P169 ANDERSEN J, 1998, 987 COM IDIAP BELLEGARDA JR, 1997, IEEE WORKSH AUT SPEE, P262 BELLEGARDA JR, 1999, P IEEE INT C AC SPEE, P717 BERROL S, 1992, PHYSICAL MED REHABIL, V6, P1 Bourland H., 1994, CONNECTIONIST SPEECH Chen S., 1998, DARPA BROADC NEWS TR COCCARO N, 1998, P INT C SPOK LANG PR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 GAROFOLO J, 1999, ESCA ETRW ACCESSING, P1 Gillick L., 1989, P ICASSP, P532 GOLUB G, 1971, HDB MATRIX COMPUTATI, V2 HOFMANN T, 1999, P 3 S INT DAT AN IDA HOFMANN T, 1998, 98042 TR INT COMP SC Honkela T., 1996, A32 HELS U TECHN LAB JOHNSON SE, 1999, P IEEE INT C AC SPEE, P49 Johnson W. B., 1984, CONT MATH, V1, P189 Kaski S., 1998, P IJCNN 98 INT JOINT, V1, P413 Kohonen T., 1997, SELF ORG MAPS Kohonen T, 1999, KOHONEN MAPS, P171, DOI 10.1016/B978-044450270-4/50013-9 KURIMO M, 1999, ESCA ETRW WORKSH ACC, P25 Kurimo M, 1999, KOHONEN MAPS, P363, DOI 10.1016/B978-044450270-4/50029-2 Lagus K., 1999, P ICANN99 9 INT C AR, V1, P371 Ng K, 1998, INT CONF ACOUST SPEE, P325, DOI 10.1109/ICASSP.1998.674433 PAPADIMITRIOU C, 1998, P 17 ACM S PRINC DAT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAUBER A, 1999, P 3 PAC AS C KNOWL D RENALS S, 1998, P 7 TEXT RETR C TREC RITTER H, 1989, BIOL CYBERN, V61, P241, DOI 10.1007/BF00203171 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robinson T., 1996, AUTOMATIC SPEECH SPE, P233 Robinson T, 1998, INT CONF ACOUST SPEE, P829, DOI 10.1109/ICASSP.1998.675393 ROBINSON T, 1999, P EUR BUD, P1067 Salton G., 1971, SMART RETRIEVAL SYST SIEGLER M, 1999, P IEEE INT C AC SPEE, P505 Simula O, 1999, KOHONEN MAPS, P375, DOI 10.1016/B978-044450270-4/50030-9 Ultsch A, 1999, KOHONEN MAPS, P33, DOI 10.1016/B978-044450270-4/50003-6 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 40 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6393 EI 1872-7182 J9 SPEECH COMMUN JI Speech Commun. PD SEP PY 2002 VL 38 IS 1-2 BP 29 EP 45 AR PII S0167-6393(01)00042-5 DI 10.1016/S0167-6393(01)00042-5 PG 17 WC Acoustics; Computer Science, Interdisciplinary Applications SC Acoustics; Computer Science GA 596KW UT WOS:000178164900003 ER PT J AU Griffiths, JM King, DW AF Griffiths, JM King, DW TI US information retrieval system evolution and evaluation (1945-1975) SO IEEE ANNALS OF THE HISTORY OF COMPUTING LA English DT Review ID PROJECT INTREX; OF-SCIENCE; RELEVANCE; SERVICES; HISTORY; INDEX AB Information retrieval has evolved through four phases: manual and mechanical devices; off line computing; online computing and vendor access; and distributed, networked, and mass computing. This article primarily addresses the first three phases. C1 Univ Pittsburgh, Pittsburgh, PA 15260 USA. RP Griffiths, JM (reprint author), Univ Pittsburgh, Pittsburgh, PA 15260 USA. EM jmgriff@pitt.edu; dwking@pitt.edu CR ATHERTON P, 1968, EVALUATION RETRIEVAL ATHERTON P, 1972, RADCTR72159 SYR U SY BAGLEY PR, 1951, AM CHEM SOC 120 M BAGLEY PR, 1951, ELECT DIGITAL MACHIN BATTEN W. E., 1951, PUNCHED CARDS, P169 BATTEN WE, 1947, P 22 C ASS SPEC LIB, P36 BAXENDALE PB, 1958, IBM J RES DEV, V2, P354 BECKER J, 1975, INFORMATION SYSTEMS, P3 BECKER J, 1964, AM LIBRARY ASS B, V58, P822 BORGMAN CL, 1989, J AM SOC INFORM SCI, V40, P99, DOI 10.1002/(SICI)1097-4571(198903)40:2<99::AID-ASI4>3.0.CO;2-7 BORKO H, 1965, TM2295024 SYST DEV BOURNE CP, 1966, ANNU REV INFORM SCI, V1, P171 BOURNE CP, 1963, ESDTDR640177 BOURNE CP, 2002, INPRESS HIST ONL INF BRYANT EC, 1966, 6 ANN M COMM INT COO, P224 BRYANT EC, 1965, SOME THEORETICAL ASP, V49, P1484 Buckland M, 1999, ASIS MONOGR, P3 Burke C, 1996, INFORM PROCESS MANAG, V32, P19, DOI 10.1016/0306-4573(95)00047-K Bush V., 1945, ATLANTIC MONTHLY, P101 Cady SA, 1999, ASIS MONOGR, P177 CAUDRA CA, 1967, P 30 ANN M AM DOC I, P95 CLARKE SH, 1936, NATURE, P535 CLEVERDON CW, 1963, ASLIB PROC, V15, P106, DOI 10.1108/eb049925 Cretsos JM, 1999, ASIS MONOGR, P117 CUADRA CA, 1975, P EUS C OSL 4 5 DEC, P53 De Solla Price D. J., 1963, LITTLE SCI BIG SCI DOEBLER PD, 1970, ANNU REV INFORM SCI, V5, P223 DOYLE LB, 1965, P S STT ASS METH MEC, P15 DOYLE LB, 1975, INFORMATION RETRIEVA DYSON GM, 1947, P 22 C ASS SPEC LIB, P23 Engelbart D.C, 1962, AFOSR3223 SRI Farkas-Conn I., 1990, DOCUMENTATION INFORM FREEMAN RR, 1968, P AM SOC INFORM SCI, V5, P193 Garfield E, 1999, ASIS MONOGR, P237 HAYES RM, 1999, P 1998 C HIST HER SC HENDERSON MM, 1973, CONF711010 AED HENDERSON MM, 1999, P 1998 C HIST HER SC HERNER S, 1966, NONCONVENTIONAL TECN, V4 HORTY JF, 1960, MODERN USES LOGIC LA, V60, P158 HORTY JF, 1960, APPL INFORMAITON RET JACOBY J, 1962, INDEXER CONSISTENCY JONES KS, 1984, INFORMATION RETRIEVA, P256 JONES KS, 1981, INFORMAITON RETRIEVA Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES PE, 1967, SELECTED COLLECTIN S KESSLER MM, 1964, MIT TECHNICAL INFORM KESSLER MM, 1965, PHYS TODAY, V18, P28 KESSLER MM, 1960, EXPT COMMUNICAITON C KESSLER MM, 1967, 671269 NTIS AD Kilgour FG, 1997, J AM SOC INFORM SCI, V48, P340, DOI 10.1002/(SICI)1097-4571(199704)48:4<340::AID-ASI7>3.0.CO;2-W King D. W., 1993, SPECIAL LIB INCREASI KING DW, 1970, STUDY COST EFFECTIVE KING DW, 1966, 4 ANN M COMM INT COO, P378 KING DW, 1971, EVALUATION INFORMATI KING DW, 1972, RADCTR71311 ROM AIR KING DW, 1981, SCI J US THEIR PROD KING DW, 1982, J AM SOC INFORM SCI, V33, P168, DOI 10.1002/asi.4630330313 Kvalnes FH, 1999, ASIS MONOGR, P107 Lancaster F. W., 1968, EVALUATION MEDLARS D LANCASTER FW, 1976, INFORM AGE ITS DEV I, P1 LANCASTER FW, 1973, INFORMATION RETRIEVA LANCASTER FW, 1979, INFORMATION RETRIEVA LANCASTER FW, 1968, INFORMAITON RETRIEVA LESK ME, 1997, MORGAN KAUFMANN SERI LESK ME, 1969, P AFIPS SPR JOINT CO, V34, P435 LILLEY DB, 1989, HIST INFORMAITON SCI LUHN HP, 1957, IBM J RES DEV, V1, P309 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MEADOWS J, 1995, PROJECT ELVYN EXPT E, P1 NEEDHAM RM, 1961, RES INFORMATION RETR NEUFELD ML, 1986, J AM SOC INFORM SCI, V37, P183, DOI 10.1002/(SICI)1097-4571(198607)37:4<183::AID-ASI2>3.0.CO;2-W Ohlman H, 1999, ASIS MONOGR, P187 OHLMAN H, 1957, PERMUTATION INDEXING OVERHAGE CF, 1974, INFORM STORAGE RET, V10, P157, DOI 10.1016/0020-0271(74)90057-6 OVERHAGE CFJ, 1972, PROJECT INTREX BRIEF PARKER EB, 1971, SPIRES 1970 71 ANN R PFEFFER H, 1966, 4 ANN M COMM INT COO PFEFFER H, 1967, 6 ANN M COMM COOP IN PRYWES NS, 1966, PR INST ELECTR ELECT, V54, P1788, DOI 10.1109/PROC.1966.5263 RASMUSSEN E, 2003, ANN REV INFORMATION Rees A., 1967, FIELD EXPT APPROACH Ro J.S., 1990, FULL TEXT DATABASES Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROGERS FB, 1974, LIBR TRENDS, V23, P73 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1964, P SPR JOINT COMP C A SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST Saracevic T., 1996, P 2 C CONC LIB INF S, P201 SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 SCHULTZ CK, 1958, PUNCHED CARDS THEIR Sparck Jones K., 1981, INFORM RETRIEVAL EXP, P213 STEVENS ME, 1965, P S STAT ASS METH ME STILES HE, 1961, J ACM, V18, P271 Tenopir C., 2000, ELECT J REALITIES SC Trolley J, 1999, ASIS MONOGR, P124 VANCAMP AJ, 1994, ONLINE, V18, P66 VRLEJS J, 1999, ASIS MONOGRAPH SERIE, P148 WHALEY FR, 1957, INFORMATION SYSTEMS WILLIAMS JH, 1965, P S STAT ASS METH ME, P217 WILLIAMS ME, 1977, DATA BASE MAPPING MO, V1 WILLIAMS ME, 1985, SCIENCE, V228, P445, DOI 10.1126/science.3885395 WILLIAMS ME, 2002, GALE DIRECTORY DATAB, V1 WILLIAMS ME, 1986, J AM SOC INFORM SCI, V37, P204 Williams M. E., 1977, Proceedings of the ASIS Annual Meeting 1977, vol.14. Information Management in the 1980's (Full papers on microfiche only) WILLIAMS ME, 1999, INFORMATION MARKET I, V60 WILLIAMS ME, 1972, P 3 CRANF INT C MECH, V8, P57 WILLIAMS ME, 1972, C615618 IITRI WILLIAMS ME, 1977, DATA BASE MAPPING MO, V2 WILLIAMS ME, 2001, GALE DIRECTORY DAT 1, V2 Wouters P, 1999, ASIS MONOGR, P127 NR 112 TC 7 Z9 7 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1058-6180 J9 IEEE ANN HIST COMPUT JI IEEE Ann. Hist. Comput. PD JUL-SEP PY 2002 VL 24 IS 3 BP 35 EP 55 DI 10.1109/MAHC.2002.1024761 PG 21 WC Computer Science, Theory & Methods; History & Philosophy Of Science SC Computer Science; History & Philosophy of Science GA 581YF UT WOS:000177322000004 ER PT J AU McMahon, C Crossland, R Lowe, A Shah, T Williams, JS Culley, S AF McMahon, C Crossland, R Lowe, A Shah, T Williams, JS Culley, S TI No zero match browsing of hierarchically categorized information entities SO AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING LA English DT Article DE information retrieval; hierarchical classification; user interface; engineering document management ID DESIGN; SYSTEM AB The two main ways to search for information in electronic document collections are by using free-text retrieval search engines or browsing information that has been organized into predefined organizational structures. However, each of these approaches has limitations. Using word or phrase search, users are faced with a compromise between overly broad searches returning an excessive amount of information or overly narrow searches that may fail to return relevant information. Browsing organizational structures is dependent on the user's knowledge of the structures, and a user may find it difficult to refine searches. This paper introduces a user interface based approach to the browsing of hierarchically organized information entities that avoids these problems by allowing the incremental narrowing down of a set of search results and by pruning the organizational structure after each user selection to show the consequences of the selection. The effect is to present to the user at all times only that part of the organizational structure that will lead to a nonnull selection. The approach is called no zero match (NZM) browsing. The paper presents the computational basis of NZM browsing before describing a trial implementation of the approach and presenting three case studies, which represent common search situations in an engineering context. C1 Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England. Univ Bristol, Fac Engn, Bristol BS8 1TR, Avon, England. CR ANICK PG, 1994, P 17 ANN INT ACM SIG, P349 BATES MJ, 1986, INTELLIGENT INFORMAT, P91 BLAIR DC, 1980, J AM SOC INFORM SCI, V31, P271, DOI 10.1002/asi.4630310407 Bruninghaus S., 2001, CASE BASED REASONING BUCKLAND M, 1994, J AM SOC INFORM SCI, V45, P12, DOI 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 Court AW, 1996, J ENG DESIGN, V7, P55, DOI 10.1080/09544829608907927 Crowder RM, 1999, COMPUT IND, V38, P31, DOI 10.1016/S0166-3615(98)00106-7 CULLEY SJ, 2001, P 13 INT C ENG DES G DAVIS HA, 1992, PAST MEETS FUTURE, P181, DOI 10.1145/168466.168522 Drucker P., 1994, POST CAPITALIST SOC DRUCKER PF, 2000, NATL PRODUCTIVITY RE, V19, P13, DOI 10.1002/1520-6734(200021)19:2<13::AID-NPR2>3.0.CO;2-V Edmunds A, 2000, INT J INFORM MANAGE, V20, P17, DOI 10.1016/S0268-4012(99)00051-1 Hall W., 1996, RETHINKING HYPERMEDI Hayman A, 2000, INT J INFORM MANAGE, V20, P297, DOI 10.1016/S0268-4012(00)00018-9 Hearst M., 1999, MODERN INFORM RETRIE, P257 Hearst MA, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P246, DOI 10.1145/258525.258582 *I SYST RES, 2001, ASIS M I SYST RES U Krellenstein M.F., 1999, US Patent, Patent No. 5924090 LARSON RR, 1992, J AM SOC INFORM SCI, V43, P130, DOI 10.1002/(SICI)1097-4571(199203)43:2<130::AID-ASI3>3.0.CO;2-S LENZ M, 1997, P 2 INT C CAS BAS RE, P84 LOWE A, 2001, P 13 INT C ENG DES G, P179 LOWE A, 2000, P ASME DES ENG TECHN Maher M., 1997, ISSUES APPL CASE BAS MCMAHON CA, 1995, J ENG COMPUTERS, V11, P123 MCMAHON CA, 1998, INFORMATION DESIGN E Meadow C.T., 1992, TEXT INFORMATION RET Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 Nielsen J., 1993, USABILITY ENG POLLIT S, 1997, P 6 INT STUD C CLASS *REUT BUS INF, 1996, DYING INF INV EFF IN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shneiderman B., 1997, DESIGNING USER INTER Waterworth J. A., 1991, Hypermedia, V3 NR 36 TC 6 Z9 6 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH ST, NEW YORK, NY 10011-4221 USA SN 0890-0604 J9 AI EDAM JI AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf. PD JUN PY 2002 VL 16 IS 3 BP 243 EP 257 DI 10.1017/S0890060402163098 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary; Engineering, Manufacturing SC Computer Science; Engineering GA 619BW UT WOS:000179454300009 ER PT J AU Zhang, J Rasmussen, EM AF Zhang, J Rasmussen, EM TI An experimental study on the iso-content-based angle similarity measure SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE similarity analysis; iso-content-based angle similarity measure; evaluation; experiment ID DOCUMENT-RETRIEVAL; INFORMATION; DISTANCE AB An experimental study was conducted to examine and evaluate retrieval performance of the iso-content-based angle similarity measure presented in an earlier paper. Retrieval performance of the iso-content-based angle similarity measure within the angle, distance, conjunction, disjunction and ellipse retrieval models is compared with retrieval performance of the distance similarity measure and the angle similarity measure. The experimental results show the iso-content-based angle similarity measure achieves satisfactory performance. (C) 2002 Elsevier Science Ltd. All rights reserved. C1 Univ Wisconsin, Sch Informat Studies, Milwaukee, WI 53211 USA. Univ Pittsburgh, Sch Informat Sci, Pittsburgh, PA 15260 USA. RP Zhang, J (reprint author), Univ Wisconsin, Sch Informat Studies, 2400 E Hartford Ave,Enderis Hall, Milwaukee, WI 53211 USA. RI zhang, jin /A-6769-2010 CR Bartell BT, 1998, J AM SOC INFORM SCI, V49, P742 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CRONIN B, 1994, J AM SOC INFORM SCI, V45, P537, DOI 10.1002/(SICI)1097-4571(199408)45:7<537::AID-ASI8>3.0.CO;2-Q Ellis D., 1993, PERSPECTIVES INFORMA, V3, P128 Falkowski BJ, 1998, J AM SOC INFORM SCI, V49, P854, DOI 10.1002/(SICI)1097-4571(199807)49:9<854::AID-ASI11>3.0.CO;2-Q Fricke M, 1997, J AM SOC INFORM SCI, V48, P882, DOI 10.1002/(SICI)1097-4571(199710)48:10<882::AID-ASI4>3.0.CO;2-Y Glass G. V., 1995, STAT METHODS ED PSYC, V3a GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 HAMERS L, 1989, INFORM PROCESS MANAG, V25, P315, DOI 10.1016/0306-4573(89)90048-4 Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 Korfhage R., 1997, INFORMATION STORAGE KWOK KL, 1985, J AM SOC INFORM SCI, V36, P242 MCGILL M, 1979, EVALUATION FACTORS A NUCHPRAYYOON A, 1996, THESIS U PITTSBURGH OLSEN KA, 1993, INFORMATION PROCESSI, V298, P6981 Olsen K. A., 1994, Proceedings. IEEE Symposium on Visual Languages (Cat. No.94TH8010), DOI 10.1109/VL.1994.363612 Qin J, 2000, J AM SOC INFORM SCI, V51, P166, DOI 10.1002/(SICI)1097-4571(2000)51:2<166::AID-ASI8>3.3.CO;2-Q RADECKI T, 1982, J DOC, V38, P14, DOI 10.1108/eb026719 RADECKI T, 1985, INFORM PROCESS MANAG, V21, P501, DOI 10.1016/0306-4573(85)90004-4 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rousseau R, 1998, INFORM PROCESS MANAG, V34, P87, DOI 10.1016/S0306-4573(97)00067-8 Salton G., 1989, AUTOMATIC TEXT PROCE TRAVISON D, 1987, INFORMATION PROCESSI, V23, P183 Tudhope D, 1997, INFORM PROCESS MANAG, V33, P233, DOI 10.1016/S0306-4573(96)00067-2 Zhang J, 1999, J AM SOC INFORM SCI, V50, P772, DOI 10.1002/(SICI)1097-4571(1999)50:9<772::AID-ASI5>3.0.CO;2-E Zhang J, 2001, INFORM PROCESS MANAG, V37, P279, DOI 10.1016/S0306-4573(00)00027-3 Zhang J, 1999, J AM SOC INFORM SCI, V50, P779, DOI 10.1002/(SICI)1097-4571(1999)50:9<779::AID-ASI6>3.0.CO;2-R NR 28 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2002 VL 38 IS 3 BP 325 EP 342 AR PII S0306-4573(01)00035-8 DI 10.1016/S0306-4573(01)00035-8 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 536WP UT WOS:000174725100002 ER PT J AU Li, H Yamanishi, K AF Li, H Yamanishi, K TI Text classification using ESC-based stochastic decision lists SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE text classification; statistical learning; stochastic decision list; rule-based method; extended stochastic complexity ID CATEGORIZATION; COMPLEXITY; RULES AB We propose a new method of text classification using stochastic decision lists. A stochastic decision list is an ordered sequence of IF-THEN-ELSE rules, and our method can be viewed as a rule-based method for text classification having advantages of readability and refinability of acquired knowledge. Our method is unique in that decision lists are automatically constructed on the basis of the principle of minimizing extended stochastic complexity (ESC), and with it we are able to construct decision lists that have fewer errors in classification. The accuracy of classification achieved with our method appears better than or comparable to those of existing rule-based methods. We have empirically demonstrated that rule-based methods like ours result in high classification accuracy when the categories to which texts are to be assigned are relatively specific ones and when the texts tend to be short. We have also empirically verified the advantages of rule-based methods over non-rule-based ones. (C) 2002 Published by Elsevier Science Ltd. C1 NEC Corp Ltd, Miyamake Ku, Kawasaki, Kanagawa 2168555, Japan. RP Li, H (reprint author), Microsoft Res China, SF Sigma Ctr No 49,Chichur Rd,Haidiar Dist, Beijing 100080, Peoples R China. CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 DAGAN I, 1997, P 2 C EMP METH NAT L Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Guthrie Louise, 1994, P COLING 94, P1059 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Joachims T., 1998, P EUR C MACH LEARN KAR G, 1978, INFORM PROCESS MANAG, V14, P57, DOI 10.1016/0306-4573(78)90063-8 Koller D., 1997, P 14 INT C MACH LEAR, P170 Lam W., 1998, P 21 ANN INT ACM SIG, P81, DOI 10.1145/290941.290961 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Lewis WH, 1996, WASH QUART, V19, P3 Li H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P122, DOI 10.1145/319950.319966 Li H., 1997, P ACL 97, P39 Masand B., 1992, P 15 ANN INT ACM SIG, P59, DOI 10.1145/133160.133177 McCallum A., 1998, P AAAI ICML 98 WORKS Moens MF, 2000, INFORM PROCESS MANAG, V36, P841, DOI 10.1016/S0306-4573(00)00012-1 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Rissanen JJ, 1996, IEEE T INFORM THEORY, V42, P40, DOI 10.1109/18.481776 Rivest R. L., 1987, Machine Learning, V2, DOI 10.1007/BF00058680 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Vapnik V, 1995, NATURE STAT LEARNING Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Yamanishi K, 1998, IEEE T INFORM THEORY, V44, P1424, DOI 10.1109/18.681319 YAMANISHI K, 1992, MACH LEARN, V9, P165, DOI 10.1007/BF00992676 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 36 TC 8 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2002 VL 38 IS 3 BP 343 EP 361 AR PII S0306-4573(01)00038-3 DI 10.1016/S0306-4573(01)00038-3 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 536WP UT WOS:000174725100003 ER PT J AU Robertson, S AF Robertson, S TI Threshold setting and performance optimization in adaptive filtering SO INFORMATION RETRIEVAL LA English DT Article DE filtering; thresholds; optimization; adaptation ID INFORMATION-RETRIEVAL; PROBABILISTIC MODEL AB An experimental adaptive filtering system, built on the Okapi search engine, is described. In addition to the regular text retrieval functions, the system requires a complex set of procedures for setting score thresholds and adapting them following feedback. These procedures need to be closely related to the evaluation measures to be used. A mixture of quantitative methods relating a threshold to the number of documents expected to be retrieved in a time period, and qualitative methods relating to the probability of relevance, is defined. Experiments under the TREC-9 Adaptive Filtering Track rules are reported. The system is seen to perform reasonably well in comparison with other systems at TREC. Some of the variables that may affect performance are investigated. C1 Microsoft Res, Cambridge CB3 OFB, England. RP Robertson, S (reprint author), Microsoft Res, JJ Thomson Ave, Cambridge CB3 OFB, England. CR Allan J., 1996, SIGIR Forum ARAMPATZIS A, 2001, NIST SPECIAL PUBLICA, P589 Arampatzis A., 2001, SIGIR Forum BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 CALLAN J, 1998, SIGIR 98 P 21 ANN IN, P224 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 ROBERTS SB, 1995, OBES RES, V3, P3 Robertson S, 2002, INFORM RETRIEVAL, V5, P257, DOI 10.1023/A:1015754113585 Robertson S, 2002, INFORM RETRIEVAL, V5, P127, DOI 10.1023/A:1015721010859 Robertson S, 2000, J DOC, V56, P312, DOI 10.1108/EUM0000000007118 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 2000, NIST SPECIAL PUBLICA, P151 ROBERTSON SE, 2001, NIST SPECIAL PUBLICA, P361 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ZHANG Y, 2001, SIGIR 2001 P 24 ANN, P294, DOI DOI 10.1145/383952.384012 NR 16 TC 9 Z9 16 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR-JUL PY 2002 VL 5 IS 2-3 BP 239 EP 256 DI 10.1023/A:1015702129514 PG 18 WC Computer Science, Information Systems SC Computer Science GA 557RU UT WOS:000175922600006 ER PT J AU Luk, RWP Leong, HV Dillon, TS Chan, ATS Croft, WB Allan, J AF Luk, RWP Leong, HV Dillon, TS Chan, ATS Croft, WB Allan, J TI A survey in indexing and searching XML documents SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review ID INFORMATION-RETRIEVAL; PROBABILISTIC MODELS; DATABASE-SYSTEMS; QUERY LANGUAGE; ALGEBRA; FRAMEWORK AB XML holds the promise to yield (1) a more precise search by providing additional information in the elements, (2) a better integrated search of documents from heterogeneous sources, (3) a powerful search paradigm using structural as well as content specifications, and (4) data and information exchange to share resources and to support cooperative search. We survey several indexing techniques for XML documents, grouping them into flat-file, semistructured, and structured indexing paradigms. Searching techniques and supporting techniques for searching are reviewed, including full text search, and multistage search. Because searching XML documents can be very flexible, various search result presentations are discussed, as well as database and information retrieval system integration and XML query languages. We also survey various retrieval models, examining how they would be used or extended for retrieving XML documents. To conclude the article, we discuss various open issues that XML poses with respect to information retrieval and database research. C1 Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. Univ Massachusetts, Dept Comp Sci, Ctr Intelligent Informat Retrieval, Amherst, MA 01003 USA. RP Luk, RWP (reprint author), Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. EM csrluk@comp.polyu.edu.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 CR Abiteboul S., 1997, International Journal on Digital Libraries, V1 Abiteboul S., 1999, SIGMOD Record, V28 Agosti M., 1991, P 14 ANN INT ACM SIG, P316, DOI 10.1145/122860.122892 AGUILERA V, 2000, P ACM SIGIR WORKSH X, P79 AHONEN H, 1996, P SGML FINL ESP FINL, P106 ALLEN BL, 1994, P 17 SIGIR C DUBL, P71 ANNAMALAI M, 2000, P ACM INT C DAT MAN, P539, DOI 10.1145/342009.335463 ARIDOR Y, 2001, P WWW10 HONG KONG, P754, DOI 10.1145/371920.372195 BAEZAYATES R, 1996, J BRAZ CS SOC, V3, P57 BAEZAYATES R, 2000, P ACM SIGIR 2000 WOR, P37 BAEZAYATES R, 1996, ACM SIGMOD RECORD, V25, P67, DOI 10.1145/381854.381890 Banerjee S., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839454 BARBARA D, 1997, ACM SIGMOD RECORD, V26, P12, DOI 10.1145/262762.262764 Baru C. K., 1999, P ACM SIGMOD INT C M, P597, DOI 10.1145/304182.304590 BENTLEY JL, 1979, IEEE T SOFTWARE ENG, V5, P333, DOI 10.1109/TSE.1979.234200 Bharadvaj H., 1998, Proceedings Seventeenth IEEE Symposium on Reliable Distributed Systems (Cat. No.98CB36281), DOI 10.1109/RELDIS.1998.740482 Bonifati A, 2000, SIGMOD RECORD, V29, P68 Brin S., 1998, P 7 INT WORLD WID WE, P107 Buneman P., 1996, P ACM SIGMOD INT C M, P505, DOI 10.1145/233269.233368 *CAP VENT, 1999, DYNAMIC CONTENT SOFT, V4 CARDENAS AF, 1975, COMMUN ACM, V18, P253, DOI 10.1145/360762.360766 CARMEL D, 2001, ACM SIGMOD RECORD, V30, P62, DOI 10.1145/373626.373705 CATTELL RGG, 1997, 20 ODMG Chakrabarti S., 2001, P 10 INT C WORLD WID, P211, DOI 10.1145/371920.372054 CHAMBERLIN D, 2000, LNCS, P1 Chan C.Y., 1998, P ACM SIGMOD INT C M, P355, DOI 10.1145/276304.276336 CHAN M, 2000, P ACM INT WORKSH DAT, P57, DOI 10.1145/355068.355316 Cheng J., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839455 Choenni S., 1994, Proceedings. The 10th International Conference Data Engineering (Cat. No.94CH3383-7), DOI 10.1109/ICDE.1994.283077 CLARKE CLA, 1995, COMPUT J, V38, P43 CLUET S, 1998, P ACM SIGMOD INT C M, P177, DOI 10.1145/276304.276321 Codd E. F., 1993, PROVIDING OLAP ONLIN CODD EF, 1979, ACM T DATABASE SYST, V4, P4 Comer D., 1979, ACM COMPUT SURV, V11, P121, DOI 10.1145/356770.356776 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROUCH C, 1990, P 13 ANN INT ACM SIG, P369, DOI 10.1145/96749.98242 CZEJDO B, 2000, P 1 INT C WEB INF SY, V1, P296, DOI 10.1109/WISE.2000.882405 Dar S, 1996, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P330 DEFAZIO S, 1995, P ACM SIGIR C INF RE, P84, DOI 10.1145/215206.215335 Deshpande P. M., 1998, P ACM SIGMOD INT C M, P259, DOI 10.1145/276304.276328 Dey D, 1996, ACM T DATABASE SYST, V21, P339, DOI 10.1145/232753.232796 DOMEL P, 1997, LECT NOTES COMPUTER, V1219, P126 DUBINKO M, 2000, XFORMS REQUIREMENTS EGNOR D, 2000, P ACM SIGIR 2000 WOR, P69 *EXC CORP, 2000, OB STOR OBJ DAT MAN FALOUTSOS C, 1985, ACM COMPUT SURV, V17, P49, DOI 10.1145/4078.4080 FAWCETT H, 1989, PAT 3 3 USERS GUIDE Fernandez M., 1997, SIGMOD Record, V26 FLORESCU D, 1999, P WWW8 TOR, P1155 FOX EA, 1985, P ACM SIGIR MONTR, P45 FOX EA, 1983, THESIS CORNELL U FRAKES WR, 1992, INFORMATION RETRIEVA FRISSE ME, 1987, P ACM HYP C CHAP HIL, P57, DOI 10.1145/317426.317433 Fuhr N., 1995, P 18 ANN INT ACM SIG, P282, DOI 10.1145/215206.215372 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1996, B TECHNICAL COMMITTE, V19, P3 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Fuhr N, 2000, J AM SOC INFORM SCI, V51, P95, DOI 10.1002/(SICI)1097-4571(2000)51:2<95::AID-ASI2>3.0.CO;2-H Fuhr N, 1997, ACM T INFORM SYST, V15, P32, DOI 10.1145/239041.239045 FUHR N, 2000, P ACM SIGIR 2000 WOR, P11 Fuller M, 1993, P 16 ANN INT ACM SIG, P204, DOI 10.1145/160688.160720 Gaede V, 1998, ACM COMPUT SURV, V30, P170, DOI 10.1145/280277.280279 Glushko RJ, 1999, COMMUN ACM, V42, P106, DOI 10.1145/295685.295720 Goldman Roy, 1999, P 2 INT WORKSH WEB D, P25 Grefenstette G., 1998, CROSS LANGUAGE INFOR GRIEFF WR, 2000, ACM T INFORM SYST, V18, P246 Gutierrez A., 2000, Proceedings 20th International Conference of the Chilean Computer Science Society, DOI 10.1109/SCCC.2000.890390 Guttman A, 1984, P ACM SIGMOD INT C M, P47 Hammer J., 1997, Proceedings of the Workshop on Management of Semi-Structured Data Hammer J., 1997, P 1 E EUR S ADV DAT, P1 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 HAYASHI Y, 2000, P ACM SIGIR 2000 WOR, P27 HOSOYA H, 2001, PRINCIPLES PROGRAMMI, P67, DOI 10.1145/360204.360209 Jang H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P383, DOI 10.1145/319950.320031 Kanne C., 2000, P 16 INT C DAT ENG, P198, DOI 10.1109/ICDE.2000.839412 KATZ H, 2000, XML QUERY ENGINE KHA DD, 2001, P 17 ICDE HEID GERM, P3123 KLEINBERG J, 1998, ACM S DISCR ALG ONL KRISTENSEN A, 1999, P WWW9 AMST, P1189 KWONG OY, 2001, P PNC Lakshmanan LVS, 1997, ACM T DATABASE SYST, V22, P419, DOI 10.1145/261124.261131 Lalmas M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P110, DOI 10.1145/258525.258546 LEE D, 2000, SIGMOD REC, V29, P64 LEE DL, 1994, P C INF KNOWL MAN GA, P64, DOI 10.1145/191246.191260 Lee JH, 2000, P SOC PHOTO-OPT INS, V4062, P264 Lee K. C. K., 2000, Proceedings 20th IEEE International Conference on Distributed Computing Systems, DOI 10.1109/ICDCS.2000.840966 LEE KCK, 1999, ACM SIGMOBILE MOBILE, V3, P28, DOI 10.1145/584027.584029 Lee N, 1998, FOOD AGR IMMUNOL, V10, P3, DOI 10.1080/09540109809354963 Lee Y. K., 1996, P 1 ACM INT C DIG LI, P91, DOI 10.1145/226931.226950 LEI H, 1999, P MOB DAT ACC 99 HON, P119 Leung KS, 1989, DATA KHOWL ENG, V4, P287, DOI 10.1016/0169-023X(89)90027-X LEVY A, 1998, NOTEXMLQL19980819 Li W.-S., 2001, P 10 INT C WORLD WID, P230, DOI 10.1145/371920.372057 LI WS, 1999, P 1998 ADV DAT S TOK Liefke H, 2000, SIGMOD RECORD, V29, P153 LUK RWP, IN PRESS BILINGUAL L LUK RWP, 2000, P ACM SIGIR 2000 WOR, P1 MACLEOD IA, 1990, INFORM PROCESS MANAG, V26, P197, DOI 10.1016/0306-4573(90)90025-W MACLEOD IA, 1991, COMPUT J, V34, P254, DOI 10.1093/comjnl/34.3.254 Manolescu I., 2000, P INT C VER LARG DAT, P623 MORRISSEY JM, 1990, ACM T INFORM SYST, V8, P159, DOI 10.1145/96105.96113 Navarro G, 1997, ACM T INFORM SYST, V15, P400, DOI 10.1145/263479.263482 NIEVERGELT J, 1984, ACM T DATABASE SYST, V9, P38, DOI 10.1145/348.318586 O'Neil P., 1997, P ACM SIGMOD INT C M, P38, DOI 10.1145/253260.253268 PETROPOULOS M, 2000, P 10 WWW C HONG KONG, P642 Psaila G., 2000, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453), DOI 10.1109/KDEX.1999.836632 QIAN X, 1992, P 8 ICDE TEMP, P48 QUINTANA Y, 1993, P ACM SIGDOC C KITCH, P259, DOI 10.1145/166025.166087 RAGGET A, 2000, CLEAN UP YOUR WEB PA RAJU KVSVN, 1988, ACM T DATABASE SYST, V13, P129, DOI 10.1145/42338.42344 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robie J., 1998, XML QUERY LANGUAGE RODDICK JF, 2000, SIGMOD RECORD, V29 Sacks-davis R., 1997, P INT S DIG MED INF, P236 SACKSDAVIS R, 1995, IEICE T INF SYST, V34, P1335 SALTON G, 1983, INTRO MODERN INFORMA SCHLIEDER T, 2000, ACM SIGIR 2000 WORKS, P53 SCHOKEN SS, 1993, INT J MAN MACH STUD, V39, P1 Selberg E, 1997, IEEE EXPERT, V12, P11, DOI 10.1109/64.577468 Shafer G, 1976, MATH THEORY EVIDENCE Shin D, 1998, P 3 ACM INT C DIG LI, P235, DOI 10.1145/276675.276702 SHIN HG, 1996, P CKIM ROCKV, P207, DOI 10.1145/238355.238495 TURAU V, 1999, MAKING LEGACY DATA A Ullman J. D., 1988, PRINCIPLES DATABASE VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VASANTHAKUMAR SR, 1996, B TECHNICAL COMMITTE, V19, P24 W3C, 1999, XML PATH LANG *W3C, 2001, XQUER 1 0 XML QUER L W3C, 2000, XML SCHEM 1 WIDOM J, 1999, B TECHNICAL COMMITTE, V22, P44 Wolff J. E., 2000, Proceedings IEEE Advances in Digital Libraries 2000, DOI 10.1109/ADL.2000.848377 WONG MH, 1990, INFORM SYST, V15, P583, DOI 10.1016/0306-4379(90)90030-S WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 YAMAMOTO Y, 1999, MARKUP TECHNOLOGIES, P235 Yu C, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P217, DOI 10.1145/319950.320005 Zadeh L. A., 1987, FUZZY SETS APPL ZANIOLO C, 1991, STPLD32891 MCC ZHANG D, 1999, P TECHN OBJ OR LANG, P83 ZHAO BY, 2000, XSEC HIGH PERFORMANC NR 142 TC 21 Z9 22 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD APR PY 2002 VL 53 IS 6 BP 415 EP 437 DI 10.1002/asi.10056 PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 534VW UT WOS:000174611300002 ER PT J AU Sebastiani, F AF Sebastiani, F TI Machine learning in automated text categorization SO ACM COMPUTING SURVEYS LA English DT Review DE algorithms; experimentation; theory; machine learning; text categorization; text classification ID PROBABILISTIC INFORMATION-RETRIEVAL; DOCUMENT CLASSIFICATION; FEATURE-SELECTION; SPECIAL ISSUE; RELEVANCE; MODELS; ASSUMPTIONS AB The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation. C1 CNR, Ist Elaboraz Informaz, I-56124 Pisa, Italy. RP Sebastiani, F (reprint author), CNR, Ist Elaboraz Informaz, Via G Moruzzi 1, I-56124 Pisa, Italy. EM fabrizio@iei.pi.cnr.it RI Sebastiani, Fabrizio/C-9501-2015 OI Sebastiani, Fabrizio/0000-0003-4221-6427 CR Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Attardi G., 1998, J UNIVERS COMPUT SCI, P719 Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Biebricher P., 1988, P 11 INT C RES DEV I, P333, DOI 10.1145/62437.62470 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Cavnar W., 1994, P 3 ANN S DOC AN INF, P161 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Clack C., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267716 Cleverdon C., 1984, Information Services & Use, V4 Cohen W., 1995, ADV INDUCTIVE LOGIC, P124 Cohen W. W., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 COHEN WW, 1995, P 12 INT C MACH LEAR, P124 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 CREECY RH, 1992, COMMUN ACM, V35, P48, DOI 10.1145/135226.135228 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENOYER L, 2001, P ECIR01 23 EUR C IN Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Escudero G., 2000, P 12 EUR C MACH LEAR, P129 ESTEBAN AD, 1998, P LREC98 1 INT C LAN, P1197 FIELD BJ, 1975, J DOC, V31, P246, DOI 10.1108/eb026605 Forsyth R.S., 1999, CAUSAL MODELS INTELL, P151 Frasconi P, 2002, J INTELL INF SYST, V18, P195, DOI 10.1023/A:1013681528748 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Fuhr N., 1984, P 3 BCS ACM S 2 6 JU, P391 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Fuhr N., 1991, P RIAO 91, P606 FUHR N, 1985, P RIAO85 1 INT C REC, P207 FUHR N, 1994, ACM T INFORM SYST, V12, P92, DOI 10.1145/174608.174612 Furnkranz J., 1999, P 3 S INT DAT AN IDA, P487 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 GALE W, 1993, COMPUT HUMANITIES, V26, P415 Govert N, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P475, DOI 10.1145/319950.320053 GRAY WA, 1971, INFORM STORAGE RET, V7, P167, DOI 10.1016/0020-0271(71)90002-7 Guthrie Louise, 1994, P COLING 94, P1059 Hayes P. J., 1990, P CAIA 90 6 IEEE C A, P320 HEAPS HS, 1973, INFORM CONTROL, V22, P268, DOI 10.1016/S0019-9958(73)90310-0 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Hull D. A., 1996, P 19 ANN INT ACM SIG, P279, DOI 10.1145/243199.243275 Ido Dagan, 1997, P 2 C EMP METH NAT L, P55 Ittner David J., 1995, P SDAIR 95 4 ANN S D, P301 Iwayama M., 1995, P 18 ANN INT ACM SIG, P273, DOI 10.1145/215206.215371 Iyer R. D., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354794 Joachims T., 1999, P 16 INT C MACH LEAR, V99, P200 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims T, 2002, J INTELL INF SYST, V18, P103, DOI 10.1023/A:1013652626023 Joachims T, 1998, P 10 EUR C MACH LEAR, P143 John G., 1994, P 11 INT C MACH LEAR, P121 JONES KS, 1997, READINGS INFORMATION JUNKER M, 1997, P RANLP 97 2 INT C R, P202 Junker M., 1998, International Journal on Document Analysis and Recognition, V1, DOI 10.1007/s100320050012 Kessler B., 1997, P 35 ANN M ASS COMP, P32 Kim Yu-Hwan, 2000, P 23 ANN INT ACM SIG, P168, DOI 10.1145/345508.345572 Klinkenberg R., 2000, P 17 INT C MACH LEAR, P487 Knight K, 1999, COMMUN ACM, V42, P58, DOI 10.1145/319382.319394 KNORZ G, 1982, P SIGIR 82 5 ACM INT, P174 Koller D., 1997, P 14 INT C MACH LEAR, P170 Korfhage R., 1997, INFORMATION STORAGE Lam S. L. Y., 1999, Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, DOI 10.1109/DASFAA.1999.765752 Lam W, 1999, IEEE T KNOWL DATA EN, V11, P865 Lam W., 1998, P 21 ANN INT ACM SIG, P81, DOI 10.1145/290941.290961 LAM W, 1997, P 15 INT JOINT C ART, P745 Lang K., 1995, P 12 INT C MACH LEAR, P331 Larkey Leah S., 1999, P DL 99 4 ACM C DIG, P179, DOI 10.1145/313238.313304 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS DD, 1995, SIGIR FORUM, V29, P2 Lewis D.D., 1994, P 11 INT C MACH LEAR, P148 LEWIS DD, 1994, ACM T INFORM SYST, V12, P231 LEWIS DD, 1995, P TREC 4 4 TEXT RETR, P165 Lewis FD, 1996, J PHOTOCH PHOTOBIO A, V96, P19, DOI 10.1016/1010-6030(95)04285-7 Li H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P122, DOI 10.1145/319950.319966 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 LIDDY ED, 1994, ACM T INFORM SYST, V12, P278, DOI 10.1145/183422.183425 Liere R., 1997, P 14 C AM ASS ART IN, P591 LIM JH, 1999, P ACM DIG LIB 99 BER, P139, DOI 10.1145/313238.313290 Manning C. D., 1999, FDN STAT NATURAL LAN MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 MASAND B, 1994, ADV GENETIC PROGRAMM, P459 Masand B., 1992, P 15 ANN INT ACM SIG, P59, DOI 10.1145/133160.133177 McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P350 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Mitchell T., 1996, MACHINE LEARNING Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Moulinier I., 1996, Proceedings. Fifth Annual Symposium on Document Analysis and Information Retrieval Moulinier I., 1996, Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing Myers K., 2000, P ICML 00 17 INT C M, P655 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Oh H J, 2000, P 23 ANN INT ACM SIG, P264, DOI 10.1145/345508.345594 PAZIENZA MT, 1997, LECT NOTES COMPUTER, V1299 RILOFF E, 1994, ACM T INFORM SYST, V12, P296, DOI 10.1145/183422.183428 Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 ROBERTSON SE, 1984, J DOC, V40, P264, DOI 10.1108/eb026768 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roth D., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Ruiz M. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312700 Sable C. L., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007990000038 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Scott S., 1999, P 16 INT C MACH LEAR, P379 Sebastiani F., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354804 Singhal A, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P25, DOI 10.1145/258525.258530 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Slonim N, 2001, P ECIR 01 23 EUR C I Taira H., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Tauritz DR, 2000, INFORM SCIENCES, V122, P121, DOI 10.1016/S0020-0255(99)00123-1 Tumer K., 1996, Connection Science, V8, DOI 10.1080/095400996116839 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Weigend A. S., 1999, Information Retrieval, V1, DOI 10.1023/A:1009983522080 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 WILLETT P, 1988, DOCUMENT RETRIEVAL S Wong J. W. T., 1996, SIGIR Forum, V30 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, J INFORMATION RETRIE, V1, P69 Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 Yu KL, 1998, P CIKM 98 7 ACM INT, P156, DOI 10.1145/288627.288652 NR 146 TC 1678 Z9 1823 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD MAR PY 2002 VL 34 IS 1 BP 1 EP 47 DI 10.1145/505282.505283 PG 47 WC Computer Science, Theory & Methods SC Computer Science GA 546HB UT WOS:000175267600001 ER PT J AU Ruthven, I Lalmas, M van Rijsbergen, K AF Ruthven, I Lalmas, M van Rijsbergen, K TI Combining and selecting characteristics of information use SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID SEARCH TERMS; RETRIEVAL AB In this article we report on a series of experiments designed to investigate the combination of term and document weighting functions in information retrieval. We describe a series of weighting functions, each of which is based on how information is used within documents and collections, and use these weighting functions in two types of experiments: one based on combination of evidence for ad hoc retrieval, the other based on selective combination of evidence within a relevance feedback situation. We discuss the difficulties involved in predicting good combinations of evidence for ad hoc retrieval, and suggest the factors that may lead to the success or failure of combination. We also demonstrate how, in a relevance feedback situation, the relevance assessments can provide a good indication of how evidence should be selected for query term weighting. he use of relevance information to guide the combination process is shown to reduce the variability inherent in combination of evidence. C1 Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. Univ London, Dept Comp Sci, London E1 4NS, England. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Ruthven, I (reprint author), Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. CR Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A CHANAKYA HN, 1997, BIOENERGY NEWS, V1, P11 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 Denos N., 1997, Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings Harman D., 1992, INFORMATION RETRIEVA, P363 HARMAN D, 1996, P 9 ANN INT ACM SIGI, P186 Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 Ingwersen P., 1994, P 17 ANN INT ACM SIG, P101 LEE JH, 1999, INFORMATION PROCESSI, V34, P681 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 PARADIS F, 1996, 2 INT C CONC LIB INF, P433 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUTHVEN I, 1999, P 3 INT C CONC LIB I SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1993, INTRO MODERN INFORMA Tombros A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290947 SMEATON A, 1998, P 20 BCS IRSG C SPRI SPARCKJONES K, 1972, J DOC, V28, P11 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VOORHEES EM, 1996, NIST SPECIAL PUBLICA, P1 Zhu X., 2000, P 23 ANN INT ACM SIG, P288, DOI 10.1145/345508.345602 NR 24 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD MAR PY 2002 VL 53 IS 5 BP 378 EP 396 DI 10.1002/asi.10046 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 530ZJ UT WOS:000174389500006 ER PT S AU Brisaboa, NR Penabad, MR Places, AS Rodriguez, FJ AF Brisaboa, NR Penabad, MR Places, AS Rodriguez, FJ BE Eaglestone, B North, S Poulovassilis, A TI A document database query language SO ADVANCES IN DATABASES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 19th British National Conference on Databases CY JUL 17-19, 2002 CL SHEFFIELD, ENGLAND HO UNIV SHEFFIELD ID INFORMATION-RETRIEVAL AB This work presents a natural language based technique to build user interfaces to query document databases through the web. We call such technique Bounded Natural Language (BNL). Interfaces based on BNL are useful to query document databases containing only structured data, containing only text or containing both of them. That is, the underlying formalism of BNL can integrate restrictions over structured and non-structured data (as text). Interfaces using BNL can be programmed ad hoc for any document database but in this paper we present a system with an ontology based architecture in which the user interface is automatically generated by a software module (User Interface Generator) capable of reading and following the ontology. This ontology is a conceptualization of the database model, which uses a label in natural language for any concept in the ontology. Each label represents the usual name for a concept in the real,world. The ontology includes general concepts useful when the user is interested in documents in any corpus in the database, and specific concepts useful when the user is interested in a specific corpus. That is, databases can store one or more corpus of documents and queries can be issued either over the whole database or over a specific corpus. The ontology guides the execution of the User Interface Generator and other software modules in such a way that any change in the database does not imply making changes in the program code, because the whole system runs following the ontology. That is, if a modification in the database schema occurs, only the ontology must be changed and the User Interface Generator will produce a new and different user interface adapted to the new database. C1 Univ A Coruna, Dep Computac, La Coruna 15071, Spain. RP Brisaboa, NR (reprint author), Univ A Coruna, Dep Computac, Campus Elvina S-N, La Coruna 15071, Spain. RI Francisco Javier, Rodriguez Martinez/B-7203-2012 CR Alonso O., 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000, DOI 10.1109/SPIRE.2000.878175 Baeza-Yates R., 1998, Proceedings. String Processing and Information Retrieval: A South American Symposium (Cat. No.98EX207), DOI 10.1109/SPIRE.1998.712977 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BAEZAYATES R, 1996, ACM SIGMOD RECORD, V25, P67, DOI 10.1145/381854.381890 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H GRUBER T, 1994, UHCS, V43, P907 Guarino N, 1998, P 1 INT C FORM ONT I, P3 HEARST MA, 1999, MODERN INFORMATION R Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 LANDAUER T, 1993, SUPERBOOK PROJECT HY MARTI AH, 1997, P 20 ANN INT ACM SIG, P246 MENA E, 1998, J DISTRIBUTED PARALL OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H PENABAD M, 1999, P INF SYST AN SYNTH ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Shneiderman B., 1998, DESIGNING USER INTER, V3rd STEVE BC, 1997, P 2 ACM INT C DIG LI, P142 Strzalkowski T., 1999, NATURAL LANGUAGE INF STUART KRR, 1992, P 5 ANN ACM S US INT, P23 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VEGAS J, 1999, THESIS U VALLADOLID Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 WORLD WIDE WEB CONSO NR 27 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-43905-6 J9 LECT NOTES COMPUT SC PY 2002 VL 2405 BP 183 EP 198 PG 16 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BW28F UT WOS:000181439000020 ER PT S AU Tsikrika, T Lalmas, M AF Tsikrika, T Lalmas, M BE Crestani, F Girolami, M VanRijsbergen, CJ TI Combining Web document representations in a Bayesian inference network model using link and content-based evidence SO ADVANCES IN INFORMATION REFTRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 24th Annual European Colloquium on Information Retrieval Research CY MAR 25-27, 2002 CL GLASGOW, SCOTLAND SP British Comp Soc, Informat Retrieval Specialist Grp, CEPIS IR, Memex Technol, Glasgow City Council ID INFORMATION-RETRIEVAL AB This paper introduces an expressive formal Information Retrieval model developed for the Web. It is based on the Bayesian inference network model and views IR as an evidential reasoning process. It supports the explicit combination of multiple Web document representations under a single framework. Information extracted from the content of Web documents and derived from the analysis of the Web link structure is used as source of evidence in support of the ranking algorithm. This content and link-based evidential information is utilised in the generation of the multiple Web document representations used in the combination. C1 Univ London Queen Mary, Dept Comp Sci, London E1 4NS, England. RP Tsikrika, T (reprint author), Univ London Queen Mary, Dept Comp Sci, Mile End Rd, London E1 4NS, England. CR AGOSTI M, 2001, INFORMATION RETRIEVA, P242 AMITAY E, IEEE INT C MULT EXP ATTARDI G, 1999, EUR S TEL HYP ART IN BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Brin S, 1998, P 7 INT WORLD WID WE Callan James, 1992, P 3 INT C DAT EXP SY, P78 Chakrabarti S., 1998, P 7 INT WORLD WID WE Croft WB, 2000, ADV INFORMATION RETR, P1 CUTLER M, 1999, 11 IEEE INT C TOOLS DAVISON BD, P 2O ANN INT ACM SIG, P272 DUNLOP MD, 1993, INFORMATION PROCESSI, V29 FISCHER H, 1972, NATURE, V238, P109 FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 GAUCH S, 1996, J UNIVERSAL COMPUTIN, V2 GERY M, 2001, INT WORKSH WEB DYN JONES KS, 1997, READINGS INFORMATION Katzer J., 1982, Information Technology: Research and Development, V1 KLEINBERG J, 1997, P 9 ACM SIAM S DISCR Lawrence S., 2000, IEEE DATA ENG B, V23, P25 Pearl J., 1997, PROBABILISTIC REASON RAJASHEKAR TB, 1995, J AM SOC INFORM SCI, V46, P272, DOI 10.1002/(SICI)1097-4571(199505)46:4<272::AID-ASI4>3.0.CO;2-T RIBEIRONETO B, 2000, SOFT COMPUTING INFOR, P259 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUTHVEN I, 2002, IN PRESS J AM SOC IN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1996, P TREC5, P489 SELBERG E, 1997, IEEE EXPERT, V12, P8 SILVA I, 2000, P 23 ANN INT ACM SIG, P96, DOI 10.1145/345508.345554 SMITAY E, 1998, P SIGIR POST C WORKS TSIKRIKA T, 2001, P 10 INT C INF KNOWL TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1989, P HYP 89 C PITTSB PE, P213, DOI 10.1145/74224.74242 TURTLE HR, THESIS VANRIJSBERGEN CJ, 1997, READINGS INFORMATION Zhu X., 2000, P 23 ANN INT ACM SIG, P288, DOI 10.1145/345508.345602 NR 37 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-43343-0 J9 LECT NOTES COMPUT SC PY 2002 VL 2291 BP 53 EP 72 PG 20 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BW25R UT WOS:000181347800004 ER PT S AU de Campos, LM Fernandez-Luna, JM Huete, JF AF de Campos, LM Fernandez-Luna, JM Huete, JF BE Crestani, F Girolami, M VanRijsbergen, CJ TI A layered Bayesian network model for document retrieval SO ADVANCES IN INFORMATION REFTRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 24th Annual European Colloquium on Information Retrieval Research CY MAR 25-27, 2002 CL GLASGOW, SCOTLAND SP British Comp Soc, Informat Retrieval Specialist Grp, CEPIS IR, Memex Technol, Glasgow City Council ID INFORMATION-RETRIEVAL AB We propose a probabilistic document retrieval model based on Bayesian networks. The network is used to compute the posterior probabilities of relevance of the documents in the collection given a query. These computations can be carried out efficiently, because of the specific network topology and conditional probability tables being considered, which allow the use of a fast and exact probabilities propagation algorithm. In the initial model, only direct relationships between the terms in the glossary and the documents that contain them are considered, giving rise to a Bayesian network with two layers. Next, we consider an extended model that also includes direct relationships between documents, using a network topology with three layers. We also report the results of a set of experiments with the two models, using several standard document collections. C1 Univ Granada, Dpto Ciencias Computac & Inteligencia Artif, ETSI Informat, E-18071 Granada, Spain. Univ Jaen, Dept Informat, Escuela Politecn Super, Jaen 23071, Spain. RP de Campos, LM (reprint author), Univ Granada, Dpto Ciencias Computac & Inteligencia Artif, ETSI Informat, E-18071 Granada, Spain. RI de Campos, Luis/B-7436-2012; Huete, Juan/C-2428-2012; Fernandez Luna, Juan Manuel/C-6845-2012 OI de Campos, Luis/0000-0001-9125-1195; CR DECAMPOS LM, 2000, 2 LUMIS WORKSH, P543 FERNANDEZLUNA JM, 2001, THESIS U GRANADA FUNG R, 1995, COMMUN ACM, V38, P42, DOI 10.1145/203330.203340 Jones K. S., 2000, Information Processing & Management, V36 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Pearl J., 1988, PROBABILISTIC REASON Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SILVA I, 2000, P 23 ANN INT ACM SIG, P96, DOI 10.1145/345508.345554 Silva I. Reis, 2000, THESIS U FEDERAL MIN TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1991, P RIA0 91 C, P644 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Turtle H. R., 1990, THESIS U MASSACHUSET VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 16 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-43343-0 J9 LECT NOTES COMPUT SC PY 2002 VL 2291 BP 169 EP 182 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BW25R UT WOS:000181347800012 ER PT S AU Lahart, O O'Riordan, C AF Lahart, O O'Riordan, C BE Oneill, M Sutcliffe, RFE Ryan, C Eaton, M Griffith, NJL TI A system for Music Information Retrieval SO ARFIFICIAL INTELLIGENCE AND COGNITIVE SICENCE, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 13th Irish Conference on Artificial Intelligence and Cognitive Science CY SEP 12-13, 2002 CL LIMERICK, IRELAND SP QAD Ireland, Univ Limerick AB Information Retrieval, and in part Music Information Retrieval, has come to the fore in recent years as a research area. In this paper we outline the design of a system which allows for the retrieval of tunes from a music database. We describe algorithms and implementation of techniques to allow for the retrieval of tunes based on the occurrence of phrases or sub-phrases together with algorithms for the comparison of tunes via the vector space model. C1 NUI, Dept IT, Galway, Ireland. RP Lahart, O (reprint author), NUI, Dept IT, Galway, Ireland. EM orlalahart@hotmail.com; colmor@geminga.nuigalway.ie CR ALEXANDRA L, 1998, ACM MULTIMEDIA FRAKES WR, 1992, INFORMATION RETRIEVA GHIAS A, 1995, ACM MULTIMEDIA LAP YC, 1999, DATABASES EXPERT SYS RICARDO A, 1992, COMBINATORIAL PATTER, P185 RICARDO A, 1999, MODERN INFORMATION R ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RODGER J, 1997, NZ DIGITAL LIB MELOD SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 STEPHAN J, 2000, P ACM SIGIR C WELSH M, QUERYING LARGE COLLE NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44184-0 J9 LECT NOTES ARTIF INT PY 2002 VL 2464 BP 197 EP 202 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BX94M UT WOS:000186905600026 ER PT S AU Stensmo, M AF Stensmo, M BE Dorronsoro, JR TI A scalable and efficient probabilistic information retrieval and text mining system SO ARTIFICIAL NEURAL NETWORKS - ICANN 2002 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 12th International Conference on Artifical Neural Networks (ICANN 2002) CY AUG 28-30, 2002 CL MADRID, SPAIN SP Univ Autonoma Madrid, Neural Network Grp Escuela Tecn Superior Ingenier Informat, European Neural Networks Soc, Spain Minist Cienc Tecnol, Inst Ingenier Conocimiento, FET Program, European Commiss, IEEE Neural Network Council, Japanese Neural Network Soc, Asia Pacific Neural Assembly, IEEE NNC, Spanish RIG AB A system for probabilistic information retrieval and text mining that is both scalable and efficient is presented. Separate feature extraction or stop-word lists are not needed since the system can remove unneeded paxameters dynamically based on a local mutual information measure. This is shown to be as effective as using a global measure. A novel way of storing system parameters eliminates the need for a ranking step during information retrieval from queries. Probability models over word contexts provide a method to suggest related words that can be added to a query. Test results are presented on a categorization task and screen shots from a live system axe shown to demonstrate its capabilities. C1 IBM Corp, Almaden Res Ctr, San Jose, CA 95120 USA. RP Stensmo, M (reprint author), IBM Corp, Almaden Res Ctr, 650 Harry Rd, San Jose, CA 95120 USA. CR Domingos P., 1996, P 13 INT C MACH LEAR LEWIS DD, 1998, REUTERS 21578 TEXT C MCCALLUM A, 1998, AM ASS ART INT WORKS Mitchell T.M., 1997, MACHINE LEARNING Papoulis A, 1984, PROBABILITY RANDOM V, V2nd ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YANG Y, 1999, P SIGIR 1999 NR 8 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44074-7 J9 LECT NOTES COMPUT SC PY 2002 VL 2415 BP 643 EP 648 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BW28K UT WOS:000181441900105 ER PT B AU Ruthven, I Lalmas, M van Rijsbergen, K AF Ruthven, I Lalmas, M van Rijsbergen, K BE Bruce, H Fidel, R Ingwersen, P Vakkari, P TI Ranking expansion terms with partial and ostensive evidence SO COLIS4: EMERGING FRAMEWORKS AND METHODS LA English DT Proceedings Paper CT 4th International Conference on Conceptions of Library and Information Science CY JUL 21-25, 2002 CL SEATTLE, WA SP Univ Washington, Inform Sch, Univ Tampere, Dept Informat Studies, Royal Sch Lib & Informat Sci, Amer Soc Informat Sci & Technol ID QUERY EXPANSION; RELEVANCE AB In this paper we examine the problem of ranking candidate expansion terms for query expansion. We show, by an extension-to the traditional F-4 scheme, how partial relevance assessments (how relevant a document is) and ostensive evidence (when a document was assessed relevant) can be incorporated into a term ranking function. We then investigate this new term ranking function in three user experiments, examining the performance of our function for automatic and interactive query expansion. We show that the new function not only suggests terms that are preferred by searchers but suggests terms that can lead to more use of expansion terms. C1 Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XH, Lanark, Scotland. CR Beaulieu M, 1997, J DOC, V53, P8, DOI 10.1108/EUM0000000007187 BORLUND P, 2000, J DOC, V56, P1 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Campbell I., 1996, P 2 INT C CONC LIB I, P251 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W Florance V., 1995, P 18 ANN INT ACM SIG, P158, DOI 10.1145/215206.215353 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Heuer R. J., 1999, PSYCHOL INTELLIGENCE Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUTHVEN I, 2001, P 24 ACM SIGIR C RES Spink A, 1998, INFORM PROCESS MANAG, V34, P599, DOI 10.1016/S0306-4573(98)00025-9 TIAMIYU MA, 1988, INFORM PROCESS MANAG, V24, P391, DOI 10.1016/0306-4573(88)90043-X Vakkari P., 2000, P RIAO 2000 C PAR CI, P894 Vakkari P., 2000, P 23 ANN INT ACM SIG, P2, DOI 10.1145/345508.345512 VOORHEES E, 1997, P 6 TEXT RETR C TREC NR 16 TC 0 Z9 0 PU LIBRARIES UNLIMITED INC PI WESTPORT PA 88 POST ROAD WEST, WESTPORT, CT 06881 USA BN 1-59158-016-1 PY 2002 BP 199 EP 219 PG 21 WC Information Science & Library Science SC Information Science & Library Science GA BW54L UT WOS:000182375000013 ER PT S AU Gaustad, T Bouma, G AF Gaustad, T Bouma, G BE Theune, M Nijholt, A Hondorp, H TI Accurate stemming of Dutch for text classification SO COMPUTATIONAL LINGUISTICS IN THE NETHERLANDS 2001 SE LANGUAGE AND COMPUTERS : STUDIES IN PRACTICAL LINGUISTICS LA English DT Proceedings Paper CT 12th Meeting on Computational Linguistics in the Netherlands (CLIN) CY NOV 30, 2001 CL ENCSHEDE, NETHERLANDS SP Netherlands Org Sci Res, Prior Programme Language & Speech Technol, Sch Informat & Knowledge Syst HO UNIV TWENTE AB This paper investigates the use of stemming for classification of Dutch (email) texts. We introduce a stemmer, which combines dictionary lookup (implemented efficiently as a finite state automaton) with a rule-based backup strategy and,how, that it outperforms the Dutch Porter stemmer in terms of accuracy. while not being substantially slower. For text classification, the most important property of a stemmer is the number of words it (correctly) reduces to the same stem. Here the dictionary - based system also outperforms Porter. However, evaluation of a Bayesian text classification system with either no stemming or the Porter or dictionary-based stemmer on an email classification and a newspaper topic classification task does not lead to significant differences in accuracy. We conclude with an analysis of why this is the case. C1 RU Groningen, Alfa Informat, NL-9700 AS Groningen, Netherlands. CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P1 Baayen R. H., 1993, CELEX LEXICAL DATABA Busemann S., 2000, P 6 C APPL NAT LANG DACIUK J, 2000, P COLING 2000 WORKSH, P34 Duda R., 1973, PATTERN CLASSIFICATI FRAKES WR, 1992, INFORMATION RETRIEVA HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P KRAAIJ W, 1996, OTSWPCL96001 OTS U U KRAAIJ W, 1995, NEW REV DOCUMENT TEX, P23 Kraaij W., 1994, INFORMATIEWETENSCHAP, P167 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 POPOVIC M, 1992, J AM SOC INFORM SCI, V43, P384, DOI 10.1002/(SICI)1097-4571(199206)43:5<384::AID-ASI6>3.0.CO;2-L PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SPITTERS M, 2000, P 6 C CONT BAS MULT, P1124 van Halteren H, 2001, COMPUT LINGUIST, V27, P199 NR 17 TC 4 Z9 4 PU EDITIONS RODOPI B V PI AMSTERDAM PA TIJNMUIDEN 7, 1046 AK AMSTERDAM, NETHERLANDS SN 0921-5034 BN 90-420-0943-8 J9 LANG COMPUT PY 2002 IS 45 BP 104 EP 117 PG 14 WC Linguistics SC Linguistics GA BV53K UT WOS:000179288800009 ER PT S AU Jones, GJF Lam-Adesina, AM AF Jones, GJF Lam-Adesina, AM BE Peters, C Braschler, M Gonzalo, J Kluck, M TI Exeter at CLEF 2001: Experiments with machine translation for bilingual retrieval SO EVLAUATION OF CROSS-LANGUAGE INFORMATION RETRIEVAL SYSTEMS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd Workshop of the Cross-Language Evaluation Forum CY SEP 03-04, 2001 CL DARMSTADT, GERMANY ID QUERY AB The University of Exeter participated in the CLEF 2001 bilingual task. The main objectives of our experiments were to compare retrieval performance for different topic languages with similar easily available machine translation resources and to explore the application of new pseudo relevance feedback techniques recently developed at Exeter to Cross-Language Information Retrieval (CLIR). This paper also describes more recent experimental results from our investigations of the combination of results from alternative machine translation outputs; specifically we look at the use of data fusion of the output from individual retrieval runs and the combination of alternative topic translations. C1 Univ Exeter, Dept Comp Sci, Exeter EX4 4PT, Devon, England. RP Jones, GJF (reprint author), Univ Exeter, Dept Comp Sci, Exeter EX4 4PT, Devon, England. CR Allan James, 1995, P 18 ANN INT ACM SIG, P337, DOI 10.1145/215206.215380 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Callan James P., 1994, P 17 ANN INT ACM SIG, P302 EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 Jones G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312690 KNAUS D, 1996, P 4 TEXT TRETR C TRE, P233 Lam-Adesina AM, 2001, P 24 ANN INT ACM SIG, P1, DOI 10.114/383952.383953 LUHN HP, 1958, IBM J RES DEV, V2, P159 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1995, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1999, OV 7 TEXT RETR C TRE, P253 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1996, OV 4 TEXT RETR C TRE, P73 Robertson SE, 1995, OV 3 TEXT RETR C TRE, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Tombros A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290947 STRZALKOWSKI T, 1998, P 17 COLING ACL 98, P1 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 19 TC 7 Z9 7 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44042-9 J9 LECT NOTES COMPUT SC PY 2002 VL 2406 BP 59 EP 77 PG 19 WC Computer Science, Artificial Intelligence SC Computer Science GA BW28G UT WOS:000181439400004 ER PT J AU Kolcz, A Alspector, J AF Kolcz, A Alspector, J TI Asymmetric missing-data problems: Overcoming the lack of negative data in preference ranking SO INFORMATION RETRIEVAL LA English DT Article DE incomplete data problems; imbalanced training data; user modeling; personalization; information retrieval ID INFORMATION; ALGORITHM; WEB; EM AB In certain classification problems there is a strong a asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for the others, accompanied by a large body of unlabeled data. Since most classification algorithms require some information about all classes involved, label estimation for the un-represented class is desired. An important representative of this group of problems is that of user interest/preference modeling where there may be a large number of examples of what the user likes with essentially no counterexamples. Recently, there has been much interest in applying the EM algorithm to incomplete data problems in the area of text retrieval and categorization. We adapt this approach to the asymmetric case of modeling user interests in news articles, where only labeled positive training data are available, with access to a large corpus of unlabeled documents. User modeling is here equivalent to that of user-specific document ranking. EM is used in conjunction with the Naive Bayes model while its output is also utilized by a Support Vector Machine and Rocchio's technique. Our findings demonstrate that the EM algorithm can be quite effective in modeling the negative class under a number of different initialization schemes. Although primarily just the negative training examples are needed, a natural question is whether using all of the estimated labels (i.e., positive and negative) would be more (or less) beneficial. This is important considering that, in this context, the initialization of the negative class for EM is likely not to be very accurate. Experimental results suggest that EM output should be limited to negative label estimates only. C1 Personal Inc, Colorado Springs, CO 80903 USA. Univ Colorado, Dept Elect & Comp Engn, Colorado Springs, CO 80918 USA. RP Kolcz, A (reprint author), Personal Inc, 24 S Weber Suite 325, Colorado Springs, CO 80903 USA. CR BILLSUS D, 1999, 7 INT C US MOD UM 99 Buckley C., 1995, P 18 ANN INT ACM SIG, P351, DOI 10.1145/215206.215383 BUCKLEY C, 1995, NIST SP CLAYPOOL M, 1999, ACM SIGIR REC SYST W COHN D, 1994, MACH LEARN, V15, P201, DOI 10.1023/A:1022673506211 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Domingos P., 1999, P 5 INT C KNOWL DISC FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 FRAKES WR, 1992, INFORMATION RETRIEVA Harman D., 1992, INFORMATION RETRIEVA, P363 IYENGAR V, 2000, P ACM SIGKDD Japkowicz N., 2000, P 2000 INT C ART INT Jennings A., 1993, Australian Telecommunication Research, V27 JOACHIMS T, 1997, LS823 U DORTM JOACHIMS T, 1997, P INT JOINT C ART IN Joachims T, 1999, ADV KERNEL METHODS S Kamba T, 1997, INT J HUM-COMPUT ST, V46, P789, DOI 10.1006/ijhc.1996.0113 Kubal M., 1997, P 9 EUR C MACH LEARN, P146 Kubat M., 1997, P 14 INT C MACH LEAR, P179 Lang K., 1995, P 12 INT C MACH LEAR, P331 Lewis D. D., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval LEWIS J, 1998, PC WEEK, V15, P98 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Losee R.M., 1998, TEXT RETRIEVAL FILTE McCallum A., 1998, AAAI 98 WORKSH LEARN MCCALLUM AK, 1997, P 1998 INT MACH LEAR, P25 McLachlan G. J., 1996, EM ALGORITHM EXTENSI Mitchell T., 1998, P 1998 C COMP LEARN MITRA M, 1997, P 20 INT ACM SIGIR C, P25 Morik K, 2000, ARTIF INTELL MED, V19, P225, DOI 10.1016/S0933-3657(00)00047-6 Morita M., 1994, P 17 ANN INT ACM SIG, P272 NICKERSON A, 2001, P 8 INT WORKSH ART I Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Nigam K., 2000, P 9 INT C INF KNOWL Pazzani M., 1994, 11 INT C MACH LEARN, P217 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Pednault E. P. D., 2000, RC21731 IBM Platt J. C., 2000, ADV LARGE MARGIN CLA PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Provost F., 2000, Learning from Imbalanced Data Sets. Papers from the AAAI Workshop (Technical Report WS-00-05) ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1971, SMART RETRIEVAL SYST Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Schohn G., 2000, P 17 INT C MACH LEAR Seung H. S., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, DOI 10.1145/130385.130417 STEVENS C, 1992, COMMUN ACM, V35, P48, DOI 10.1145/138859.138863 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Vapnik VN, 1998, STAT LEARNING THEORY Yang Y., 1999, P 22 INT ACM SIGIR C NR 51 TC 1 Z9 1 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PY 2002 VL 5 IS 1 BP 5 EP 40 DI 10.1023/A:1012714523368 PG 36 WC Computer Science, Information Systems SC Computer Science GA 492KR UT WOS:000172168000001 ER PT B AU Cai, D van Rijsbergen, CJ AF Cai, D van Rijsbergen, CJ GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI Automatic query expansion based on directed divergence SO INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, PROCEEDINGS LA English DT Proceedings Paper CT 3rd International Conference on Information Technology: Coding and Computing (ITCC 2002) CY APR 08-10, 2002 CL LAS VEGAS, NV SP IEEE Comp Soc ID INFORMATION-RETRIEVAL; SEARCH TERMS; SELECTION AB To extract informative terms through known relevance information for query expansion is an important issue in the design of an effective text retrieval system. Various discriminant functions have been developed as a device for eliciting good terms. In this paper the notion of informativeness of terms is interpreted and discriminant functions are formalised both based on information-theoretic ideas for auromatic query expansion. Preliminary, results show that the approach adopted in this paper is worthy of continued investigation. C1 Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland. RP Cai, D (reprint author), Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland. CR Buckley C., 1995, P 18 ANN INT ACM SIG, P351, DOI 10.1145/215206.215383 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Good I.J., 1950, PROBABILITY WEIGHING KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Robertson S. E., 1999, NIST SPECIAL PUBLICA, P151 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 SMEATON A, 1983, COMPUT J, V26, P10 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 NR 17 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1507-X PY 2002 BP 8 EP 15 PG 8 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BU41K UT WOS:000175939100002 ER PT J AU Karamuftuoglu, M Jones, S Robertson, S Venuti, F Wang, XK AF Karamuftuoglu, M Jones, S Robertson, S Venuti, F Wang, XK TI Challenges posed by web-based retrieval of scientific papers: Okapi participation in TIPS SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID MEDICAL LITERATURES; MODELS; SEARCH AB This paper reviews some of the past Okapi research and discusses the role of Okapi in the current TIPS project. The main purpose is to report new challenges faced by probabilistic text retrieval in the web environment and to indicate some of the solutions that are currently under investigation. In this context, extraction of indexing units from formatted document sources, user interface design, implementation of field searching and query expansion within the framework of probabilistic searching are discussed. The problem of maintaining session continuity in the web environment and a possible solution to this problem are outlined. Other challenges posed by the open nature of the web environment are also indicated. These include the difficulty of delimiting the boundaries of a search session and the potential of the web for collaborative information retrieval. A system for collaboratively filtering documents based on their contents is described in this connection. Issues surrounding the integration of Okapi with other pieces of software being developed for the TIPS project are also briefly discussed. C1 City Univ London, Dept Informat Sci, Ctr Interact Syst Res, London EC1V 0HB, England. RP Karamuftuoglu, M (reprint author), City Univ London, Dept Informat Sci, Ctr Interact Syst Res, Northampton Sq, London EC1V 0HB, England. CR Bodoff D, 1999, J AM SOC INFORM SCI, V50, P49, DOI 10.1002/(SICI)1097-4571(1999)50:1<49::AID-ASI7>3.0.CO;2-9 BRAJNIK G, 1996, P 19 ANN INT ACM SIG, P128, DOI 10.1145/243199.243249 CHEVALLET JP, INFORMATION RETRIEVA GOOSSENS M, XML DOCUMENT REPOSIT Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1994, J DOC, V50, P233, DOI 10.1108/eb026932 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SWANSON DR, 1989, J AM SOC INFORM SCI, V40, P356, DOI 10.1002/(SICI)1097-4571(198909)40:5<356::AID-ASI9>3.0.CO;2-B SWANSON DR, 1987, J AM SOC INFORM SCI, V38, P228, DOI 10.1002/(SICI)1097-4571(198707)38:4<228::AID-ASI2>3.0.CO;2-G 1997, J DOCUMENTATION, V53 NR 11 TC 2 Z9 2 PU BOWKER-SAUR PI E GRINSTEAD PA WINDSOR COURT, EAST GRINSTEAD HOUSE, E GRINSTEAD RH19 1XA, W SUSSEX, ENGLAND SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 2002 VL 28 IS 1 BP 3 EP 17 DI 10.1177/0165551024233904 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 527BU UT WOS:000174166300002 ER PT S AU Cuenca-Acuna, FM Nguyen, TD AF Cuenca-Acuna, FM Nguyen, TD BE Gregori, E Cherkasova, L Cugola, G Panzieri, F Picco, GP TI Text-based content search and retrieval in ad hoc P2P communities SO WEB ENGINEERING AND PEER TO PEER COMPUTING SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT International Workshop on Web Engineering/International Workshop on Peer-to-Peer Computing held Jointly with Networking 2002 CY MAY 19-24, 2002 CL PISA, ITALY SP Compaq, Microsoft, Softec, CNUCE, iiT, Pisa Cassa Risparmio Spa AB We consider the problem of content search and retrieval in peer-to-peer (P2P) communities. P2P computing is a potentially powerful model for information sharing between ad hoc groups of users because of its low cost of entry and natural model for resource scaling. As P2P communities grow, however, locating information distributed across the large number of peers becomes problematic. We address this problem by adapting a state-of-the-art text-based document ranking algorithm, the vector-space model instantiated with the TFxIDF ranking rule, to the P2P environment. We make three contributions: (a) we show how to approximate TFxIDF using compact summaries of individual peers' inverted indexes rather than the inverted index of the entire communal store; (b) we develop a heuristic for adaptively determining the set of peers that should be contacted for a query; and (c) we show that our algorithm tracks TFADF's performance very closely, giving P2P communities a search and retrieval algorithm as good as that possible assuming a centralized server. C1 Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08854 USA. RP Cuenca-Acuna, FM (reprint author), Rutgers State Univ, Dept Comp Sci, 110 Frelinghuysen Rd, Piscataway, NJ 08854 USA. CR BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BUCKLEY C, 1985, TR85686 CORN U Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Clarke I., 2000, WORKSH DES ISS AN UN, P46 CUENCAACUNA FM, 2001, DCSTR465 RUTG U Demers A, 1987, P 6 ANN ACM S PRINC, P1, DOI [10.1145/41840.41841, DOI 10.1145/41840.41841] Douglis Fred, 1997, USENIX S INT TECHN S FRENCH JC, 1999, RES DEV INFORMATION, P238 GIFFORD DK, 1991, P 13 ACM S OP SYST P Gravano Luis, 1994, P 1994 ACM SIGMOD IN, P126, DOI 10.1145/191839.191869 Harman D., 1993, P 16 ANN INT ACM SIG ORAM A, 2001, PEER TO PEER HARNESS RATNASAMY S, 2001, P ACM SIGCOMM 01 C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roselli D., 2000, P 2000 USENIX ANN TE Rowstron A. I. T., 2001, P IFIP ACM INT C DIS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saroiu S., 2002, P MULT COMP NETW STOICA I, 2001, P ACM SIGCOMM 01 C A Weatherspoon Hakim, 2000, P ACM ASPLOS Witten I. H., 1999, MANAGING GIGABYTES C Yang B., 2002, P INT C DISTR COMP S ZHAO Y, 2000, UCBCDS011141 NR 24 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44177-8 J9 LECT NOTES COMPUT SC PY 2002 VL 2376 BP 220 EP 234 PG 15 WC Computer Science, Theory & Methods SC Computer Science GA BY01P UT WOS:000187251900020 ER PT J AU Jones, G Collier, N Sakai, T Sumita, K Hirakawa, H AF Jones, G Collier, N Sakai, T Sumita, K Hirakawa, H TI A framework for cross-language information access: Application to English and Japanese SO COMPUTERS AND THE HUMANITIES LA English DT Article DE cross-language information retrieval; information access; Japanese-English; machine translation; probabilistic retrieval ID RETRIEVAL AB Internet search engines allow access to online information from all over the world. However, there is currently a general assumption that users are fluent in the languages of all documents that they might search for. This has for historical reasons usually been a choice between English and the locally supported language. Given the rapidly growing size of the Internet, it is likely that future users will need to access information in languages in which they are not fluent or have no knowledge of at all. This paper shows how information retrieval and machine translation can be combined in a cross-language information access framework to help overcome the language barrier. We present encouraging preliminary experimental results using English queries to retrieve documents from the standard Japanese language BMIR-J2 retrieval test collection. We outline the scope and purpose of cross-language information access and provide an example application to suggest that technology already exists to provide effective and potentially useful applications. C1 Toshiba Corp, Ctr Res & Dev, Knowledge Media Lab, Saiwai Ku, Kawasaki, Kanagawa 2108582, Japan. RP Jones, G (reprint author), Univ Exeter, Dept Comp Sci, Exeter EX4 4QJ, Devon, England. CR Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 Beaulieu M, 1997, P TREC 5, P143 Brown M.G., 1995, P ACM MULT 95 SAN FR, P35, DOI 10.1145/217279.215080 Carbonell J.G., 1997, P 15 INT JOINT C ART, P708 Chien L. F., 1995, P 18 ANN INT ACM SIG, P112, DOI 10.1145/215206.215345 COLLIER N, 1998, P COLINGACL 98 MONTR, P263 Franz M., 1999, P 7 TEXT RETR C TREC, P157 FUJII H, 1993, P 16 ANN INT ACM SIG, P237, DOI 10.1145/160688.160728 GEY FC, 1999, P 7 TEXT RETR C TREC, P527 HARMAN D, 1998, 6 TEXT RETR C TREC 6 HAYASHI Y, 1997, P AAAI 97 SPRING S C, P56 Hearst Marti A., 1995, P ACM SIGCHI C HUM F, P59, DOI 10.1145/223904.223912 HIRAKAWA H, 1991, P MACH TRANSL SUMM, V3, P73 Hull D. A., 1996, P 19 ANN INT ACM SIG, P49, DOI 10.1145/243199.243212 Hutchins W. J., 1992, INTRO MACHINE TRANSL Jones G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312690 Jones GJF, 1996, P 19 ANN INT ACM SIG, P30, DOI 10.1145/243199.243208 Jones G. J. F., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290992 JONES GJF, 1998, INF PROC SOC JAP JOI, P57 JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1996, LECT NOTES ARTIFICIA, V1083 KAJIURA M, 1997, P 54 INF PROC SOC JA KANDO N, 1999, P 1 NTCIR WORKSH RES Kitani T., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291033 Lee J.H., 1996, P 19 ANN INT ACM SIG, P216, DOI 10.1145/243199.243269 Nie J. Y., 1996, P 19 ANN INT ACM SIG, P225, DOI 10.1145/243199.243270 OARD DW, 1996, UMIACSTR9619 U MARYL Oard DW, 1999, INFORM PROCESS MANAG, V35, P363, DOI 10.1016/S0306-4573(98)00066-1 Ogawa Y, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P226, DOI 10.1145/258525.258576 Ogawa Yasushi, 1995, P 18 ANN INT ACM SIG, P121 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1997, 356 U CAMBR COMP LAB ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEKINE S, 1999, P INF RETR EXTR EX I Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 WALKER S, 1998, P 6 TEXT RETR C TREC, P125 NR 41 TC 0 Z9 0 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 0010-4817 J9 COMPUT HUMANITIES JI Comput. Humanit. PD NOV PY 2001 VL 35 IS 4 BP 371 EP 388 DI 10.1023/A:1011851209975 PG 18 WC Computer Science, Interdisciplinary Applications SC Computer Science GA 472PC UT WOS:000170991800002 ER PT J AU Losee, RM AF Losee, RM TI Term dependence: A basis for Luhn and Zipf models SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Editorial Material ID WORD PHRASES; RETRIEVAL AB There are regularities in the statistical information provided by natural language terms about neighboring terms. We find that when phrase rank increases, moving from common to less common phrases, the value of the expected mutual information measure (EMIM) between the terms regularly decreases. Luhn's model suggests that midrange terms are the best index terms and relevance discriminators. We suggest reasons for this principle based on the empirical relationships shown here between the rank of terms within phrases and the average mutual information between terms, which we refer to as the Inverse Representation - EMIM principle. We also suggest an Inverse EMIM term weight for indexing or retrieval applications that is consistent with Luhn's distribution. An information theoretic interpretation of Zipf's Law is provided. Using the regularity noted here, we suggest that Zipf's Law is a consequence of the statistical dependencies that exist between terms, described here using information theoretic concepts. C1 Univ N Carolina, Chapel Hill, NC 27599 USA. RP Losee, RM (reprint author), Univ N Carolina, Manning Hall,CB 3360, Chapel Hill, NC 27599 USA. CR ACZEL J, 1975, MEASURES INFORMATION Anderson RB, 1997, MEM COGNITION, V25, P724, DOI 10.3758/BF03211315 CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 Cover T. M., 1991, ELEMENTS INFORMATION COX DR, 1993, STAT SCI, V8, P204, DOI 10.1214/ss/1177010887 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 Egghe L, 1999, J AM SOC INFORM SCI, V50, P233, DOI 10.1002/(SICI)1097-4571(1999)50:3<233::AID-ASI6>3.0.CO;2-8 GEY FC, 1993, THESIS U CALIFORNIA Hartley RVL, 1928, BELL SYST TECH J, V7, P535 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X LOSEE RM, 2001, IN PRESS INFORMATION Losee RM, 1996, INFORM PROCESS MANAG, V32, P747, DOI 10.1016/S0306-4573(96)00017-9 Losee R.M., 1998, TEXT RETRIEVAL FILTE LOSEE RM, 1990, SCI INFORMATION MEAS LUHN HP, 1958, IBM J RES DEV, V2, P159 Mandelbrot B., 1961, P S APPL MATH, V12, P190 MOON SB, 1993, THESIS U N CAROLINA Naranan S., 1998, J QUANT LINGUIST, V5, P35, DOI 10.1080/09296179808590110 Nyquist H, 1924, BELL SYST TECH J, V3, P324 Rapoport A, 1982, QUANTITATIVE LINGUIS, V16, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Shannon C. E., 1949, MATH THEORY COMMUNIC SHANNON CE, 1951, AT&T TECH J, V30, P50 SIMON HA, 1955, BIOMETRIKA, V42, P425 SMITH FJ, 1985, INFORM PROCESS MANAG, V21, P215, DOI 10.1016/0306-4573(85)90106-2 SPARCKJONES K, 1972, J DOC, V28, P11 YAVUZ D, 1974, IEEE T INFORM THEORY, V20, P650, DOI 10.1109/TIT.1974.1055269 YNGVE VH, 1986, LINGUISTICS SCI YU CT, 1983, INFORM TECHNOL R & D, V2, P129 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 31 TC 9 Z9 9 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD OCT PY 2001 VL 52 IS 12 BP 1019 EP 1025 DI 10.1002/asi.1155 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 473NL UT WOS:000171050400004 ER PT J AU An, AJ Cercone, N AF An, AJ Cercone, N TI Rule quality measures for rule induction systems: Description and evaluation SO COMPUTATIONAL INTELLIGENCE LA English DT Article DE rule quality measures; rule induction; meta-learning AB A rule quality measure is important to a rule induction system for determining when to stop generalization or specialization. Such measures are also important to a rule-based classification procedure for resolving conflicts among rules. We describe a number of statistical and empirical rule quality formulas and present an experimental comparison of these formulas on a number of standard machine learning datasets. We also present a meta-learning method for generating a set of formula-behavior rules from the experimental results which show the relationships between a formula's performance and the characteristics of a dataset. These formula-behavior rules arc combined into formula-selection rules that can be used in a rule induction system to select a rule quality formula before rule induction. We will report the experimental results showing the effects of formula-selection on the predictive performance of a rule induction system. C1 Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. RP An, AJ (reprint author), Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. CR ALI KM, 1993, P 13 INT JOINT C ART AN A, 1998, LECT NOTES ARTIFICIA, V1418 AN A, 1999, P 7 INT WORKSH ROUGH ARKIN H, 1970, STAT METHODS BISHOP YMM, 1991, DISCRETE MULTIVARIAT Brazdil P. B., 1990, CURRENT TRENDS KNOWL BRUHA I, 1996, MACH LEARNING STAT Bruha I, 1993, INFORMATICA, V17, P233 Bruning J. L., 1997, COMPUTATIONAL HDB ST CLARK AJL, 1989, J MOL ENDOCRINOL, V2, P3 Clark P, 1991, P 5 EUR WORK SESS LE, P151 COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104 Duda R., 1979, EXPERT SYSTEMS MICRO Holte R., 1989, P 11 INT JOINT C ART KONONENKO I, 1991, MACH LEARN, V6, P67, DOI 10.1007/BF00153760 MICHALSKI RS, 1990, IEEE T PATTERN ANAL, V2, P4 Quinlan J. R., 1993, C4 5 PROGRAMS MACH L ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 TING K. M., 1994, P 10 CAN C ART INT TORGO L, 1993, ECML 93, P185 NR 20 TC 30 Z9 30 PU BLACKWELL PUBLISHERS PI MALDEN PA 350 MAIN STREET, STE 6, MALDEN, MA 02148 USA SN 0824-7935 J9 COMPUT INTELL JI Comput. Intell. PD AUG PY 2001 VL 17 IS 3 BP 409 EP 424 DI 10.1111/0824-7935.00154 PG 16 WC Computer Science, Artificial Intelligence SC Computer Science GA 468MN UT WOS:000170761600003 ER PT J AU Sandberg, R Winberg, G Branden, CI Kaske, A Ernberg, I Coster, J AF Sandberg, R Winberg, G Branden, CI Kaske, A Ernberg, I Coster, J TI Capturing whole-genome characteristics in short sequences using a naive Bayesian classifier SO GENOME RESEARCH LA English DT Article ID HORIZONTAL GENE-TRANSFER; BACTERIAL GENOMES; DNA-SEQUENCES; DINUCLEOTIDE; SIGNATURE AB Bacterial genomes have diverged during evolution, resulting in clearcut differences in their nucleotide ge composition, such as their GC content. The analysis of complete sequences of bacterial genomes also reveals the presence of nonrandom. sequence variation, manifest in the frequency profile of specific short oligonucleotides. These frequency profiles constitute highly specific genomic signatures. Based on these differences in oligonucleotide frequency between bacterial genomes, we investigated the possibility of predicting the genome of origin for a specific genomic sequence. To this end, we developed a naive Bayesian classifier and systematically analyzed 28 eubacterial and archaeal genomes. We found that sequences as short as 400 bases could be correctly classified with an accuracy of 85%. We then applied the classifier to the identification of horizontal gene transfer events In whole-genome sequences and demonstrated the validity of our approach by correctly predicting the transfer of both the superoxide dismutase (sodC) and the bioC gene from Haemophilus influenzae to Neisseria meningitis, correctly identifying both the donor and recipient species. We believe that this classification methodology could be a valuable tool in biodiversity studies. C1 Karolinska Inst, Ctr Microbiol & Tumor Biol, S-17177 Stockholm, Sweden. Virtual Genet Lab AB, S-17177 Stockholm, Sweden. RP Sandberg, R (reprint author), Karolinska Inst, Ctr Microbiol & Tumor Biol, S-17177 Stockholm, Sweden. RI Winberg, Gosta/I-5686-2013 CR DEICH RA, 1980, MOL GEN GENET, V177, P369, DOI 10.1007/BF00271475 Deschavanne PJ, 1999, MOL BIOL EVOL, V16, P1391 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Doolittle WF, 1999, SCIENCE, V284, P2124, DOI 10.1126/science.284.5423.2124 Durbin R., 1998, BIOL SEQUENCE ANAL Eisen JA, 2000, CURR OPIN GENET DEV, V10, P606, DOI 10.1016/S0959-437X(00)00143-X Garcia-Vallve S, 2000, GENOME RES, V10, P1719, DOI 10.1101/gr.130000 GOLDMAN N, 1993, NUCLEIC ACIDS RES, V21, P2487, DOI 10.1093/nar/21.10.2487 KARLIN S, 1995, TRENDS GENET, V11, P283 Karlin S, 1997, J BACTERIOL, V179, P3899 KARLIN S, 1992, NUCLEIC ACIDS RES, V20, P1363, DOI 10.1093/nar/20.6.1363 KARLIN S, 1994, P NATL ACAD SCI USA, V91, P12832, DOI 10.1073/pnas.91.26.12832 Kroll JS, 1998, P NATL ACAD SCI USA, V95, P12381, DOI 10.1073/pnas.95.21.12381 LANGLEY P, 1992, AAAI92 LEWIS D, 1994, SIGIR94 Mrazek J, 1999, ANN NY ACAD SCI, V870, P314, DOI 10.1111/j.1749-6632.1999.tb08893.x Nakashima H, 1998, DNA Res, V5, P251, DOI 10.1093/dnares/5.5.251 Nakashima H, 1997, DNA Res, V4, P185, DOI 10.1093/dnares/4.3.185 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ronaghi M, 1996, ANAL BIOCHEM, V242, P84, DOI 10.1006/abio.1996.0432 Rubin GM, 2000, SCIENCE, V287, P2204, DOI 10.1126/science.287.5461.2204 NR 21 TC 101 Z9 108 PU COLD SPRING HARBOR LAB PRESS PI PLAINVIEW PA 1 BUNGTOWN RD, PLAINVIEW, NY 11724 USA SN 1088-9051 J9 GENOME RES JI Genome Res. PD AUG PY 2001 VL 11 IS 8 BP 1404 EP 1409 DI 10.1101/gr.186401 PG 6 WC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Genetics & Heredity SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Genetics & Heredity GA 459RC UT WOS:000170263900012 PM 11483581 ER PT J AU Bodoff, D Enache, D Kambil, A Simon, G Yukhimets, A AF Bodoff, D Enache, D Kambil, A Simon, G Yukhimets, A TI A unified maximum likelihood approach to document retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID RELEVANCE FEEDBACK; MODEL; TERMS AB Empirical work shows significant benefits from using relevance feedback data to improve information retrieval (IR) performance. Still, one fundamental difficulty has limited the ability to fully exploit this valuable data. The problem is that it is not clear whether the relevance feedback data should be used to train the system about what the users really mean, or about what the documents really mean. In this paper, we resolve the question using a maximum likelihood framework. We show how all the available data can be used to simultaneously estimate both documents and queries in proportions that are optimal in a maximum likelihood sense. The resulting algorithm is directly applicable to many approaches to IR, and the unified framework can help explain previously reported results as well as guide the search for new methods that utilize feedback data in IR. C1 Hong Kong Univ Sci & Technol, Dept Informat & Syst Management, Hong Kong, Hong Kong, Peoples R China. SAF Germany GmbH, Simulat Anal Forecasting, D-78467 Constance, Germany. Andersen Consulting Inst Strateg Change, Wellesley, MA USA. NYU, Leonard N Stern Sch Business, Dept Stat & Operat Res, New York, NY 10012 USA. NYU, Dept Phys, New York, NY 10003 USA. RP Bodoff, D (reprint author), Hong Kong Univ Sci & Technol, Dept Informat & Syst Management, Clear Water Bay, Hong Kong, Hong Kong, Peoples R China. RI bodoff, david/A-3775-2009 CR BARTELL B, 1992, DENM 15 INT SIGIR BARTELL BT, 1995, J AM SOC INFORM SCI, V46, P254, DOI 10.1002/(SICI)1097-4571(199505)46:4<254::AID-ASI3>3.0.CO;2-S Bartell BT, 1998, J AM SOC INFORM SCI, V49, P742 BODOFF D, 1999, REUNIFICATION 2 COMP Bodoff D, 1999, J AM SOC INFORM SCI, V50, P49, DOI 10.1002/(SICI)1097-4571(1999)50:1<49::AID-ASI7>3.0.CO;2-9 BRAUEN, 1971, SMART RETRIEVAL SYST, P456 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H COOPER WS, 1994, SIGIR 94 DUBL IR COOPER WS, 1992, DENM 15 INT SIGIR CRESTANI F, 1994, SCOTL 16 BCS IR C Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 HARMAN DK, 1992, DENM 15 INT SIGIR Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 MARGULIS EL, 1993, INFORM PROCESS MANAG, V29, P215, DOI 10.1016/0306-4573(93)90005-X MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1982, UNIFIED PROBABILISTI Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1994, J DOC, V50, P233, DOI 10.1108/eb026932 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE DE, 1998, SIGCHI B, V30 Salton G., 1989, AUTOMATIC TEXT PROCE TAI X, 2000, P 5 INT WORKSH INF R WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 2000, BIG PICTURE TRAFFIC NR 29 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD AUG PY 2001 VL 52 IS 10 BP 785 EP 796 DI 10.1002/asi.1137.abs PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 457EJ UT WOS:000170123900002 ER PT J AU Hanani, U Shapira, B Shoval, P AF Hanani, U Shapira, B Shoval, P TI Information filtering: Overview of issues, research and systems SO USER MODELING AND USER-ADAPTED INTERACTION LA English DT Review DE evaluation methods; information filtering; information retrieval; learning; measurement; user modeling; user profile ID USER MODELS; RETRIEVAL; WEB; EXPERIMENTATION; PERSONALIZATION; STEREOTYPES; PREFERENCES; PRIVACY; SERVICE; NEWS AB An abundant amount of information is created and delivered over electronic media. Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation. Information filtering (IF) is one of the methods that is rapidly evolving to manage large information flows. The aim of IF is to expose users to only information that is relevant to them. Many IF systems have been developed in recent years for various application domains. Some examples of filtering applications are: filters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them only to suitable pages, filters for e-commerce applications that address products and promotions to potential customers only, and many more. The different systems use various methods, concepts, and techniques from diverse research areas like: Information Retrieval, Artificial Intelligence, or Behavioral Science. Various systems cover different scope, have divergent functionality, and various platforms. There are many systems of widely varying philosophies, but all share the goal of automatically directing the most valuable information to users in accordance with their User Model, and of helping them use their limited reading time most optimally. This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems. The paper defines a framework to classify IF systems according to several parameters, and illustrates the approach with commercial and academic systems. The paper describes the underlying concepts of IF systems and the techniques that are used to implement them. It discusses methods and measurements that are used for evaluation of IF systems and limitations of the current systems. In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and integration with digital libraries and Web repositories. C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-841055 Beer Sheva, Israel. RP Hanani, U (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-841055 Beer Sheva, Israel. EM shoval@bgumail.bgu.ac.il RI Shoval, Peretz/F-1229-2012; SHAPIRA, BRACHA/F-1999-2012 OI Shoval, Peretz/0000-0002-0183-2832; CR Abiteboul S, 2000, DATA WEB RELATIONS S ALLEN RB, 1990, INT J MAN MACH STUD, V32, P511, DOI 10.1016/S0020-7373(05)80032-X Ambrosini L., 1997, US MODE P 6 INT C WI, P59 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 ARAMPATZIS AT, 2000, RIAO 2000 C P APR 12, V2, P1221 Arms W., 2000, DIGITAL LIB ARNHEIM LA, 1996, COLL FILT WORKSH MAR Asnicar F., 1997, P WORKSH AD SYST US Avery C, 1999, AM ECON REV, V89, P564, DOI 10.1257/aer.89.3.564 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Balaban AT, 1998, SAR QSAR ENVIRON RES, V8, P1, DOI 10.1080/10629369808033259 BALABANOVIC M, 1997, COMMUN ACM, V40, P60 BATES ME, 1994, ONLINE, V18, P43 BEERUD S, 1994, NEW PERSONALIZED INF BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BENHAZEZ S, 2000, RIAO2000 C P PAR FRA, V2 Billsus D, 1999, CISM COUR L, P99 Binkley J., 1995, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V5, DOI 10.1007/BF00962625 Bollacker KD, 2000, IEEE INTELL SYST APP, V15, P42, DOI 10.1109/5254.850826 Bollacker K.D., 1999, P 4 ACM C DIG LIB, P105, DOI 10.1145/313238.313270 BORCHERS AJ, 1998, IEEE COMPUT, V3, P106 BRAJNIK G, 1990, IEEE T SYST MAN CYB, V20, P166, DOI 10.1109/21.47819 BREWESTER K, 1991, ONLINE, V15, P56 *BRM TECHN, 2000, BACKW PUSH SERV Calvi L, 1997, USER MODEL USER-ADAP, V7, P257, DOI 10.1023/A:1008258421355 CAMPIONE M, 1998, JAVA TUTORIAL CHEN C, 1999, INFORMATION VISUALIS Cingil I, 2000, COMMUN ACM, V43, P136, DOI 10.1145/345124.345168 *COMM CORP, 2000, NEWSCL NEWS FILT LAN Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336 DESJARDINS G, 2000, RIAO 2000 C P APR 12, V2, P1676 DRAGAN RV, 1997, PC MAGAZINE SEP Edmunds A, 2000, INT J INFORM MANAGE, V20, P17, DOI 10.1016/S0268-4012(99)00051-1 ELIENS A, 2000, PRINCIPLES OBJECT OR Fisher Danyel, 2000, P SIGIR, P366, DOI 10.1145/345508.345658 Fleming M., 1999, P 7 INT C US MOD, P67 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 FOLTZ PW, 1990, ACM SIG OIS, P40 Foner L. N., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267732 FRAKES WR, 1992, INFORMATION RETRIEVA GOECK J, 2000, P 2000 INT C INT US GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Gosling J., 1996, JAVA LANGUAGE ENV WH GURNEY K, 1996, INTRO NEURAL NETWORK HANANI U, 2000, P 2000 KYOT INT C DI, P260 HANANI U, 2000, ECIS 2000 P VIENN JU, P306 HARMAN D, 1999, P 8 TEXT RETR C TREC, P1 HENSLEY P, 1997, W3C OP PROF Hirashima T, 1997, USER MODEL USER-ADAP, V7, P239, DOI 10.1023/A:1008239629258 HOASHI K, 1999, P 8 TEXT RETR C TREC HOFFERER M, 1994, P INT MULT INF RETR, P314 HULL DA, 1998, NIST SP, V5240, P45 HULL DA, 2000, 8 TEXT RETR C TREC 7 HULL DA, 1999, NIST SP, V5242, P3 JENNINGS A, 1992, IEICE T INF SYST, VE75D, P198 Jennings A., 1993, User Modeling and User-Adapted Interaction, V3, DOI 10.1007/BF01099423 KANTOR P, 1999, P ASIS 99, P536 Kantor PB, 2000, COMMUN ACM, V43, P112, DOI 10.1145/345124.345162 KASS R, 1989, INT J INTELL SYST, V4, P81, DOI 10.1002/int.4550040106 Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 KAY J, 1995, CUSTOMIZATION DELIVE KAY J, 1990, 2 INT US MOD WORKSH, P11 KILLANDER F, 1994, GHOSTS DESIGN SYSTEM Kim Yu-Hwan, 2000, P 23 ANN INT ACM SIG, P168, DOI 10.1145/345508.345572 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Korfhage R., 1997, INFORMATION STORAGE KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 Kuflik T., 2000, P 23 ANN INT ACM SIG, P313, DOI 10.1145/345508.345615 Lang K., 1995, P 12 INT C MACH LEAR, P331 Lanquillon C, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P538, DOI 10.1145/319950.320061 LOSEE RM, 1995, INFORM PROCESS MANAG, V31, P555, DOI 10.1016/0306-4573(95)00072-O Losee RM, 2000, J AM SOC INFORM SCI, V51, P834, DOI 10.1002/(SICI)1097-4571(2000)51:9<834::AID-ASI60>3.3.CO;2-T MAES P, 1993, PROCEEDINGS OF THE ELEVENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, P459 MALONE TW, 1987, COMMUN ACM, V30, P390, DOI 10.1145/22899.22903 Manber U, 2000, COMMUN ACM, V43, P35, DOI 10.1145/345124.345136 MARINILLI M, 1999, 2 WORKSH AD SYST US McCalla G., 1996, P 5 INT C US MOD, P13 MCCLEARY H, 1994, ONLINE, V4, P35 METZ C, 2000, PC MAGAZINE, V19, P59 MICHEL C, 2000, RIAO 2000 C P APR 12, V2, P1578 Mock KJ, 1997, INFORM PROCESS MANAG, V33, P633, DOI 10.1016/S0306-4573(97)00022-8 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 Morita M., 1994, P 17 ANN INT ACM SIG, P272 Mostafa J, 1997, ACM T INFORM SYST, V15, P368, DOI 10.1145/263479.263481 MOUKAS A, 1996, P C PRACT APPL AG MU De Carvalho Moura A MCM, 1998, WORLD WIDE WEB, V1, P221 Newell SC, 1997, USER MODEL USER-ADAP, V7, P223, DOI 10.1023/A:1008292003163 OARD WD, 1998, P AM ASS ART INT WOR, P80 OARD WD, 2000, INFORMATION FILTERIN OARD WD, 1997, USER MODEL USER-ADAP, V7, P141 OLSSON T, 1998, REC SYST WORKSH PAP, P84 Orfali R., 1996, ESSENTIAL CLIENT SER Pazzani M. J., 1999, Proceedings of the Third International Conference on Autonomous Agents, DOI 10.1145/301136.301256 PINAR A, 1995, P ISCIS 10 10 INT S, P281 PROCTER R, 1997, 5 DELOS WORKSH COLL QUIROGA LM, 1990, P 4 ACM C DIG LIB AU, P238 Raskutti B, 1997, USER MODEL USER-ADAP, V7, P179, DOI 10.1023/A:1008291330418 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Rhodes B. J., 1996, PAAM 96. Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology Rhodes B., 1997, PERSONAL TECHNOLOGIE, V1, P218, DOI 10.1007/BF01682024 Rich E., 1989, USER MODELS DIALOG S, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTWITT S, 2000, PC MAGAZINE 0509, V19, P190 Rocchio J.J., 1971, SMART RETRIEVAL SYST, P57 RUST G, 1998, D LIB MAGAZINE JUL, P1082 RYAN ME, 1999, PC MAGAZINE 0504, V8, P273 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SARWAR BM, 1909, P ACM 1998 C COMP SU, P345 Seo Y.-W., 2000, P 2000 INT C INT US, P248, DOI 10.1145/325737.325859 Shapira B., 1997, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V8, DOI 10.1023/A:1008676625559 Shapira B, 1997, INFORM PROCESS MANAG, V33, P273, DOI 10.1016/S0306-4573(97)00003-4 Shapira B, 1999, DECIS SUPPORT SYST, V27, P5, DOI 10.1016/S0167-9236(99)00034-2 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 SHARON T, 2000, 66 IFLA COUNC GEN C Sheth B. D., 1994, THESIS MIT Smyth B, 2000, COMMUN ACM, V43, P107, DOI 10.1145/345124.345161 STADNYK I, 1992, COMMUN ACM, V35, P49, DOI 10.1145/138859.138864 STANEK WR, 1997, PC MAGAZINE ONLINE, V16 Stefani A., 1999, 2 WORKSH AD SYST US SU L, 1991, THESIS RUTGERS STATE TAGUESUTCLIFFE J, 1992, INFORM PROCESS MANAG, V28, P467, DOI 10.1016/0306-4573(92)90005-K TATEMURA J, 1999, P 1999 INT C INT US, P189, DOI 10.1145/291080.291114 TATEMURA L, 2000, P 2000 INT C INT US, P272 TERRY DB, 1993, TOUR TAPESTRY Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 Thomas C., 1996, P 5 INT C US MOD, P5 TJOA AM, 1997, APPL EV ALG PROBL IN TURNBULL D, 2000, AUGMENTING INFORMATI TURNBULL D, 1997, KMDI FINAL SUMMARY C VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Volokh E, 2000, COMMUN ACM, V43, P84, DOI 10.1145/345124.345155 *W3C, 2000, RES DESCR FRAM RDF WAERN A, 1999, P 1999 INT C INT US, P185, DOI 10.1145/291080.291110 WAERN A, 1999, INT C US MOD UM 99 U WOLINSKI F, 2000, RAIO 2000 C P APR 12, V2, P1208 *WORLDN, 2000, LEX DAT ENGL WU Y, 2000, 20 INT C DISTR COMP YAN TW, 1994, STANFORD INFORMATION Moukas A., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267747 NR 140 TC 129 Z9 132 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-1868 J9 USER MODEL USER-ADAP JI User Model. User-Adapt. Interact. PD AUG PY 2001 VL 11 IS 3 BP 203 EP 259 DI 10.1023/A:1011196000674 PG 57 WC Computer Science, Cybernetics SC Computer Science GA 441GK UT WOS:000169222500001 ER PT J AU Danilowicz, C Balinski, J AF Danilowicz, C Balinski, J TI Document ranking based upon Markov chains SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE document similarity; document ranking; Markov chain ID BOOLEAN INFORMATION-RETRIEVAL; SYSTEMS; MODEL; WEB AB One of the most important problems in information retrieval is determining the order of documents in the answer returned to the user. Many methods and algorithms fur document ordering have been proposed. The method introduced in this paper differs from them especially in that it uses a probabilistic model of a document set. In this model documents are regarded as states of a Markov chain, where transition probabilities are directly proportional to similarities between documents. Steady-state probabilities reflect similarities of particular documents to the whole answer set. If documents are ordered according to these probabilities, at the top of a list there will be documents that are the best representatives of the set, and at the bottom those which are the worst representatives. The method was tested against databases INSPEC and Networked Computer Science Technical Reference Library (NCSTRL). Test results are positive. Values of the Kendall rank correlation coefficient indicate high similarity between rankings generated by the proposed method and rankings produced by experts. Results are comparable with rankings generated by the vector model using standard weighting schema tf . idf. (C) 2001 Elsevier Science Ltd. All rights reserved. C1 Wroclaw Univ Technol, Dept Informat Syst, PL-50370 Wroclaw, Poland. RP Danilowicz, C (reprint author), Wroclaw Univ Technol, Dept Informat Syst, Wybrzeze S Wyspianskiego 27, PL-50370 Wroclaw, Poland. RI Mafutsana, Mopedi/C-5157-2015 CR BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I Carriere SJ, 1997, COMPUT NETWORKS ISDN, V29, P1257, DOI 10.1016/S0169-7552(97)00062-7 CHOROS K, 1982, INFORM PROCESS MANAG, V18, P207, DOI 10.1016/0306-4573(82)90060-7 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 CROFT WB, 1993, INFORM PROCESS MANAG, V29, P313, DOI 10.1016/0306-4573(93)90058-L DANILOWICZ C, 1994, INFORM PROCESS MANAG, V30, P363, DOI 10.1016/0306-4573(94)90050-7 DANILOWICZ C, 1983, INFORM PROCESS MANAG, V19, P159, DOI 10.1016/0306-4573(83)90069-9 Davies N. J., 1997, Software agents and soft computing. Towards enhancing machine intelligence. Concepts and applications El-Beltagy S., 1999, PAAM99. Proceedings of the Fourth International Conference on the Practical Applications of Intelligent Agents and Multi-agent Technology Feller W., 1961, INTRO PROBABILITY TH Ferber J., 1999, MULTIAGENT SYSTEMS GUPTA MM, 1991, FUZZY SET SYST, V40, P431, DOI 10.1016/0165-0114(91)90171-L HSIANG J, 1999, PRIMA 98, P118 Kendall M. G., 1948, RANK CORRELATION MET Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Marchiori M, 1997, COMPUT NETWORKS ISDN, V29, P1225, DOI 10.1016/S0169-7552(97)00036-6 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SEWELL W, 1986, J AM SOC INFORM SCI, V37, P234, DOI 10.1002/(SICI)1097-4571(198607)37:4<234::AID-ASI9>3.0.CO;2-A SOERGEL D, 1967, INFORM STORAGE RET, V3, P129, DOI 10.1016/0020-0271(67)90006-X SPARCK JK, 1973, INFORMATION STORAGE, V9, P619 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 NR 25 TC 6 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2001 VL 37 IS 4 BP 623 EP 637 DI 10.1016/S0306-4573(00)00038-8 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 436CK UT WOS:000168918800005 ER PT J AU Zhang, J Rasmussen, EM AF Zhang, J Rasmussen, EM TI Developing a new similarity measure from two different perspectives SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE new similarity measure; angle-based similarity measure; distance-based similarity measure ID DOCUMENT-RETRIEVAL; INFORMATION; RELEVANCE AB In this paper two distinct similarity measures in a document vector space, the distance-based and angle-based similarity measures, are compared, and a newly developed similarity measure based upon both the distance and angle strengths of two compared objects is presented. The concept of the iso-extent contour, which facilitates the understanding of the nature of the newly developed similarity measure, is introduced. The three different similarity measures are compared and the properties of the newly developed similarity measure are addressed. (C) 2001 Elsevier Science Ltd. All rights reserved. C1 Univ Wisconsin, Sch Lib & Informat Sci, Milwaukee, WI 53211 USA. Univ Pittsburgh, Sch Informat Sci, Pittsburgh, PA 15260 USA. RP Zhang, J (reprint author), Univ Wisconsin, Sch Lib & Informat Sci, 2400 E Hartford Ave,Enderis Hall, Milwaukee, WI 53211 USA. RI zhang, jin /A-6769-2010 CR Bartell BT, 1998, J AM SOC INFORM SCI, V49, P742 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CRONIN B, 1994, J AM SOC INFORM SCI, V45, P537, DOI 10.1002/(SICI)1097-4571(199408)45:7<537::AID-ASI8>3.0.CO;2-Q Ellis D., 1993, PERSPECTIVES INFORMA, V3, P128 Falkowski BJ, 1998, J AM SOC INFORM SCI, V49, P854, DOI 10.1002/(SICI)1097-4571(199807)49:9<854::AID-ASI11>3.0.CO;2-Q FRAKES WR, 1992, INFORMATION RETRIEVA Fricke M, 1997, J AM SOC INFORM SCI, V48, P882, DOI 10.1002/(SICI)1097-4571(199710)48:10<882::AID-ASI4>3.0.CO;2-Y GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 HAMERS L, 1989, INFORM PROCESS MANAG, V25, P315, DOI 10.1016/0306-4573(89)90048-4 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Korfhage R., 1997, INFORMATION STORAGE KWOK KL, 1985, J AM SOC INFORM SCI, V36, P242 MCGILL M, 1979, EVALUATION FACTORS A Meadow C.T., 1992, TEXT INFORMATION RET NUCHPRAYYOON A, 1996, THESIS U PITTSBURGH OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 Olsen K. A., 1994, Proceedings. IEEE Symposium on Visual Languages (Cat. No.94TH8010), DOI 10.1109/VL.1994.363612 RADECKI T, 1982, J DOC, V38, P14, DOI 10.1108/eb026719 RADECKI T, 1985, INFORM PROCESS MANAG, V21, P501, DOI 10.1016/0306-4573(85)90004-4 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rousseau R, 1998, INFORM PROCESS MANAG, V34, P87, DOI 10.1016/S0306-4573(97)00067-8 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, AUTOMATIC INFORMATIO TRAVISON D, 1987, INFORMATION PROCESSI, V23, P183 TUDHOPE D, 1996, P 59 ANN M AM SOC IN, P67 Tudhope D, 1997, INFORM PROCESS MANAG, V33, P233, DOI 10.1016/S0306-4573(96)00067-2 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Zhang J, 1999, J AM SOC INFORM SCI, V50, P772, DOI 10.1002/(SICI)1097-4571(1999)50:9<772::AID-ASI5>3.0.CO;2-E ZHANG J, 1999, THESIS U PITTSBURGH NR 30 TC 29 Z9 32 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2001 VL 37 IS 2 BP 279 EP 294 DI 10.1016/S0306-4573(00)00027-3 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 402VL UT WOS:000167008800006 ER PT J AU Kim, W Wilbur, WJ AF Kim, W Wilbur, WJ TI Corpus-based statistical screening for content-bearing terms SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID RETRIEVAL; WORDS AB An important problem in the indexing of natural language text is how to identify those words and phrases that reflect the content of the text. In general, automatic indexing has dealt with this problem by removing instances of a few hundred common words known as stop words, and treating the remaining words as though they were content bearing. This approach is acceptable for some applications such as statistical estimates of the similarity of queries and documents for the purpose of document retrieval. However, when the indexing terms are to be examined by a human as a means of accessing the literature, it greatly improves efficiency if most of the noncontent-bearing words and phrases can be eliminated from the indexing, Here we present three statistical techniques for identifying content-bearing phrases within a natural language database. We demonstrate the effectiveness of the methods on test data, and show how all three methods can be combined to produce a single improved method. C1 NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20894 USA. RP Kim, W (reprint author), NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bldg 38A,Rm 8S806,8600 Rockville Pike, Bethesda, MD 20894 USA. CR BOOKSTEIN A, 1995, P 18 ANN INT ACM SIG Bookstein A, 1998, J AM SOC INFORM SCI, V49, P102 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 CARROLL JM, 1969, AM DOC, V20, P227, DOI 10.1002/asi.4630200308 CURTICE RM, 1967, AM DOC I ANN M CAMBR DAMERAU FJ, 1965, AM DOC, V16, P283, DOI 10.1002/asi.5090160403 DILLON M, 1982, J AM SOC INFORM SCI, V33, P245 HARMAN D, 1994, P TIPSTER TEXT PROGR HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 LANGLEY P, 1994, 10 C UNC ART INT SEA Langley P., 1996, ELEMENTS MACHINE LEA LANGLEY P, 1992, 10 NAT C ART INT SAN Larson H, 1982, INTRO PROBABILITY TH PONTE JM, 1998, SIGIR98 MELB AUSTR RESNICK A, 1961, SCIENCE, V134, P1004, DOI 10.1126/science.134.3484.1004 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1969, AM DOCUMENTATION JAN, P61 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, THEORY INDEXING, V18 SALTON G, 1992, INFORM PROCESS MANAG, V28, P441, DOI 10.1016/0306-4573(92)90002-H STAFF NLM, 2000, MED SUBJECT HEADINGS STORE DC, 1968, AM DOC, V19, P411 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Wilbur WJ, 1996, COMPUT BIOL MED, V26, P209, DOI 10.1016/0010-4825(95)00055-0 Witten Ian H., 1999, MANAGING GIGABYTES, V2nd Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 33 TC 9 Z9 9 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB 1 PY 2001 VL 52 IS 3 BP 247 EP 259 DI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1588>3.0.CO;2-7 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 406CU UT WOS:000167198000006 ER PT J AU Spink, A Greisdorf, H AF Spink, A Greisdorf, H TI Regions and levels: Measuring and mapping users' relevance judgments SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; RECALL AB The dichotomous bipolar approach to relevance has produced an abundance of information retrieval IIR) research. However, relevance studies that include consideration of users' partial relevance judgments are moving to a greater relevance clarity and congruity to impact the design of more effective IR systems. The study reported in this paper investigates the various regions of across a distribution of users' relevance judgments, including how these regions may be categorized, measured, and evaluated. An instrument was designed using four scales for collecting, measuring, and describing end-user relevance judgments. The instrument was administered to 21 end-users who conducted searches on their own information problems and made relevance judgments on a total of 1059 retrieved items. Findings include: (1) overlapping regions of relevance were found to impact the usefulness of precision ratios as a measure of in system effectiveness, (2) both positive and negative levels of relevance are important to users as they make relevance judgments, (3) topicality was used more to reject rather than accept items as highly relevant, (4) utility was more used to judge items highly relevant, and (5) the nature of relevance judgment distribution suggested a new IR evaluation measure-median effect. Findings suggest that the middle region of a distribution of relevance judgments, also called "partial relevance," represents a key avenue for ongoing study. The findings provide implications for relevance theory, and the evaluation of in systems. C1 Penn State Univ, Sch Informat Sci & Technol, University Pk, PA 16801 USA. Univ N Texas, Sch Lib & Informat Sci, Denton, TX 76103 USA. RP Spink, A (reprint author), Penn State Univ, Sch Informat Sci & Technol, 511 Rider Bldg,120 S Burrowes St, University Pk, PA 16801 USA. CR BARHILLEL Y, 1958, P INT C SCI INF WASH, P1402 BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J Brooks TA, 1997, INFORM PROCESS MANAG, V33, P69, DOI 10.1016/S0306-4573(96)00051-9 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Cormack G. V., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291009 Crow E. L., 1960, STAT MANUAL EISENBERG MB, 1988, INFORM PROCESS MANAG, V24, P373, DOI 10.1016/0306-4573(88)90042-8 FAIRTHORNE RA, 1958, P INT C SCI INF WASH, P1406 FOSKETT DJ, 1972, INFORM STORAGE RET, V8, P77, DOI 10.1016/0020-0271(72)90009-5 Gluck M, 1996, INFORM PROCESS MANAG, V32, P89, DOI 10.1016/0306-4573(95)00031-B GOFFMAN W, 1964, INFORM STORAGE RET, V2, P201, DOI 10.1016/0020-0271(64)90004-X GREISDORF H, IN PRESS REGIONS R 2 GREISDORF H, 1999, IRSG 99 21 C INF RET HILLMAN DJ, 1964, AM DOCUMENTATION JAN, P26 HOWARD DL, 1994, J AM SOC INFORM SCI, V45, P172, DOI 10.1002/(SICI)1097-4571(199404)45:3<172::AID-ASI7>3.0.CO;2-V JANES JW, 1993, P AM SOC INFORM SCI, P104 JANES JW, 1991, J AM SOC INFORM SCI, V42, P754, DOI 10.1002/(SICI)1097-4571(199112)42:10<754::AID-ASI9>3.0.CO;2-C Mizzaro S, 1998, INTERACT COMPUT, V10, P303, DOI 10.1016/S0953-5438(98)00012-5 Mosteller F., 1977, DATA ANAL REGRESSION OCONNOR J, 1967, AM DOCUMENTATION JUL, P165 PAO ML, 1993, INFORM PROCESS MANAG, V29, P95, DOI 10.1016/0306-4573(93)90026-A PARK TK, 1993, LIBR QUART, V63, P318 Rees A., 1967, FIELD EXPT APPROACH REES AM, 1966, ASLIB PROC, V18, P316, DOI 10.1108/eb050068 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Saracevic T., 1996, 2 INT C CONC LIB INF, P201 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Saracevic T., 1969, P AM SOC INFORM SCI, V6, P293 Sperber D., 1986, RELEVANCE COMMUNICAT Spink A, 1998, INFORM PROCESS MANAG, V34, P599, DOI 10.1016/S0306-4573(98)00025-9 Sternberg R. J., 1996, COGNITIVE PSYCHOL SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 Tang R, 1999, J AM SOC INFORM SCI, V50, P254, DOI 10.1002/(SICI)1097-4571(1999)50:3<254::AID-ASI8>3.0.CO;2-Y TAUBE M, 1965, AM DOC, V16, P69, DOI 10.1002/asi.5090160204 TIAMIYU MA, 1988, INFORM PROCESS MANAG, V24, P391, DOI 10.1016/0306-4573(88)90043-X Wallis P, 1996, INFORM PROCESS MANAG, V32, P273, DOI 10.1016/0306-4573(95)00061-5 WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X WOEFEL J, 1980, MEASUREMENT COMMUNIC Yang Yiming, 1998, P 21 ANN INT ACM SIG, P28, DOI DOI 10.1145/290941.290953 NR 40 TC 27 Z9 27 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JAN 15 PY 2001 VL 52 IS 2 BP 161 EP 173 DI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1564>3.3.CO;2-C PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 404KY UT WOS:000167098100007 ER PT S AU Yao, YY Zhong, N AF Yao, YY Zhong, N GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI Mining market value functions for targeted marketing SO 25TH ANNUAL INTERNATIONAL COMPUTER SOFTWARE & APPLICATIONS CONFERENCE SE PROCEEDINGS - INTERNATIONAL COMPUTER SOFTWARE & APPLICATIONS CONFERENCE LA English DT Proceedings Paper CT 25th Annual International Computer Software and Applications Conference (COMPSAC 2001) CY OCT 08-12, 2001 CL CHICAGO, IL SP IEEE Comp Soc ID DISCOVERY; DATABASES; MODEL AB Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects of interest according to their market values, instead of classifying the objects. The theoretical results reported in this paper establish a basis on which further studies and experimental evaluation can be carried out. C1 Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada. RP Yao, YY (reprint author), Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada. EM yyao@cs.uregina.ca; zhong@maebashi-it.ac.jp RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 FISHBURN PC, 1974, J MATH PSYCHOL, V11, P294, DOI 10.1016/0022-2496(74)90024-8 HAN JW, 1993, IEEE T KNOWL DATA EN, V5, P29, DOI 10.1109/69.204089 JONES KS, 1997, READINGS INFORMATION KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 LEAKE DB, 1996, CASE BASE REASONING Lingjaerde O, 1998, ACTA PSYCHIAT SCAND, V98, P73, DOI 10.1111/j.1600-0447.1998.tb10045.x Pawlak Z, 1991, ROUGH SETS THEORETIC Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RAO CR, 1982, THEOR POPUL BIOL, V21, P24, DOI 10.1016/0040-5809(82)90004-1 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA WATANABE S, 1981, PATTERN RECOGN, V13, P381, DOI 10.1016/0031-3203(81)90094-7 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1992, J AM SOC INFORM SCI, V43, P54, DOI 10.1002/(SICI)1097-4571(199201)43:1<54::AID-ASI5>3.0.CO;2-A WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 Yao Y.Y., 1999, P 3 PAC AS C KNOWL D, P133 YAO YY, 1999, UNPUB GRANULAR COMPU Zhong N, 1999, LECT NOTES ARTIF INT, V1704, P136 Zhong N, 2001, NEUROCOMPUTING, V36, P171, DOI 10.1016/S0925-2312(00)00341-6 Zhong N, 2000, INFORM SCIENCES, V126, P99, DOI 10.1016/S0020-0255(00)00012-8 NR 22 TC 7 Z9 11 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 0730-3157 BN 0-7695-1372-7 J9 P INT COMP SOFTW APP PY 2001 BP 517 EP 522 DI 10.1109/CMPSAC.2001.960662 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BT20L UT WOS:000172264100079 ER PT B AU Papineni, K AF Papineni, K GP ACL TI Why inverse document frequency? SO 2ND MEETING OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE LA English DT Proceedings Paper CT 2nd Meeting of the North American Chapter of the Association-for-Computational-Linguistics CY JUN 02-07, 2001 CL Pittsburgh, PA SP Assoc Computat Linguist, N Amer Chapter HO Carnegie Mellon Univ AB Inverse Document Frequency (IDF) is a popular measure of a word's importance. The IDF invariably appears in a host of heuristic measures used in information retrieval. However, so far the IDF has itself been a heuristic. In this paper, we show IDF to be optimal in a principled sense. We show that IDF is the optimal weight of a word with respect to minimization of a Kullback-Leibler distance suitably generalized to nonnegative functions which need not be probability distributions. This optimization problem is closely related to maximum entropy problem. We show that the IDF is the optimal weight associated with a word-feature in an information retrieval setting where we treat each document as the query that retrieves itself. That is, IDF is optimal for document self-retrieval. C1 IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA. CR COLLINS M, 2000, P 13 ANN C COMP LEAR CSISZAR I, 1991, ANN STAT, V19, P2032, DOI 10.1214/aos/1176348385 DellaPietra S, 1997, IEEE T PATTERN ANAL, V19, P380, DOI 10.1109/34.588021 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 LAFFERTY J, 1997, CANADIAN WORKSH INFO, P77 LAU R, 1993, P ARPA HUM LANG TECH, P108, DOI 10.3115/1075671.1075695 PAPINENI K, 2000, RC21815 IBM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA WONG SKM, 1992, J AM SOC INFORM SCI, V43, P54, DOI 10.1002/(SICI)1097-4571(199201)43:1<54::AID-ASI5>3.0.CO;2-A NR 10 TC 0 Z9 0 PU ASSOCIATION COMPUTATIONAL LINGUISTICS PI SOMERSET PA PO BOX 6090, SOMERSET, NJ 08875 USA BN 1-55860-775-7 PY 2001 BP 25 EP 32 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Statistics & Probability SC Computer Science; Linguistics; Mathematics GA BAP03 UT WOS:000223095200004 ER PT J AU Carpineto, C De Mori, R Romano, G Bigi, B AF Carpineto, C De Mori, R Romano, G Bigi, B TI An information-theoretic approach to automatic query expansion SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE information retrieval; automatic query expansion; pseudorelevance feedback; information theory ID CO-OCCURRENCE DATA; DOCUMENT-RETRIEVAL; SEARCH TERMS; MODELS; PROBABILITIES; FEEDBACK AB Techniques for automatic query expansion from top retrieved documents have shown promise for improving retrieval effectiveness on large collections; however, they often rely on an empirical ground, and there is a shortage of cross-system comparisons. Using ideas from Information Theory, we present a computationally simple and theoretically justified method for assigning scores to candidate expansion terms. Such scores are used to select and weight expansion terms within Rocchio's framework for query reweighting. We compare ranking with information-theoretic query expansion versus ranking with other query expansion techniques, showing that the former achieves better retrieval effectiveness on several performance measures. We also discuss the effect on retrieval effectiveness of the main parameters involved in automatic query expansion, such as data sparseness, query difficulty, number of selected documents, and number of selected terms, pointing out interesting relationships. C1 Fdn Ugo Bordoni, I-00142 Rome, Italy. Univ Avignon, Lab Informat, F-84911 Avignon 9, France. RP Carpineto, C (reprint author), Fdn Ugo Bordoni, Via B Castiglione 59, I-00142 Rome, Italy. CR AMATI G, 2000, PROBABILISTIC MODELS ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BALLERINI JP, 1996, P 5 C TEXT RETR TREC, P217 Bigi B., 1997, P 1997 IEEE WORKSH A, P535 Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 BRAJNIK G, 1996, P 19 ANN INT ACM SIG, P128, DOI 10.1145/243199.243249 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley Chris, 1995, P 3 TEXT RETR C TREC, P69 CARPINETO C, 2000, P 8 C TEXT RETR TREC, P377 CARPINETO C, 1999, P 3 EUR C DIG LIB EC, P126 Carpineto C, 2000, J AM SOC INFORM SCI, V51, P587 Carpineto C., 1998, P 3 INT C FLEX QUER, P83 Cooper J., 1997, P 2 ACM INT C DIG LI, P237, DOI 10.1145/263690.263828 Cover T. M., 1991, ELEMENTS INFORMATION CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Dagan I, 1999, MACH LEARN, V34, P43, DOI 10.1023/A:1007537716579 De Mori R., 1998, SPOKEN DIALOGUES COM DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dietterich TG, 1997, AI MAG, V18, P97 DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 EFTHIMIADIS E. N., 1993, P 16 ANN INT ACM SIG, P146, DOI 10.1145/160688.160710 Fitzpatrick L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P306, DOI 10.1145/258525.258597 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Harman D., 1992, INFORMATION RETRIEVA, P241 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HAWKING D, 1998, NIST SPECIAL PUBLICA, P275 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Karp D., 1992, P 14 INT C COMP LING, P950 KATZ SM, 1987, IEEE T ACOUST SPEECH, V35, P400, DOI 10.1109/TASSP.1987.1165125 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 LOSEE RM, 1990, SCI INFORMATION MEAS MARGULIS EL, 1993, INFORM PROCESS MANAG, V29, P215, DOI 10.1016/0306-4573(93)90005-X Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Porter M. F., 1982, Information Technology: Research and Development, V1 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 1994, P 3 TEXT RETR C TREC, P109 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Singhal A., 1999, P TREC 7 NIST GAITH, P239 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Velez B, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P6, DOI 10.1145/258525.258528 Voorhees E., 1998, NIST SPECIAL PUBLICA, P1 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Voorhees E.M., 1999, P TREC, P1 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 YANG K, 1999, P 7 C TEXT RETR TREC, P555 NR 58 TC 115 Z9 118 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN PY 2001 VL 19 IS 1 BP 1 EP 27 DI 10.1145/366836.366860 PG 27 WC Computer Science, Information Systems SC Computer Science GA 419TD UT WOS:000167964000001 ER PT B AU Kim, BM Kim, JY Kim, J AF Kim, BM Kim, JY Kim, J BE Smith, MH Gruver, WA Hall, LO TI Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference SO JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5 LA English DT Proceedings Paper CT 9th International-Fuzzy-Systems-Association World Congress/20th North-American-Fuzzy-Information-Processing-Society, International Conference CY JUL 25-28, 2001 CL VANCOUVER, CANADA SP Int Fuzzy Syst Assoc, N Amer Fuzzy Informat Proc Soc, IEEE Syst, Man & Cybernet Soc, IEEE, Neural Networks Council ID RETRIEVAL AB To improve the effectiveness of the classic relevance techniques for the vector model, in this paper, a new technique for term expansion and term, reweighting is suggested. The advantages of the classic techniques are simplicity and good results. However, due to the simplicity, the term occurrence pattern is not considered explicitly, To supplement the classic relevance techniques, we introduce the term co-occurrence similarity as a measure of how similar the distributions within the feedbacked documents of a given term and the initial query are. With this similarity and additional information, the weight in the new query of the term is calculated by fuzzy inference, Although the experiments are performed on the small collection, the results show that the technique proposed in this paper yields substantial improvements in retrieval effectiveness. C1 Kumoh Natl Univ Tech, Sch Comp & Software Eng, Kumi, Kyungpook, South Korea. RP Kim, BM (reprint author), Kumoh Natl Univ Tech, Sch Comp & Software Eng, Kumi, Kyungpook, South Korea. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 FRAKES WR, 1992, INFORMATION RETRIEVA Ide E., 1971, SMART RETRIEVAL SYST, P337 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 RADECKI T, 1988, INFORMATION PROCESSI, V24, P513 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7078-3 PY 2001 BP 715 EP 720 PG 6 WC Computer Science, Artificial Intelligence; Mathematics, Applied SC Computer Science; Mathematics GA BT52S UT WOS:000173245100125 ER PT J AU Wilbur, WJ AF Wilbur, WJ TI Global term weights for document retrieval learned from TREC data SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; PROBABILISTIC MODEL AB A key element in modern text retrieval systems is the weighting of individual words for importance. Early in the development of document retrieval methods it was recognized that performance could be improved if weights were based at least in part on the frequencies of individual terms in the database. This observation led investigators to propose inverse document frequency weighting, which has become the most commonly used approach. Inverse document frequency weighting can be given some justification based on probabilistic arguments. However, many different formulas have been tried and it is difficult to distinguish between these on a purely theoretical basis. Witten, Moffat and Bell, have proposed a monotonicity condition as fundamental: 'a term that appears in many documents should not be regarded as more important than a term that appears in a few'. Based on this monotonicity assumption and probabilistic arguments we show here how the TREC data can be used to learn ideal global weights. Using cross-validation we show that these weights are a modest but statistically significant improvement over IDF weights. One conclusion is that IDF weights are close to optimal within the probabilistic assumptions that are commonly made. C1 Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Bethesda, MD 20894 USA. RP Wilbur, WJ (reprint author), Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Bldg 38A,Rm 8S806,8600 Rockville Pike, Bethesda, MD 20894 USA. CR AYER M, 1955, ANN MATH STAT, V26, P641, DOI 10.1214/aoms/1177728423 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 GREIFF WR, 1998, SIGIR 98 MELB AUSTR, P21 Hardle W, 1991, SMOOTHING TECHNIQUES HARMAN DK, 1994, SPECIAL PUBLICATION HARMAN DK, 1995, SPECIAL PUBLICATION Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 1998, 446 U CAMBR PONTE JM, 1998, SIGIR 98 MELB AUSTR RO JS, 1988, J AM SOC INFORM SCI, V39, P147, DOI 10.1002/(SICI)1097-4571(198805)39:3<147::AID-ASI1>3.0.CO;2-O ROBERTSON SE, 1997, SIGIR 97 PHIL PA, P20 ROBERTSON SE, 1994, 17 ANN INT ACM SIGIR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE Sparck Jones K., 1999, ARTIF INTELL, V141, P257 SPARCKJONES K, 1972, J DOC, V28, P11 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILBUR WJ, 1994, J INFORM SCI, V20, P270, DOI 10.1177/016555159402000405 WILLIAMS JH, 1968, IBM INF SYST S WASH Witten Ian H., 1999, MANAGING GIGABYTES, V2nd Zobel Justin, 1998, ACM SIGIR FORUM, V32, P18, DOI 10.1145/281250.281256 NR 24 TC 1 Z9 1 PU BOWKER-SAUR PI E GRINSTEAD PA WINDSOR COURT, EAST GRINSTEAD HOUSE, E GRINSTEAD RH19 1XA, W SUSSEX, ENGLAND SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 2001 VL 27 IS 5 BP 303 EP 310 DI 10.1177/0165551014233725 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 499QH UT WOS:000172581500002 ER PT J AU Lim, JH AF Lim, JH TI Building visual vocabulary for image indexation and query formulation SO PATTERN ANALYSIS AND APPLICATIONS LA English DT Article DE content-based retrieval; image indexation; image retrieval; query formulation; visual thesaurus; visual vocabulary ID RETRIEVAL AB With advances in digital imaging, the amount of digital images will increase tremendously. To locate relevant images in a large collection of images presents a challenging and genuine problem fur content-based retrieval research. This paper presents a novel framework called visual keywords for image indexation and query formulation. Visual keywords are flexible and intuitive visual prototypes specified perceptually from sample domain images. A visual content is described and indexed by flexible spatial aggregation of the soft presence of visual keywords. A new query method based on visual constraints is also proposed to allow direct and explicit. content specification. Last but nor least, we have developed a digital album prototype to demonstrate query and retrieval on both home photos and stuck photos based on visual keywords. C1 RWCP, KRDL Lab, Informat Base Funct, Singapore 119613, Singapore. RP Lim, JH (reprint author), RWCP, KRDL Lab, Informat Base Funct, 21 Heng Mui Kent Terrace, Singapore 119613, Singapore. CR Bach JR, 1996, P SOC PHOTO-OPT INS, V2670, P76, DOI 10.1117/12.234785 Bishop CM, 1995, NEURAL NETWORKS PATT CARSON C, 1999, UNPUB IEE ET PAMI COX I, 1996, P ICPR, VC, P361 Huang J., 1998, P 6 ACM INT C MULT, P219, DOI 10.1145/290747.290774 JACOBS C, 1995, P SIGGRAPH 95 Klir G. J., 1992, FUZZY SETS UNCERTAIN LIM JH, 1995, P ICARCV 96 SING, P933 LIM JH, 1995, PROCEEDINGS OF 1995 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS I-IV, P2305 LIM JH, 1999, P IEEE ICMCS 99 FLOR, P169 LIM JH, 1993, P INT C ART NEUR NET LIM JH, 1969, VIS INF INF SYST P V, P367 LIM JH, 1999, P ACM DIG LIB 99 BER, P139, DOI 10.1145/313238.313290 Lim JH, 2000, NEW GENERAT COMPUT, V18, P147 LIM JH, 2000, SOFT COMPUTING INFOR, P77 Lipson P, 1997, PROC CVPR IEEE, P1007, DOI 10.1109/CVPR.1997.609453 Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 NIBLACK W, 1993, P SOC PHOTO-OPT INS, V1908, P13 PAPAGEORGIOU PC, 1997, P ICCV, P555 Pass G, 1999, MULTIMEDIA SYST, V7, P234, DOI 10.1007/s005300050125 Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143 PICARD PW, 1995, P SPRING VERL WORKSH ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMITH JR, 1996, P ACM MULT 96 BOST M SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487 WOOD MEJ, 1997, P 1997 BRIT MACH VIS, P620 NR 26 TC 20 Z9 20 PU SPRINGER-VERLAG PI NEW YORK PA 175 FIFTH AVE, NEW YORK, NY 10010 USA SN 1433-7541 J9 PATTERN ANAL APPL JI Pattern Anal. Appl. PY 2001 VL 4 IS 2-3 BP 125 EP 139 DI 10.1007/PL00014574 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA 449UL UT WOS:000169705500005 ER PT J AU Kwok, KL AF Kwok, KL TI Improving English and Chinese ad-hoc retrieval: A Tipster Text Phase 3 project report SO INFORMATION RETRIEVAL LA English DT Article DE language model and PIRCS retrieval model; ad-hoc two-stage retrieval; pseudo-relevance feedback; collection enrichment; segmentation and Chinese IR ID PROBABILISTIC INFORMATION-RETRIEVAL; TERMS AB Both English and Chinese ad-hoc information retrieval were investigated in this Tipster 3 project. Part of our objectives is to study the use of various term level and phrasal level evidence to improve retrieval accuracy. For short queries, we studied five term level techniques that together can lead to good improvements over standard ad-hoc 2-stage retrieval for TREC5-8 experiments. For long queries. we studied the use of linguistic phrases to re-rank retrieval lists. its effect is small but consistently positive. For Chinese IR, we investigated three simple representations for documents and queries: short-words, bigrams and characters. Both approximate short-word segmentation or bigrams, augmented with characters, give highly effective results. Accurate word segmentation appears not crucial For overall result of a query set. Character indexing by itself is not competitive. Additional improvements may be obtained using collection enrichment and combination of retrieval lists. Our PIRCS document-focused retrieval is also shown to have similarity with a simple language model approach to IR. C1 CUNY Queens Coll, Dept Comp Sci, Flushing, NY 11367 USA. RP Kwok, KL (reprint author), CUNY Queens Coll, Dept Comp Sci, 65-30 Kissena Blvd, Flushing, NY 11367 USA. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BOISEN S, 1996, P TIPST TEXT PROGR P, P109, DOI 10.3115/1119018.1119047 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cavnar W., 1995, NIST SPECIAL PUBLICA, P269 Chen AT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42, DOI 10.1145/258525.258532 CHIEN LF, 1995, P 18 ANN INT ACM SIG, P21 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 FAGAN JL, 1987, 87868 TR CORN U DEP FRAKES WR, 1992, INFORMATION RETRIEVA HIEMSTRA D, 1999, NIST SPECIAL PUBLICA, P133 Kwok K. L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291003 Kwok KL, 1999, J AM SOC INFORM SCI, V50, P709, DOI 10.1002/(SICI)1097-4571(1999)50:8<709::AID-ASI8>3.0.CO;2-V KWOK KL, 1997, NIST SPECIAL PUBLICA, P133 KWOK KL, 1998, NIST SPECIAL PUBLICA, P207 Kwok K.L., 1997, P 2 C EMP METH NLP N, P141 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 KWOK KL, IN PRESS TREC 8 AD H KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 Kwok Kuilam L., 1996, P 19 ANN INT ACM SIG, P187, DOI 10.1145/243199.243266 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 PONTE J, 1996, S DOC AN INF RETR SD Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUGE G, 1992, INFORM PROCESS MANAG, V28, P317, DOI 10.1016/0306-4573(92)90078-E Singhal A, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P25, DOI 10.1145/258525.258530 SMEATON AF, 1995, NIST SPECIAL PUBLICA, P55 Sproat R, 1996, COMPUT LINGUIST, V22, P377 STRZALKOWSKI T, 1996, NIST SPECIAL PUBLICA, P245 TAGUESUTCLIFFE J, 1995, NATL I STANDARDS TEC, P385 VOORHEES E, 1997, NIST SPECIAL PUBLICA, P1 Voorhees E., 1998, NIST SPECIAL PUBLICA, P1 WU ZM, 1995, J AM SOC INFORM SCI, V46, P83, DOI 10.1002/(SICI)1097-4571(199503)46:2<83::AID-ASI2>3.0.CO;2-0 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 35 TC 5 Z9 5 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2000 VL 3 IS 4 BP 313 EP 338 DI 10.1023/A:1009955715597 PG 26 WC Computer Science, Information Systems SC Computer Science GA 407RE UT WOS:000167283900002 ER PT J AU Roberts, D Souter, C AF Roberts, D Souter, C TI The automation of controlled vocabulary subject indexing of medical journal articles SO ASLIB PROCEEDINGS LA English DT Article ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; KNOWLEDGE; SYSTEM AB This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of 'concept strings' from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules. C1 Univ Leeds, Sch Comp Studies, Leeds LS2 9JT, W Yorkshire, England. RP Roberts, D (reprint author), Univ Leeds, Sch Comp Studies, Woodhouse Lane, Leeds LS2 9JT, W Yorkshire, England. CR BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 BELKIN NJ, 1982, J DOC, V38, P144 BELKIN NJ, 1983, J INFORM SCI, V5, P153 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BERRUT C, 1990, INFORMATION PROCESSI, V25, P93 Biber Douglas, 1988, VARIATION SPEECH WRI Blair D. C., 1990, LANGUAGE REPRESENTAT Brier S, 1996, J DOC, V52, P296, DOI 10.1108/eb026970 CHIARAMELLA Y, 1987, INFORM PROCESS MANAG, V23, P285, DOI 10.1016/0306-4573(87)90019-7 Cleverdon C. W., 1962, REPORT TESTING ANAL COHEN PR, 1987, INFORM PROCESS MANAG, V23, P255, DOI 10.1016/0306-4573(87)90017-3 CROFT WB, 1992, TEXT-BASED INTELLIGENT SYSTEMS : CURRENT RESEARCH AND PRACTICE IN INFORMATION EXTRACTION AND RETRIEVAL, P127 DEW P, 2000, CRIS 2000 KNOWL WORK ELEW RK, 1989, P 12 INT SIGIR C RES, P11 Ellis D., 1996, PROGR PROBLEMS INFOR FROHMANN B, 1990, J DOC, V46, P81, DOI 10.1108/eb026855 Humphrey SM, 1999, J AM SOC INFORM SCI, V50, P661, DOI 10.1002/(SICI)1097-4571(1999)50:8<661::AID-ASI4>3.0.CO;2-R JONES LP, 1988, P 11 INT SIGIR C RES, P191, DOI 10.1145/62437.62450 Keen M., 1966, FACTORS DETERMINING, V2 Keen M., 1966, FACTORS DETERMINING, V1 Lancaster F. W., 1968, EVALUATION MEDLARS D *NAT LIB MED, MEDLARS IND MAN *NAT LIB MED, 1986, NLM TECHN B, V205, P11 *NAT LIB MED, 1999, UN MED LANG SYST ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 POLLITT S, 1987, INFORM PROCESS MANAG, V23, P119, DOI 10.1016/0306-4573(87)90053-7 Roberts D.J., 1995, COMPLEMENTARY THERAP, V3, P255, DOI 10.1016/S0965-2299(95)80085-9 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST SCHWARZ C, 1990, INFORM PROCESS MANAG, V26, P219, DOI 10.1016/0306-4573(90)90027-Y SMEATON AF, 1992, COMPUT J, V35, P268, DOI 10.1093/comjnl/35.3.268 SNOW B, 1986, DATABASE, V9, P94 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 33 TC 1 Z9 2 PU ASLIB PI LONDON PA STAPLE HALL, STONE HOUSE COURT, LONDON EC3A 7PB, ENGLAND SN 0001-253X J9 ASLIB PROC JI Aslib Proc. PD NOV-DEC PY 2000 VL 52 IS 10 BP 384 EP 401 DI 10.1108/EUM0000000007030 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 389RL UT WOS:000166251500002 ER PT J AU Huang, XJ Robertson, S Cercone, N An, AJ AF Huang, XJ Robertson, S Cercone, N An, AJ TI Probability-based Chinese text processing and retrieval SO COMPUTATIONAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 4th Meeting of the Pacific-Association-for-Computational-Linguistics (PACLING 99) CY AUG, 1999 CL WATERLOO, CANADA SP Pacific Assoc Computat Linguist HO UNIV WATERLOO DE information retrieval; word-based and character-based Chinese text processing; single-unit and compound-unit weighting ID RELEVANCE INFORMATION AB We discuss the use of probability-based natural language processing for Chinese text retrieval. We focus on comparing different text extraction methods and probabilistic weighting methods. Several document processing methods and probabilistic weighting functions are presented. A number of experiments have been conducted on large standard text collections. We present the experimental results that compare a word-based text processing method with a character-based method. The experimental results also compare a number of term-weighting functions including both single-unit weighting and compound-unit weighting functions. C1 Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. City Univ London, Dept Informat Sci, London EC1V 0HB, England. RP Huang, XJ (reprint author), Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. CR BEAULIEU M, 1996, NIST SPECIAL PUBLICA, P143 BUCKLEY C, 1996, NIST SPECIAL PUBLICA, P105 CHEN G, 1992, J INFORMATION, V11, P11 CHIEN LF, 1995, SIGIR, V95, P112 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 GEY F, 1996, P 5 TEXT RETR C TREC, P181 HUANG X, 1999, P C PAC ASS COMP LIN, P223 Huang XJ, 1997, J DOC, V53, P74, DOI 10.1108/EUM0000000007193 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KWOK KL, 1997, NIST SPECIAL PUBLICA, P207 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VOORHEES E, 1997, NIST SPECIAL PUBLICA, P1 WILLETT P, 1979, J DOC, V35, P296, DOI 10.1108/eb026684 WU ZM, 1993, J AM SOC INFORM SCI, V44, P532, DOI 10.1002/(SICI)1097-4571(199310)44:9<532::AID-ASI3>3.0.CO;2-M NR 15 TC 2 Z9 2 PU BLACKWELL PUBLISHERS PI MALDEN PA 350 MAIN STREET, STE 6, MALDEN, MA 02148 USA SN 0824-7935 J9 COMPUT INTELL JI Comput. Intell. PD NOV PY 2000 VL 16 IS 4 BP 552 EP 569 DI 10.1111/0824-7935.00126 PG 18 WC Computer Science, Artificial Intelligence SC Computer Science GA 372VP UT WOS:000165255000005 ER PT J AU Sparck-Jones, K Walker, S Robertson, SE AF Sparck-Jones, K Walker, S Robertson, SE TI A probabilistic model of information retrieval: development and comparative experiments Part 1 SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; retrieval history; probabilistic model; term weighting; experiments ID RELEVANCE INFORMATION AB The paper combines a comprehensive account of a probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects of retrieval data and a wider range of system functions. Each step in the argument is matched by comparative retrieval tests, to provide a single coherent account of a major line of research. The experiments demonstrate, for a large test collection, that the probabilistic model is effective and robust, and that it responds appropriately, with major improvements in performance, to key features of retrieval situations. Part 1 covers the foundations and the model development for document collection and relevance data, along with the test apparatus. Part 2 covers the further development and elaboration of the model, with extensive testing, and briefly considers other environment conditions and tasks, model training, concluding with comparisons with other approaches and an overall assessment. Data and results tables for both parts ave given in Part 1. Key results are summarised in Part 2. (C) 2000 Elsevier Science Ltd. All rights reserved. C1 Univ Cambridge, Comp Lab, Cambridge CB2 3QG, England. Microsoft Res Ltd, Cambridge CB2 3NH, England. City Univ London, Dept Informat Sci, London EC1V 0HB, England. RP Sparck-Jones, K (reprint author), Univ Cambridge, Comp Lab, New Museums Site,Pembroke St, Cambridge CB2 3QG, England. EM ksj@cl.cam.ac.uk CR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 Cox D, 1970, ANAL BINARY DATA CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARMAN DK, 1993, SPECIAL PUBLICATION HARMAN DK, 1996, SPECIAL PUBLICATION HARMAN DK, 1994, SPECIAL PUBLICATION HARMAN DK, 1995, SPECIAL PUBLICATION HARMAN DK, 1997, SPECIAL PUBLICATION Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1975, J DOC, V31, P266, DOI 10.1108/eb026606 JONES KS, 2000, INFORM PROCESS MANAG, V36, P37 JONES KS, 1998, 446 TR U CAMBR COMP JONES KS, 1980, 5553 BL R D Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 1999, NAT I STANDARDS TECH LANCASTER FW, 1969, AM DOC, V20, P119, DOI 10.1002/asi.4630200204 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1975, THEORY INDEXING VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, 1997, SPECIAL PUBLICATION VOORHEES EM, 1999, SPECIAL PUBLICATION Voorhees E.M., 1998, SPECIAL PUBLICATION NR 33 TC 32 Z9 32 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2000 VL 36 IS 6 BP 779 EP 808 DI 10.1016/S0306-4573(00)00015-7 PG 30 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 354HZ UT WOS:000089325100001 ER PT J AU Ng, K Zue, VW AF Ng, K Zue, VW TI Subword-based approaches for spoken document retrieval SO SPEECH COMMUNICATION LA English DT Article DE spoken document retrieval; audio indexing; information retrieval ID RECOGNITION; ALGORITHM AB This paper explores approaches to the problem of spoken document retrieval (SDR), which is the task of automatically indexing and then retrieving relevant items from a large collection of recorded speech messages in response to a user specified natural language text query. We investigate the use of subword unit representations for SDR as an alternative to words generated by either keyword spotting or continuous speech recognition. In this study, we explore the space of possible subword units to determine the complexity of the subword units needed for SDR; describe the development and application of a phonetic recognition system to extract subword units from the speech signal; examine the behavior and sensitivity of the subword units to speech recognition errors; measure the effect of speech recognition performance on retrieval performance; and investigate a number of robust indexing and retrieval methods in an effort to improve retrieval performance in the presence of speech recognition errors. We find that with the appropriate subword units, it is possible to achieve performance comparable to that of text-based word units if the underlying phonetic units are recognized correctly. In the presence of speech recognition errors, retrieval performance degrades to 60% of the clean reference level. This performance can be improved by 23% (to 74% of the clean reference) with use of the robust methods. (C) 2000 Elsevier Science B.V. All rights reserved. C1 MIT, Comp Sci Lab, Spoken Language Syst Grp, Cambridge, MA 02139 USA. RP Ng, K (reprint author), MIT, Comp Sci Lab, Spoken Language Syst Grp, 545 Technol Sq,Room 638, Cambridge, MA 02139 USA. EM kng@mit.edu CR Acero A., 1990, P ICASSP, P849 Buckley C., 1985, 85686 CORN U COMP SC CHANG J, 1997, P EUR RHOD GREEC OCT, P1199 Chase L., 1997, P EUR C SPEECH COMM, P815 Chomsky Noam, 1968, SOUND PATTERN ENGLIS DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 DELIGNE S, 1995, P ICASSP, P169 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DHARANIPRAGADA S, 1998, 7 TEXT RETR C TREC 7 Duda R., 1973, PATTERN CLASSIFICATI FISHER W, 1996, AUTOMATIC SYLLABIFIC Foote J.T., 1995, P EUR 95 MADR SPAIN, P2145 GAROFOLO JS, 1993, 4930 NISTIR NAT I ST GLASS J, 1996, P ICSLP, V4, P2277, DOI 10.1109/ICSLP.1996.607261 GLASS JR, 1988, P ICASSP NEW YORK AP, P429 GLAVITSCH U, 1992, P 15 ANN INT ACM SIG, P168, DOI 10.1145/133160.133194 Halberstadt A. K., 1998, THESIS MIT CAMBRIDGE HARMAN DK, 1998, 7 TEXT RETR C TREC 7 HARMAN DK, 1997, 6 TEXT RETR C TREC 6 Hartigan J. A., 1975, CLUSTERING ALGORITHM HAUPTMANN AG, 1997, P 1997 IEEE INT C AC, P195 Hazen TJ, 1998, INT CONF ACOUST SPEE, P653, DOI 10.1109/ICASSP.1998.675349 James D.A., 1995, THESIS U CAMBRIDGE C JOHNSON S, 1998, 7 TEXT RETR C TREC 7 Jones GJF, 1996, P 19 ANN INT ACM SIG, P30, DOI 10.1145/243199.243208 JONES GJF, 1995, P ICASSP 95, V1, P309 Jourlin P., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312701 Kahn D, 1976, THESIS MIT CAMBRIDGE Lee K.-F., 1989, AUTOMATIC SPEECH REC Marukawa K, 1997, PATTERN RECOGN, V30, P1361, DOI 10.1016/S0031-3203(96)00155-0 Ng C., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291055 NG K, 1998, P ICSLP 98 SYDN AUST Ng K., 1997, P EUR C SPEECH COMM, P1607 Ng K, 1998, INT CONF ACOUST SPEE, P325, DOI 10.1109/ICASSP.1998.674433 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Rijsbergen CJV, 1979, INFORMATION RETRIEVA, V2nd ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE RC, 1991, P ICASSP 91 TOR CAN, P317, DOI 10.1109/ICASSP.1991.150340 SALTON G, 1983, INTRO MODERN INFORMA SCHAUBLE P, 1994, P ARPA HUM LANG TECH, P370, DOI 10.3115/1075812.1075897 Schmandt C., 1994, VOICE COMMUNICATION SINGHAL A, 1998, 7 TEXT RETR C TREC 7 Siu M., 1997, P EUR C SPEECH COMM, P831 SPINA MS, 1996, P ICSLP 96 PHIL PA, V2, P594, DOI 10.1109/ICSLP.1996.607431 SPINA MS, 1997, P EUR 97 RHOD GREEC, P1547 VITERBI AJ, 1967, IEEE T INFORM THEORY, V13, P260, DOI 10.1109/TIT.1967.1054010 Wechsler M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290950 WECHSLER M, 1995, P MIRO WORKSH GLASG Winston P.H., 1992, ARTIFICIAL INTELLIGE WITBROCK MJ, 1997, P DARPA SPEECH REC W ZHAI C, 1996, P 5 TEXT RETR C TREC NR 52 TC 29 Z9 36 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6393 EI 1872-7182 J9 SPEECH COMMUN JI Speech Commun. PD OCT PY 2000 VL 32 IS 3 BP 157 EP 186 DI 10.1016/S0167-6393(00)00008-X PG 30 WC Acoustics; Computer Science, Interdisciplinary Applications SC Acoustics; Computer Science GA 362ME UT WOS:000089781100001 ER PT J AU Renals, S Robinson, T AF Renals, S Robinson, T TI Accessing information in spoken audio SO SPEECH COMMUNICATION LA English DT Editorial Material C1 Univ Sheffield, Dept Comp Sci, Sheffield S1 4DP, S Yorkshire, England. Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England. RP Renals, S (reprint author), Univ Sheffield, Dept Comp Sci, 211 Portobello St, Sheffield S1 4DP, S Yorkshire, England. EM s.renals@dcs.shef.ac.uk; ajr@eng.cam.ac.uk CR HAUPTMANN AG, 1999, INTELLIGENT MULTIMED, P213 JONES GJF, 1996, P IEEE INT C AC SPEE, P311 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBINSON T, 1999, P ESCA WORKSH ACC IN SALTON G, 1983, INTRO MODERN INFORMA SCHAUBLE P, 1997, MULTIMEDIA INFORMATI NR 6 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6393 EI 1872-7182 J9 SPEECH COMMUN JI Speech Commun. PD SEP PY 2000 VL 32 IS 1-2 BP 1 EP 3 DI 10.1016/S0167-6393(00)00019-4 PG 3 WC Acoustics; Computer Science, Interdisciplinary Applications SC Acoustics; Computer Science GA 350HM UT WOS:000089095100001 ER PT J AU Renals, S Abberley, D Kirby, D Robinson, T AF Renals, S Abberley, D Kirby, D Robinson, T TI Indexing and retrieval of broadcast news SO SPEECH COMMUNICATION LA English DT Article DE spoken document retrieval; information retrieval; broadcast speech; large vocabulary speech recognition ID DOCUMENT-RETRIEVAL; RELEVANCE AB This paper describes a spoken document retrieval (SDR) system for British and North American Broadcast News. The system is based on a connectionist large vocabulary speech recognizer and a probabilistic information retrieval (IR) system. We discuss the development of a real-time Broadcast News speech recognizer, and its integration into an SDR system. Two advances were made for this task: automatic segmentation and statistical query expansion using a secondary corpus. Precision and recall results using the Text Retrieval Conference (TREC) SDR evaluation infrastructure are reported throughout the paper, and we discuss the application of these developments to a large scale SDR task based on an archive of British English broadcast news. (C) 2000 Elsevier Science B.V. All rights reserved. C1 Univ Sheffield, Dept Comp Sci, Sheffield S1 4DP, S Yorkshire, England. Univ Cambridge, Dept Engn, Cambridge CB2 1TN, England. RP Renals, S (reprint author), Univ Sheffield, Dept Comp Sci, 211 Portobello St, Sheffield S1 4DP, S Yorkshire, England. EM s.renals@dcs.shef.ac.uk CR Abberley D, 1998, INT CONF ACOUST SPEE, P3781, DOI 10.1109/ICASSP.1998.679707 ABBERLEY D, 1999, P ESCA WORKSH ACC IN, P19 ALLAN J, 1998, P 6 TEXT RETR C TREC, P169 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 COOK G, 1999, P DARPA BROADC NEWS, P161 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Dharanipragada S, 1998, INT CONF ACOUST SPEE, P233, DOI 10.1109/ICASSP.1998.674410 Ferrari A., 1999, Elettronica Oggi Foote JT, 1997, COMPUT SPEECH LANG, V11, P207, DOI 10.1006/csla.1997.0027 GAROFOLO J, 1999, ESCA ETRW ACCESSING, P1 Harman D., 1992, INFORMATION RETRIEVA, P241 HARMAN DK, 1996, P 4 TEXT RETR C TREC, pA6 Hauptmann A., 1997, INTELLIGENT MULTIMED, P213 Hearst MA, 1997, COMPUT LINGUIST, V23, P33 JAMES DA, 1994, P INT C AC SPEECH SI, V1, P377 JOHNSON SE, 1999, P IEEE INT C AC SPEE, P49 JONES GJF, 1996, P IEEE INT C AC SPEE, P311 JONES K. S., 1998, TR446 CAMBR U COMP L Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 KEMP T, 1998, P INT C SPOK LANG PR, P1839 KRAAIJ W, 1998, P 14 TWENT WORKSH LA, P141 MORRISON P, 1998, SUM HUMAN KNOWLEDGE Ng C., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291055 Ng K, 1998, INT CONF ACOUST SPEE, P325, DOI 10.1109/ICASSP.1998.674433 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1994, P ACM SIGIR, P16 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBINSON AJ, 2000, UNPUB SPEECH COMMUNI ROBINSON AJ, 1994, IEEE T NEURAL NETWOR, V5, P298, DOI 10.1109/72.279192 Robinson T., 1996, AUTOMATIC SPEECH SPE, P233 Robinson T, 1998, INT CONF ACOUST SPEE, P829, DOI 10.1109/ICASSP.1998.675393 ROBINSON T, 1999, P EUR BUD, P1067 ROBINSON T, 2000, UNPUB SPEECH COMMUNI SIEGLER M, 1999, P IEEE INT C AC SPEE, P505 Singhal A., 1999, P TREC 7 NIST GAITH, P239 Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 SMEATON AF, 1998, LNCS, V1513, P429 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Wechsler M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290950 Williams HE, 1999, COMPUT J, V42, P193, DOI 10.1093/comjnl/42.3.193 Witbrock M. J., 1997, P 2 ACM INT C DIG LI, P30, DOI 10.1145/263690.263779 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Yamron JP, 1998, INT CONF ACOUST SPEE, P333, DOI 10.1109/ICASSP.1998.674435 NR 45 TC 17 Z9 17 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6393 EI 1872-7182 J9 SPEECH COMMUN JI Speech Commun. PD SEP PY 2000 VL 32 IS 1-2 BP 5 EP 20 DI 10.1016/S0167-6393(00)00020-0 PG 16 WC Acoustics; Computer Science, Interdisciplinary Applications SC Acoustics; Computer Science GA 350HM UT WOS:000089095100002 ER PT J AU Edgar, SJ Holliday, JD Willett, P AF Edgar, SJ Holliday, JD Willett, P TI Effectiveness of retrieval in similarity searches of chemical databases: A review of performance measures SO JOURNAL OF MOLECULAR GRAPHICS & MODELLING LA English DT Review DE evaluation; fragment bit-strings; performance; retrieval effectiveness; similarity searching ID COMPOUND SELECTION; DESCRIPTORS AB This article reviews measures for evaluating the effectiveness of similarity searches in chemical databases, drawing principally upon the many measures that have been described previously for evaluating the performance of text search engines. The use of the various measures is exemplified by fragment-based 2D similarity searches on several databases for which both structural and bioactivity data are available. It is concluded that the cumulative recall and G-H score measures are the most useful of those tested. (C) 2000 by Elsevier Science Inc. C1 Univ Sheffield, Krebs Inst Biomolec Res, Sheffield S10 2TN, S Yorkshire, England. Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. RP Willett, P (reprint author), Univ Sheffield, Krebs Inst Biomolec Res, Western Bank, Sheffield S10 2TN, S Yorkshire, England. CR [Anonymous], WORLD DRUG INDEX BAWDEN D, 1985, J CHEM INF COMP SCI, V25, P36, DOI 10.1021/ci00045a009 BOHM HJ, IN PRESS VIRTUAL SCR BOYCE BR, 1994, MEASUREMENT INFORMAT Brown RD, 1996, J CHEM INF COMP SCI, V36, P572, DOI 10.1021/ci9501047 CLEVERDO.CW, 1972, J DOC, V28, P195, DOI 10.1108/eb026538 Cosgrove DA, 1998, J MOL GRAPH MODEL, V16, P19, DOI 10.1016/S1093-3263(98)00014-X Dean P.M., 1994, MOL SIMILARITY DRUG FRAKES WR, 1992, INFORMATION RETRIEVA GUNER OF, FORMULA DETERMINING HEINE MH, 1973, INFORM STORAGE RET, V9, P181, DOI 10.1016/0020-0271(73)90066-1 Johnson M. A., 1990, CONCEPTS APPL MOL SI JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1997, READINGS INFORMATION Patterson DE, 1996, J MED CHEM, V39, P3049, DOI 10.1021/jm960290n PEPPERRELL CA, 1991, J COMPUT AID MOL DES, V5, P455, DOI 10.1007/BF00125665 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 Sheridan RP, 1996, J CHEM INF COMP SCI, V36, P128, DOI 10.1021/ci950275b Snarey M, 1997, J MOL GRAPH MODEL, V15, P372, DOI 10.1016/S1093-3263(98)00008-4 TagueSutcliffe JM, 1996, J AM SOC INFORM SCI, V47, P1 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VICKERY BC, 1966, FACTORS DETERMINING Voiskunskii VG, 1997, J AM SOC INFORM SCI, V48, P133 Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 1992, INF P MANAG, V28, P439 NR 27 TC 43 Z9 43 PU ELSEVIER SCIENCE INC PI NEW YORK PA 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010 USA SN 1093-3263 J9 J MOL GRAPH MODEL JI J. Mol. Graph. PD AUG-OCT PY 2000 VL 18 IS 4-5 BP 343 EP 357 DI 10.1016/S1093-3263(00)00061-9 PG 15 WC Biochemical Research Methods; Biochemistry & Molecular Biology; Computer Science, Interdisciplinary Applications; Crystallography; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Computer Science; Crystallography; Mathematical & Computational Biology GA 378PF UT WOS:000165592200005 PM 11143554 ER PT J AU Greiff, WR Ponte, JM AF Greiff, WR Ponte, JM TI The maximum entropy approach and probabilistic IR models SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE theory; binary independence model; combination match; linked dependence; idf weighting; probability ranking principle ID INFORMATION-RETRIEVAL SYSTEMS; CO-OCCURRENCE DATA; DOCUMENT-RETRIEVAL; PRINCIPLE AB This paper takes a fresh look at modeling approaches to information retrieval that have been the basis of much of the probabilistically motivated IR research over the last 20 years. We shall adopt a subjectivist Bayesian view of probabilities and argue that classical work on probabilistic retrieval is best understood from this perspective. The main focus of the paper will be the ranking formulas corresponding to the Binary Independence Model (BIM), presented originally by Roberston and Sparck Jones [1977] and the Combination Match Model (CMM), developed shortly thereafter by Croft and Harper [1979]. We will show how these same ranking formulas can result from a probabilistic methodology commonly known as Maximum Entropy (MAXENT). C1 Mitre Corp, Bedford, MA 01730 USA. RP Greiff, WR (reprint author), Mitre Corp, 202 Burlington Rd, Bedford, MA 01730 USA. CR BEEFERMAN D, 1997, P EMP METH NAT LANG Chiang A., 1967, FUNDAMENTAL METHODS Cooper W., 1991, P 14 ANN INT ACM SIG, P57, DOI 10.1145/122860.122866 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DARROCH JN, 1972, ANN MATH STAT, V43, P1470, DOI 10.1214/aoms/1177692379 Dawid A. P., 1986, ENCY STATISTICAL SCI, V7, P210 DEGROOT M, 1982, STATISTICIAN, V32, P12 DELLAPIETRA S, 1997, IEEE PAMI, V19, P3 Erickson G. J., 1988, MAXIMUM ENTROPY BAYE, V1, P75 Erickson G.J., 1988, MAXIMUM ENTROPY BAYE Fine T., 1973, THEORIES PROBABILITY Golan A., 1996, MAXIMUM ENTROPY ECON GOOD IJ, 1960, J ROY STAT SOC B, V22, P319 Good I.J., 1950, PROBABILITY WEIGHING GULL SF, 1978, NATURE, V272, P686, DOI 10.1038/272686a0 Hacking I., 1965, LOGIC STAT INFERENCE HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Jaynes E., 1979, MAXIMUM ENTROPY FORM, P15 JAYNES ET, 1963, BRANDEIS SUMMER I LE, V3, P182 JAYNES ET, 1957, PHYS REV, V108, P171, DOI 10.1103/PhysRev.108.171 Jaynes E.T., 1994, PROBABILITY THEORY L JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 KANTOR PB, 1986, P 1986 ACM C RES DEV, P269, DOI 10.1145/253168.253225 Kantor PB, 1998, J AM SOC INFORM SCI, V49, P557 LEE JJ, 1991, J AM SOC INFORM SCI, V42, P166, DOI 10.1002/(SICI)1097-4571(199104)42:3<166::AID-ASI2>3.0.CO;2-A Marshall K. T., 1995, DECISION MAKING FORE ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SHANNON CE, 1948, AT&T TECH J, V27, P379 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SPARCKJONES K, 1972, J DOC, V28, P11 TRIBUS M, 1979, MAXIMUM ENTROPY FORM, P1 Tribus M., 1969, RATIONAL DESCRIPTION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 39 TC 7 Z9 7 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 2000 VL 18 IS 3 BP 246 EP 287 DI 10.1145/352595.352597 PG 42 WC Computer Science, Information Systems SC Computer Science GA 371BB UT WOS:000165156800002 ER PT J AU Sacco, GM AF Sacco, GM TI Dynamic taxonomies: A model for large information bases SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE information storage and retrieval; indexing methods; retrieval models; large text archives; information interfaces and presentation; multimedia information systems; hypertext navigation and maps; knowledge representation formalisms and methods; semantic networks AB A new taxonomic model for structuring and accessing large heterogeneous information bases is presented. The model is designed to simplify both classification and access by computer-illiterate people. It defines simple and intuitive operations to access large information bases at the conceptual level and at different levels of abstraction. in a totally assisted way. through a simple, yet effective visual interface. The model can also be used to summarize result sets computed by other query methods, such as information retrieval. shape retrieval. etc., and to provide user maps for complex hypermedia networks. The experience gained by applying this model to commercial applications is reported. C1 Univ Turin, Dipartimento Informat, I-10149 Turin, Italy. HyperSyst Srl, I-10138 Turin, Italy. RP Sacco, GM (reprint author), Univ Turin, Dipartimento Informat, Corso Svizzera 185, I-10149 Turin, Italy. CR ANKESARLA F, 1993, INTERNET GOPHER BERNERSLEE T, 1994, COMMUN ACM, V37, P76, DOI 10.1145/179606.179671 CHAMBERLIN D, 1976, IBM J RES DEV, V20, P6 CUTTING DR, 1993, P ACM SIGIR 93 CUTTING DR, 1992, P ACM SIGIR 92 Fellbaum C, 1998, WORDNET ELECT LEXICA FISCHER G, 1989, P ACM HUM FACT COMP GROENBAEK K, 1994, COMM ACM 37, V2, P26 NELSON T, 1980, P IFIP C, P1013 Nielsen J., 1990, Communications of the ACM, V33, DOI 10.1145/77481.77483 PIROLLI P, 1996, P ACM HUM FACT COMP ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROTTA D, 1994, THESIS U TURIN SACCO GM, 1987, P INT C BUS CDROM SACCO GM, 1984, P ACM SIGOA C OFF IN, P104, DOI 10.1145/800023.808338 SACCO GM, 1995, INFORMATION DISCOVER Salton G., 1971, SMART RETRIEVAL SYST STRAUBE DD, 1990, ACM T INFORMATION SY, V8 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA 1994, ACM COMPUTING REV, V35, P4 NR 20 TC 44 Z9 44 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD MAY-JUN PY 2000 VL 12 IS 3 BP 468 EP 479 DI 10.1109/69.846296 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 325GQ UT WOS:000087668200008 ER PT J AU Robertson, S Walker, S AF Robertson, S Walker, S TI Threshold setting in adaptive filtering SO JOURNAL OF DOCUMENTATION LA English DT Article AB A major problem in using current best-match methods in a filtering task is that of setting appropriate thresholds, which are required in order to force a binary decision on notifying a user of a document. We discuss methods for setting such thresholds and adapting them as a result of feedback information on the performance of the profile. These methods fit within the probabilistic approach to retrieval, and are applied to a probabilistic system. Some experiments, within the framework of the TREC-7 adaptive filtering track, are described. C1 Microsoft Res Ltd, Cambridge CB2 3NH, England. RP Robertson, S (reprint author), Microsoft Res Ltd, St George House,1 Guildhall St, Cambridge CB2 3NH, England. CR BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 Callan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290998 CARROLL G, 1992, INTELLIGENT PROBABIL, P25 COOPER WS, 1995, NIST SPECIAL PUBLICA, P127 HULL D, 1999, NIST SPECIAL PUBLICA, P33 JONES KS, 1998, 446 U CAMBR COMP LAB KWOK KL, 1999, NIST SPECIAL PUBLICA, P343 LEWIS DD, 1999, REFERENCE LIST ACCOM Mitchell T.M., 1997, MACHINE LEARNING Robertson S., 1999, NIST SPECIAL PUBLICA, P253 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VOORHEES EM, 1999, NIST SPECIAL PUBLICA Walker S., 1998, NIST SPECIAL PUBLICA, P125 NR 14 TC 9 Z9 11 PU ASLIB PI LONDON PA STAPLE HALL, STONE HOUSE COURT, LONDON EC3A 7PB, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PD MAY PY 2000 VL 56 IS 3 BP 312 EP 331 DI 10.1108/EUM0000000007118 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 310VE UT WOS:000086849100005 ER PT J AU Dominich, S AF Dominich, S TI A unified mathematical definition of classical information retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Review ID VECTOR-SPACE MODEL; PROBABILISTIC INFERENCE; DOCUMENT-RETRIEVAL; SYSTEMS; LOGIC; RELEVANCE; PRECISION; RECALL; HYPERTEXT; FRAMEWORK AB A unified mathematical definition for the classical (vector and probabilistic) models of information retrieval is given. Also, a mathematical structure (Diophantine set) behind relevance feedback is identified. C1 Univ Veszprem, Dept Comp Sci, H-8200 Veszprem, Hungary. Buckinghamshire Chilterns Univ Coll, Dept Comp Sci & Informat Technol, High Wycombe HP11 2JZ, Bucks, England. RP Dominich, S (reprint author), Univ Veszprem, Dept Comp Sci, Egyetem U10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu CR ALLEN J, 1996, SIGIR 96 P 19 INT C, P270 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Belkin N. J., 1996, TREC 4 P 4 TEXT RETR, P181 BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I BORDOGNA G, 1995, INT J INTELL SYST, V10, P233, DOI 10.1002/int.4550100205 BUCKLAND M, 1994, J AM SOC INFORM SCI, V45, P12, DOI 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L Buckland MK, 1997, J AM SOC INFORM SCI, V48, P804, DOI 10.1002/(SICI)1097-4571(199709)48:9<804::AID-ASI5>3.0.CO;2-V CAHOON B, 1996, ACM SIGIR P 19 INT C, P110 CAID WR, 1995, INFORM PROCESS MANAG, V31, P419, DOI 10.1016/0306-4573(94)00056-9 Callan James, 1992, P 3 INT C DAT EXP SY, P78 Carrick C, 1997, INFORM PROCESS MANAG, V33, P615, DOI 10.1016/S0306-4573(97)00021-6 CATER SC, 1989, INFORM PROCESS MANAG, V25, P15, DOI 10.1016/0306-4573(89)90088-5 CHEN PS, 1994, INFORM PROCESS MANAG, V30, P43, DOI 10.1016/0306-4573(94)90023-X CHIARAMELLA Y, 1992, COMPUT J, V35, P233, DOI 10.1093/comjnl/35.3.233 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CRESTANI F, 1994, INFORMATION RETRIEVA, P47 CRESTANI F, 1995, ACM SIGIR 18 INT C R, P291 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 Croft W. B., 1998, P ACM SIGIR, P275 CROFT WB, 1995, IEEE EXPERT DEC, P59 CROFT WB, 1993, IEEE EXPERT APR, P8 CROFT WB, 1997, RES ADV TECHNOLOGY D, P345 CSASZAR A, 1978, GEN TOPOLOGY DISQUIV DASILVA WT, 1993, J AM SOC INFORM SCI, V44, P10, DOI 10.1002/(SICI)1097-4571(199301)44:1<10::AID-ASI2>3.0.CO;2-V DOMINICH S, 1993, THESIS HUNGARIAN ACA DOMINICH S, 1994, J DOC, V50, P197, DOI 10.1108/eb026930 DOMINICH S, 1997, ENCY COMPUTER SC S22, V37, P175 DOMINICH S, 1997, ENCY COMPUTER SC S22, V59, P218 Egghe L, 1997, J DOC, V53, P488, DOI 10.1108/EUM0000000007208 Egghe L, 1998, J AM SOC INFORM SCI, V49, P1144, DOI 10.1002/(SICI)1097-4571(1998)49:13<1144::AID-ASI2>3.0.CO;2-Z Ellis D, 1996, J AM SOC INFORM SCI, V47, P23, DOI 10.1002/(SICI)1097-4571(199601)47:1<23::AID-ASI3>3.0.CO;2-4 EVERETT DM, 1992, J AM SOC INFORM SCI, V43, P658, DOI 10.1002/(SICI)1097-4571(199212)43:10<658::AID-ASI3>3.0.CO;2-H FLORIAN D, 1990, P FIT 90 C SALZB WIE, P13 FORD N, 1991, J AM SOC INFORM SCI, V42, P72, DOI 10.1002/(SICI)1097-4571(199101)42:1<72::AID-ASI8>3.0.CO;2-9 FRANTS VI, 1997, AUTOMATED INFORMATIO FROEHLICH TJ, 1994, J AM SOC INFORM SCI, V45, P124, DOI 10.1002/(SICI)1097-4571(199404)45:3<124::AID-ASI2>3.0.CO;2-8 FROELICH TJ, 1991, P AM SOC INFORM SCI, P118 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Fuhr N, 1997, ACM T INFORM SYST, V15, P32, DOI 10.1145/239041.239045 FUHR N, 1998, P 6 INT C EXT DAT TE, P24 GORDON M, 1989, J AM SOC INFORM SCI, V40, P145, DOI 10.1002/(SICI)1097-4571(198905)40:3<145::AID-ASI1>3.0.CO;2-I GORDON MD, 1990, J AM SOC INFORM SCI, V41, P313, DOI 10.1002/(SICI)1097-4571(199007)41:5<313::AID-ASI1>3.0.CO;2-G Harel D, 1998, LECT NOTES COMPUT SC, V1450, P36 Huang XJ, 1997, J DOC, V53, P74, DOI 10.1108/EUM0000000007193 Huibers TWC, 1996, INFORMATION RETRIEVAL, P3 INGWERSEN P, 1992, INFORMATION RETRIEVA JONES KS, 1997, READINGS INFORMATION Kang HK, 1997, INFORM PROCESS MANAG, V33, P289, DOI 10.1016/S0306-4573(96)00074-X Karamuftuoglu M, 1998, J AM SOC INFORM SCI, V49, P1070, DOI 10.1002/(SICI)1097-4571(1998)49:12<1070::AID-ASI3>3.0.CO;2-S KNEALE K, 1962, DEV LOGIC KOLMOGOROFF A, 1950, FDN PROBABILITY Korfhage R., 1997, INFORMATION STORAGE KOWALSKI G, 1997, INFORMATION RETRIEVA KRAFT DH, 1995, HDB FUZZY SETS POSSI, V2, P119 KRAFT DH, 1994, FUZZY SETS POSSIBILI, P330 KRAFT DH, 1995, ADV DATABAS ART INT, V1, P243 KUHN HP, 1959, READINGS AUTOMATIC L, P159 KWOK KL, 1989, SIGIR FORUM, V23, P21, DOI 10.1145/75334.75338 LALMAS M, 1993, P WORKSH INC UNC INF, P62 LALMAS M, 1992, BCS 14 INF RETR C LA, P1 LEE JJ, 1991, J AM SOC INFORM SCI, V42, P166, DOI 10.1002/(SICI)1097-4571(199104)42:3<166::AID-ASI2>3.0.CO;2-A Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 Liu GZ, 1997, J AM SOC INFORM SCI, V48, P395 LUCARELLA D, 1993, INFORM PROCESS MANAG, V29, P299, DOI 10.1016/0306-4573(93)90057-K Macfarlane A, 1997, J DOC, V53, P274, DOI 10.1108/EUM0000000007201 MARON ME, 1960, J ASSOC COMPUT MACH, V7, P219 Meghini C., 1993, ACM SIGIR 16 INT C R, P298 Meinke K., 1992, HDB LOGIC COMPUTER S, VI MIYAMOTO S, 1990, FUZZY SETS INFORMATI Miyamoto S, 1998, J AM SOC INFORM SCI, V49, P195, DOI 10.1002/(SICI)1097-4571(1998)49:3<195::AID-ASI2>3.3.CO;2-E Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Mock KJ, 1997, INFORM PROCESS MANAG, V33, P633, DOI 10.1016/S0306-4573(97)00022-8 *NAT I STAND TECHN, 1997, TREC HOM PAG NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Nie JY, 1995, COMPUT J, V38, P643, DOI 10.1093/comjnl/38.8.643 NIE JY, 1992, ACM SIGIR 15 INT C R, P140 Pearce C, 1996, J AM SOC INFORM SCI, V47, P263, DOI 10.1002/(SICI)1097-4571(199604)47:4<263::AID-ASI2>3.0.CO;2-V PHILIPS ICC, 1992, HDB LOGIC COMPUTER S, V1 RADECKI T, 1976, INFORMATION PROCESSI, V12, P131 RAGHAVAN V, 1990, ENCY COMPUTER SCI TE, V22, P423 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO, 1971, SMART RETRIEVAL SYST, P313 Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 SALTON G, 1965, READINGS AUTOMATIC L, P169 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO Salton G, 1996, INFORM PROCESS MANAG, V32, P127, DOI 10.1016/S0306-4573(96)85001-1 Salton G., 1971, SMART RETRIEVAL SYST Sebastiani F, 1994, ACM SIGIR 17 INT C R, P122 Shaw WM, 1997, INFORM PROCESS MANAG, V33, P1, DOI 10.1016/S0306-4573(96)00043-X Smyth M.B., 1992, HDB LOGIC COMPUTER S, VI STRZALKOWSKI T, 1995, INFORM PROCESS MANAG, V31, P397, DOI 10.1016/0306-4573(94)00055-8 SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 Tarski A., 1956, LOGIC SEMANTICS META, P60 VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 van Rijsbergen C. J., 1986, SIGIR Forum, V21 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA vanRijsbergen CJ, 1996, J AM SOC INFORM SCI, V47, P385 WATTERS CR, 1989, J AM SOC INFORM SCI, V40, P311, DOI 10.1002/(SICI)1097-4571(198909)40:5<311::AID-ASI3>3.0.CO;2-B Willard S., 1970, GEN TOPOLOGY WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 WONG SKM, 1993, J AM SOC INFORMATION, V44 WONG SKM, 1991, INFORM SYST, V16, P301, DOI 10.1016/0306-4379(91)90003-R WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 WONG SKM, 1991, GEN SYST, V19, P101 WONG SKM, 1995, P 11 C UNC ART INT M, P156 YU CT, 1989, ACM T DATABASE SYST, V14, P147, DOI 10.1145/63500.63519 ZADEH LA, 1984, SELECTED PAPERS LA Z, P29 Zadeh LA, 1989, IEEE T KNOWL DATA EN, V1, P89, DOI DOI 10.1109/69.43406 Zimmerman HJ, 1996, FUZZY SET THEORY ITS 1994, J AM SOC INFORMATION, V45 NR 117 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAY PY 2000 VL 51 IS 7 BP 614 EP 624 DI 10.1002/(SICI)1097-4571(2000)51:7<614::AID-ASI4>3.0.CO;2-S PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 307KA UT WOS:000086651400004 ER PT J AU Nigam, K McCallum, AK Thrun, S Mitchell, T AF Nigam, K McCallum, AK Thrun, S Mitchell, T TI Text classification from labeled and unlabeled documents using EM SO MACHINE LEARNING LA English DT Article DE text classification; Expectation-Maximization; integrating supervised and unsupervised learning; combining labeled and unlabeled data; Bayesian learning ID SAMPLES AB This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. We introduce an algorithm for learning from labeled and unlabeled documents based on the combination of Expectation-Maximization (EM) and a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents, and probabilistically labels the unlabeled documents. It then trains a new classifier using the labels for all the documents, and iterates to convergence. This basic EM procedure works well when the data conform to the generative assumptions of the model. However these assumptions are often violated in practice, and poor performance can result. We present two extensions to the algorithm that improve classification accuracy under these conditions: (1) a weighting factor to modulate the contribution of the unlabeled data, and (2) the use of multiple mixture components per class. Experimental results, obtained using text from three different real-world tasks, show that the use of unlabeled data reduces classification error by up to 30%. C1 Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. Just Res, Pittsburgh, PA 15213 USA. RP Nigam, K (reprint author), Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. CR Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 CASTELLI V, 1995, PATTERN RECOGN LETT, V16, P105, DOI 10.1016/0167-8655(94)00074-D Cheeseman P., 1996, ADV KNOWLEDGE DISCOV Cohen W. W., 1996, SIGIR Forum Cover T. M., 1991, ELEMENTS INFORMATION Craven M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Dagan I., 1995, Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dietterich TG, 1998, NEURAL COMPUT, V10, P1895, DOI 10.1162/089976698300017197 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Friedman JH, 1997, DATA MIN KNOWL DISC, V1, P55, DOI 10.1023/A:1009778005914 Ghahramani Z., 1993, ADV NEURAL INFORMATI, V6, P120 JAAKKOLA TS, 1998, LEARNING GRAPHICAL M Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joachims T., 1998, Machine Learning: ECML-98. 10th European Conference on Machine Learning. Proceedings Koller D., 1997, P 14 INT C MACH LEAR, P170 Lang K., 1995, Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning Larkey L. S., 1996, SIGIR Forum Lewis DD, 1997, INFORM PROCESS MANAG, V33, P209, DOI 10.1016/S0306-4573(96)00063-5 Lewis D., 1994, 3 ANN S DOC AN INF R, P81 Lewis D. D., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Lewis D. D., 1995, SIGIR Forum, V29 LEWIS DD, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P37 Lewis D. D., 1998, Machine Learning: ECML-98. 10th European Conference on Machine Learning. Proceedings Li H., 1997, P ACL 97, P39 Liere R., 1997, P 14 C AM ASS ART IN, P591 McCallum A., 1998, AAAI 98 WORKSH LEARN McCallum A., 1998, Machine Learning. Proceedings of the Fifteenth International Conference (ICML'98) MCCALLUM AK, 1998, MACH LEARN, P350 Mclachlan G., 1988, MIXTURE MODELS McLachlan G. J., 1997, EM ALGORITHM EXTENSI Miller DJ, 1997, ADV NEUR IN, V9, P571 Mitchell T.M., 1997, MACHINE LEARNING NG AY, 1997, MACH LEARN, P245 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 RISSANEN J, 1983, ANN STAT, V11, P416, DOI 10.1214/aos/1176346150 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST Sahami M., 1998, AAAI 98 WORKSH LEARN SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SCHUURMANS D, 1997, P 14 NAT C ART INT A, P552 SHAHSHAHANI BM, 1994, IEEE T GEOSCI REMOTE, V32, P1087, DOI 10.1109/36.312897 SHAVLIK J, 1998, AAAI 98 WORKSH LEARN STOLCKE A, 1994, TR94003 ICSI U CAL B Yang Y., 1999, J INFORMATION RETRIE, V1, P69 Yang Y., 1994, SIGIR 94 P 17 ANN IN, V94, P13 Yang Y., 1997, MACH LEARN INT WORKS, P412 NR 47 TC 734 Z9 787 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 0885-6125 J9 MACH LEARN JI Mach. Learn. PD MAY PY 2000 VL 39 IS 2-3 BP 103 EP 134 DI 10.1023/A:1007692713085 PG 32 WC Computer Science, Artificial Intelligence SC Computer Science GA 295HF UT WOS:000085960100002 ER PT J AU Menczer, F Belew, RK AF Menczer, F Belew, RK TI Adaptive retrieval agents: Internalizing local context and scaling up to the Web SO MACHINE LEARNING LA English DT Article DE InfoSpiders; distributed information retrieval; evolutionary algorithms; local selection; internalization; reinforcement learning; neural networks; relevance feedback; linkage topology; scalability; selective query expansion; adaptive on-line Web agents ID WORLD-WIDE-WEB; HYPERTEXT AB This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information environments on-line in search of pages relevant to the user, by traversing hyperlinks in an autonomous and intelligent fashion. Each agent adapts to the spatial and temporal regularities of its local context thanks to a combination of machine learning techniques inspired by ecological models: evolutionary adaptation with local selection, reinforcement learning and selective query expansion by internalization of environmental signals, and optional relevance feedback. We evaluate the feasibility and performance of these methods in three domains: a general class of artificial graph environments, a controlled subset of the Web, and (preliminarly) the full Web. Our results suggest that InfoSpiders could take advantage of the starting points provided by search engines, based on global word statistics, and then use linkage topology to guide their search on-line. We show how this approach can complement the current state of the art, especially with respect to the scalability challenge. C1 Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA. Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA. RP Menczer, F (reprint author), Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA. CR ARMSTRONG R, 1995, AAAI SPRING S INF GA AROCENA GO, 1997, P 6 INT WORLD WID WE Balabanovic Marko, 1997, P 1 INT C AUT AG BELEW R.K., 1996, ADAPTIVE INDIVIDUALS BELEW RK, IN PRESS FINDING OUT BOLLACKER KD, 1998, P 2 INT C AUT AG BOWMAN CM, 1994, COMMUN ACM, V37, P98, DOI 10.1145/179606.179704 CHAKRABARTI S, 1998, ACM SIGIR WORKSH HYP Chakrabarti S., 1998, P 7 INT WORLD WID WE CHAKRABARTI S, 1998, P ACM SIGMOD SEATTL Cohen WW, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P709 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CROFT WB, 1993, INFORM PROCESS MANAG, V29, P313, DOI 10.1016/0306-4573(93)90058-L De Bra P. M. E., 1994, P 1 INT WORLD WID WE De Jong K., 1995, P 6 INT C GEN ALG DeJong K., 1975, THESIS U MICHIGAN Eichmann D., 1994, P 1 INT WORLD WID WE Fisher D., 1997, P 14 INT C MACH LEAR FOX C, 1992, INFORMATION RETRIEVA FRAKES WR, 1992, INFORMATION RETRIEVA Goldberg D. E., 1987, P 2 INT C GEN ALG HARMAN D, 1992, INFORAMTION RETRIEVA HART WE, 1996, ADAPTIVE INDIVIDUALS Huberman BA, 1998, SCIENCE, V280, P95, DOI 10.1126/science.280.5360.95 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Kaelbling LP, 1996, J ARTIF INTELL RES, V4, P237 Koutsoupias E., 1996, P 23 INT C AUT LANG, P280 KWOK KL, 1988, INFORM PROCESS MANAG, V24, P123, DOI 10.1016/0306-4573(88)90104-5 LARSON RR, 1996, P 1996 ANN ASIS M Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 LEWIS D, 1996, P NRC MASS DAT SETS Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 LEWIS DD, 1997, P 9 ANN C COMP LEARN LEWIS DD, 1995, AAAI FALL S ACT LEAR LEWIS DD, 1994, ACM T INFORM SYST, V12, P231 Lieberman H., 1997, P ACM C COMP HUM INT LIN LJ, 1992, MACH LEARN, V8, P293, DOI 10.1007/BF00992699 LUKOSE RM, 1998, P 4 INT C COMP EC Mahfoud S.W., 1995, P 6 INT C GEN ALG MAHFOUD SW, 1994, FDN GENETIC ALGORITH, P3 Menczer F., 1996, ADAPTIVE INDIVIDUALS MENCZER F, 1998, P 7 ANN C EV PROGR S Menczer F., 1998, THESIS U CALIFORNIA MENCZER F, 1998, P 2 INT C AUT AG MIN MENCZER F, 1999, INTELLIGENT INFORMAT Menczer F, 1994, CIKM WORKSH INT INF Mitchell T.M., 1997, MACH LEARN MONGE AE, 1996, P 3 INT C COMP SCI R MOUKAS A, 1997, P 1 INT C AUT AG PASQUALE J, 1998, UCSD ACTIVE WEB PINKERTON B, 1994, P 1 INT WORKD WID WE RIVLIN E, 1994, COMMUN ACM, V37, P87, DOI 10.1145/175235.175242 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rumelhart DE, 1986, PARALLEL DISTRIBUTED, V1 SALTON G, 1963, J ACM, V10, P440, DOI 10.1145/321186.321188 SALTON G, 1983, INTRO MODERN INFORMA Savoy J, 1996, INFORM PROCESS MANAG, V32, P155, DOI 10.1016/S0306-4573(96)85003-5 SHAKES J, 1997, P 6 INT WORLD WID WE Sparck Jones K., 1972, J DOC, V28, P111 SPERTUS E, 1997, P 6 INT WORLD WID WE SRINIVASAN P, 1998, COMMUNICATION STEIER AM, 1994, THESIS U C SAN DIEG STEIER AM, 1994, 2 S DOC AN INF RETR SUTTON R, 1996, AAAI SPRING S MACH L VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WEISS R, 1996, P 7 ACM C HYP NR 66 TC 74 Z9 79 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 0885-6125 J9 MACH LEARN JI Mach. Learn. PD MAY PY 2000 VL 39 IS 2-3 BP 203 EP 242 DI 10.1023/A:1007653114902 PG 40 WC Computer Science, Artificial Intelligence SC Computer Science GA 295HF UT WOS:000085960100005 ER PT J AU Robertson, SE Walker, S Beaulieu, M AF Robertson, SE Walker, S Beaulieu, M TI Experimentation as a way of life: Okapi at TREC SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT 6th Text Retrieval Conference (TREC-6) CY NOV 19-21, 1999 CL GAITHERSBURG, MARYLAND SP TREC 6 AB The Okapi system has been used in a series of experiments on the TREC collections, investigating probabilistic models, relevance feedback and query expansion, and interaction issues. The TREC-6 ad hoc task was used to test an application of a new relevance weighting formula, which takes account of documents judged nonrelevant. The application was to a form Of blind feedback (using the top-ranked documents from an initial search to improve the query formulation for a subsequent search, without actual relevance feedback, on the assumption that these top-ranked documents are likely to be relevant). In the routing task, the problem is one of query optimization based on a training set with known relevant documents; investigations for TREC-6 included using a form of simulated annealing for this purpose. A significant feature of this work is the need to avoid overfitting of the training sample. In the interactive:track, methodology remains the major problem we do not yet know how to conduct controlled laboratory experiments which provide good information about information retrieval interaction. The Okapi team has been particularly interested in the relation between the functionalities associated with relevance feedback and the ability of searchers to make use of these functionalities. TREC provides an excellent environment and set of tools for,investigating automatic systems; its value for interactive systems is not yet proven. (C) 1999 Elsevier Science Ltd. All rights reserved. C1 City Univ London, Dept Informat Sci, Ctr Interact Syst Res, London EC1V 0HB, England. RP Robertson, SE (reprint author), City Univ London, Dept Informat Sci, Ctr Interact Syst Res, Northampton Sq, London EC1V 0HB, England. CR Beaulieu M., 1997, NIST SPECIAL PUBLICA, P143 Beaulieu M, 1996, J AM SOC INFORM SCI, V47, P85 BEAULIEU MM, 1998, NIST SPECIAL PUBLICA, P143 HAWKING D, 1999, INFORMATION RETRIEVA, V1, P113 ROBERTS NA, 1995, INT ANTIVIRAL NEWS, V3, P2 ROBERTSON SA, 1990, REPROD FERT DEVELOP, V2, P359, DOI 10.1071/RD9900359 Robertson S. E., 1997, SIGIR Forum ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Voorhees E., 1998, NIST SPECIAL PUBLICA, V500-242, P1 Walker S., 1998, NIST SPECIAL PUBLICA, P125 NR 12 TC 79 Z9 81 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2000 VL 36 IS 1 BP 95 EP 108 DI 10.1016/S0306-4573(99)00046-1 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 265UX UT WOS:000084265200005 ER PT J AU Murakami, T Orihara, R AF Murakami, T Orihara, R TI Friendly information retrieval through adaptive restructuring of information space SO NEW GENERATION COMPUTING LA English DT Article DE information retrieval; relevance feedback; vector space model; dictionary vector; Frequently Asked Questions AB Although a technique of relevance feedback is common in the field of information retrieval(IR), the feedback: is usually done by means of query refinement; restructuring of the information space has not been attempted yet. The restructuring not only allows useful applications such as clustering but also is indispensable for IR if a modeling function employs correlation of terms. In this paper we present a new method of relevance feedback through the restructuring of the information space. Our method adapts document space to the user's mental model by manipulating a dictionary vector. Therefore, user's viewpoint is preserved after a series of retrieval processes and reused for retrieval performed later. We show its effectiveness through the retrieval experiments on FAQ(Frequently Asked Questions) documents. C1 Informat Base Funct Toshiba Lab, Saiwai Ku, Kawasaki, Kanagawa 2108501, Japan. RP Murakami, T (reprint author), Informat Base Funct Toshiba Lab, Saiwai Ku, 70 Yanagi Cho, Kawasaki, Kanagawa 2108501, Japan. CR Buckley C., 1995, P 18 ANN INT ACM SIG, P351, DOI 10.1145/215206.215383 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESSING MA, V24, P515 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 NR 6 TC 1 Z9 1 PU SPRINGER VERLAG PI NEW YORK PA 175 FIFTH AVE, NEW YORK, NY 10010 USA SN 0288-3635 J9 NEW GENERAT COMPUT JI New Gener. Comput. PY 2000 VL 18 IS 2 BP 137 EP 146 PG 10 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA 277XP UT WOS:000084959100006 ER PT J AU Daranyi, S AF Daranyi, S TI A field approach to word semantics SO SEMIOTICA LA English DT Article ID INFORMATION-RETRIEVAL CR ARFKEN G, 1985, MATH MODELS PHYSICIS BARHILLEL Y, 1964, LANGUAGE INFORMATION BROWNE S, 1994, NETLIB SERVICES RESO CARNAP R., 1947, MEANING NECESSITY CHUTE CG, 1992, PROC SYMP COMPUT APP, P185 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 DARANYI S, 1998, UNPUB FORMAL THEORY Daranyi S, 1996, J DOC, V52, P86, DOI 10.1108/eb026963 DARANYI S, 1998, UNPUB STYLE FINGERPR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Edwards Paul, 1967, ENCY PHILOS, V1-8 Fodor J.D., 1977, SEMANTICS THEORIES M Gordon MD, 1998, J AM SOC INFORM SCI, V49, P674 Gray A., 1997, MODERN DIFFERENTIAL, V2nd Hjelmslev Louis, 1970, LANGUAGE INTRO Hjorland B, 1998, KNOWL ORGAN, V25, P16 HONKELA T, 1996, P 1996 IEEE INT C NE, V1, P56, DOI 10.1109/ICNN.1996.548866 HONKELA T, 1991, ARTIFICIAL NEURAL NETWORKS, VOLS 1 AND 2, P897 Jackendoff Ray, 1990, SEMANTIC STRUCTURES Johnson Michael L, 1988, MIND LANGUAGE MACHIN Kohonen T., 1995, SELF ORG MAPS KRAMPEN M, 1979, SEMIOTIC LANDSCAPE P, P169 Lehrer Adrienne, 1974, SEMANTIC FIELDS LEXI LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 LYONS J, 1968, INTRO THEORETICAL LI Morse PM, 1953, METHODS THEORETICAL OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 Osgood C.E., 1957, MEASUREMENT MEANING Paijmans H, 1997, J DOC, V53, P520, DOI 10.1108/EUM0000000007209 PITT VH, 1977, PENGUIN DICT PHYS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SANFILIPPO A, 1998, EAGLES PRILIMINARY R SKIDMORE A, 1981, SEMIOTIC THEMES, P33 STEEN FF, 1996, CONCEPTUAL STRUCTURE Thom Rene, 1975, STRUCTURAL STABILITY Trier J., 1934, NEUE JB WISSENSCHAFT, V10, P428 van Rijsbergen CJ, 1986, P 1986 ACM C RES DEV, P194, DOI 10.1145/253168.253208 VOIGT V, 1998, UNPUB AUTOMATED MOTI WISE JA, 1998, IN PRESS J AM SOC IN Wittgenstein L., 1958, PHILOS INVESTIGATION NR 41 TC 0 Z9 0 PU WALTER DE GRUYTER & CO PI BERLIN PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY SN 0037-1998 J9 SEMIOTICA JI Semiotica PY 2000 VL 128 IS 3-4 BP 259 EP 280 DI 10.1515/semi.2000.128.3-4.259 PG 22 WC Humanities, Multidisciplinary SC Arts & Humanities - Other Topics GA 318DC UT WOS:000087267800006 ER PT B AU MacFarlane, A McCann, JA Robertson, SE AF MacFarlane, A McCann, JA Robertson, SE GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI Parallel search using partitioned inverted files SO SPIRE 2000: SEVENTH INTERNATIONAL SYMPOSIUM ON STRING PROCESSING AND INFORMATION RETRIEVAL - PROCEEDINGS LA English DT Proceedings Paper CT 7th International Symposium on String Processing and Information Retrieval (SPIRE 2000) CY SEP 27-29, 2000 CL LA CORUNA, SPAIN SP Cyted-Amyri Res Project, IEEE ID OKAPI AB We examine the search of partitioned inverted files with particular emphasis on issues that arise from different types of partitioning methods. Two types of index partitions are investigated: namely TermId and DocId. We describe the search operations implemented in order to support parallelism in probabilistic search. We also describe higher level features such as search topologies in parallel search methods. The results from runs on the two types of partitioning are compared and contrasted. We conclude that within our framework the DocId method is the best. C1 City Univ London, Sch Informat, London EC1V 0HB, England. RP MacFarlane, A (reprint author), City Univ London, Sch Informat, London EC1V 0HB, England. CR CORMACK GV, 1997, P 6 TEXT RETR C TREC, P303 Frakes W. B., 1992, INFORMATION RETRIEVA, P1 Harman D, 1992, INFORMATION RETRIEVA, P28 HAWKING D, 1998, P 7 TEXT RETR C GAIT, P257 JEONG BS, 1995, IEEE T PARALL DISTR, V6, P142 MACFARLANE A, 2000, P 8 TEXT RETR C GAIT Macfarlane A, 1997, J DOC, V53, P274, DOI 10.1108/EUM0000000007201 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 Robertson SE, 1994, 356 U CAMBR COMP LAB Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Silverstein C., 1999, SIGIR Forum, V33 Singhal A, 1998, P 6 TEXT RETR C TREC, P215 Tomasic A., 1993, Proceedings of the Second International Conference on Parallel and Distributed Information Systems (Cat. No.93TH0493-7), DOI 10.1109/PDIS.1993.253078 NR 15 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-0746-8 PY 2000 BP 209 EP 220 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BR08V UT WOS:000165615200024 ER PT J AU Ellis, D Allen, D Wilson, T AF Ellis, D Allen, D Wilson, T TI Information science and information systems: Conjunct subjects disjunct disciplines SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Review ID RETRIEVAL CONFERENCE TREC-2; ASSESSMENT EXERCISE RATINGS; INTELLECTUAL STRUCTURE; COCITATION ANALYSIS; AUTHOR COCITATION; SEARCH INTERFACE; CITATION COUNTS; TECHNOLOGY; DESIGN; USER AB The relationship between information science and information systems (IS) research is examined through analysis of the subject literature of each field and by citation and cc-citation analysis of highly cited researchers in each field. The subfields of user studies (US) and information retrieval (IR) research were selected to represent information science research as these subject areas are central to information science and seemed also to represent areas in which there was most overlap of interest or subject matter. The two forms of analysis revealed that although there seemed considerable overlap or potential far overlap in research subjects that there was almost none in relation to the disciplinary fields as assessed by the cc-citation analysis of the most highly cited authors in the three subject areas. An examination of the reasons for this is offered in relation to the nature of scientific disciplines, the socialization process of researchers in the different fields and with institutional pressures. C1 Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. RP Ellis, D (reprint author), Univ Sheffield, Dept Informat Studies, Western Bank, Sheffield S10 2TN, S Yorkshire, England. RI Wilson, Thomas/N-9363-2013 OI Wilson, Thomas/0000-0003-4941-8443 CR ALLEN D, 1997, P 5 EUR C INF SYST U, P737 Allen T. J., 1977, MANAGING FLOW TECHNO *ASS INF SYST, 1997, P ASS INF SYST 3 AM AVISON D, 1998, P 3 UKAIS C BAETS WJ, 1998, P 6 EUR C INF SYST S BATES MJ, 1990, INFORM PROCESS MANAG, V26, P575, DOI 10.1016/0306-4573(90)90103-9 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 BAYER AE, 1990, J AM SOC INFORM SCI, V41, P444, DOI 10.1002/(SICI)1097-4571(199009)41:6<444::AID-ASI12>3.0.CO;2-J BELKIN NJ, 1990, J INFORM SCI, V16, P11, DOI 10.1177/016555159001600104 Benbasat I., 1978, MIS Q, P43, DOI 10.2307/248940 Benbasat I, 1996, INFORM SYST RES, V7, P389, DOI 10.1287/isre.7.4.389 BENBASAT I, 1981, COMMUN ACM, V24, P752, DOI 10.1145/358790.358795 BJORNANDERSEN N, 1985, P IFIP WG 8 2 C AMST BJORNANDERSEN N, 1983, P IFIP WG 8 2 C MINN BJORNANDERSEN N, 1988, COMPUT J, V31, P386, DOI 10.1093/comjnl/31.5.386 BOTTLE RT, 1997, INT ENCY INFORMATION, P212 Bourdieu Pierre, 1988, HOMOACADEMICUS BOYNTON AC, 1987, MIS QUART, V11, P59, DOI 10.2307/248826 BOYNTON AC, 1994, MIS Q, V18, P229 BRADLEY SJ, 1992, J INFORM SCI, V18, P225, DOI 10.1177/016555159201800307 BRITTAIN JM, 1970, INFORMATION ITS USER BROOKES BC, 1977, INT WORKSHOP COGNITI, P195 Checkland P, 1981, SYSTEMS THINKING SYS Checkland P. B., 1998, INFORMATION SYSTEMS Checkland P. B, 1990, SOFT SYSTEMS METHODO Checkland P. B., 1972, Journal of Systems Engineering, V3 CIBORRA CU, 1994, STRATEGIC INFORMATIO Ciborra C.U., 1996, GROUPWARE TEAMWORK CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 CRONIN B, 1989, J INFORM SCI, V15, P13, DOI 10.1177/016555158901500103 CUADRA CA, 1967, J DOC, V23, P291, DOI 10.1108/eb026436 CULNAN MJ, 1990, J AM SOC INFORM SCI, V41, P453, DOI 10.1002/(SICI)1097-4571(199009)41:6<453::AID-ASI13>3.0.CO;2-E DERVIN B, 1983, OVERVIEW SENSE MAKIN Diesing P., 1971, PATTERNS DISCOVERY S Earl M.H., 1989, MANAGEMENT STRATEGIE Ellis D., 1996, PROGR PROBLEMS INFOR ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 GALLIERS R, 1987, INFORMATION ANAL SEL GALLIERS RD, 1992, INFORMATION SYSTEMS GALLIERS RD, 1997, KEY ISSUES INFORMATI, P143 GALLIERS RD, 1994, STRATEGIC INFORMATIO Galliers R.D., 1991, J INFORMATION SYSTEM, V1, P89 Garvey W. D., 1979, COMMUNICATION ESSENC GILLBERG M, 1991, SLEEP, V14, P147 HARMAN D, 1995, INFORM PROCESS MANAG, V31, P271, DOI 10.1016/0306-4573(94)00047-7 HARMAN D, 1995, INFORM PROCESS MANAG, V31, P269, DOI 10.1016/0306-4573(94)00046-6 HARMAN DK, 1993, INFORM PROCESS MANAG, V29, P411, DOI 10.1016/0306-4573(93)90037-E HENDERSON JC, 1993, IBM SYST J, V32, P4 Hirschheim R, 1995, INFORMATION SYSTEMS Hjorland B., 1997, INFORMATION SEEKING Holsapple C. W., 1994, Journal of Management Information Systems, V11 *I INF SCI, 1996, CRIT INF SCI INGWERSEN P, 1992, INFORMATION RETRIEVA INGWERSEN P, 1996, 2 INT C CONC LIB INF INTRONA LD, 1997, MANAGEMENT INFORMATI IVES B, 1983, COMMUN ACM, V26, P785, DOI 10.1145/358413.358430 Ives B, 1996, SLOAN MANAGE REV, V37, P33 IVES B, 1984, MANAGE SCI, V30, P586, DOI 10.1287/mnsc.30.5.586 JACKSON MC, 1997, INFORMATION SYSTEMS, P201 JACKSON MC, 1992, J INFORMATION SYSTEM, V2, P83 KLING R, 1996, COMPUTERS CONTROVERS KLING R, 1994, INFORMATION SYSTEMS, V5, P139 KRAEMER KL, 1991, INFORMATION SYSTEMS, V3 Kuhlthau C. C., 1993, SEEKING MEANING PROC KUMAR K, 1990, COMMUN ACM, V33, P528, DOI 10.1145/78607.78613 Lancaster F. W., 1968, EVALUATION MEDLARS D LEDERER AL, 1988, MIS QUART, V12, P445, DOI 10.2307/249212 LEDERER AL, 1986, INFORM MANAGE, V10, P245, DOI 10.1016/0378-7206(86)90027-3 LIEVROUW LA, 1988, P AM SOC INF SCI ANN, V88, P54 Lyytinen K., 1987, CRITICAL ISSUES INFO, P3 MARKUS ML, 1983, COMMUN ACM, V26, P430, DOI 10.1145/358141.358148 MARKUS ML, 1987, COMMUN ACM, V30, P498, DOI 10.1145/214762.214764 MARKUS ML, 1994, SLOAN MANAGE REV, V35, P11 MARTYN J, 1975, J DOC, V31, P290, DOI 10.1108/eb026610 MARTYN J, 1974, ANNU REV INFORM SCI, V9, P3 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q MCFARLAN FW, 1983, HARVARD BUS REV, V61, P91 MCFARLAN FW, 1984, HARVARD BUS REV, V62, P98 MINGERS J, 1992, SYST PRACTICE, V5, P173, DOI 10.1007/BF01059939 MINGERS JC, 1995, INFORM SYST J, V5, P285, DOI 10.1111/j.1365-2575.1995.tb00100.x MINGERS J, 1997, INFORMATION SYSTEMS Mooney JG, 1996, DATA BASE ADV INF SY, V27, P68 Mumford E, 1987, COMPUTERS DEMOCRACY Mumford E., 1879, PARTICIPATIVE APPROA Mumford E., 1983, DESIGNING PARTICIPAT Mumford E., 1979, COMPUTER SYSTEMS WOR Mumford E, 1995, EFFECTIVE REQUIREMEN Mumford E., 1983, DESIGNING HUMAN SYST OPPENHEIM C, 1995, J DOC, V51, P18, DOI 10.1108/eb026940 Oppenheim C, 1997, J DOC, V53, P477, DOI 10.1108/EUM0000000007207 Orlikowski W. J., 1991, INFORMATION SYSTEMS, V2, P1, DOI DOI 10.1287/ISRE.2.1.1 PAISLEY W, 1990, J AM SOC INFORM SCI, V41, P459, DOI 10.1002/(SICI)1097-4571(199009)41:6<459::AID-ASI14>3.0.CO;2-5 PAISLEY WJ, 1968, ANNU REV INFORM SCI, V3, P1 Pinsonneault A, 1997, MANAGE SCI, V43, P659, DOI 10.1287/mnsc.43.5.659 RACKOFF N, 1985, MIS QUART, V9, P285, DOI 10.2307/249229 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBEY D, 1984, MIS QUART, V8, P5, DOI 10.2307/249240 Salton G., 1971, SMART RETRIEVAL SYST SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SAUNDERS C, 1996, MIS J RANKINGS IS WO SMALL H, 1985, J INFORM SCI, V11, P147, DOI 10.1177/016555158501100402 SMEATON AF, 1992, COMPUT J, V35, P268, DOI 10.1093/comjnl/35.3.268 *UKAIS, 1997, NEWSL UKAIS 3 Vakkari P., 1992, CONCEPTIONS LIB INFO Vakkari P., 1994, ADV LIBRARIANSHIP, V18, P1, DOI 10.1108/S0065-2830(1994)0000018003 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VENKATRAMAN N, 1994, SLOAN MANAGE REV, V35, P73 VICKERY A, 1987, J DOC, V43, P1, DOI 10.1108/eb026798 VICKERY BC, 1961, RETRIEVAL SYSTEM THE WALSHAM G, 1993, INTERPRETING INFORMA WALSTROM KA, 1995, COMMUN ACM, V38, P93, DOI 10.1145/203330.203348 Ward J., 1996, STRATEGIC PLANNING I WERSIG G, 1971, INFORMATION KOMMUNIK White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302 WILLETT P, 1988, DOCUMENT RETRIEVAL S WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702 Wilson TD, 1997, INFORM PROCESS MANAG, V33, P551, DOI 10.1016/S0306-4573(97)00028-9 Wilson T.D., 1997, INT ENCY INFORMATION, P187 Wilson T.D., 1994, 50 YEARS INFORM PROG, P15 Winder R., 1997, PHILOS ASPECTS INFOR Wiseman C., 1985, STRATEGY COMPUTERS I ZMUD RW, 1984, MIS QUART, V8, P79, DOI 10.2307/249345 NR 126 TC 37 Z9 37 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD OCT PY 1999 VL 50 IS 12 BP 1095 EP 1107 DI 10.1002/(SICI)1097-4571(1999)50:12<1095::AID-ASI9>3.3.CO;2-Q PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 238FP UT WOS:000082700500008 ER PT J AU Amati, G Crestani, F AF Amati, G Crestani, F TI Probabilistic learning for selective dissemination of information SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT RIAO 97 Conference CY 1997 CL QUEBEC CITY, CANADA ID RETRIEVAL; FEEDBACK AB New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest, In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of Information Retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile. (C) 1999 Elsevier Science Ltd. All rights reserved. C1 Fdn Ugo Bordoni, I-00142 Rome, Italy. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Amati, G (reprint author), Fdn Ugo Bordoni, Via B Castiglione 59, I-00142 Rome, Italy. CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 Allan J, 1996, P 19 ANN INT ACM SIG, P270, DOI 10.1145/243199.243274 AMATI G, 1995, P AICA 95 CAGL IT, P27 AMATI G, 1997, P RIAO C MONTR CAN, V1, P513 AMATI G, 1995, P 5 INT WORKSH INF R BAR-HILLEL Y., 1953, BRIT J PHILOS SCI, V4, P147, DOI DOI 10.1093/BJPS/IV.14.147 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Blair DC, 1996, J AM SOC INFORM SCI, V47, P4 Callan J.P., 1996, P 19 ANN INT ACM SIG, P262, DOI 10.1145/243199.243273 Carnap R., 1950, LOGICAL FDN PROBABIL Cleverdon C.W., 1966, ASLIB CRANFIELD RES CRESTANI F, 1998, IN PRESS ACM COMPUTI, V30 Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 Fidel R, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P198, DOI 10.1145/258525.258568 FRAKES WR, 1992, INFORMATION RETRIEVA GHOSH G, 1991, BRIEF HIST SEQUENTIA HARMAN D, 1992, INFORMATION RETRIEVA, pCH11 HARMAN D, 1996, P TREC C GAITH MD US Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HINTIKKA J, 1970, INFORMATION INFERENC HULL D, 1998, TREC C WASH DC US HULL D, 1998, P TREC C WASH DC US, P45 Jeffrey R., 1965, LOGIC DECISION KILANDER F, 1995, UNPUB BRIEF COMP NEW Lang K., 1995, P 12 INT C MACH LEAR, P331 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Lewis D. D., 1995, SIGIR Forum, V29 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 RENYI A, 1969, FDN PROBABILITY ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON S, 1993, P TREC C TREC 1 GAIT, P21 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, SMART RETRIEVAL SYST SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 Turtle H. R., 1990, THESIS U MASSACHUSET VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YAN TW, 1995, PROCEEDINGS OF THE 1995 USENIX TECHNICAL CONFERENCE, P177 NR 39 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 1999 VL 35 IS 5 BP 633 EP 654 DI 10.1016/S0306-4573(99)00012-6 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 235WL UT WOS:000082565800003 ER PT J AU Losee, RM Paris, LAH AF Losee, RM Paris, LAH TI Measuring search-engine quality and query difficulty: Ranking with target and freestyle SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; VECTOR-SPACE; RELEVANCE; FOUNDATION; BEHAVIOR; SYSTEMS AB Instead of using traditional performance measures such as precision and recall, information retrieval performance may be measured by considering the probability that the search engine is optimal and the difficulty associated with retrieving documents with a given query or on a given topic. These measures of desirable characteristics are more easily and more directly interpretable than are traditional measures. The performance of the Target and Freestyle search engines is examined, and is very good. Each query in the CF database is assigned a difficulty number, and these numbers: are found to strongly correlate with other measures of retrieval performance such as an E or F value, The query difficulty correlates weakly with query length. C1 Univ N Carolina, Sch Informat & Lib Studies, Chapel Hill, NC 27599 USA. Oklahoma Christian Univ Lib, Oklahoma City, OK 73136 USA. RP Losee, RM (reprint author), Univ N Carolina, Sch Informat & Lib Studies, Manning Hall,CB 3360, Chapel Hill, NC 27599 USA. CR BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 Burgin R, 1999, J AM SOC INFORM SCI, V50, P181, DOI 10.1002/(SICI)1097-4571(1999)50:2<181::AID-ASI8>3.0.CO;2-9 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 Duval B. K., 1994, Library Software Review, V13 HARTER SP, 1997, ANN REV INFORMATION, V32, P1 Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 KEEN EM, 1994, ONLINE CDROM REV, V18, P285, DOI 10.1108/eb024502 Losee R.M., 1998, TEXT RETRIEVAL FILTE LU XA, 1995, OV 3 TEXT RETR C TRE, P231 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 *MEAD DAT CENTR, 1994, HDB US LEX NEX Paris LAH, 1998, INFORM PROCESS MANAG, V34, P175, DOI 10.1016/S0306-4573(97)00065-4 PARIS LAH, 1998, THESIS U N CAROLINA ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 Shaw WM, 1997, INFORM PROCESS MANAG, V33, P15, DOI 10.1016/S0306-4573(96)00044-1 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SPARCKJONES K, 1972, J DOC, V28, P11 Tang R, 1998, INFORM PROCESS MANAG, V34, P237, DOI 10.1016/S0306-4573(97)00081-2 TENOPIR C, 1994, ONLINE, V18, P31 TURTLE H, 1994, ACM ANN C RES DEV IN, P212 VANRIJSBERGEN CJ, 1974, J DOC, V30, P365 Wilson D., 1995, RELEVANCE COMMUNICAT NR 29 TC 13 Z9 13 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD AUG PY 1999 VL 50 IS 10 BP 882 EP 889 DI 10.1002/(SICI)1097-4571(1999)50:10<882::AID-ASI5>3.0.CO;2-6 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 219QE UT WOS:000081617400005 ER PT J AU Sebastiani, F AF Sebastiani, F TI Towards a logical reconstruction of information retrieval theory SO CYBERNETICS AND SYSTEMS LA English DT Article ID TEXT RETRIEVAL; MODEL AB Here we expand on a previous paper concerning the role of logic in information retrieval (IR) modelling. In that paper, among other things, we had pointed out how different ways of understanding the contribution of logic to IR have sprung from the (always unstated) adherence to either the total or the partial knowledge assumption. Here we make our analysis more precise by relating this dichotomy to the notion of vividness, as used in knowledge representation, and to another dichotomy that has had a profound influence in database theory, namely, the distinction between the proof-theoretic and the model-theoretic views of a database, spelled out by Kelter in his "logical reconstruction of database theory." We show that precisely the same distinction can be applied to logical models of IR developed so far. The strengths and weaknesses of the adoption of either approach in logical models of IR are discussed. C1 CNR, Ist Elaborazione Informaz, I-56126 Pisa, Italy. RP Sebastiani, F (reprint author), CNR, Ist Elaborazione Informaz, Via S Maria 46, I-56126 Pisa, Italy. RI Sebastiani, Fabrizio/C-9501-2015 OI Sebastiani, Fabrizio/0000-0003-4221-6427 CR Baayen R. H., 1997, COMPUT HUMANITIES, V30, P281 COOPER WS, 1981, INFORMATION RETRIEVA, P199 CRESTANI F, 1996, P 2 INT WORKSH LOG U, P48 CRESTANI F, 1995, P 1 INT WORKSH LOG U, P35 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 CRESTANI F, 1995, P ACM SIGIR C RES DE, P291, DOI 10.1145/215206.215373 Fuhr N., 1995, P 18 ANN INT ACM SIG, P282, DOI 10.1145/215206.215372 Garey MR, 1979, COMPUTERS INTRACTABI HALPERN JY, 1991, PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P325 HALPERN JY, 1990, ARTIF INTELL, V46, P311, DOI 10.1016/0004-3702(90)90019-V HARMAN D, 1995, INFORM PROCESS MANAG, V31, P271, DOI 10.1016/0306-4573(94)00047-7 HARPER WL, 1986, IFS CONDITIONALS BEL JONES KS, 1997, READINGS INFORMATION LEVESQUE HJ, 1988, J PHILOS LOGIC, V17, P355, DOI 10.1007/BF00297511 LEVESQUE HJ, 1986, ARTIF INTELL, V30, P81, DOI 10.1016/0004-3702(86)90068-8 LEWIS D, 1976, PHILOS REV, V85, P297, DOI 10.2307/2184045 LUHN HP, 1957, IBM J RES DEV, V1, P309 Lukaszewicz W., 1990, NONMONOTONIC REASONI MEGHINI C, 1993, P 16 ANN INT ACM SIG, P298, DOI 10.1145/160688.160753 Meghini C., 1996, P 19 ANN INT ACM SIG, P197, DOI 10.1145/243199.243267 MYLOPOULOS J, 1989, READINGS ARTIFICIAL NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 NIE JY, 1992, P 15 ANN ACM SIGIR C, P140, DOI 10.1145/133160.133188 Ohlbach H. J., 1991, Journal of Logic and Computation, V1, DOI 10.1093/logcom/1.5.691 Reiter R., 1984, CONCEPTUAL MODELLING, P191 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROLLEKE T, 1995, P SIGIR 95, P374 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F., 1994, P 17 ANN INT ACM SIG, P122 SEBASTIANI F, 1988, INFORMATION PROCESSI, V34, P1 Sebastiani F, 1998, COMPUT ARTIF INTELL, V17, P35 SMEATON AF, 1997, INFORMATION RETRIEVA, P3 Stalnaker Robert C., 1970, THEORIA-SPAIN, V36, P23 van Benthem J, 1976, THESIS U AMSTERDAM VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 WILLETT P, 1988, DOCUMENT RETRIEVAL S WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 41 TC 2 Z9 2 PU TAYLOR & FRANCIS LTD PI LONDON PA ONE GUNPOWDER SQUARE, LONDON EC4A 3DE, ENGLAND SN 0196-9722 J9 CYBERNET SYST JI Cybern. Syst. PD JUL-AUG PY 1999 VL 30 IS 5 BP 411 EP 428 DI 10.1080/019697299125145 PG 18 WC Computer Science, Cybernetics SC Computer Science GA 212TW UT WOS:000081233600003 ER PT J AU Chang, CH Hsu, CC AF Chang, CH Hsu, CC TI Enabling concept-based relevance feedback for information retrieval on the WWW SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE query expansion; relevance feedback; concept-based feedback; keyword extraction; document clustering; document-based browsing; cluster-based browsing ID SEARCH AB The World Wide Web is a world of great richness, but finding information on the Web is also a great challenge. Keyword-based querying has been an immediate and efficient way to specify and retrieve related information that the user inquires. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given, as in most cases. In order to clarify the ambiguity of the short queries given by users, we propose the idea of concept-based relevance feedback for Web information retrieval. The idea is to have users give two to three times more feedback in the same amount of time that would be required to give feedback for conventional feedback mechanisms. Under this design principle, we apply clustering techniques to the initial search results to provide concept-based browsing. We show the performances of various feedback interface designs and compare their pros and cons. We shall measure precision and relative recall to show how clustering improves performance over conventional similarity ranking and, most importantly, we shall show how the assistance of concept-based presentation reduces browsing labor. C1 Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan. RP Chang, CH (reprint author), Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan. RI Chang, Chia-Hui/C-1049-2009 CR Anick PG, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P314, DOI 10.1145/258525.258601 Chang CH, 1997, COMPUT NETWORKS ISDN, V29, P1217, DOI 10.1016/S0169-7552(97)00053-6 CHANG CH, 1998, P 10 IEEE INT C TOOL CROFT WB, 1995, P 2 INT C THEOR PRAC, P19 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DREILINGER D, 1996, CS96111 COL STAT U Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 HEARST MA, 1996, P ACM SIGIR INT C RE PINKERTON B, 1994, P 2 INT WWW C CHIC PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rasmussen E., 1992, INFORM RETRIEVAL DAT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SELEBERG E, 1995, P 4 INT WWW C BOST Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 17 TC 42 Z9 47 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JUL-AUG PY 1999 VL 11 IS 4 BP 595 EP 609 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 231WV UT WOS:000082335100007 ER PT J AU Zhang, J Korfhage, RR AF Zhang, J Korfhage, RR TI A distance and angle similarity measure method SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RELEVANCE AB This article presents a distance and angle similarity measure. The integrated similarity measure takes the strengths of both the distance and direction of measured documents into account. This article analyzes the features of the similarity measure by comparing it with the traditional distance-based similarity measure and the cosine measure, providing the iso-similarity contour, investigating the impacts of the parameters and variables on the new similarity measure. it also gives the further research issues on the topic. C1 Univ Pittsburgh, Sch Informat Sci, Pittsburgh, PA 15260 USA. RP Zhang, J (reprint author), Univ Wisconsin, Sch Lib & Informat Sci, 2400 E Hartford Ave, Milwaukee, WI 53211 USA. RI zhang, jin /A-6769-2010 CR CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 FRAKES WR, 1992, INFORMATION RETRIEVA JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Korfhage R., 1997, INFORMATION STORAGE KWOK KL, 1985, J AM SOC INFORM SCI, V36, P242 MCGILL M, 1979, EVALUATION FACTORS A Meadow C.T., 1992, TEXT INFORMATION RET Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, AUTOMATIC INFORMATIO VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 12 TC 27 Z9 28 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUL PY 1999 VL 50 IS 9 BP 772 EP 778 DI 10.1002/(SICI)1097-4571(1999)50:9<772::AID-ASI5>3.0.CO;2-E PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 212CK UT WOS:000081198600005 ER PT J AU Boughanem, M Chrisment, C Soule-Dupuy, C AF Boughanem, M Chrisment, C Soule-Dupuy, C TI Query modification based on relevance back-propagation in an ad hoc environment SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT RIAO 97 Conference CY 1997 CL QUEBEC CITY, CANADA ID INFORMATION-RETRIEVAL; GENETIC ALGORITHMS; DOCUMENT-RETRIEVAL AB It is well-known that relevance feedback is a method significant in improving the effectiveness of information retrieval systems. Improving effectiveness is important since these information retrieval systems must gain access to large document collections distributed over different distant sites. As a consequence, efforts to retrieve relevant documents have become significantly greater. Relevance feedback can be viewed as an aid to the information retrieval task. In this paper, a relevance feedback strategy is presented. The strategy is based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. This paper describes a neural information retrieval model and emphasizes the results obtained with the associated relevance back-propagation algorithm in three different environments: manual ad hoc, automatic ad hoc and mixed ad hoc strategy (automatic plus manual ad hoc). (C) 1999 Elsevier Science Ltd. All rights reserved. C1 IRIT, SIG, F-31062 Toulouse 4, France. RP Boughanem, M (reprint author), IRIT, SIG, 118 Route Narbonne, F-31062 Toulouse 4, France. CR BEIN J, 1988, CUCS40588 U COL DEP BELEW RK, 1989, ACM SIGIR INT C RES BOUGHANEM M, 1994, 4 RIAO INT C COMP AS BOUGHANEM M, 1997, NIST SP BOUGHANEM M, 1992, THESIS P SABATIER U BOUGHANEM M, 1997, 5 RIAO INT C COMP AS BRACHMAN RJ, 1988, P 11 INT C R D IR GR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 1995, NIST SP Chang Y, 1971, SMART RETRIEVAL SYST, P355 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S DOSZKOCS TE, 1990, ANNU REV INFORM SCI, V25, P209 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 HAINES D, 1993, ACM SIGIR INT C RES HARMAN DK, 1995, NIST SP HARMAN DK, 1992, ACM SIGIR INT C RES HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Ide E., 1971, SMART RETRIEVAL SYST, P337 KRAFT DH, 1994, IEEE S EV C KWOK KL, 1997, NIST SP KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 LECUN Y, 1985, COGN C FRONT INT ART QIU Y, 1993, ACM SIGIR INT C RES ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rumelhart DE, 1986, PARALLEL DISTRIBUTED, V1 SALTON G, 1970, INFORM STORAGE RET, V6, P29, DOI 10.1016/0020-0271(70)90011-2 SINGHAL A, 1997, ACM SIGIR INT C RES TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1986, ACM SIGIR INT C RES WILKINSON R, 1991, ACM SIGIR INT C RES WONG SKM, 1993, ACM SIGIR INT C RES YANG JJ, 1992, L150451592001 NR 34 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 1999 VL 35 IS 2 BP 121 EP 139 DI 10.1016/S0306-4573(99)00008-4 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 199DC UT WOS:000080463400002 ER PT J AU Cohen, E Lewis, DD AF Cohen, E Lewis, DD TI Approximating matrix multiplication for pattern recognition tasks SO JOURNAL OF ALGORITHMS LA English DT Article; Proceedings Paper CT 8th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 97) CY JAN 05-07, 1997 CL NEW ORLEANS, LOUISIANA SP ACM, SIAM ID RETRIEVAL; INFORMATION AB Many pattern recognition tasks, including estimation, classification, and the finding of similar objects, make use of linear models. The fundamental operation in such tasks is the computation of the dot product between a query vector and a large database of instance vectors. Often we are interested primarily in those instance vectors which have high dot products with the query. We present a random sampling based algorithm that enables us to identify, for any given query vector, those instance vectors which have large dot products, while avoiding explicit computation of all dot products. We provide experimental results that demonstrate considerable speedups for text retrieval tasks. Our approximate matrix multiplication algorithm is applicable to products of k greater than or equal to 2 matrices and is of independent interest. Our theoretical and experimental analysis demonstrates that in many scenarios, our method dominates standard matrix multiplication, (C) 1999 Academic Press. C1 AT&T Labs Res, Florham Park, NJ 07932 USA. RP Cohen, E (reprint author), AT&T Labs Res, Florham Park, NJ 07932 USA. EM edith@research.att.com; lewis@research.att.com CR ARYA S, 1994, PROCEEDINGS OF THE FIFTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P573 ATKINSON KE, 1989, NUMERICAL ANAL BENTLEY JL, 1980, ACM T MATH SOFTWARE, V6, P563, DOI 10.1145/355921.355927 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Buckley C., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval CHERNOFF H, 1952, ANN MATH STAT, V23, P493, DOI 10.1214/aoms/1177729330 CLARKSON KL, 1996, COMMUNICATION DEC CLARKSON KL, 1996, UNPUB RAPID ESTIMATI Coppersmith D., 1987, P 19 ANN ACM S THEOR CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Dasarthy B., 1991, NEAREST NEIGHBOR NN DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUDA RO, 1973, PATTENR CLASSIFICATI DUMAIS ST, 1995, 3 TEXT RETR C TREC 3, P219 Feller W, 1968, INTRO PROBABILITY TH Golub GH, 1989, MATRIX COMPUTATIONS Harman D., 1992, INFORMATION RETRIEVA, P241 Hastie T.J., 1990, MONOGRAPHS STAT APPL, V43 JONES KS, 1971, AUTOMATIC KEYWORD CL LEWIS DD, 1992, ACM SIGIR C RES DEV, P37 Lewis D. D., 1996, SIGIR Forum MICHIE D, 1994, MACHINERY LEARNING N ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA THOMPSON RH, 1989, INT J MAN MACH STUD, V30, P639, DOI 10.1016/S0020-7373(89)80014-8 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Witten I.H., 1994, MANAGING GIGABYTES C YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 29 TC 12 Z9 12 PU ACADEMIC PRESS INC PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 0196-6774 J9 J ALGORITHM JI J. Algorithms PD FEB PY 1999 VL 30 IS 2 BP 211 EP 252 DI 10.1006/jagm.1998.0989 PG 42 WC Computer Science, Theory & Methods; Mathematics, Applied; Logic SC Computer Science; Mathematics; Science & Technology - Other Topics GA 164LD UT WOS:000078464800002 ER PT S AU Indrawan, M Wilson, C Srinivasan, B AF Indrawan, M Wilson, C Srinivasan, B BE Mohammadian, M TI Relevance feedback in an information retrieval system based on Bayesian belief networks SO COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - INTELLIGENT IMAGE PROCESSING, DATA ANALYSIS & INFORMATION RETRIEVAL SE CONCURRENT SYSTEMS ENGINEERING SERIES LA English DT Proceedings Paper CT International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 99) CY FEB 17-19, 1999 CL VIENNA, AUSTRIA ID MODELS AB People have been using information retrieval systems for decades, however users do not always find the retrieval results to be relevant. The discrepencies between what the systems consider relevant and what the users consider as relevant may not be totally avoided due to the uncertainty involved during the indexing process. Many IR systems adopt relevance feedback as a learning mechanism for understanding users' information needs. This paper discusses the issues of incorporating relevance feedback into an Tp system based on Bayesian networks. Methods for supporting the addition of evidence and for altering dependencies in the model are presented. C1 Monash Univ, Fac Informat Technol, Clayton, Vic 3168, Australia. RP Indrawan, M (reprint author), Monash Univ, Fac Informat Technol, Clayton, Vic 3168, Australia. CR CHAZFAN D, 1995, P 5 HEL C INF, P639 CHAZFAN D, 1996, P 6 INT C INF PROC M, P841 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1990, P 16 INT C VER LARG, P696 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SPARCKJONES K, 1979, J DOC, V35, P30 TUTLE H, 1990, THESIS U MASSACHUSET VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU C, 1988, P 11 INT C RES DEV I, P201, DOI 10.1145/62437.62451 NR 12 TC 1 Z9 1 PU I O S PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 1383-7575 BN 90-5199-475-3 J9 CONCUR SYST ENGN SER PY 1999 VL 56 BP 266 EP 271 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA BM72W UT WOS:000079620000045 ER PT J AU Cercone, N An, AJ Chan, C AF Cercone, N An, AJ Chan, C TI Rule-induction and case-based reasoning: Hybrid architectures appear advantageous SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE case-based reasoning; rule induction; machine learning; classification; numeric prediction AB Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule-induction methods with case-based reasoning techniques to engender performance improvements over more-traditional one-representation architectures. We briefly survey several major rule-induction and case-based reasoning ML systems. We then examine some interesting hybrid combinations of these systems, and explain their strengths and weaknesses as learning systems. We present a balanced approach to constructing a hybrid architecture, along with arguments in favor of this balance and mechanisms for achieving a proper balance. Finally, we present some initial empirical results from testing our ideas and draw some conclusions based on those results. C1 Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada. RP Cercone, N (reprint author), Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. CR AAMODT A, 1994, ARTIFICIAL INTELLIGE, V7 ALTHOFF K, 1995, P 8 WORKSH GERM SIG AN A, 1995, P 15 ANN C BRIT COMP, P85 An A., 1998, P 12 BIENN C CAN SOC AN A, 1997, THESIS U REGINA SASK BAREISS ER, 1987, P 4 INT WORKSH MACH BEAULIEU M, 1997, NIST SPECIAL PUBLICA CLARK P, 1990, ARTIFICIAL INTELLIGE Clark P, 1991, P 5 EUR WORK SESS LE, P151 COOPER WS, 1973, J AM SOC INFORMATION, V24 CREECY RH, 1992, COMMUN ACM, V35, P48, DOI 10.1145/135226.135228 DOMINGOS P, P IJCAI 95 MONTR QUE, P1226 GOLDING AR, 1991, PROCEEDINGS : NINTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P22 Koton P., 1989, MITLCSTR441 LANGLEY P, 1995, COMM ACM, V38 Leake D. B., 1996, CASE BASED REASONING Michalski R. S., 1986, Proceedings AAAI-86: Fifth National Conference on Artificial Intelligence Murphy P.M., 1994, UCI RESPOSITORY MACH MURTHY SK, 1994, J ARTIFICIAL INTELLI, P1 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Robertson S. E., 1977, J DOCUMENTATION, V33 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SKALAK DB, 1990, PROCEEDINGS : EIGHTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P840 WATSON I, 1996, P 2 UK WORKSH CAS BA ZHANG JP, 1990, PROCEEDINGS OF THE 2ND INTERNATIONAL IEEE CONFERENCE ON TOOLS FOR ARTIFICIAL INTELLIGENCE, P31, DOI 10.1109/TAI.1990.130306 NR 26 TC 25 Z9 25 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JAN-FEB PY 1999 VL 11 IS 1 BP 166 EP 174 DI 10.1109/69.755625 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 178YK UT WOS:000079296600018 ER PT J AU Ding, Y Chowdhury, G Foo, S AF Ding, Y Chowdhury, G Foo, S TI Mapping the intellectual structure of information retrieval studies: an author co-citation analysis, 1987-1997 SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID DOCUMENT-RETRIEVAL; SEARCH KEYS; COCITATION ANALYSIS; ONLINE CATALOGS; SYSTEM-DESIGN; MODEL; SEEKING; TEXT; PROBABILITY; RELEVANCE AB Author co-citation analysis (ACA) has been used to explore changes in the intellectual base of the information retrieval (LR) held over two consecutive time periods: 1987-1991 and 1992-1997. Thirty-nine highly cited IR researchers were selected as the research sample. Multidimensional scaling (MDS) and clustering techniques (CT) were used to create the two-dimensional maps to display the dynamic intellectual structure of IR, based on scholars citing their work over these two time periods. Factor analysis (FA) has been used to reveal the 'breadth' of the authors' research areas. ACA offers a good technique that contributes to the understanding of intellectual structure in the sciences and possibly in other areas to the extent that those areas rely on formal scholarly communication such as serial publications. Nonetheless, obvious drawbacks exist in ACA. These include the subjective nature of the interpretation of results, the difficulty of readily identifying clusters and the inability to distinguish collaborative research relationships between authors, Thus, ACA by itself is insufficient. However, ACA can be enhanced significantly when combined with FA to give a more accurate acid useful picture of the MDS results. C1 Nanyang Technol Univ, Sch Appl Sci, Div Informat Studies, Singapore 639798, Singapore. RP Ding, Y (reprint author), Nanyang Technol Univ, Sch Appl Sci, Div Informat Studies, Nanyang Ave, Singapore 639798, Singapore. RI Foo, Schubert /H-2070-2013 CR Aldenderfer M., 1984, CLUSTER ANAL BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P205, DOI 10.1002/asi.4630300406 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 BAYER AE, 1990, J AM SOC INFORM SCI, V41, P444, DOI 10.1002/(SICI)1097-4571(199009)41:6<444::AID-ASI12>3.0.CO;2-J BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 Borg I., 1997, MODERN MULTIDIMENSIO Borgman C. L., 1990, SCHOLARLY COMMUNICAT BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P387, DOI 10.1002/(SICI)1097-4571(198611)37:6<387::AID-ASI3>3.0.CO;2-8 BRAAM RR, 1991, MAPPING SCI FOCI INT, P267 Cleverdon C. W., 1966, FACTORS DETERMINING COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 Cronin B., 1984, CITATION PROCESS ROL CULNAN MJ, 1990, J AM SOC INFORM SCI, V41, P453, DOI 10.1002/(SICI)1097-4571(199009)41:6<453::AID-ASI13>3.0.CO;2-E DERVIN B, 1986, ANNU REV INFORM SCI, V21, P3 DHAR V, 1991, INFORMATION PROCESSI, V27, P405 Ding Y, 1998, INT FORUM INFORM DOC, V23, P25 DING Y, 1998, BIBLIOMETRIC ANAL IN ELLIS D, 1984, J INFORM SCI, V8, P25, DOI 10.1177/016555158400800105 ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 FIDEL R, 1991, J AM SOC INFORM SCI, V42, P501, DOI 10.1002/(SICI)1097-4571(199108)42:7<501::AID-ASI5>3.0.CO;2-V FIDEL R, 1991, J AM SOC INFORM SCI, V42, P490, DOI 10.1002/(SICI)1097-4571(199108)42:7<490::AID-ASI4>3.0.CO;2-V FIDEL R, 1986, J AM SOC INFORM SCI, V37, P37, DOI 10.1002/asi.4630370107 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Garfield E., 1979, CITATION INDEXING Griffith B. C., 1990, SCHOLARLY COMMUNICAT, P31 HARMAN D, 1990, J AM SOC INFORM SCI, V41, P581, DOI 10.1002/(SICI)1097-4571(199012)41:8<581::AID-ASI4>3.0.CO;2-U Harter S. P., 1986, ONLINE INFORMATION R HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q INGWERSEN P, 1992, INFORMATION RETRIEVA INGWERSEN P, 1982, J DOC, V38, P165, DOI 10.1108/eb026727 INGWERSEN P, 1984, ONLINE REV, V8, P465, DOI 10.1108/eb024167 JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1971, AUTOMATIC KEYWORD CL Karki R, 1996, J INFORM SCI, V22, P323, DOI 10.1177/016555159602200501 Kruskal J.B., 1978, MULTIDIMENSIONAL SCA KUHLTHAU CC, 1990, LIBR INFORM SCI RES, V12, P5 KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-# Lancaster F., 1986, VOCABULARY CONTROL I LANCASTER FW, 1968, INFORMATION RETRIEVA LENK P, 1983, J AM SOC INFORM SCI, V34, P115, DOI 10.1002/asi.4630340204 Lewis-Beck M. S., 1994, FACTOR ANAL RELATED LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W Marchionini G., 1988, IEEE COMPUT, V21, P70 MARCHIONINI G, 1989, J AM SOC INFORM SCI, V40, P54, DOI 10.1002/(SICI)1097-4571(198901)40:1<54::AID-ASI6>3.0.CO;2-R MARKEY K, 1981, INFORM PROCESS MANAG, V17, P215, DOI 10.1016/0306-4573(81)90016-9 Markey K., 1987, Cataloging & Classification Quarterly, V7, DOI 10.1300/J104v07n03_05 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MCCAIN KW, 1986, J AM SOC INFORM SCI, V37, P111, DOI 10.1002/(SICI)1097-4571(198605)37:3<111::AID-ASI2>3.0.CO;2-D MCCAIN KW, 1984, J AM SOC INFORM SCI, V35, P351, DOI 10.1002/asi.4630350607 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q MEADOW CT, 1989, J AM SOC INFORM SCI, V40, P86, DOI 10.1002/(SICI)1097-4571(198903)40:2<86::AID-ASI3>3.0.CO;2-K MEADOW CT, 1988, INFORM PROCESS MANAG, V24, P449, DOI 10.1016/0306-4573(88)90047-7 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 ODDY RN, 1991, INFORM PROCESS MANAG, V27, P317, DOI 10.1016/0306-4573(91)90087-3 PAISLEY W, 1990, SCHOLARLY COMMUNICAT PERSSON O, 1994, J AM SOC INFORM SCI, V45, P31, DOI 10.1002/(SICI)1097-4571(199401)45:1<31::AID-ASI4>3.0.CO;2-G Pierce S. J., 1990, SCHOLARLY COMMUNICAT, P46 RADA R, 1987, INFORM PROCESS MANAG, V23, P195, DOI 10.1016/0306-4573(87)90004-5 RADA R, 1989, J AM SOC INFORM SCI, V40, P304, DOI 10.1002/(SICI)1097-4571(198909)40:5<304::AID-ASI2>3.0.CO;2-6 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P219, DOI 10.1016/0306-4573(88)90089-1 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P177, DOI 10.1002/(SICI)1097-4571(198805)39:3<177::AID-ASI3>3.0.CO;2-F SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SCHRAMM W, 1980, COMMUNICATION YB, V4 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SPINK A, 1992, P 55 ANN M ASIS PITT SWANSON DR, 1988, J AM SOC INFORM SCI, V39, P94 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA WHITE HD, 1983, J AM SOC INFORM SCI, V34, P307, DOI 10.1002/asi.4630340502 WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 Zadeh LA, 1965, J INFORM CONTROL, P338, DOI DOI 10.1016/S0019-9958(65)90241-X NR 93 TC 39 Z9 39 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD,, E GRINSTEAD RH19 1HH, W SUSSEX, ENGLAND SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1999 VL 25 IS 1 BP 67 EP 78 DI 10.1177/016555159902500107 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 228EK UT WOS:000082124000007 ER PT J AU Bodoff, D AF Bodoff, D TI A re-unification of two competing models for document retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID ONLINE; FEEDBACK; TERMS AB Two competing approaches for document retrieval were first identified by Robertson, Maron, & Cooper (1982) for probabilistic retrieval. The difficulty of unifying those approaches was introduced as a problem of resolving query-focused with document-focused retrieval, and an approach towards unification was offered. That approach rests on a re-conceptualization of the meaning of term weight estimates. In this work, we propose a new unified model. The unification problem is re-framed as resulting from a lack of theory regarding the relationship of two sorts of data, absolute and relative. This new unified model is valid even for traditional interpretations of term estimates. C1 NYU, Dept Informat Syst, Stern Sch Business, Management Educ Ctr, New York, NY 10012 USA. RP Bodoff, D (reprint author), NYU, Dept Informat Syst, Stern Sch Business, Management Educ Ctr, 44 W 4th St,Room 9-181, New York, NY 10012 USA. EM dbodoff@rnd.stern.nyu.edu RI bodoff, david/A-3775-2009 CR Bartell B., 1992, P 15 ANN INT ACM SIG, P161, DOI 10.1145/133160.133191 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 BELEW RK, 1989, SIGIR FORUM, V23, P11, DOI 10.1145/75334.75337 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 Brauen T., 1971, SMART RETRIEVAL SYST, P456 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CAVNAR WB, 1993, USING N GRAM BASED D COOPER WS, 1992, P 15 INT C RES DEV I COOPER WS, 1995, EXPT PROBABILISTIC R FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 HARMAN D, 1992, 1 TEXT RETRIEVAL C T HARMAN DK, 1992, P 15 INT C RES DEV I HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER SP, 1975, J AM SOC INFORM SCI, P280 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Hert CA, 1996, J AM SOC INFORM SCI, V47, P504, DOI 10.1002/(SICI)1097-4571(199607)47:7<504::AID-ASI4>3.0.CO;2-Y Jones S, 1997, J DOC, V53, P35, DOI 10.1108/EUM0000000007189 KATZER J, 1990, ANN M AM SOC INF SCI Kruskal J.B., 1978, SAGE U PAPER SERIES, V07-011 KWOK KL, 1989, SIGIR FORUM, V23, P21, DOI 10.1145/75334.75338 KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 LEWIS DD, 1992, THESIS U MASS, P280 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MASAND B, 1992, 15 INT SIGIR DENM NE MICHALEWICZ Z, 1996, GENETIC ALGORITHMS P ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1982, PROBABILITY RELEVANC, P1 ROBERTSON SE, 1983, LECT NOTES COMPUTER, V146 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1994, J DOC, V50, P233, DOI 10.1108/eb026932 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 TENOPIR C, 1991, LIBR INFORM SCI RES, V13, P237 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 NR 39 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1999 VL 50 IS 1 BP 49 EP 64 DI 10.1002/(SICI)1097-4571(1999)50:1<49::AID-ASI7>3.0.CO;2-9 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 157UX UT WOS:000078081000006 ER PT B AU Kurimo, M AF Kurimo, M BE Oja, E Kaski, S TI Indexing audio documents by using latent semantic analysis and SOM SO KOHONEN MAPS LA English DT Proceedings Paper CT High-Level Workshop on the Theory of Methodology and Applications of the SOM CY JUL, 1999 CL FINLAND SP Acad Finland ID MAPS AB This paper describes an important application for state-of-art automatic speech recognition, natural language processing and information retrieval systems. Methods for enhancing the indexing of spoken documents by using latent semantic analysis and self organizing maps are presented, motivated and tested. The idea is to extract extra information from the structure of the document collection and use it for more accurate indexing by generating new index terms and stochastic index weights. Indexing methods are evaluated for two broadcast news databases (one French and one English) using the average document perplexity defined in this paper and test queries analyzed by human experts. C1 IDIAP, CH-1920 Martigny, Switzerland. RP Kurimo, M (reprint author), IDIAP, CP 592,Rue Simplon 4, CH-1920 Martigny, Switzerland. EM Mikko.Kurimo@idiap.ch RI Kurimo, Mikko/F-6647-2012 CR ABBERLEY D, 1999, ESCA ETRW WORK ACC I Abberley D, 1998, INT CONF ACOUST SPEE, P3781, DOI 10.1109/ICASSP.1998.679707 ALLAN J, 1998, P 6 TEXT RETR C TREC, P169 ANDERSEN J, 1998, 987 COM IDIAP BELLEGARDA JR, 1997, IEEE WORKSH AUT SPEE, P262 BERROL S, 1992, PHYSICAL MED REHABIL, V6, P1 Bourlard H, 1994, CONNECTIONIST SPEECH CHEN S, 1998, DARPA BROADCAST NEWS DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 GAROFOLO JS, 1999, ESCA ETRW WORKSH ACC Honkela T., 1996, A32 HELS U TECHN LAB IYER R, 1997, IEEE WORKSH SPEECH R, P254 JOHNSON SE, 1999, P IEEE INT C AC SPEE, P49 Kaski S., 1998, P IJCNN 98 INT JOINT, V1, P413 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kohonen T., 1997, SELF ORG MAPS KURIMO M, 1999, ESCA ETRW WORKSH ACC KURIMO M, 1997, THESIS HELSINKI U TE Ng K, 1998, INT CONF ACOUST SPEE, P325, DOI 10.1109/ICASSP.1998.674433 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RENALS S, 1998, P 7 TEXT RETR C TREC RITTER H, 1989, BIOL CYBERN, V61, P241, DOI 10.1007/BF00203171 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robinson T., 1996, AUTOMATIC SPEECH SPE, P233 Salton G., 1971, SMART RETRIEVAL SYST SIEGLER M, 1999, P IEEE INT C AC SPEE, P505 XU J, 1996, P ACM SIGIR NR 27 TC 8 Z9 8 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS BN 0-444-50270-X PY 1999 BP 363 EP 374 DI 10.1016/B978-044450270-4/50029-2 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BP21Z UT WOS:000084436200029 ER PT S AU An, A Cercone, N AF An, A Cercone, N BE Zhong, N Skowron, A Ohsuga, S TI An empirical study on rule quality measures SO NEW DIRECTIONS IN ROUGH SETS, DATA MINING, AND GRANULAR-SOFT COMPUTING SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 7th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (RSFDGrC99) CY NOV 09-11, 1999 CL UBE, JAPAN SP Int Rough Set Soc, BISC Special Interest Grp Granular Comp, Polish Japanese Inst Informat Technol, Yamaguchi Univ, Kayamuri Fdn Informat Sci Advancement, USAF, Asian Off Aerosp Res & Dev, Yamaguchi Ind Technol Dev Organizer AB We describe statistical and empirical rule quality formulas and present an empirical comparison of them on standard machine learning datasets. From the experimental results, a set of formula-behavior rules are generated which show relationships between a formula's performance and dataset characteristics. These formula-behavior rules are combined into formula-selection rules which can be used in a rule induction system to select a rule quality formula before rule induction. C1 Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. RP An, A (reprint author), Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada. CR ALI K, 1993, P IJCAI 93 CHAMB FRA AN A, 1998, LECT NOTES ARTIFICIA, V1418 BISHOP YMM, 1991, DISCRETE MULTIVARIAT Brazdil P. B., 1990, CURRENT TRENDS KNOWL Bruha I, 1993, INFORMATICA, V17, P233 Bruning J. L., 1997, COMPUTATIONAL HDB ST CLARK AJL, 1989, J MOL ENDOCRINOL, V2, P3 COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104 HOLTE R, 1989, P IJCAI 89 DETR MICH KONONENKO I, 1991, MACH LEARN, V6, P67, DOI 10.1007/BF00153760 MICHALSKI RS, 1990, IEEE T PATTERN ANAL, P4 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 TORGO L, 1993, ECML 93, P185 NR 14 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-66645-1 J9 LECT NOTES ARTIF INT PY 1999 VL 1711 BP 482 EP 491 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BR08F UT WOS:000165604900059 ER PT B AU Li, H Yamanishi, K AF Li, H Yamanishi, K BE Gauch, S TI Text classification using ESC-based stochastic decision lists SO PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99 LA English DT Proceedings Paper CT 8th International Conference on Information Knowledge Management (CIKM 99) CY NOV 02-06, 1999 CL KANSAS CITY, MO SP Assoc Comp Machinery, Special Interest Grp Management Informat Syst, Special Interest Grp Management Informat, Special Interest Grp Informat Retrieval ID COMPLEXITY; RULES AB We propose a new method of text classification using stochastic decision lists. A stochastic decision list is an ordered sequence of IF-THEN rules, and our method can be viewed as a rule-based method for text classification having advantages of readability and refinability of acquired knowledge. Our method is unique in that decision lists are automatically constructed on the basis of the principle of minimizing Extended Stochastic Complexity (ESC), and with it we are able to construct decision lists that have fewer errors in classification. The accuracy of classification achieved with our method appears better than or comparable to those of existing rule-based methods. C1 NEC Corp Ltd, C&C Media Res Labs, Kawasaki, Kanagawa, Japan. RP Li, H (reprint author), NEC Corp Ltd, C&C Media Res Labs, Kawasaki, Kanagawa, Japan. CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 COHEN WW, 1998, CONTEXT SENSITIVE LE DUMAIS ST, 1998, P CIKM 98 JOACHIMS T, 1998, P ECML 98 KAR G, 1978, INFORM PROCESS MANAG, V14, P57, DOI 10.1016/0306-4573(78)90063-8 Koller D., 1997, P 14 INT C MACH LEAR, P170 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 LEWIS DD, 1996, P SIGIR 96 Li H., 1997, P ACL 97, P39 Rissanen JJ, 1996, IEEE T INFORM THEORY, V42, P40, DOI 10.1109/18.481776 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SCHAPIRE RE, 1998, P SIGIR 98 SCHUTZE H, 1995, P SIGIR 95 Vapnik V, 1995, NATURE STAT LEARNING Yamanishi K, 1998, IEEE T INFORM THEORY, V44, P1424, DOI 10.1109/18.681319 YAMANISHI K, 1992, MACH LEARN, V9, P165, DOI 10.1007/BF00992676 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 18 TC 18 Z9 19 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 1-58113-146-1 PY 1999 BP 122 EP 130 DI 10.1145/319950.319966 PG 9 WC Computer Science, Information Systems SC Computer Science GA BP17F UT WOS:000084311500017 ER PT S AU Hiemstra, D de Jong, F AF Hiemstra, D de Jong, F BE Abiteboul, S Vercoustre, AM TI Disambiguation strategies for Cross-Language Information Retrieval SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd European Conference on Research and Advanced Technology for Digital Libraries (ECDL 99) CY SEP 22-24, 1999 CL PARIS, FRANCE SP French Natl Inst Res Comp Sci & Control, Frence Natl Lib, European Commiss, TMR Program, European Consortium Informat & Math DE Cross-Language Information Retrieval; statistical machine translation AB This paper gives an overview of tools and methods for Cross-Language Information Retrieval (CLIR) that arp developed within the Twenty-One project. The tools and methods are evaluated with the TREC CLIR task document collection using Dutch queries on the English document base. The main issue addressed here is an evaluation of two approaches to disambiguation. The underlying question is whether a lot of effort should be: put in finding the correct translation for each query term before searching, or whether searching with more than one possible translation leads to better results? The experimental study suggests that the quality of search methods is more important than the quality of disambiguation methods. Good retrieval methods are able to disambiguate translated queries implicitly during searching. C1 Univ Twente, Ctr Telemat & Informat Technol, NL-7500 AE Enschede, Netherlands. RP Hiemstra, D (reprint author), Univ Twente, Ctr Telemat & Informat Technol, POB 217, NL-7500 AE Enschede, Netherlands. CR Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 BOD R, 1995, ENRICHING LINGUISTIC BRASCHLER M, 1999, P 7 TEXT RETR C TREC HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 HIEMSTRA D, IN PRESS INT J DIGIT HIEMSTRA D, 1998, P 8 CLIN M, P41 HIEMSTRA D, 1999, P 7 TEXT RETR C TREC HULL D, 1996, P 19 ACM SIGIR C RES HULL DA, 1997, AAAI S CROSS LANG TE KRAAIJ W, 1998, P 6 TEXT RETR C TREC, P500 KRAAJI W, 1997, AAAI S CROSS LANG TE MILLER DRH, 1999, P 7 TEXT RETR C TREC Mood A.M., 1963, INTRO THEORY STAT Oard D. W., 1996, SURVEY MULTILINGUAL Oard D. W., 1998, P 3 C ASS MACH TRANS PONTE J, 1998, P 21 ACM SIGIR C RES ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA NR 20 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-66558-7 J9 LECT NOTES COMPUT SC PY 1999 VL 1696 BP 274 EP 293 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BP88L UT WOS:000086484700016 ER PT B AU Miller, DRH Leek, T Schwartz, RM AF Miller, DRH Leek, T Schwartz, RM BE Hearst, M Gey, F Tong, R TI A hidden Markov model information retrieval system SO SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 22nd International Conference on Research and Development in Information Retrieval CY AUG, 1999 CL BERKELEY, CA SP British Comp Soc, Info Retriev Special Grp, Gesell Info Germany, Microsoft, West Grp, AT&T Labs, Sun Microsyst, Verity, Xerox Parc, IBM, Lexis-Nexis HO UNIV CA ID SPEECH RECOGNITION; ALGORITHM AB We present a new method for information retrieval using hidden Markov models (HMMs). We develop a general framework for incorporating multiple word generation mechanisms within the same model. We then demonstrate that an extremely simple realization of this model substantially outperforms standard tf.idf ranking on both the TREC-6 and TREC-7 ad hoc retrieval tasks. We go on to present a novel method for performing blind feedback in the HMM framework, a more complex HMM that models bigram production, and several other algorithmic refinements. Together, these methods form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task. C1 BBN Syst & Technol Corp, Cambridge, MA 02138 USA. RP Miller, DRH (reprint author), BBN Syst & Technol Corp, Cambridge, MA 02138 USA. CR ALLAN J, NIST SPECIAL PUBLICA Bikel D. M., 1997, 5 C APPL NAT LANG PR, P194 BYRNE W, 1993, THESIS U MARYLAND CO Cohen W, 1996, P 19 ANN INT ACM SIG, P307, DOI 10.1145/243199.243278 COLBATH S, 1998, PERC US INT C SAN FR DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Harman D. K., 1996, NIST SPECIAL PUBLICA, P1 HIEMSTRA D, 1999, IN PRESS P 7 TEXT RE MAKHOUL J, 1995, P NATL ACAD SCI USA, V92, P9956, DOI 10.1073/pnas.92.22.9956 Makhoul J, 1998, PATTERN RECOGN, V31, P1285, DOI 10.1016/S0031-3203(97)00152-0 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Robertson S., 1995, NIST SPECIAL PUBLICA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 SCHWARTZ R, 1997, P EUR 97 RHOD GREEC, P1455 Voorhees E., 1998, NIST SPECIAL PUBLICA, P1 Voorhees E., 1998, NIST SPECIAL PUBLICA, V500-242, P1 VOORHEES EM, 1999, IN PRESS P 7 TEXT RE XU J, 1998, COMMUNICATION OCT NR 23 TC 9 Z9 9 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 1-58113-096-1 PY 1999 BP 214 EP 221 PG 8 WC Computer Science, Information Systems SC Computer Science GA BN86U UT WOS:000083284000029 ER PT B AU Berger, A Lafferty, J AF Berger, A Lafferty, J BE Hearst, M Gey, F Tong, R TI Information retrieval as statistical translation SO SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 22nd International Conference on Research and Development in Information Retrieval CY AUG, 1999 CL BERKELEY, CA SP British Comp Soc, Info Retriev Special Grp, Gesell Info Germany, Microsoft, West Grp, AT&T Labs, Sun Microsyst, Verity, Xerox Parc, IBM, Lexis-Nexis HO UNIV CA ID PROBABILISTIC MODELS AB We propose a new probabilistic approach to information retrieval based upon the ideas and methods of statistical machine translation. The central ingredient in this approach is a statistical model of how a user might distill or "translate" a given document into a query. To assess the relevance of a document to a user's query, we estimate the probability that the query would have been generated as a translation of the document, and factor in the user's general preferences in the form of a prior distribution over documents. We propose a simple, well motivated model of the document-to-query translation process, and describe an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents. As we show, one can view this approach as a generalization and justification of the "language modeling" strategy recently proposed by Ponte and Croft. In a series of experiments on TREC data, a simple translation-based retrieval system performs well in comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval. C1 Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. RP Berger, A (reprint author), Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. CR BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BRODER A, 1998, INFORMATION RETRIEVA BROWN P, 1993, P ARPA HUM LANG TECH Brown P. F., 1990, Computational Linguistics, V16 Brown P. F., 1993, Computational Linguistics, V19 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 GALE WA, 1991, 4 DARPA WORKSH SPEEC, P152 Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTSN S, 1992, P 1 TEXT RETR C TREC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 TURTLE H, 1991, P RIAO 3 WEAVER W., 1949, MACHINE TRANSLATION NR 15 TC 8 Z9 8 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 1-58113-096-1 PY 1999 BP 222 EP 229 PG 8 WC Computer Science, Information Systems SC Computer Science GA BN86U UT WOS:000083284000030 ER PT J AU Crestani, F Lalmas, M Van Rijsbergen, CJ Campbell, I AF Crestani, F Lalmas, M Van Rijsbergen, CJ Campbell, I TI "Is this document relevant? ... probably": A survey of probabilistic models in information retrieval SO ACM COMPUTING SURVEYS LA English DT Article DE information retrieval; probabilistic indexing; probabilistic modeling; probabilistic retrieval; uncertain inference modeling ID INFERENCE; LOGIC; SYSTEMS; TERMS AB This article surveys probabilistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the development of IR are described, classified, and compared using a common formalism. New approaches that constitute the basis of future research are described. C1 Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Crestani, F (reprint author), Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. CR AMATI G, 1995, P 1 INT WORKSH INF R AMATI G, 1992, 5B04892 FOND UG BORD Biebricher P., 1988, P 11 INT C RES DEV I, P333, DOI 10.1145/62437.62470 BOOKSTEIN A, 1976, LIBR Q, V46, P2 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BORGOGNA G, 1993, J AM SOC INFORM SCI, V2, P44 BRUZA PD, 1992, COMPUT J, V35, P208, DOI 10.1093/comjnl/35.3.208 BRUZA PD, 1993, THESIS KATHOLIEKE U Campbell I., 1996, P COLIS, V2, P251 CHIARAMELLA Y, 1992, COMPUT J, V35, P233, DOI 10.1093/comjnl/35.3.233 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 Cox D, 1970, ANAL BINARY DATA CRESTANI F, 1995, P ACM SIGIR C RES DE, P291, DOI 10.1145/215206.215373 CRESTANI F, 1995, J DOC, V51, P1 CROFT W, 1988, P ACM SIGIR GREN FRA CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1992, P ACM SIGIR COP JUN, P223, DOI 10.1145/133160.133203 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 DEMPSTER AP, 1968, J ROY STAT SOC B, V30, P205 DESILVA WT, 1993, J AM SOC INFORM SCI, V4, P10 DUNLOP M, 1991, THESIS U GLASGOW GLA FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1993, P 16 ANN INT ACM SIG, P309, DOI 10.1145/160688.160754 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Fuhr N., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium FUHR N, 1993, SPECIAL PUBLICATION, P89 FUHR N, 1992, P 15 ANN INT ACM SIG, P211, DOI 10.1145/133160.133202 FUHR N, 1991, P 14 ANN INT ACM SIG, P46, DOI 10.1145/122860.122865 FUHR N, 1990, P 16 INT C VER LARG, P696 FUNG RM, 1990, P ACM SIGIR BRUX BEL, P455, DOI 10.1145/96749.98252 Good I.J., 1950, PROBABILITY WEIGHING HARMAN D, 1992, INFORMATION RETRIEVA, pCH11 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 HARMAN D, 1996, P TREC C GAITH MD US Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 HARPER DJ, 1992, COMPUT J, V35, P256, DOI 10.1093/comjnl/35.3.256 HARTER PS, 1975, J ASIS 1, V26, P197 HUIBERS TWC, 1996, THESIS UTRECHT U NET Jeffrey R., 1965, LOGIC DECISION JONES KS, 1981, INFORMATION RETRIEVA KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 Lalmas M, 1998, INFORM PROCESS MANAG, V34, P19, DOI 10.1016/S0306-4573(97)00041-1 LALMAS M, 1992, P 14 BCS INF RETR C Margulis E., 1992, P ACM SIGIR COP JUN, P177, DOI 10.1145/133160.133195 MARGULIS EL, 1993, INFORM PROCESS MANAG, V29, P215, DOI 10.1016/0306-4573(93)90005-X MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 MIZZARO S, 1996, UDMI1296RR U UD DIP Neapolitan R. E., 1990, PROBABILISTIC REASON Nie J., 1988, P ACM SIGIR GREN FRA, P495, DOI 10.1145/62437.62493 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Nie JY, 1995, COMPUT J, V38, P643, DOI 10.1093/comjnl/38.8.643 NIE JY, 1992, P 15 ANN ACM SIGIR C, P140, DOI 10.1145/133160.133188 Pearl J., 1988, PROBABILISTIC REASON PEARL J, 1990, KNOWLEDGE REPRESENTA, P245 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SAVOY J, 1992, INFORM PROCESS MANAG, V28, P389, DOI 10.1016/0306-4573(92)90082-B SCHOKEN SS, 1993, INT J MAN MACH STUD, V39, P1 Sebastiani F., 1994, P 17 ANN INT ACM SIG, P122 SEMBOK TMT, 1993, P BCS C INF RETR GLA, P91 SERACEVIC T, 1970, INTRO INFORMATION SC, pCH14 Shafer G, 1976, MATH THEORY EVIDENCE SMITH S, 1988, ANAL EFFECTS DATA CO STALNAKER R, 1981, IFS, P107 THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P383, DOI 10.1016/0306-4573(90)90098-M THOMPSON R, 1989, DESIGN IMPLEMENTATIO THOMPSON RJ, 1990, J PEDIATR PSYCHOL, V1, P3 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H. R., 1990, THESIS U MASSACHUSET TURTLE HR, 1992, COMPUT J, V35, P279, DOI 10.1093/comjnl/35.3.279 TURTLE HR, 1992, UNPUB UNCERTAINTY IN TURTLE HR, 1990, P ACM SIGIR BRUSS BE VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1989, P ACM SIGIR CAMBR MA VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1992, 1992R2 U GLASG COMP VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA vanRijsbergen CJ, 1996, J AM SOC INFORM SCI, V47, P385 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 Zadeh L. A., 1987, FUZZY SETS APPL SELE NR 90 TC 69 Z9 69 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD DEC PY 1998 VL 30 IS 4 BP 528 EP 552 DI 10.1145/299917.299920 PG 25 WC Computer Science, Theory & Methods SC Computer Science GA 197BE UT WOS:000080344100004 ER PT J AU Bodoff, D Kambil, A AF Bodoff, D Kambil, A TI Partial coordination. I. The best of pre-coordination and post-coordination SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID ONLINE CATALOG; RETRIEVAL-SYSTEMS; SUBJECT ACCESS; DESIGN; LIBRARY; SEARCHES; MODEL AB The introduction of computerized post-coordination has solved many of the problems of pre-coordinated subject access. However, the adoption of computerized post-coordination results in the loss of some pre-coordination benefits. Specifically, the effect of hiding terms within the context of others is lost In post-coordination which gives lead status to every document term. This results in spurious matches of terms out of context. Library patrons and Internet searchers are increasingly dissatisfied with subject access performance, in part because of unmanageably large retrieval sets. The need to enhance precision and limit the size of retrieval sets motivates this work which proposes partial coordination, an approach which incorporates the advantages of computer search with the ability of pre-coordination to limit spurious partial matches and thereby enhance precision. C1 NYU, Stern Sch Business, Dept Informat Syst, New York, NY 10012 USA. RP Bodoff, D (reprint author), NYU, Stern Sch Business, Dept Informat Syst, 44 W 4th St, New York, NY 10012 USA. RI bodoff, david/A-3775-2009 CR AUSTIN D, 1977, LIBR RESOUR TECH SER, V21, P13 BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BATES MJ, 1977, J AM SOC INFORM SCI, V28, P161, DOI 10.1002/asi.4630280304 Beaulieu M, 1996, J AM SOC INFORM SCI, V47, P491, DOI 10.1002/(SICI)1097-4571(199607)47:7<491::AID-ASI2>3.0.CO;2-P BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BLAIR DC, 1990, INFORM PROCESS MANAG, V26, P437, DOI 10.1016/0306-4573(90)90102-8 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 Borgman CL, 1996, J AM SOC INFORM SCI, V47, P493, DOI 10.1002/(SICI)1097-4571(199607)47:7<493::AID-ASI3>3.0.CO;2-P BORGMAN CL, 1984, THESIS STANFORD U Cleverdon C. W., 1966, FACTORS DETERMINING COCHRANE PA, 1985, REDESIGN CATALOGS IN COCHRANE PA, 1983, INFORM TECHNOL LIBR, V2, P3 COSTELLO JC, 1961, AM DOC, V12, P20, DOI 10.1002/asi.5090120105 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P389 Drabenstott K. M., 1994, USING SUBJECT HEADIN Drabenstott KM, 1996, J AM SOC INFORM SCI, V47, P519, DOI 10.1002/(SICI)1097-4571(199607)47:7<519::AID-ASI5>3.0.CO;2-X DYKSTRA M, 1987, PRECIS PRIMER FAGAN J, 1987, THESIS CORNELL U Foskett A. C., 1977, SUBJECT APPROACH INF FUGGMAN R, 1985, THEORY SUBJECT ANAL, P392 HARMAN DK, 1995, P 3 TEXT RETR C NOV HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HILDRETH CR, 1989, ONLINE CATALOG DEV D KAMBIL A, 1997, IS9715 STERN SCH BUS LARSON RR, 1989, P 52 ANN M AM SOC IN, P129 LARSON RR, 1991, J AM SOC INFORM SCI, V42, P197, DOI 10.1002/(SICI)1097-4571(199104)42:3<197::AID-ASI6>3.0.CO;2-T LARSON RR, 1983, INFORM TECHNOL LIBR, V2, P93 Larson RR, 1996, J AM SOC INFORM SCI, V47, P555, DOI 10.1002/(SICI)1097-4571(199607)47:7<555::AID-ASI7>3.0.CO;2-T LAWRENCE GS, 1985, LIBR RESOUR TECH SER, V29, P16 LIPETZ BA, 1987, LIBR TRENDS, V35, P597 LYNCH C, 1989, OPAC P JOINT M BRIT, P51 LYNCH C, 1997, SCI AM, V277, P51 MARKEY K, 1988, INFORMATION TECHNOLO, V7, P297 MARKEY K, 1980, ANAL REV CATALOG USE MARKEY K, 1985, LIBR RESOUR TECH SER, V29, P34 MARKEY K, 1984, SUBJECT SEARCHING LI MARON ME, 1988, INFORM PROCESS MANAG, V24, P249, DOI 10.1016/0306-4573(88)90092-1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MATTHEWS JR, 1984, INFORM TECHNOL LIBR, V3, P354 MISCHO WH, 1979, J LIBR AUTOMAT, V12, P338 MISCHO WH, 1980, P 43 ASIS ANN M AN C, P155 MISCHO WH, 1981, OCLCDDTR814 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PETERS TA, 1991, ONLINE CATALOG CRITI PRABHA C, 1989, OPAC P JOINT M BRIT, P33 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P281, DOI 10.1016/0306-4573(88)90095-7 Rijsbergen C.J.V., 1977, J DOC, V33, P106, DOI 10.1108/eb026637 Robertson S. E., 1982, Information Technology: Research and Development, V1 Robertson SE, 1997, J DOC, V53, P3, DOI 10.1108/EUM0000000007186 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, COMMUN ACM, V26, P1021 SALTON G, 1972, J AM SOC INFORM SCI, V23, P75, DOI 10.1002/asi.4630230202 SALTON G, 1982, LECT NOTES COMPUTER, P151 SIEVERT M, 1989, P 52 ASIS ANN M WASH, P34 STEINBERG D, 1984, COLL RES LIBR, V45, P66 Stoll C., 1995, SILICON SNAKE OIL 2 SVENONIUS E, 1986, J AM SOC INFORM SCI, V37, P331, DOI 10.1002/asi.4630370509 TAUBE M, 1961, AM DOC, V12, P98, DOI 10.1002/asi.5090120203 TENOPIR C, 1985, ONLINE REV, V9, P149, DOI 10.1108/eb024180 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WILSON N, 1991, SELECT, V4, P6 NR 65 TC 7 Z9 7 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD DEC PY 1998 VL 49 IS 14 BP 1254 EP 1269 DI 10.1002/(SICI)1097-4571(1998)49:14<1254::AID-ASI4>3.0.CO;2-O PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 141AD UT WOS:000077119700004 ER PT J AU Bollmann-Sdorra, P Raghavan, VV AF Bollmann-Sdorra, P Raghavan, VV TI On the necessity of term dependence in a query space for weighted retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; MODEL AB In recent years, in the context of the vector space model, the view, held by many researchers, that documents, queries, terms, etc., are all elements of a common space has been challenged (Bollmann-Sdorra & Raghavan, 1993). In particular, it was noted that term independence has to be investigated in the context of user preferences and it was shown, through counterexamples, that term independence can hold in the document space, but not in the query space and vice versa. In this article, we continue the investigation of query and document spaces with respect to the property of term independence. We prove, under realistic assumptions, that requiring term independence to hold in the query space is inconsistent with the goal of achieving better performance by means of weighted retrieval. The result that term independence in the query space is undesirable is obtained without making any assumption about whether or not the property of term independence holds in the document space. The results of this article reinforce our position that the properties of document and query spaces must be investigated separately, since the document and query spaces do not necessarily have the same properties. C1 Tech Univ Berlin, Fachbereich Informat, D-10587 Berlin, Germany. Univ SW Louisiana, Ctr Adv Comp Studies, Lafayette, LA 70504 USA. RP Bollmann-Sdorra, P (reprint author), Tech Univ Berlin, Fachbereich Informat, FR 6-9,Franklin Str 28-29, D-10587 Berlin, Germany. EM raghavan@cacs.usl.edu CR Bollmann P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42023 BOLLMANN P, 1985, P 8 ANN INT ACM SIGI, P213, DOI 10.1145/253495.253527 BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, P579 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 CHERNIAVSKY VS, 1970, AUTOMATIC DOCUMENTAT, V4, P9 Krantz D.H., 1971, FDN MEASUREMENT RAGHAVAN V. V., 1995, P 18 ANN INT ACM SIG, P344, DOI 10.1145/215206.215381 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965, P AFIPS, V27, P293 Salton G., 1989, AUTOMATIC TEXT PROCE Sneath P.H., 1973, NUMERICAL TAXONOMY SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 WONG SKM, 1989, INFORM SYST, V14, P47, DOI 10.1016/0306-4379(89)90024-0 WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 YAO YY, 1995, J AM SOC INFORM SCI, V46, P133, DOI 10.1002/(SICI)1097-4571(199503)46:2<133::AID-ASI6>3.0.CO;2-Z YU CT, 1974, J AM SOC INFORM SCI, V25, P218, DOI 10.1002/asi.4630250403 YU CT, 1978, J ACM, V25, P210, DOI 10.1145/322063.322066 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 21 TC 13 Z9 13 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD NOV PY 1998 VL 49 IS 13 BP 1161 EP 1168 DI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 129XT UT WOS:000076489900003 ER PT J AU Crestani, F Melucci, M AF Crestani, F Melucci, M TI A case study of automatic authoring: From a textbook to a hyper-textbook SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE automatic authoring; hypertext; information retrieval; hyperbook; hyper-textbook ID INFORMATION-RETRIEVAL; CONSTRUCTION; SYSTEMS; DESIGN AB This paper presents a case-study of automatic construction of a hypertext from a large full-text document. The document we used as the input of the automatic authoring process is a well known textbook of Information Retrieval (IR). We explain in detail the steps performed to transform the textbook into.a hyper-textbook. A hyper-textbook is the hypertextual equivalent of a textbook, in the same way as a hyperbook is the hypertextual equivalent of a book. A hyper-textbook is designed to be used both as a self-instruction manual and as a reference source. Moreover, it can be printed out into its textual form in total or only partially for easy consultation without a computer. The hyper-textbook is written in HTML and can be accessed and navigated using a common WWW browser. (C) 1998 Elsevier Science B.V. C1 Univ Padua, Dept Elect & Informat, I-35131 Padua, Italy. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Melucci, M (reprint author), Univ Padua, Dept Elect & Informat, Via Gradenigo 6A, I-35131 Padua, Italy. EM melo@dei.unipd.it CR AGOSTI M, 1993, P ACM S APPL COMP IN, P745, DOI 10.1145/162754.167175 Agosti M, 1996, INFORM PROCESS MANAG, V32, P459, DOI 10.1016/0306-4573(95)00075-5 AGOSTI M, 1995, MULTIMEDIA SYST, V3, P15, DOI 10.1007/BF01236576 AGOSTI M, 1992, COMPUT J, V35, P194, DOI 10.1093/comjnl/35.3.194 AGOSTI M, 1991, P ACM INT C RES DEV, P3126 AGOSTI M, 1988, 12TH P INT ONL INF M, V1, P57 AGOSTI M, 1997, P 7 IFIP 2 6 WORK C AGOSTI M, 1996, INFORMATION RETRIEVA AGOSTI M, 1992, INFORM PROCESS MANAG, V28, P371, DOI 10.1016/0306-4573(92)90081-A AITCHISON J, 1987, THESAURUS CONSTRUCTI BOTAFOGO RA, 1992, ACM T INFORM SYST, V10, P142, DOI 10.1145/146802.146826 BRUZA PD, 1992, COMPUT J, V35, P208, DOI 10.1093/comjnl/35.3.208 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 CASATI F, 1996, ATT CONV SIST EV BAS, P253 CATENAZZI N, 1991, P INT MULT INF RETR, V2, P45 CATENAZZI N, 1995, J INFORM SCI, V21, P161, DOI 10.1177/016555159502100302 CHOQUETTE M, 1995, P 6 DAT EXP SYST APP, P449 COMPAGNONI FR, 1989, ACM T INFORM SYST, V7, P271 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 DUNLOP M, 1991, THESIS U GLASGOW GLA DUNLOP M, P 2 MIR WORKSH MONS FRISSE ME, 1988, COMMUN ACM, V31, P880, DOI 10.1145/48511.48518 FURNER J, 1996, INFORMATION RETRIEVA, P75 Furuta R., 1989, Electronic Publishing: Origination, Dissemination and Design, V2 GABELBECKER I, 1996, 6606 ID ESPRIT NETW LANDONI M, 1997, THESIS U STRATHCLYDE Mittendorf E., 1994, P 17 ANN INT ACM SIG, P318 MULLER P, 1995, WRITING HYPERTEXT BO PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RADA R, 1992, ACM T INFORM SYST, V10, P294, DOI 10.1145/146760.146791 RAYMOND DR, 1988, COMMUN ACM, V31, P871, DOI 10.1145/48511.48517 REMDE JR, 1987, HYPERTEXT 87 PAPERS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1988, P ACM INT C RES DEV Salton G, 1996, INFORM PROCESS MANAG, V32, P127, DOI 10.1016/S0306-4573(96)85001-1 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1983, SMART RETRIEVAL SYST SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Savoy J, 1996, INFORM PROCESS MANAG, V32, P155, DOI 10.1016/S0306-4573(96)85003-5 *SHAPE, 2 GEN HYP APPL PROJ SMEATON AF, 1995, P IWHD C MONTPL JUN SMEATON AF, 1995, CA0295 DUBL CIT U SC SRINIVADSAN P, 1992, INFORMATION RETRIEVA THOMPSON R, 1989, DESIGN IMPLEMENTATIO VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 46 TC 9 Z9 9 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X J9 DATA KNOWL ENG JI Data Knowl. Eng. PD AUG PY 1998 VL 27 IS 1 BP 1 EP 30 DI 10.1016/S0169-023X(97)00043-8 PG 30 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA ZX561 UT WOS:000074530400001 ER PT J AU Crestani, F Van Rijsbergen, CJ AF Crestani, F Van Rijsbergen, CJ TI A study of probability kinematics in information retrieval SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE performance; logical imaging; probabilistic modeling; probabilistic retrieval ID MODELS; RELEVANCE; LOGIC; INFERENCE AB We analyze the kinematics of probabilistic term weights at retrieval time for different Information Retrieval models. We present four models based on different notions of probabilistic retrieval. Two of these models are based on classical probability theory and can be considered as prototypes of models long in use in Information Retrieval, like the Vector Space Model and the Probabilistic Model. The two other models are based on a logical technique of evaluating the probability of a conditional called imaging; one is a generalization of the other. We analyze the transfer of probabilities occurring in the term space at retrieval time for these four models, compare their retrieval performance using classical test collections, and discuss the results. We believe that our results provide useful suggestions on how to improve existing probabilistic models of Information Retrieval by taking into consideration term-term similarity. C1 Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Crestani, F (reprint author), Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. EM fabio@dcs.gla.ac.uk; keith@dcs.gla.ac.uk CR AMATI G, 1992, 5BO4892 FOND UG BORD Brown P. F., 1992, Computational Linguistics, V18 BRUZA PD, 1993, THESIS KATHOLIEKE U CHIARAMELLA Y, 1992, COMPUT J, V35, P233, DOI 10.1093/comjnl/35.3.233 Church K., 1989, P ACL, P76, DOI DOI 10.3115/981623.981633 Cleverdon C.W., 1966, ASLIB CRANFIELD RES COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1994, P 17 ANN INT ACM C R, P242 CRESTANI F, 1995, J DOC, V51, P1 CRESTANI F, 1995, P 1995 TREC C WASH CRESTANI F, 1995, P 1 WORKSH UNC LOG G CRESTANI F, 1996, NEW REV DOC TEXT MAN, V1, P277 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Fuhr N., 1995, P 18 ANN INT ACM SIG, P282, DOI 10.1145/215206.215372 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 GARDENFORS P, 1982, J PHILOS, V79, P747, DOI 10.2307/2026039 Gardenfors P., 1988, KNOWLEDGE FLUX MODEL HUGHES G. E., 1968, INTRO MODAL LOGIC Huibers T. W. C., 1996, SIGIR Forum, V30 Kripke S., 1971, REFERENCE MODALITY, P63 LALMAS M, 1993, P WORKSH INC UNC INF, P62 Lewis D., 1986, COUNTERFACTUALS LEWIS D, 1981, U W ONTARIO SERIES P, P129 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Nie JY, 1995, COMPUT J, V38, P643, DOI 10.1093/comjnl/38.8.643 NIE JY, 1992, P 15 ANN ACM SIGIR C, P140, DOI 10.1145/133160.133188 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, THESIS U LONDON LOND ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROLLEKE T, 1995, P 18 ANN INT ACM SIG SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SEMBOK TMT, 1993, P BCS C INF RETR GLA, P91 Sparck Jones K., 1976, J DOC, V32, P59, DOI 10.1108/eb026616 Srinivasanm P, 1992, INFORMATION RETRIEVA, P161 STALNAKER R, 1981, IFS, P107 VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 VOORHEES EM, 1993, P 1993 TREC C GAITH, P223 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 Wong S. K. M., 1993, P 16 ANN INT ACM SIG, P107, DOI 10.1145/160688.160703 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 WONG SKM, 1991, INFORM SYST, V16, P301, DOI 10.1016/0306-4379(91)90003-R NR 50 TC 12 Z9 12 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 1998 VL 16 IS 3 BP 225 EP 255 DI 10.1145/290159.290161 PG 31 WC Computer Science, Information Systems SC Computer Science GA 147ET UT WOS:000077475900002 ER PT J AU Chang, CH Hsu, CC AF Chang, CH Hsu, CC TI Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval SO COMPUTER NETWORKS AND ISDN SYSTEMS LA English DT Article; Proceedings Paper CT 7th International World Wide Web Conference CY APR 14-18, 1998 CL BRISBANE, AUSTRALIA DE query expansion; relevance feedback; document clustering AB Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. In this poster, we propose an idea to integrate two existing techniques: query expansion and relevance feedback to achieve a concept-based information search for the Web. (C) 1998 Published by Elsevier Science B.V. All rights reserved. C1 Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan. RP Chang, CH (reprint author), Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan. EM f2506005.CCHsu@csie.ntu.edu.tw CR Anick PG, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P314, DOI 10.1145/258525.258601 CHANG CH, 1997, P 6 INT WWW C SANT C Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 HEARST MA, 1996, P ACM SIGIR 96 INT C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J. J., SMART RETRIEVAL SYST, P313 NR 6 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-7552 J9 COMPUT NETWORKS ISDN JI Comput. Netw. ISDN Syst. PD APR PY 1998 VL 30 IS 1-7 BP 621 EP 623 DI 10.1016/S0169-7552(98)00076-2 PG 3 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA ZK763 UT WOS:000073360600072 ER PT J AU Cosgrove, DA Willett, P AF Cosgrove, DA Willett, P TI SLASH: A program for analysing the functional groups in molecules SO JOURNAL OF MOLECULAR GRAPHICS & MODELLING LA English DT Article DE molecular similarity; high-throughput screening; functional groups ID FEATURES AB The program SLASH, which is designed to enable large numbers of compounds to be analysed in terms of the functional groups they contain, is described. The usefulness of the groups for analysing the activities of compounds tested in high-throughput biological screens is investigated. The functional group fragments are also studied as a means of determining similarity within groups of compounds. (C) 1998 by Elsevier Science Inc. C1 Zeneca Pharmaceut, Macclesfield SK10 4TG, Cheshire, England. Univ Sheffield, Krebs Inst Biomolec Res, Sheffield, S Yorkshire, England. Univ Sheffield, Dept Informat Studies, Sheffield S10 2TN, S Yorkshire, England. RP Cosgrove, DA (reprint author), Zeneca Pharmaceut, Alderly Pk, Macclesfield SK10 4TG, Cheshire, England. CR ADAMSON GW, 1980, J CHEM INF COMP SCI, V20, P97, DOI 10.1021/ci60022a009 ADAMSON GW, 1977, J CHEM INF COMP SCI, V17, P164, DOI 10.1021/ci60011a012 ADAMSON GW, 1976, J CHEM SOC PERK T 1, P168, DOI 10.1039/p19760000168 ADAMSON GW, 1976, J CHEM INF COMP SCI, V16, P161, DOI 10.1021/ci60007a011 ADAMSON GW, 1974, NATURE, V248, P406, DOI 10.1038/248406a0 ADAMSON GW, 1975, J CHEM INF COMP SCI, V15, P215, DOI 10.1021/ci60004a006 BRUGGER WE, 1976, J CHEM INF COMP SCI, V16, P105, DOI 10.1021/ci60006a015 CARHART RE, 1985, J CHEM INF COMP SCI, V25, P64, DOI 10.1021/ci00046a002 COSGROVE DA, 1996, THESIS U SHEFFIELD S CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 HODES L, 1981, J CHEM INF COMP SCI, V21, P132, DOI 10.1021/ci00031a004 HODES L, 1977, J MED CHEM, V20, P469, DOI 10.1021/jm00214a002 JURS PC, 1979, J MED CHEM, V22, P476, DOI 10.1021/jm00191a004 KLOPMAN G, 1986, MOL PHARMACOL, V136, P67 KLOPMAN G, 1995, TOXICOL LETT, V79, P145, DOI 10.1016/0378-4274(95)03366-S ORMEROD A, 1984, J AM CHEM SOC, V106, P7315 ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shemetulskis NE, 1996, J CHEM INF COMP SCI, V36, P862, DOI 10.1021/ci950169+ TAKAHASHI Y, 1992, J CHEM INF COMP SCI, V32, P639, DOI 10.1021/ci00010a009 WILLETT P, 1987, SIMILARITY CLUSTERIN, P54 NR 21 TC 23 Z9 23 PU ELSEVIER SCIENCE INC PI NEW YORK PA 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010 USA SN 1093-3263 J9 J MOL GRAPH MODEL JI J. Mol. Graph. PD FEB PY 1998 VL 16 IS 1 BP 19 EP 32 DI 10.1016/S1093-3263(98)00014-X PG 14 WC Biochemical Research Methods; Biochemistry & Molecular Biology; Computer Science, Interdisciplinary Applications; Crystallography; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Computer Science; Crystallography; Mathematical & Computational Biology GA 123EF UT WOS:000076112300005 PM 9783257 ER PT J AU Melucci, M AF Melucci, M TI Passage retrieval: A probabilistic technique SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID INFORMATION-RETRIEVAL AB This paper presents a probabilistic technique to retrieve passages from texts having a large size or heterogeneous semantic content. The proposed technique is independent on any supporting auxiliary data, such as text structure, topic organization, or pre-defined text segments. A Bayesian framework implements the probabilistic technique. We carried out experiments to compare the probabilistic technique to one based on a text segmentation algorithm. In particular, the probabilistic technique is more effective than, or as effective as the one based on the text segmentation to retrieve small:passages. Results show that passage size affects passage retrieval performance. Result; do also suggest that text organization and query generality may have an impact on the difference in effectiveness between the two techniques. (C) 1998 Elsevier Science Ltd. All rights reserved. C1 Univ Padua, Dipartimento Elettron & Informat, I-35131 Padua, Italy. RP Melucci, M (reprint author), Univ Padua, Dipartimento Elettron & Informat, Via Gradenigo N 6-A, I-35131 Padua, Italy. CR Agosti M, 1996, INFORM PROCESS MANAG, V32, P459, DOI 10.1016/0306-4573(95)00075-5 AGOSTI M, 1997, INFORMATION PROCESSI, V33 Agosti M, 1997, INFORM PROCESS MANAG, V33, P133, DOI 10.1016/S0306-4573(96)00058-1 Allan J, 1997, INFORM PROCESS MANAG, V33, P145, DOI 10.1016/S0306-4573(96)00059-3 Allan James, 1995, P 18 ANN INT ACM SIG, P337, DOI 10.1145/215206.215380 BLOCH DA, 1967, ANN MATH STAT, V38, P1423, DOI 10.1214/aoms/1177698697 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 CALLAN J, 1994, P ACM INT C RES DEV, P303 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 HEARST MA, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P9 Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LINDLEY DV, 1964, ANN MATH STAT, V35, P1622, DOI 10.1214/aoms/1177700386 Mittendorf E., 1994, P 17 ANN INT ACM SIG, P318 O'Hagan A., 1994, BAYESIAN INFERENCE OCONNOR J, 1980, J AM SOC INFORM SCI, V32, P227 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILKINSON R, 1996, INFORM RETRIEVAL HYP, P257 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 NR 26 TC 17 Z9 17 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 1998 VL 34 IS 1 BP 43 EP 68 DI 10.1016/S0306-4573(97)00047-2 PG 26 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ZA695 UT WOS:000072391800004 ER PT J AU Ding, Y AF Ding, Y TI Visualization of intellectual structure in information retrieval: Author cocitation analysis SO INTERNATIONAL FORUM ON INFORMATION AND DOCUMENTATION LA English DT Article DE author cocitation analysis; information retrieval; intellectual structure; multidimensional scaling; clustering; factor analysis ID SYSTEM-DESIGN; RELEVANCE; PROBABILITY; RANKING; SCIENCE; TEXT AB This study presents author cocitation analysis from the information retrieval research field from 1987 to 1997. The data were retrieved from Social Scisearch via Dialog. The top 40 authors were submitted to author cocitation analysis to yield the intellectual structure of information retrieval. The resulting multidimensional scaling map shows 1. identifiable author groups of information retrieval, 2. locations of these groups with respect to each other, 3. the extent of centrality and peripherality of authors within groups, 4. proximities of authors within group and across group boundaries; and 5. the meaning of the axes of the map, which adds interpretation. Factor analysis was used to reveal the extent of the author's research areas. Statistical routines included ALSCAL, clustering analysis and factor analysis. Cocitation analysis of authors offers a good technique that contributes to the understanding of intellectual structures in the sciences and possibly in other areas to the extent that those areas rely on formal scholarly communication (serial publications). C1 Nanyang Technol Univ, Informat Studies Div, Sch Appl Sci, Singapore 639798, Singapore. RP Ding, Y (reprint author), Nanyang Technol Univ, Informat Studies Div, Sch Appl Sci, Blk N4 2A-36, Singapore 639798, Singapore. CR BAYER AE, 1990, J AM SOC INFORM SCI, V41, P444, DOI 10.1002/(SICI)1097-4571(199009)41:6<444::AID-ASI12>3.0.CO;2-J Blair D. C., 1990, LANGUAGE REPRESENTAT BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 CULNAN MJ, 1990, J AM SOC INFORM SCI, V41, P453, DOI 10.1002/(SICI)1097-4571(199009)41:6<453::AID-ASI13>3.0.CO;2-E DERVIN B, 1986, ANNU REV INFORM SCI, V21, P3 DING Y, 1998, BIBLIOMETRIC ANAL SC ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 HARMAN D, 1990, J AM SOC INFORM SCI, V41, P581, DOI 10.1002/(SICI)1097-4571(199012)41:8<581::AID-ASI4>3.0.CO;2-U Harter S. P., 1986, ONLINE INFORMATION R HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q INGWERSEN P, 1992, INFORMATION RETRIEVA INGWERSEN P, 1982, J DOC, V38, P165, DOI 10.1108/eb026727 JONES KS, 1981, INFORMATION RETRIEVA Karki R, 1996, J INFORM SCI, V22, P323, DOI 10.1177/016555159602200501 LANCASTER F, 1993, INFORMATION RETRIEVA LANCASTER FW, 1988, IF YOU WANT EVALUATE LANCASTER FW, 1993, LIB FUTURE ESSAYS LI LENK P, 1983, J AM SOC INFORM SCI, V34, P115, DOI 10.1002/asi.4630340204 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W MARCHIONINI G, 1989, J AM SOC INFORM SCI, V40, P54, DOI 10.1002/(SICI)1097-4571(198901)40:1<54::AID-ASI6>3.0.CO;2-R MCCAIN KW, 1986, J AM SOC INFORM SCI, V37, P111, DOI 10.1002/(SICI)1097-4571(198605)37:3<111::AID-ASI2>3.0.CO;2-D MCCAIN KW, 1984, J AM SOC INFORM SCI, V35, P351, DOI 10.1002/asi.4630350607 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q MEADOW CT, 1989, J AM SOC INFORM SCI, V40, P86, DOI 10.1002/(SICI)1097-4571(198903)40:2<86::AID-ASI3>3.0.CO;2-K PAISLEY W, 1990, SCHOLARLY COMMUNICAT PERSSON O, 1994, J AM SOC INFORM SCI, V45, P31, DOI 10.1002/(SICI)1097-4571(199401)45:1<31::AID-ASI4>3.0.CO;2-G PRITCHARD A, 1972, RES LIB, V4 PRYTHERCH R, 1995, HARRODS LIB GLOSSARY ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1971, INFORMATION TECHNOLO, V1, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SWANSON DR, 1986, LIBR QUART, V56, P389 SWANSON DR, 1988, J AM SOC INFORM SCI, V39, P94 SWANSON DR, 1977, LIBR QUART, V47, P128 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA White H. D., 1990, SCHOLARLY COMMUNICAT WHITE HD, 1983, J AM SOC INFORM SCI, V34, P307, DOI 10.1002/asi.4630340502 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 NR 49 TC 6 Z9 6 PU INT FEDERAT INFORMATION & DOCUMENTATION PI THE HAGUE PA PO BOX 90402, 2509 LK THE HAGUE, NETHERLANDS SN 0304-9701 J9 INT FORUM INFORM DOC JI Int. Forum Inf. Doc. PY 1998 VL 23 IS 1 BP 25 EP 36 PG 12 WC Information Science & Library Science SC Information Science & Library Science GA 285DR UT WOS:000085374900004 ER PT J AU Balabanovic, M AF Balabanovic, M TI Exploring versus exploiting when learning user models for text recommendation SO USER MODELING AND USER-ADAPTED INTERACTION LA English DT Article DE recommender systems; information filtering; user modeling; relevance feedback; Selective Dissemination of Information; machine learning; adaptive information retrieval ID PROBABILITY RANKING PRINCIPLE; INFORMATION-RETRIEVAL; RELEVANCE AB The text recommendation task involves delivering sets of documents to users on the basis of user models. These models are improved over time, given feedback on the delivered documents. When selecting documents to recommend, a system faces an instance of the exploration/exploitation tradeoff. whether to deliver documents about which there is little certainty, or those which are known to match the user model learned so far. In this paper, a simulation is constructed to investigate the effects of this tradeoff on the rate of learning user models, and the resulting compositions of the sets of recommended documents, in particular World-Wide Web pages. Document selection strategies are developed which correspond to different points along the tradeoff. Using an exploitative strategy, our results show that simple preference functions can successfully be learned using a vector-space representation of a user model in conjunction with a gradient descent algorithm, but that increasingly complex preference functions lead to a slowing down of the learning process. Exploratory strategies are shown to increase the rate of user model acquisition at the expense of presenting users with suboptimal recommendations; in addition they adapt to user preference changes more rapidly than exploitative strategies. These simulated tests suggest an implementation for a simple control that is exposed to users, allowing them to vary a system's document selection behavior depending on individual circumstances. C1 Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA. RP Balabanovic, M (reprint author), Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA. CR Allan J, 1996, P 19 ANN INT ACM SIG, P270, DOI 10.1145/243199.243274 ALLEN RB, 1990, INT J MAN MACH STUD, V32, P511, DOI 10.1016/S0020-7373(05)80032-X ASNICAR FA, 1997, UM97 WORKSH AD SYST Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Balabanovic M., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267744 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bollmann P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42023 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BRAJNIK G, 1987, INFORM PROCESS MANAG, V23, P305, DOI 10.1016/0306-4573(87)90020-3 CARBONELL J, 1983, CMUCS83115 CARN MELL COHEN WW, 1997, ADV NEURAL INFORMATI, V10 Fisk D, 1996, BT TECHNOL J, V14, P124 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 FREDERKING R, 1997, P AAAI SPRING S CROS FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 Graber DA, 1988, PROCESSING NEWS PEOP, V2nd HARMAN D, 1996, P 5 TEXT RETR C GAIT Hill W., 1995, C HUM FACT COMP SYST Jennings A., 1993, User Modeling and User-Adapted Interaction, V3, DOI 10.1007/BF01099423 Koller D., 1996, P 13 INT C MACH LEAR Koster M., 1994, STANDARD ROBOT EXCLU Lang K., 1995, P 12 INT C MACH LEAR LESK ME, 1971, SMART RETRIEVAL SYST, P506 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 Lewis D.D., 1994, P 11 INT C MACH LEAR, P148 Minio M., 1996, UM96 WORKSH US MOD I Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Oard DW, 1997, USER MODEL USER-ADAP, V7, P141, DOI 10.1023/A:1008287121180 PAZZANI M, 1996, P 13 NAT C ART INT P PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Resnick P., 1994, P ACM C COMP SUPP CO Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 Roberts F.S., 1979, MEASUREMENT THEORY ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RORVIG ME, 1988, ANNU REV INFORM SCI, V23, P157 ROSENBLATT F, 1960, VG1196G4 CORN AER LA Rucker J, 1997, COMMUN ACM, V40, P73, DOI 10.1145/245108.245125 SALTON G, 1983, INTRO MODERN INFORMA SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SCHAMBER L, 1996, P ANN M AM SOC INFOR, P218 SHARDANAND U, 1995, C HUMAN FACTORS COMP SHETH B, 1993, P 9 IEEE C ART INT A STADNYK I, 1991, CFAR91003 EDS CTR AD SUMNER RG, 1996, P 5 TEXT RETR C GAIT TAYLOR RS, 1962, AM DOC, V13, P391, DOI 10.1002/asi.5090130405 WANG P, 1996, P 59 ANN M AM SOC IN, P87 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, 1991, J AM SOC INFORM SCI, V42, P723, DOI 10.1002/(SICI)1097-4571(199112)42:10<723::AID-ASI5>3.0.CO;2-U WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 YANG Y, 1997, CMUCS97127 CARN MELL Yang Y, 1994, P 17 ANN INT ACM SIG, P13 YAO YY, 1995, J AM SOC INFORM SCI, V46, P133, DOI 10.1002/(SICI)1097-4571(199503)46:2<133::AID-ASI6>3.0.CO;2-Z Moukas A., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267747 NR 57 TC 24 Z9 24 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 0924-1868 J9 USER MODEL USER-ADAP JI User Model. User-Adapt. Interact. PY 1998 VL 8 IS 1-2 BP 71 EP 102 DI 10.1023/A:1008205606173 PG 32 WC Computer Science, Cybernetics SC Computer Science GA ZY292 UT WOS:000074605500004 ER PT J AU Crestani, F AF Crestani, F TI Application of spreading activation techniques in information retrieval SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article DE spreading activation; information storage and retrieval; semantic networks; associative information retrieval; information processing; knowledge representation ID INFERENCE; KNOWLEDGE; NETWORKS; SYSTEMS AB This paper surveys the use of Spreading Activation techniques on Semantic Networks in Associative Information Retrieval. The major Spreading Activation models are presented and their applications to IR is surveyed. A number of works in this area are critically analyzed in order to study the relevance of Spreading Activation for associative IR. RP Crestani, F (reprint author), UNIV PADUA,DIPARTIMENTO ELETTRON & INFORMAT,I-35131 PADUA,ITALY. CR AGOSTI M, 1990, P IEEE INT C COMP CO AGOSTI M, 1993, P ACM S APPL COMP IN, P745, DOI 10.1145/162754.167175 AGOSTI M, 1995, MULTIMEDIA SYST, V3, P15, DOI 10.1007/BF01236576 Chung M., 1994, IEEE EXPERT, P36 COHEN PR, 1987, INFORM PROCESS MANAG, V23, P255, DOI 10.1016/0306-4573(87)90017-3 CRESTANI F, 1993, IR932 U GLASG DEP CO CRESTANI F, 1991, 585 CNR LIN RIC COOR CROFT W, 1988, P ACM SIGIR GREN FRA CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 Fahlman S.E., 1981, P INT JOINT C ART IN, P257 FRAKES WR, 1992, INFORMATION RETRIEVA FUJII H, 1993, P 16 ANN INT ACM SIG, P237, DOI 10.1145/160688.160728 HARMAN D, 1993, 16TH P ANN INT ACM S, P36 Jacobs P.S., 1992, TEXT BASED INTELLIGE JACOBS PS, 1993, ARTIF INTELL, V63, P143, DOI 10.1016/0004-3702(93)90016-5 KIM JT, 1993, IEEE T KNOWL DATA EN, V5, P753 KIMOTO H, 1990, P ACM SIGIR BRUSS BE KJELDSEN R, 1987, IEEE EXPERT SUM, P73 Pearl J., 1988, PROBABILISTIC REASON Preece S, 1981, THESIS U ILLINOIS UR Quillian M. R., 1968, SEMANTIC INFORMATION, P216 RAU LF, 1987, INFORM PROCESS MANAG, V23, P269, DOI 10.1016/0306-4573(87)90018-5 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rumelhart D, 1986, PARALLEL DISTRIBUTED Rumelhart D. E., 1983, REPRESENTATION MEMOR Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1988, P ACM SIGIR GREN FRA SAVOY J, 1992, INFORM PROCESS MANAG, V28, P389, DOI 10.1016/0306-4573(92)90082-B SCHIEL U, 1989, SIGART NEWSLETTERS, V107, P25 Shoval P., 1981, P ACM SIGIR, P145, DOI 10.1145/511754.511775 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 NR 35 TC 120 Z9 121 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 0269-2821 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD DEC PY 1997 VL 11 IS 6 BP 453 EP 482 DI 10.1023/A:1006569829653 PG 30 WC Computer Science, Artificial Intelligence SC Computer Science GA XX903 UT WOS:A1997XX90300002 ER PT J AU Kang, HK Choi, KS AF Kang, HK Choi, KS TI Two-level document ranking using mutual information in natural language information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID TEXT; SEARCHES AB Information retrieval is to retrieve relevant information that satisfies user's information needs. There arises a problem of how to select only information that is relevant to the user. Ranking techniques are used to find the documents in a collection of documents that are most likely to be relevant to the user's query. However, we find out that there could be retrieved documents whose contexts may not be consistent to the query. Mutual information is a measure which represents the relation between a word and another word. So, we will re-evaluate the relation between the terms in the retrieved document and the terms in the query. In this paper, we discuss a model of natural language information retrieval system that is based on a two-level document ranking method using mutual information. At the first-level, we retrieve documents based on automatically constructed index terms. At the second-level, we reorder the retrieved documents using mutual information. We will show that our method achieves considerable retrieval effectiveness improvement over a traditional linear searching method. Also, we will analyse seven newly developed formulas that reorder the retrieved documents. Among the seven formulas, we will recommend one formula that dominates the others in terms of the retrieval effectiveness. (C) 1997 Elsevier Science Ltd. C1 KOREA ADV INST SCI & TECHNOL,DEPT COMP SCI,YUSONG GU,TAEJON 305701,SOUTH KOREA. RP Kang, HK (reprint author), ELECT & TELECOMMUN RES INST,NATL LANGUAGE PROCESSING SECT,YUSONG GU,161 KAJONG DONG,TAEJON 305350,SOUTH KOREA. RI Choi, Key-Sun/C-1978-2011 CR ALLAN J, 1995, 18 INT C RES DEV INF BELKIN NJ, 1993, 16 INT C RES DEV INF BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BUCKLEY C, 1995, 18 INT C RES DEV INF BUCKLEY C, 1994, 17 INT C RES DEV INF SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 1985, 8 INT C RES DEV INF COOPER WS, 1988, INFORM PROCESS MANAG, V24, P243, DOI 10.1016/0306-4573(88)90091-X CROFT WB, 1988, ACM T INFORM SYST, V6, P42, DOI 10.1145/42279.45947 Fano RM, 1961, TRANSMISSION INFORMA Church K. W., 1990, Computational Linguistics, V16 Harman D., 1992, INFORMATION RETRIEVA, P363 HARMAN D, 1992, 15 INT C RES DEV INF HARMAN D, 1990, J AM SOC INFORM SCI, V41, P581, DOI 10.1002/(SICI)1097-4571(199012)41:8<581::AID-ASI4>3.0.CO;2-U HIANES D, 1993, 16 INT C RES DEV INF Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KANG HK, 1994, 3 PAC RIM INT C ART *KEM, 1992, KEM COMP NEW ENC LEE JH, 1995, 18 INT C RES DEV INF NELSON PE, 1995, EXCALIBUR TREC 4 SYS NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 Palermo David S., 1964, WORD ASS NORMS GRADE RADECKI T, 1988, INFORM PROCESS MANAG, V24, P219, DOI 10.1016/0306-4573(88)90089-1 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 WILBUR WJ, 1994, INFORM PROCESS MANAG, V30, P253, DOI 10.1016/0306-4573(94)90068-X WONG WYP, 1993, INFORM PROCESS MANAG, V29, P647, DOI 10.1016/0306-4573(93)90085-R NR 34 TC 11 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 1997 VL 33 IS 3 BP 289 EP 306 DI 10.1016/S0306-4573(96)00074-X PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA XC397 UT WOS:A1997XC39700002 ER PT J AU Dunlop, MD AF Dunlop, MD TI The effect of accessing nonmatching documents on relevance feedback SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE experimentation; theory; free-text information retrieval; hypertext; negative feedback; probabilistic model; relevance feedback; vector space model ID RETRIEVAL AB Traditional information retrieval (IR) systems only allow users access to documents that match their current query, and therefore, users can only give relevance feedback on matching documents (or those with a matching strength greater than a set threshold). This article shows that, in systems that allow access to nonmatching documents (e.g., hybrid hypertext and information retrieval systems), the strength of the effect of giving relevance feedback varies between matching and nonmatching documents. For positive feedback the results shown here are encouraging, as they can be justified by an intuitive view of the process. However, for negative feedback the results show behavior that cannot easily be justified and that varies greatly depending on the model of feedback used. RP Dunlop, MD (reprint author), UNIV GLASGOW,DEPT COMP SCI,GLASGOW G12 8QQ,LANARK,SCOTLAND. CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DUNLOP MD, 1993, INFORM PROCESS MANAG, V29, P287, DOI 10.1016/0306-4573(93)90056-J DUNLOP MD, 1991, 1991R21 U GLASG COMP FRAKES WR, 1992, INFORMATION RETRIEVA Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 Harman D., 1992, 15TH P ANN INT ACM S, P1 Harman D., 1992, INFORMATION RETRIEVA, P241 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1969, ISR15 CORN U DEP COM IDE E, 1969, ISR16 CORN U DEP COM Norman D., 1986, USER CTR SYSTEM DESI, P31 ROBERTSON SE, 1979, J DOC, V34, P294 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965, ISR9 CORN U DEP COMP Salton G., 1971, SMART RETRIEVAL SYST Sanderson M., 1991, Electronic Publishing: Origination, Dissemination and Design, V4 Sebastiani F., 1994, P 17 ANN INT ACM SIG, P122 SMEATON AF, 1995, J DOC TEXT MANAGE, V2, P67 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA vanRijsbergen CJ, 1996, J AM SOC INFORM SCI, V47, P385 NR 21 TC 15 Z9 15 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 1997 VL 15 IS 2 BP 137 EP 153 DI 10.1145/248625.248650 PG 17 WC Computer Science, Information Systems SC Computer Science GA XC752 UT WOS:A1997XC75200003 ER PT J AU Fujihara, H Simmons, DB Ellis, NC Shannon, RE AF Fujihara, H Simmons, DB Ellis, NC Shannon, RE TI Knowledge conceptualization tool SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE knowledge acquisition; information retrieval; intelligent system; knowledge-based system; knowledge engineering; conceptualization ID TEXT RETRIEVAL AB Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a knowledge acquisition process,and few automated support tools exist to help knowledge engineers enhance their performance. This paper presents a knowledge conceptualization tool (KCT) in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is accurate and complete. The KCT uses information retrieval technique to facilitate conceptualization, which is one of the human intensive activities of knowledge acquisition. Two information retrieval techniques employing best-match strategies are used: vector space model and probabilistic ranking principle model. A prototype of the KCT was implemented to demonstrate the concept. The results from KCT are compared with the outputs from a manual knowledge acquisition process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, and uncertainty) of KCT is about half that of the manual process, and the number of knowledge components retrieved from knowledge acquisition activities is four times more than that retrieved through a manual process. Furthermore, KCT captured every knowledge component that the knowledge engineer manually captured. KCT demonstrates the effectiveness of the knowledge acquisition process model proposed in this paper. C1 TEXAS A&M UNIV,COGNIT SYST LAB,COLLEGE STN,TX 77843. RP Fujihara, H (reprint author), HEWLETT PACKARD CORP,R&D LAB,BOISE,ID 83714, USA. CR ADMODT A, 1990, CURRENT TRENDS KNOWL, P1 ALLARD F, 1992, P EKAW 92 6 EUR KNOW, P1 BOOSE J, 1988, KNOWL ACQUIS, V1, P3 BREUKER J, 1987, P1098 U AMST BUCHANAN B, 1983, BUILDING EXPERT SYST COOKSON M, 1985, RES DEV EXPERT SYSTE Feigenbaum E., 1988, RISE EXPERT CO Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 FODOR JA, 1968, J PHILOS, V65, P627, DOI 10.2307/2024316 GAINES B, 1989, P 3 EUR WORKSH KNOWL Haugeland J., 1981, MIND DESIGN, P161 Hayes-Roth F., 1983, BUILDING EXPERT SYST JARDINE N, 1976, INFORMATION STORAGE, V27, P129 JOHANNSEN G, 1991, AUTOMATICA, V27, P97, DOI 10.1016/0005-1098(91)90009-Q KAGAN J, 1972, PSYCHOL INTRO McGraw K. L., 1989, KNOWLEDGE ACQUISITIO Mitchell T. M., 1986, Machine Learning, V1, DOI 10.1007/BF00116250 Musen M. A., 1989, Knowledge Acquisition, V1, DOI 10.1016/S1042-8143(89)80006-8 Musen M.A., 1989, AUTOMATED GENERATION PEARL T, 1988, PROBABILISTIC REASON Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ryle G., 1949, CONCEPT MIND Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1970, SCIENCE, P335 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TURTLE HR, 1992, COMPUT J, V35, P279, DOI 10.1093/comjnl/35.3.279 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 WATERMAN DA, 1988, GUIDE EXPERT SYSTEMS NR 35 TC 8 Z9 8 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD MAR-APR PY 1997 VL 9 IS 2 BP 209 EP 220 DI 10.1109/69.591447 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA WV231 UT WOS:A1997WV23100002 ER PT J AU Losee, RM AF Losee, RM TI Comparing Boolean and probabilistic information retrieval systems across queries and disciplines SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RELEVANCE FEEDBACK; DOCUMENT-RETRIEVAL; TERM DEPENDENCE; SEARCH TERMS; FUZZY SET; PERFORMANCE; ALGORITHM; RANKING; MODELS AB Whether using Boolean queries or ranking documents using document and term weights will result in better retrieval performance has been the subject of considerable discussion among document retrieval system users and researchers. We suggest a method that allows one to analytically compare the two approaches to retrieval and examine their relative merits. The performance of information retrieval systems may be determined either by using experimental simulation, or through the application of analytic techniques that directly estimate the retrieval performance, given values for query and database characteristics. Using these performance predicting techniques, sample performance figures are provided for queries using the Boolean and and or, as well as for probabilistic systems assuming statistical term independence or term dependence. The variation of performance across sublanguages (used in different academic disciplines) and queries is examined. The performance of models failing to meet statistical and other assumptions is examined. RP Losee, RM (reprint author), UNIV N CAROLINA,MANNING HALL,CB 3360,CHAPEL HILL,NC 27599, USA. CR Bartell B. T., 1994, ACM ANN C RES DEV IN, P173 BECHER T, 1987, STUD HIGH EDUC, V12, P261, DOI 10.1080/03075078712331378052 BELKIN NJ, 1993, ACM ANN C RES DEV IN, P339 BONZI S, 1984, J DOC, V40, P247, DOI 10.1108/eb026767 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1985, P 8 ANN INT ACM SIGI, P11, DOI 10.1145/253495.253505 BOOKSTEIN A, 1982, RES DEV INFORMATION BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 EVANS R, 1994, P 15 NAT ONL M MEDF, P121 EVERETT DM, 1992, J AM SOC INFORM SCI, V43, P658, DOI 10.1002/(SICI)1097-4571(199212)43:10<658::AID-ASI3>3.0.CO;2-H FOX EA, 1990, J AM SOC INFORM SCI, V41, P10, DOI 10.1002/(SICI)1097-4571(199001)41:1<10::AID-ASI2>3.0.CO;2-6 GUPTA PD, 1987, J AM SOC INFORM SCI, V38, P245 HAAS SW, 1995, P 4 ANN S DOC AN INF, P137 Kneale W., 1986, DEV LOGIC LAM K, 1982, ACM T DATABASE SYST, V7, P500, DOI 10.1145/319732.319756 LEE JH, 1994, ACM ANN C RES DEV IN, P182 LEE JH, 1995, ACM ANN C RES DEV IN, P180 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X Losee RM, 1996, J AM SOC INFORM SCI, V47, P95 LOSEE RM, 1995, INFORM PROCESS MANAG, V31, P555, DOI 10.1016/0306-4573(95)00072-O LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P193, DOI 10.1016/0306-4573(94)90064-7 LOSEE RM, 1995, J AM SOC INFORM SCI, V46, P519, DOI 10.1002/(SICI)1097-4571(199508)46:7<519::AID-ASI5>3.0.CO;2-R LOSEE RM, 1986, ACM C RES DEV INFORM, P258 LOSEE RM, 1988, INFORM PROCESS MANAG, V24, P315, DOI 10.1016/0306-4573(88)90097-0 LOSEE RM, 1995, P 5 INT C SCI INF ME, P265 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W LOSEE RM, 1991, INFORM PROCESS MANAG, V27, P1, DOI 10.1016/0306-4573(91)90027-J Markey K., 1981, ONTAP ONLINE TRAININ MOON SB, 1993, THESIS U N CAROLINA NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1990, INFORMATICS 10, P153 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGER N, 1981, P 44 ASIS ANN M WHIT, P199 SALTON G, 1984, 84588 TR CORN U SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 SPINK A, 1995, INFORM PROCESS MANAG, V31, P161, DOI 10.1016/0306-4573(94)00043-3 TIBBO HR, 1992, LIBR INFORM SCI RES, V14, P31 TURTLE H, 1994, 17TH P ANN INT ACM S, P212 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 49 TC 12 Z9 12 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD FEB PY 1997 VL 48 IS 2 BP 143 EP 156 DI 10.1002/(SICI)1097-4571(199702)48:2<143::AID-ASI5>3.0.CO;2-Y PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA WD675 UT WOS:A1997WD67500005 ER PT B AU An, AJ Cercone, N Chan, C AF An, AJ Cercone, N Chan, C BE Leake, DB Plaza, E TI Integrating rule induction and case-based reasoning to enhance problem solving SO CASE-BASED REASONING RESEARCH AND DEVELOPMENT SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 2nd International Conference on Case-Based Reasoning (ICCBR-97) CY JUL 25-27, 1997 CL PROVIDENCE, RHODE ISLAND AB We present a new method that integrates rule induction and case-based reasoning. The method is new in two aspects. First, it applies a novel feature weighting function for assessing similarities between cases. By using this weighting function, optimal case retrieval is achieved in that the most relevant cases can be retrieved from the case base. Second, the method handles both classification and numeric prediction tasks under a mixed paradigm of rule-based and case-based reasoning. Before problem solving, rule induction is performed to induce a set of decision rules from a set of training data. The rules are then employed to determine some parameters in the new weighting function. The induced rules are also used to detect possible noise in the training set so that noisy cases are not used in case-based reasoning. For classification tasks, rules are applied to make decisions; if there is a conflict between matched rules, case-based reasoning is performed. The method was implemented in ELEM2-CBR, a learning and problem solving system. We demonstrate the performance of ELEM2-CBR by comparing it with other methods on a number of designed and real-world problems. C1 Univ Regina, Regina, SK S4S 0A2, Canada. RP An, AJ (reprint author), Univ Regina, Regina, SK S4S 0A2, Canada. CR ALTHOFF K, 1995, P 8 WORKSH GERM SPEC AN A, 1997, THESIS U REGINA REGI COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 Domingos P., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence GREECY RH, 1992, COMMUN ACM, V35, P48 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ZHANG JP, 1990, PROCEEDINGS OF THE 2ND INTERNATIONAL IEEE CONFERENCE ON TOOLS FOR ARTIFICIAL INTELLIGENCE, P31, DOI 10.1109/TAI.1990.130306 NR 9 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN 33 PA HEIDELBERGER PLATZ 3, W-1000 BERLIN 33, GERMANY BN 3-540-63233-6 J9 LECT NOTES ARTIF INT PY 1997 VL 1266 BP 499 EP 508 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BK96Z UT WOS:000073947800047 ER PT J AU Shaw, WM Burgin, R Howell, P AF Shaw, WM Burgin, R Howell, P TI Performance standards and evaluations in IR test collections: Cluster-based retrieval models SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID FIBROSIS DOCUMENT COLLECTION; COMPOSITE REPRESENTATIONS; SUBJECT DESCRIPTIONS; RANDOM GRAPHS; CF DATABASE; CLASSIFICATION; METHODOLOGIES; INFORMATION; COMPONENTS; ALGORITHM AB Low performance standards for the group of queries in 13 retrieval test collections have been computed. Derived from the random graph hypothesis, these standards represent the highest levels of retrieval effectiveness that can be obtained from meaningless clustering structures. Operational levels of cluster-based performance reported in selected sources during the past 20 years have been compared to the standards. Comparisons show that typical levels of operational cluster-based retrieval can be explained on the basis of chance. Indeed, most operational results in retrieval test collections are lower than those predicted by random graph theory. A tentative explanation for the poor performance of cluster-based retrieval reveals weaknesses in both fundamental assumptions and operational implementations. The cluster hypothesis offers no guarantee that relevant documents are naturally grouped together, clustering algorithms may not reveal the inherent structure in a set of documents, and retrieval strategies do not reliably retrieve the most effective cluster or clusters of documents. That most cluster-based retrieval implementations implicitly rely on topical relatedness to be equivalent to a relevance relationship contributes to the poor performance. Clustering strategies capable of adapting to relevance information may succeed where static clustering techniques have failed. Copyright (C) 1997 Elsevier Science Ltd C1 N CAROLINA CENT UNIV,SCH LIB & INFORMAT SCI,DURHAM,NC 27707. RP Shaw, WM (reprint author), UNIV N CAROLINA,SCH LIB & INFORMAT SCI,CHAPEL HILL,NC 27599, USA. CR BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J Bullen P. S., 1988, MEANS THEIR INEQUALI BURGIN R, 1995, J AM SOC INFORM SCI, V46, P562, DOI 10.1002/(SICI)1097-4571(199509)46:8<562::AID-ASI2>3.0.CO;2-B CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 DAVIES A, 1983, THESIS U SHEFFIELD U DICE LR, 1945, ECOLOGY, V26, P297, DOI 10.2307/1932409 DUBES R, 1980, ADV COMPUT, V19, P113, DOI 10.1016/S0065-2458(08)60034-0 DUBES R, 1979, PATTERN RECOGN, V11, P235, DOI 10.1016/0031-3203(79)90034-7 ELHAMDOUCHI A, 1989, COMPUT J, V32, P220, DOI 10.1093/comjnl/32.3.220 ELHAMDOUCHI A, 1987, J INFORM SCI, V13, P361, DOI 10.1177/016555158701300607 FOX EA, 1983, 83561 CORN U DEP COM Freedman D. A., 1980, STATISTICS GORDON MD, 1991, J AM SOC INFORM SCI, V42, P311, DOI 10.1002/(SICI)1097-4571(199106)42:5<311::AID-ASI1>3.0.CO;2-J GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 Harary F., 1969, GRAPH THEORY KEEN EM, 1992, INFORM PROCESS MANAG, V28, P491, DOI 10.1016/0306-4573(92)90006-L Kenney J., 1947, MATH STAT KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 LING RF, 1975, J MATH PSYCHOL, V12, P90, DOI 10.1016/0022-2496(75)90051-6 LING RF, 1976, J AM STAT ASSOC, V71, P293, DOI 10.2307/2285300 LING RF, 1973, ANN PROBAB, V1, P876, DOI 10.1214/aop/1176996856 MACLEOD KJ, 1991, INFORM PROCESS MANAG, V27, P337, DOI 10.1016/0306-4573(91)90088-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1969, AM DOC, V20, P61, DOI 10.1002/asi.4630200109 SALTON G, 1972, J AM SOC INFORM SCI, V23, P75, DOI 10.1002/asi.4630230202 SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 SHAW WM, 1995, INFORM PROCESS MANAG, V31, P491, DOI 10.1016/0306-4573(95)00011-5 SHAW RJ, 1993, INFORM PROCESS MANAG, V29, P449, DOI 10.1016/0306-4573(93)90040-K Shaw WM, 1997, INFORM PROCESS MANAG, V33, P15, DOI 10.1016/S0306-4573(96)00044-1 SHAW WM, 1990, INFORM PROCESS MANAG, V26, P339, DOI 10.1016/0306-4573(90)90095-J SHAW WM, 1991, J AM SOC INFORM SCI, V42, P669 SHAW WM, 1990, INFORM PROCESS MANAG, V26, P693, DOI 10.1016/0306-4573(90)90046-5 SHAW WM, 1994, INFORM PROCESS MANAG, V30, P711, DOI 10.1016/0306-4573(94)90079-5 SHAW WM, 1990, INFORM PROCESS MANAG, V26, P705, DOI 10.1016/0306-4573(90)90047-6 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SHAW WM, 1991, J AM SOC INFORM SCI, V42, P676 SHAW WM, 1993, INFORM PROCESS MANAG, V29, P751, DOI 10.1016/0306-4573(93)90104-L Sparck Jones K., 1976, J DOC, V32, P59, DOI 10.1108/eb026616 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 WILBUR WJ, 1994, INFORM PROCESS MANAG, V30, P253, DOI 10.1016/0306-4573(94)90068-X WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 NR 46 TC 51 Z9 52 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 1997 VL 33 IS 1 BP 1 EP 14 DI 10.1016/S0306-4573(96)00043-X PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA WB742 UT WOS:A1997WB74200001 ER PT J AU Robertson, SE AF Robertson, SE TI Overview of the Okapi projects SO JOURNAL OF DOCUMENTATION LA English DT Article RP Robertson, SE (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,CTR INTERACT SYST RES,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. CR MITEV N, 1985, 39 BRIT LIB ROBERTSON SE, 1996, 356 COMP LAB ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WALKER S, 1987, 24 BRIT LIB WALKER S, 1991, 6056 BRIT LIB WALKER S, 1987, 72 BRIT LIB NR 6 TC 69 Z9 71 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD JAN PY 1997 VL 53 IS 1 BP 3 EP 7 DI 10.1108/EUM0000000007186 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA WE244 UT WOS:A1997WE24400002 ER PT J AU Robertson, SE Walker, S Beaulieu, M AF Robertson, SE Walker, S Beaulieu, M TI Laboratory experiments with Okapi: Participation in the TREC programme SO JOURNAL OF DOCUMENTATION LA English DT Article ID RETRIEVAL AB A brief review of the history of laboratory testing of information retrieval systems focuses on the idea of a general-purpose test collection of documents, queries and relevance judgements. The TREC programme is introduced in this context, and an overview is given of the methods used in TREC. The Okapi team's participation in TREC is then discussed. The team has made use of TREC to improve some of the automatic techniques used in Okapi, specifically the term weighting function and the algorithms for term selection for query expansion. The consequence of this process has been a very good showing for Okapi in terms of the TREC evaluation results. Some of the issues around the much more difficult problem of interactive evaluation in TREC are then discussed. Although some interesting interactive experiments have been performed at TREC, the problems of reconciling the requirements of the laboratory context with the concerns of interactive retrieval are still largely unresolved. RP Robertson, SE (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,CTR INTERACT SYST RES,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. CR Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Cleverdon C. W., 1966, FACTORS DETERMINING CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARMAN DK, 1996, 1 TEX RETR C GAITH HARMAN DK, IN PRESS 4 TEXT RETR HEARST M, IN PRESS 4 TEXT RETR JONES KS, 1975, 5266 BRIT LIB RES DE KNAUS D, IN PRESS 4 TEXT RETR Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1996, 356 COMP LAB ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 Robertson S.E., 1981, INFORMATION RETRIEVA, P9 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, IN PRESS 4 TEXT RETR ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Sparck Jones K., 1995, INFORM PROCESS MANAG, V31, P291 NR 18 TC 7 Z9 7 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD JAN PY 1997 VL 53 IS 1 BP 20 EP 34 DI 10.1108/EUM0000000007188 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA WE244 UT WOS:A1997WE24400004 ER PT J AU Huang, XJ Robertson, SE AF Huang, XJ Robertson, SE TI Application of probabilistic methods to Chinese text retrieval SO JOURNAL OF DOCUMENTATION LA English DT Article AB The use of text retrieval methods based on the probabilistic model with Chinese language material is discussed. Since Chinese text has no natural word boundaries, we must either apply a dictionary-based word segmentation method to the text, or index and search in terms of single Chinese characters. In either case, it becomes important to have a good way of dealing with phrases or contiguous strings of characters; the probabilistic model does not at present have such a facility. Some ad hoc modifications of the probabilistic weighting function and matching method are proposed for this purpose. RP Huang, XJ (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,CTR INTERACT SYST RES,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. CR CHEN G, 1992, J INFORMATION, V11, P11 Chien L. F., 1995, P 18 ANN INT ACM SIG, P112, DOI 10.1145/215206.215345 ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WILLETT P, 1979, J DOC, V35, P296, DOI 10.1108/eb026684 WU ZM, 1993, J AM SOC INFORM SCI, V44, P532, DOI 10.1002/(SICI)1097-4571(199310)44:9<532::AID-ASI3>3.0.CO;2-M NR 6 TC 3 Z9 3 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD JAN PY 1997 VL 53 IS 1 BP 74 EP 79 DI 10.1108/EUM0000000007193 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA WE244 UT WOS:A1997WE24400009 ER PT S AU Smeaton, AF Crimmins, F AF Smeaton, AF Crimmins, F BE Peters, C Thanos, C TI Relevance feedback and query expansion for searching the web: A model for searching a digital library SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 1st European Conference on Research and Advanced Technology for Digital Libraries (ECDL 97) CY SEP 01-03, 1997 CL PISA, ITALY SP CNR, Ist Elaborazione Informazione, European Res Consortium Informat & Math, Centr Off, TMR, CEU Telemat, European Res Consortium Informat & Math, CNR, Natl Advisory Comm Phys, CNR, Natl Advisory Comm Informat Sci & Technol AB A fully operational large scale digital library is likely to be based on a distributed architecture and because of this it is likely that a number of independent search engines may be used to index different overlapping portions of the entire contents of the library. In any case, different media, text, audio, image, etc., will be indexed for retrieval by different search engines so techniques which provide a coherent and unified search over a suite of underlying independent search engines are thus likely to be an important part of navigating in a digital library. In this paper we present an architecture and a system for searching the world's largest DL, the world wide web. What makes our system novel is that we use a suite of underlying web search engines to do the bulk of the work while our system orchestrates them in a parallel fashion to provide a higher level of information retrieval functionality. Thus it is our meta search engine and not the underlying direct search engines that provide the relevance feedback and query expansion options for the user. The paper presents the design and architecture of the system which has been implemented, describes an initial version which has been operational for almost a year, and outlines the operation of the advanced version. C1 Dublin City Univ, Sch Comp Applicat, Dublin 9, Ireland. RP Smeaton, AF (reprint author), Dublin City Univ, Sch Comp Applicat, Glasnevin, Dublin 9, Ireland. CR BOWMAN C, 1995, CUCS73294 U COL EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W Eriksson H, 1996, IEEE EXPERT, V11, P14, DOI 10.1109/64.506749 GRAVANO L, 1994, P 1994 ACM SIGMOD C HARMAN D, 1996, NIST SPECIAL PUBLICA KOENEMANN J, 1996, P CHI 96 MORRISSEY J, 1982, LECT NOTES COMPUTER, V146 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMEATON AF, 1997, WWW6 C SANT CLAR CAL SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 NR 11 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN 33 PA HEIDELBERGER PLATZ 3, W-1000 BERLIN 33, GERMANY SN 0302-9743 BN 3-540-63554-8 J9 LECT NOTES COMPUT SC PY 1997 VL 1324 BP 99 EP 112 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BK99Z UT WOS:000074029800008 ER PT J AU Robertson, AM Willett, P AF Robertson, AM Willett, P TI An upperbound to the performance of ranked-output searching: Optimal weighting of query terms using a genetic algorithm SO JOURNAL OF DOCUMENTATION LA English DT Article ID RETRIEVAL PERFORMANCE; DOCUMENT-RETRIEVAL; RELEVANCE INFORMATION AB This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked-output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson-Sparck Jones F4 retrospective relevance weight. Extended experiments with seven document test collections show that the GA can often find weights that are slightly superior to those produced by the deterministic weighting scheme. That said, there are many cases where the two approaches give the same results, and a few cases where the F4 weights are superior to the GA weights. Since the GA has been designed to identify weights yielding the best possible level of retrospective performance, these results indicate that the F4 weights provide an excellent and practicable alternative. Evidence is presented to suggest that negative weights may play an important role in retrospective relevance weighting. C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Davis L., 1991, HDB GENETIC ALGORITH FRAKES WR, 1992, INFORMATION RETRIEVA Frieder O., 1991, SIGIR Forum Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 HEINE MH, 1991, J AM SOC INFORM SCI, V42, P267, DOI 10.1002/(SICI)1097-4571(199105)42:4<267::AID-ASI3>3.0.CO;2-Y JACKSON DM, 1971, ADV COMPUT, V11, P60 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1975, J DOC, V31, P266, DOI 10.1108/eb026606 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KEEN EM, 1991, J DOC, V47, P1, DOI 10.1108/eb026869 KEEN EM, 1992, INFORM PROCESS MANAG, V28, P491, DOI 10.1016/0306-4573(92)90006-L LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P193, DOI 10.1016/0306-4573(94)90064-7 MICHALEWICZ Z, 1992, GENETIC ALGORITHMS P Mitchell M., 1996, INTRO GENETIC ALGORI Petry F. E., 1993, ASIS '93. Proceedings of the 56th ASIS Annual Meeting PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 PRITCHARDSCHOCH T, 1993, ONLINE, V17, P33 Raghavan V. V., 1987, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms ROBERTSON AM, 1995, 6201 BRIT LIB R D ROBERTSON AM, 1994, J DOC, V50, P213, DOI 10.1108/eb026931 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST SCHAUBLE P, 1989, COMPATIBILITY RETRIE SHAW WM, 1995, INFORM PROCESS MANAG, V31, P491, DOI 10.1016/0306-4573(95)00011-5 SMITH M, 1995, NEW REV DOCUMENT TEX, V1, P101 STIRLING KH, 1977, THESIS U CALIFORNIA TENOPIR C, 1994, ONLINE, V18, P31 WADE SJ, 1995, NEW REV DOCUMENT TEX, V1, P147 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Wilkinson R., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Yang J., 1993, NIST SPECIAL PUBLICA, P31 NR 38 TC 18 Z9 18 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD DEC PY 1996 VL 52 IS 4 BP 405 EP 420 DI 10.1108/eb026973 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VU414 UT WOS:A1996VU41400002 ER PT J AU Tani, M Kamiya, T Ichiyama, S AF Tani, M Kamiya, T Ichiyama, S TI User interfaces for information strolling in a digital library SO NEC RESEARCH & DEVELOPMENT LA English DT Article DE virtual stacks; CG librarian; associated data retrieval; multimodal interface; information visualization; VRML; WWW; user interface; digital library AB This paper describes the issues involved in developing a digital library user interface and how to realize information strolling. It further reports our three user interface systems: virtual open stacks and a CG librarian to simulate a conventional library, associated data retrieval to understand multimodal dialogue, and a WWW-based interface to realize the high quality relevance feedback and graphical visualization of the retrieved data. With a digital library, users will be able to access any part of the primary sources. In the user interface and retrieval, it will be necessary to handle various relationships between multimedia data and to easily and freely identify and utilize those relationships. Information strolling is designed to allow users to ''walk'' around in the information space with full knowledge of and control over the relationships between the data. RP Tani, M (reprint author), NEC CORP LTD,KANSAI C&C RES LABS,TOKYO,JAPAN. CR Cassell J., 1994, P SIGGRAPH 94, P413, DOI 10.1145/192161.192272 EKMAN P, 1980, 3 CLASSES NONVERBAL FOX EA, 1993, 16TH P ANN INT ACM S, P248 FOX EA, 1995, COMMUN ACM, V38, P23 FOX EA, 1993, J AM SOC INFORM SCI, V44, P480, DOI 10.1002/(SICI)1097-4571(199309)44:8<480::AID-ASI7>3.0.CO;2-B GAUCH S, 1994, P DIG LIB 94 COLL ST, P47 Hill W.C., 1995, CHI 95 C P HUM FACT, P194 ICHIYAMA S, 1996, DIGITAL LIB JUN, P32 *JAP EL DICT RES I, 1994, TR042 JAP EL DICT RE, pCH5 KAMIYA T, 1995, IPSJ SIG NOTES, V95, P27 KOIZUMI A, 1994, P COLING 94 AUG, P257 LAMPING J, 1995, CHI 95 C P MAY, P67 LU S, 1995, HCI INT 95 TOKYO JUL, P17 MACKINLAY JD, 1995, CHI 95 C P MAY, P202 MIURA M, 1992, P COLING 92 AUG, P693 MUKHERJEA S, 1996, IEEE VIS 96 S OCT RAO R, 1995, COMMUN ACM, V38, P29, DOI 10.1145/205323.205326 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SATO M, 1991, IPSJ SIG NOTES, V91, P1 SATO M, 1988, 4 IWT MAY CAEN SATO M, 1989, 5 IWT SEP DENV SHAKIR HS, 1994, P 2 MED INT TECH WOR Shardanand U., 1995, CHI 95, P210 TANI M, 1994, IPSJ SIG NOTES, V94, P143 TANI M, 1995, P ISDL 95 AUG, P167 NR 26 TC 2 Z9 2 PU NEC CORPORATION PI TOKYO PA 7-1 SHIBA 5-CHOME, MINATO-KU, TOKYO 108-01, JAPAN SN 0547-051X J9 NEC RES DEV JI NEC Res. Dev. PD OCT PY 1996 VL 37 IS 4 BP 535 EP 545 PG 11 WC Engineering, Electrical & Electronic SC Engineering GA VR668 UT WOS:A1996VR66800013 ER PT J AU Singhal, A Salton, G Mitra, M Buckley, C AF Singhal, A Salton, G Mitra, M Buckley, C TI Document length normalization SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article AB In the TREC collection-a large full-text experimental text collection with widely varying document lengths-we observe that the likelihood of a document being judged relevant by a user increases with the document length. We show that a retrieval strategy, such as the vector-space cosine match, that retrieves documents of different lengths with roughly equal chances, will not optimally retrieve useful documents from such a collection. We present a modified technique-pivoted cosine normalization-that attempts to match the likelihood of retrieving documents of all lengths to the likelihood of their relevance, and show that this technique yields significant improvements in retrieval effectiveness. Copyright (C) 1996 Elsevier Science Ltd RP Singhal, A (reprint author), CORNELL UNIV,DEPT COMP SCI,4130 UPSON HALL,ITHACA,NY 14853, USA. CR BROGLIO J, 1995, NIST SPECIAL PUBLICA, P29 Buckley C., 1995, NIST SPECIAL PUBLICA, P69 BUCKLEY C, 1993, HUMAN LANGUAGE TECHN Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Harman D., 1995, P 3 TEXT RETR C TREC, P1 Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 KWOK KL, 1995, NIST PUBLICATION, P247 Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 Robertson S.E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1994, 941438 CORN U DEP CO SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1994, RIAO 94 CONFERENCE PROCEEDINGS - INTELLIGENT MULTIMEDIA INFORMATION RETRIEVAL SYSTEMS AND MANAGEMENT, P6 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1995, TR951507 CORN U DEP Turtle H., 1990, INFERENCE NETWORKS D NR 20 TC 49 Z9 49 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 1996 VL 32 IS 5 BP 619 EP 633 DI 10.1016/0306-4573(96)00008-8 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VJ870 UT WOS:A1996VJ87000010 ER PT J AU Robertson, S AF Robertson, S TI Untitled SO INFORMATION PROCESSING & MANAGEMENT LA English DT Letter ID RELEVANCE RP Robertson, S (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,CTR INTERACT SYST RES,LONDON EC1V 0HB,ENGLAND. CR Buckley C., 1995, SIGIR Forum HARMAN D, 1995, 3 TEXT RETR C TREC 3 Robertson SE, 1995, OV 3 TEXT RETR C TRE, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAW WM, 1995, INFORM PROCESS MANAG, V31, P491, DOI 10.1016/0306-4573(95)00011-5 NR 5 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 1996 VL 32 IS 5 BP 635 EP 636 DI 10.1016/S0306-4573(96)90121-1 PG 2 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VJ870 UT WOS:A1996VJ87000011 ER PT J AU Shaw, WM AF Shaw, WM TI Untitled - Reply SO INFORMATION PROCESSING & MANAGEMENT LA English DT Letter ID SEARCH TERMS RP Shaw, WM (reprint author), UNIV N CAROLINA,SCH LIB & INFORMAT SCI,CHAPEL HILL,NC 27599, USA. CR BUCKLEY C, 1995, P 18 ANN INT ACM SIG Buckley C., 1995, OV 3 TEXT RETR C TRE, V500-225, P69 Robertson SE, 1995, OV 3 TEXT RETR C TRE, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAW WM, 1995, INFORM PROCESS MANAG, V31, P491, DOI 10.1016/0306-4573(95)00011-5 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 8 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 1996 VL 32 IS 5 BP 636 EP 637 DI 10.1016/S0306-4573(96)90123-5 PG 2 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VJ870 UT WOS:A1996VJ87000012 ER PT J AU Agosti, M Crestani, F Melucci, M AF Agosti, M Crestani, F Melucci, M TI Design and implementation of a tool for the automatic construction of hypertexts for information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID USER AB The paper describes the design and implementation of TACHIR, a tool for the automatic construction of hypertexts for Information Retrieval. Through the use of an authoring methodology employing a set of well known Information Retrieval techniques, TACHIR automatically builds up a hypertext from a document collection. The structure of the hypertext reflects a three level conceptual model that has proved to be quite effective for Information Retrieval. Using this model it is possible to navigate among documents, index terms, and concepts using automatically determined links. The hypertext is implemented using the HTML hypertext mark up language, the mark up language of the World Wide Web project. It can be distributed on different sites and different machines over the Internet, and it can be navigated using any of the interfaces developed in the framework World Wide Web project, for example NETSCAPE. Copyright (C) 1996 Elsevier Science Ltd RP Agosti, M (reprint author), UNIV PADUA,DIPARTIMENTO ELETTRON & INFORMAT,I-35100 PADUA,ITALY. CR Aalbersberg IJsbrand J., 1992, P 15 ANN INT ACM SIG, P11, DOI 10.1145/133160.133169 AGOSTI M, 1993, INFORM PROCESS MANAG, V29, P283, DOI 10.1016/0306-4573(93)90055-I AGOSTI M, 1993, P ACM S APPL COMP IN, P745, DOI 10.1145/162754.167175 AGOSTI M, 1990, NINTH ANNUAL INTERNATIONAL PHOENIX CONFERENCE ON COMPUTERS AND COMMUNICATIONS, P500, DOI 10.1109/PCCC.1990.101662 Agosti M., 1991, P 14 ANN INT ACM SIG, P316, DOI 10.1145/122860.122892 AGOSTI M, 1994, P RIAO 94 C NEW YORK AGOSTI M, 1992, COMPUT J, V35, P194, DOI 10.1093/comjnl/35.3.194 AITCHISON J, 1987, THESAURUS CONSTRUCTI ALLAN J, 1987, THESAURUS CONSTRUCTI ALLAN J, 1995, P ACM SIGIR SEATTL W Berners-Lee T., 1992, Electronic Networking: Research, Applications and Policy, V2 CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUNLOP M, 1991, THESIS U GLASGOW GLA DUNLOP MD, 1993, INFORM PROCESS MANAG, V29, P287, DOI 10.1016/0306-4573(93)90056-J EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 FRAKES WR, 1992, INFORMATION RETRIEVA FRISSE ME, 1988, COMMUN ACM, V31, P880, DOI 10.1145/48511.48518 FURUTA R, 1989, ELECT PUBLISHING, V4, P211 HANCOCKBEAULIEU M, 1991, INFORM PROCESS MANAG, V27, P523, DOI 10.1016/0306-4573(91)90067-V JONES PE, 1968, CR1020 NASA LOOMIS MES, 1987, P EUR C OBJ OR PROGR, P325 MELUCCI M, 1994, TOOL AUTOMATIC CONST ODDY RN, 1975, THESIS U NEWCASTLE U PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QIU Y, 1994, CONSTRUCTING UPDATIN RADA R, 1992, ACM T INFORM SYST, V10, P294, DOI 10.1145/146760.146791 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1989, AUTOMATIC GENERATION Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 SMEATON AF, 1995, P IWHD C MONTP JUN 1 SRINIVASAN P, 1992, INFORMATION RETRIEVA, pCH9 STROUSTRUP B, 1991, C PLUS PLUS PROGRAMM THOMPSON R, 1989, DESIGN IMPLEMENTATIO VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 41 TC 16 Z9 16 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 1996 VL 32 IS 4 BP 459 EP 476 DI 10.1016/0306-4573(95)00075-5 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA UT277 UT WOS:A1996UT27700005 ER PT J AU Mathe, N Chen, JR AF Mathe, N Chen, JR TI User-centered indexing for adaptive information access SO USER MODELING AND USER-ADAPTED INTERACTION LA English DT Article; Proceedings Paper CT 4th International Conference on User Modeling (UM 94) CY 1994 CL HYANNIS, MA DE user-centered indexing; long-term adaptation; adaptive information retrieval; adaptive navigation; user feedback; shared information access ID RETRIEVAL; MODEL AB We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments. RP Mathe, N (reprint author), NASA, AMES RES CTR, MAIL STOP 269-2, MOFFETT FIELD, CA 94035 USA. CR AGOSTI M, 1995, MULTIMEDIA SYST, V3, P15, DOI 10.1007/BF01236576 ARMSTRONG R, 1995, P AAAI SPR S INF GAT BARREAU DK, 1995, J AM SOC INFORM SCI, V46, P327, DOI 10.1002/(SICI)1097-4571(199506)46:5<327::AID-ASI4>3.0.CO;2-C BELEW RK, 1989, 12TH P ANN INT ACM S, P11 BELKIN NJ, 1993, INFORM PROCESS MANAG, V29, P325, DOI 10.1016/0306-4573(93)90059-M BERGSTROM P, 1994, THESIS BOY GA, 1991, P 3 ACM C HYP SAN AN, P51, DOI 10.1145/122974.122980 BRADSHAW JM, 1996, IN PRESS SOFTWARE AG BRADSHAW JM, 1993, P 4 INT C HUM MACH I BRUSILOVSKY P, 1994, 4 INT C US MOD HYANN CALLAN JP, 1992, 3RD P INT C DAT EXP, P78 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S CHEN J, 1994, 4 INT C US MOD HYANN DUMAIS ST, 1988, MAY CHI 88 C P HUM F, P281 Fischer G., 1991, Human Factors in Computing Systems. Reaching Through Technology. CHI '91. Conference Proceedings FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 JENNINGS A, 1992, IEICE T INF SYST, VE75D, P198 Kaplan C., 1993, User Modeling and User-Adapted Interaction, V3, DOI 10.1007/BF01257889 KATSUMOTO M, 1995, P 1995 PAC WORKSH DI, P76 KIBBY MR, 1989, HYPERTEXT THEORY PRA, P164 KLARK P, 1995, P WORLD C ED MULT HY, P372 LASHKARI L, 1994, P 12 NAT C ART INT S, P444 MAES P, 1993, 11TH P NAT C ART INT, P459 MATHE N, 1994, P 4 INT C US MOD HYA, P107 Monk A., 1989, Interacting with Computers, V1, DOI 10.1016/0953-5438(89)90026-X PARGMAN D, 1994, P WORKSH US MOD INF RABINOWITZ J, 1995, P AAAI FALL S ART IN, P110 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE DE, 1991, INT J MAN MACH STUD, V35, P1, DOI 10.1016/S0020-7373(07)80006-X SALTON G, 1989, AUTOMATIC TEST PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHETH B, 1993, NINTH CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR APPLICATIONS : PROCEEDINGS, P345 TORRANCE M, 1995, P AAAI FALL S ART IN, P131 TUDHOPE D, 1995, P C HYP INF RETR MUL, P203 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VASSILEVA J, 1994, P 4 INT C US MOD HYA, P115 NR 40 TC 10 Z9 10 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-1868 J9 USER MODEL USER-ADAP JI User Model. User-Adapt. Interact. PD JUL PY 1996 VL 6 IS 2-3 BP 225 EP 261 DI 10.1007/BF00143968 PG 37 WC Computer Science, Cybernetics SC Computer Science GA VE967 UT WOS:A1996VE96700006 ER PT J AU Wilbur, WJ Yang, YM AF Wilbur, WJ Yang, YM TI An analysis of statistical term strength and its use in the indexing and retrieval of molecular biology texts SO COMPUTERS IN BIOLOGY AND MEDICINE LA English DT Article DE molecular biology; stop terms; text retrieval; text classification; linear least squares fit; weight; strength; vector model; Bayesian model AB The biological literature presents a difficult challenge to information processing in its complexity, diversity, and in its sheer volume. Much of the diversity resides in its technical terminology, which has also become voluminous. In an effort to deal more effectively with this large vocabulary and improve information processing, a method of focus has been developed which allows one to classify terms based on a measure of their importance in describing the content of the documents in which they occur. The measurement is called the strength of a term and is a measure of how strongly the term's occurrences correlate with the subjects of documents in the database. If term occurrences are random then there will be no correlation and the strength will be zero, but if for any subject, the term is either always present or never present its strength will be one. We give here a new, information theoretical interpretation of term strength, review some of its uses in focusing the processing of documents for information retrieval and describe new results obtained in document categorization. Copyright (C) 1996 Elsevier Science Ltd. C1 MAYO CLIN & MAYO FDN,SECT MED INFORMAT RESOURCES,ROCHESTER,MN 55905. RP Wilbur, WJ (reprint author), NIH,NATL CTR BIOTECHNOL INFORMAT,NATL LIB MED,BETHESDA,MD 20894, USA. CR BARKLA JK, 1969, UNPUB CONSTRUCTION W BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUCKLEY C, 1985, 85686 CORN U DEP COM COOPER WS, 1991, P 54 ANN M AM SOC IN, V28, P366 CROFT WB, 1982, 8221 COINS U MASS FANRIJSBEWRGEN CJ, 1979, INFORMATION RETRIEVA FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 HARMAN D, 1994, 2 TEXT RETR C TREC 2, P1 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 LINDBERG DAB, 1993, JAMA-J AM MED ASSOC, V269, P3124, DOI 10.1001/jama.269.24.3124 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 *NAT CTR BIOT INF, 1992, ENTR SEQ DAT CDROM V *NAT LIB MED, 1995, MEDLINE MED LIT AN R *NAT LIB MED, 1995, MED SUBJ HEAT MESH ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILBUR WJ, 1994, INFORM PROCESS MANAG, V30, P253, DOI 10.1016/0306-4573(94)90068-X WILBUR WJ, 1993, P 55 ANN M AM SOC IN, V29, P216 Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium YANG Y, IN PRESS AM SOC INFO Yang Y, 1994, P 17 ANN INT ACM SIG, P13 YANG Y, 1992, 14TH P INT C COMP LI, P447 YANG Y, 1993, P 16 ANN INT ACM SIG, P281, DOI 10.1145/160688.160738 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 31 TC 57 Z9 58 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0010-4825 J9 COMPUT BIOL MED JI Comput. Biol. Med. PD MAY PY 1996 VL 26 IS 3 BP 209 EP 222 DI 10.1016/0010-4825(95)00055-0 PG 14 WC Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology SC Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology GA UN160 UT WOS:A1996UN16000004 PM 8725772 ER PT J AU Losee, RM AF Losee, RM TI Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering: An empirical basis for grammatical rules SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article AB The grammars of natural languages may be learned by using genetic algorithms that reproduce and mutate grammatical rules and part-of-speech tags, improving the quality of later generations of grammatical components. Syntactic rules are randomly generated and then evolve; those rules resulting in improved parsing and occasionally improved retrieval and filtering performance are allowed to further propagate. The LUST system learns the characteristics of the language or sublanguage used in document abstracts by learning from the document rankings obtained from the parsed abstracts. Unlike the application of traditional linguistic rules to retrieval and filtering applications, LUST develops grammatical structures and tags without the prior imposition of some common grammatical assumptions (e.g. part-of-speech assumptions), producing grammars that are empirically based and are optimized for this particular application. RP Losee, RM (reprint author), UNIV N CAROLINA,MANNING HALL,CB 3360,CHAPEL HILL,NC 27599, USA. CR BONZI S, 1990, J AM SOC INFORM SCI, V41, P121, DOI 10.1002/(SICI)1097-4571(199003)41:2<121::AID-ASI5>3.0.CO;2-S BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 Briscoe T., 1993, Computational Linguistics, V19 BURGIN R, 1992, J AM SOC INFORM SCI, V43, P101, DOI 10.1002/(SICI)1097-4571(199203)43:2<101::AID-ASI1>3.0.CO;2-Y CHARNIAK E, 1993, STATISTICAL LANGUAGE CHITRAO MV, 1990, SPEECH NATURAL LANGU, P263 Chomsky N, 1965, ASPECTS THEORY SYNTA CLARK R, 1993, LINGUIST INQ, V24, P299 DAMERAU FJ, 1990, INFORM PROCESS MANAG, V26, P791, DOI 10.1016/0306-4573(90)90052-4 Filho J. L. R., 1994, COMPUTER, V27, P28 Gazdar Gerald, 1985, GENERALIZED PHRASE S Goldberg D. E., 1989, GENETIC ALGORITHMS S Grishman R., 1986, ANAL LANGUAGE RESTRI HAAS SW, 1994, INFORM PROCESS MANAG, V30, P619, DOI 10.1016/0306-4573(94)90074-4 HAAS SW, 1993, INFORM PROCESS MANAG, V29, P721, DOI 10.1016/0306-4573(93)90101-I LANKHORST MM, 1994, BREEDING GRAMMARS GR LANKHORST MM, 1994, AUTOMATIC WORD CATEG LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P293, DOI 10.1016/0306-4573(94)90071-X LOSEE RM, 1995, INFORM PROCESS MANAG, V31, P555, DOI 10.1016/0306-4573(95)00072-O LOSEE RM, 1995, J AM SOC INFORM SCI, V46, P519, DOI 10.1002/(SICI)1097-4571(199508)46:7<519::AID-ASI5>3.0.CO;2-R LOSEE RM, 1986, ACM C RES DEV INFORM, P258 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W LOSEE RM, 1991, INFORM PROCESS MANAG, V27, P1, DOI 10.1016/0306-4573(91)90027-J MASAND B, 1994, ADV GENETIC PROGRAMM, P445 Newmeyer Frederick J., 1986, LINGUISTIC THEORY AM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SRINIVAS M, 1994, COMPUTER, V27, P17, DOI 10.1109/2.294849 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YANG JJ, 1993, THESIS U PITTSBURGH YNGVE V, 1908, LINGUISTICS SCI NR 31 TC 18 Z9 18 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 1996 VL 32 IS 2 BP 185 EP 197 DI 10.1016/S0306-4573(96)85005-9 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TK998 UT WOS:A1996TK99800005 ER PT J AU Robertson, S AF Robertson, S TI Use of genetic algorithms in information retrieval - Robertson,AM, Willett,P SO JOURNAL OF DOCUMENTATION LA English DT Book Review RP Robertson, S (reprint author), CITY UNIV LONDON,LONDON EC1V 0HB,ENGLAND. CR Buckley C., 1995, SIGIR Forum CRINGEAN JK, 1990, TETRAHEDRON COMP MET, V3, P37, DOI 10.1016/0898-5529(90)90119-S ROBERTSON AM, 1995, USE GENETIC ALGORITH ROBERTSON AM, 1994, J DOC, V50, P213, DOI 10.1108/eb026931 ROBERTSON SE, 1995, 3 TEXT RETR C TREC 3 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 6 TC 0 Z9 0 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD MAR PY 1996 VL 52 IS 1 BP 100 EP 103 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TW912 UT WOS:A1996TW91200006 ER PT J AU Spink, A Losee, RM AF Spink, A Losee, RM TI Feedback in information retrieval SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review ID RELEVANCE FEEDBACK; DOCUMENT-RETRIEVAL; ONLINE SEARCH; SEEKING STRATEGIES; QUERY EXPANSION; BOOLEAN QUERIES; FUZZY-SET; SYSTEMS; PERFORMANCE; DESIGN C1 UNIV N CAROLINA,CHAPEL HILL,NC 27599. RP Spink, A (reprint author), UNIV N TEXAS,POB 13796,DENTON,TX 76203, USA. CR Allan J., 1995, SIGIR Forum ALLEN BL, 1991, ANNU REV INFORM SCI, V26, P3 Ashby W. R., 1956, INTRO CYBERNETICS ASHFORD SJ, 1983, ORGAN BEHAV HUM PERF, V32, P370, DOI 10.1016/0030-5073(83)90156-3 Ashworth MJ, 1982, FEEDBACK DESIGN SYST ATTAR R, 1981, INFORM PROCESS MANAG, V17, P115 BATES MJ, 1984, RQ, V24, P43 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 Beer S., 1967, CYBERNETICS MANAGEME BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BELKIN NJ, 1995, IN PRESS P 4 TEXT RE BELKIN NJ, 1995, EXPERT SYST APPL, V9, P379, DOI 10.1016/0957-4174(95)00011-W BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BORGMAN CL, 1984, ANNU REV INFORM SCI, V19, P33 Boulding K. E., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality BRANDES A, 1991, CONSTRUCTIONISM RES, P391 BRENNAN SE, 1995, KNOWL-BASED SYST, V8, P143, DOI 10.1016/0950-7051(95)98376-H BUCKLEY C, 1995, INFORM PROCESS MANAG, V31, P315, DOI 10.1016/0306-4573(94)00049-9 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CALLAN JP, 1992, 3RD P INT C DAT EXP, P78 CATHEART R, 1985, INFORMATION BEHAV, V1, P113 CHESEBRO JW, 1985, INFORMATION BEHAVIOR, V1, P202 Church K. W., 1995, SIGIR Forum COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DALRYMPLE PW, 1990, J AM SOC INFORM SCI, V41, P272, DOI 10.1002/(SICI)1097-4571(199006)41:4<272::AID-ASI6>3.0.CO;2-5 Daniels J. J., 1995, SIGIR Forum Dechert C. R., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality DEMPSEY JV, 1993, INTERACTIVE INSTRUCT Deutsch K. W., 1968, MODERN SYSTEMS RES B, P387 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X DOSZKOCS TE, 1990, ANNU REV INFORM SCI, V25, P209 EASTON D, 1957, WORLD POLIT, V9, P383, DOI 10.2307/2008920 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W EFTHIMIADIS FN, 1989, PERSPECTIVES INFORMA, V1, P257 Fenichel C.H., 1980, LIBR RES, V2, P107 FIDEL R, 1983, J AM SOC INFORM SCI, V34, P163, DOI 10.1002/asi.4630340302 FIDEL R, 1985, ONLINE REV, V9, P61, DOI 10.1108/eb024176 FIDEL R, 1987, CAN J INFORM SCI, V12, P54 FOX EA, 1995, SIGIR 95 FOX EA, 1996, DIGITAL LIB 1996 FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 GAUCH S, 1991, ACM T INFORM SYST, V9, P249, DOI 10.1145/125187.125193 GIGNAC P, 1994, INFORMATION INDUSTRY IN TRANSITION - CAIS/ACSI '94, P236 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P311, DOI 10.1002/(SICI)1097-4571(199106)42:5<311::AID-ASI1>3.0.CO;2-J Haines D., 1993, SIGIR Forum HARMAN D, 1995, INFORM PROCESS MANAG, V31, P269, DOI 10.1016/0306-4573(94)00046-6 HARMAN D, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1 HARMAN DK, 1993, INFORM PROCESS MANAG, V29, P411, DOI 10.1016/0306-4573(93)90037-E HARMAN DK, 1994, 500215 NIST HARMAN DK, 1993, P 14 NAT ONL M 1993, P181 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Harter S. P., 1986, ONLINE INFORMATION R Heims Steve J., 1993, CONSTRUCTING SOCIAL HOLLAND JH, 1992, ADAPTATION NATURAL A Hume David, 1975, ENQUIRIES HUMAN UNDE, V3rd Ide E., 1971, SMART RETRIEVAL SYST, P337 INGWERSEN P, 1992, INFORMATION RETRIEVA Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 JONES KS, 1980, RES RELEVANCE WEIGHT JONS KS, 1981, INFORMATION RETRIEVA, P256 KANTOR PB, 1994, ANNU REV INFORM SCI, V29, P53 KANTOR PB, 1982, ANNU REV INFORM SCI, V17, P99 KASS RE, 1996, IN PRESS J AM STAT A KIM JY, 1971, THESIS U IOWA IOWA C KIM JY, 1972, GEN SYSTEMS THEORY H, P207 KNAPP SD, 1978, RO, V17, P373 KOENEMANN J, 1996, CHI, P205 KOENEMANN J, 1995, 500225 NIST, P145 KRAFT DH, J AM SOC INFORMATION KRAFT DH, 1979, INFORM PROCESS MANAG, V15, P47, DOI 10.1016/0306-4573(79)90007-4 Kramer E., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality LANCASTER FW, 1972, 101 U ILL GRAD SCH L Layaida R., 1994, Database and Expert Systems Applications. 5th International Conference, DEXA '94 Proceedings LEE DL, 1996, IN PRESS IEEE SOFTWA Losee RM, 1996, J AM SOC INFORM SCI, V47, P95 LOSEE RM, 1995, INFORM PROCESS MANAG, V31, P555, DOI 10.1016/0306-4573(95)00072-O Losee R. M., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42024 LOSEE RM, 1994, INFORM PROCESS MANAG, V30, P193, DOI 10.1016/0306-4573(94)90064-7 LOSEE RM, 1986, ACM C RES DEV INFORM, P258 LOSEE RM, 1988, INFORM PROCESS MANAG, V24, P315, DOI 10.1016/0306-4573(88)90097-0 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W MANIER E, 1972, BOSTON STUDIES PHILO, V8, P225 MARCHIONINI G, 1989, J AM SOC INFORM SCI, V40, P54, DOI 10.1002/(SICI)1097-4571(198901)40:1<54::AID-ASI6>3.0.CO;2-R MARKEY K, 1984, LIBR INFORM SCI RES, V6, P155 MARKEY K, 1986, IMPACT ONLINE CATALO, P35 MARUYAMA M, 1963, AM SCI, V51, P164 Maruyama M., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality MAYR O, 1970, FRUHGESCHICHTE TECHN MCGILL MJ, 1979, ANNU REV INFORM SCI, V14, P93 MCNEIL D, 1979, CONCEPTUAL BASIS LAN Meadow C.T., 1992, TEXT INFORMATION RET MEADOW CT, 1985, COMPUTER SCI INFORMA, P12 Miller JG, 1978, LIVING SYSTEMS Milsum J. H., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality Mooers C. N., 1951, AM DOC, V2, P20, DOI 10.1002/asi.5090020107 MORRISON EW, 1991, ACAD MANAGE REV, V16, P533 Norman D. A., 1988, PSYCHOL EVERYDAY THI ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 OLDROYD BK, 1977, ONLINE REV, V1, P295, DOI 10.1108/eb023957 PAYNTER MH, 1968, POSITIVE FEEDBACK GE, P66 Perez-Quinones M. A., 1996, COMMON GROUND CHI 96, P316 PETRY FE, 1993, P ASIS ANNU MEET, V30, P122 PIETILAINEN P, 1983, ONLINE REV, V7, P391, DOI 10.1108/eb024137 PIETILAINEN P, 1983, INFORM PROCESS MANAG, V19, P51, DOI 10.1016/0306-4573(83)90035-3 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 POWERS WT, 1978, PSYCHOL REV, V85, P417, DOI 10.1037//0033-295X.85.5.417 PRATT JW, 1995, INTRO STATISTICAL DE Raghavan V. V., 1995, SIGIR Forum Rapoport A., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality RESNICK M, 1991, CONSTRUCTIONISM RES, P151 *RIAO, 1994, RIAO 94 INT MULT INF Richardson G. P., 1991, FEEDBACK THOUGHT SOC ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 ROBERTSON SE, 1982, STAT PROBLEMS APPL P ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCCHIO JJ, 1966, THESIS HARVARD U CAM Rosenblueth Arturo, 1943, PHILOS SCI, V10, P18, DOI DOI 10.1086/286788 Rubin M. D., 1968, Positive feedback. A general systems approach to positive/negative feedback and mutual causality SALTON G, 1971, SMART SYSTEM EXPT AU, P324 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SARACEVIC T, 1990, P ASIS ANNU MEET, V27, P47 SARACEVIC T, 1991, P 12 NAT ONL M 1991, P329 SARACEVIC T, 1996, IN PRESS ASIS 95, V33 Saracevic T., 1995, SIGIR Forum SARAVECIC T, INFORMATION PROCESSI SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 Schutze H., 1995, SIGIR Forum SEMBOK TMT, 1994, INFORMATION RETRIEVAL, P91 SHAW WM, 1995, INFORM PROCESS MANAG, V31, P491, DOI 10.1016/0306-4573(95)00011-5 SHAW WM, 1996, IN PRESS COMMUNICATI SHENOUDA WA, 1991, THESIS STATE U NEW J Sheth B., 1993, Proceedings. The Ninth Conference on Artificial Intelligence for Applications (Cat. No.93CH3254-0), DOI 10.1109/CAIA.1993.366590 Shneiderman B, 1987, DESIGNING USER INTER SHUTE SJ, 1989, THESIS OHIO STATE U Simon H., 1976, ADM BEHAV STUDY DECI, V3rd Slobodkin LB, 1968, POSITIVE FEEDBACK GE, P149 SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SMITH KU, 1969, PRINCIPLES SKILL ACQ, P103 SOLAK JJ, 1983, INFORM PROCESS MANAG, V19, P141, DOI 10.1016/0306-4573(83)90067-5 Sparck Jones K., 1995, INFORM PROCESS MANAG, V31, P291 SPINK A, 1987, IN PRESS J AM SOC IN SPINK A, 1996, IN PRESS ASIS 9L, V33 SPINK A, 1995, INFORM PROCESS MANAG, V31, P161, DOI 10.1016/0306-4573(94)00043-3 SPINK A, 1992, ASIS 92, V26, P249 SPINK A, 1997, IN PRESS J AM SOC IN SPINK A, 1993, P ASIS ANNU MEET, V30, P63 Spink A., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval SPINK A, 1993, P ASIS ANNU MEET, V30, P115 SPINK AH, 1993, THESIS STATE U NEW J STORRS G, 1994, INTERACT COMPUT, V6, P173, DOI 10.1016/0953-5438(94)90023-X Taghva K, 1996, INFORM PROCESS MANAG, V32, P317, DOI 10.1016/0306-4573(95)00058-5 Taube M, 1955, AM DOC, V6, P1 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VIGIL PJ, 1981, INFORMATION COMMUNIT, V18, P212 VIGIL PJ, 1983, J AM SOC INFORM SCI, V34, P281, DOI 10.1002/asi.4630340407 VONBERTALANFFY L, 1968, MODERN SYSTEMS RES B, P11 VONFOERSTER H, 1949, CYBERNETICS CIRCULAR, V6 WEINBERG BH, 1983, P AM SOC INFORM SCI, V20, P32 WIENER N, 1961, CYBERNETICS OR CONTR Wiener N., 1953, READINGS PHILOS SCI, P100 Wiener N, 1950, HUMAN USE HUMAN BEIN Wong S. K. M., 1993, SIGIR Forum WOOD F, 1994, J INFORM SCI, V20, P29, DOI 10.1177/016555159402000104 WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 YANG JJ, 1993, THESIS U PITTSBURGH NR 175 TC 40 Z9 39 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1996 VL 31 BP 33 EP 78 PG 46 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VU798 UT WOS:A1996VU79800004 ER PT J AU Efthimiadis, EN AF Efthimiadis, EN TI Query expansion SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review ID DOCUMENT-RETRIEVAL SYSTEMS; AUTOMATIC RELEVANCE FEEDBACK; INFORMATION SEARCH TACTICS; FULL-TEXT RETRIEVAL; CO-OCCURRENCE DATA; RANKING ALGORITHMS; EXPERT-SYSTEMS; CONTROLLED VOCABULARY; HUMANITIES SCHOLARS; TEACHING PACKAGE RP Efthimiadis, EN (reprint author), UNIV CALIF LOS ANGELES,300 CIRCLE DR N,405 HILGARD AVE,MAILBOX 951520,LOS ANGELES,CA 90095, USA. RI WANG, Haoming/A-5033-2009 CR AHLSWEDE T, 1988, RIAO 88 PROGR, V1 AIGRAIN P, 1994, J AM SOC INFORM SCI, V45, P225, DOI 10.1002/(SICI)1097-4571(199405)45:4<225::AID-ASI1>3.0.CO;2-1 ALLEN BL, 1994, SIGIR 94 P ASS COMP ANICK PG, 1993, INTEGRATING DYNAMIC, P136 ARAYA JE, 1990, THESIS U MICROFILMS ARONSON AR, 1994, RIAO 94 C P PRES PRO ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 ATTAR R, 1981, INFORM PROCESS MANAG, V17, P115 BATES MJ, 1994, DESIGN DATABASES OTH, V18, P331 BATES MJ, 1987, ONLINE, V11, P47 BATES MJ, 1981, ANNU REV INFORM SCI, V16, P139 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P280 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P205, DOI 10.1002/asi.4630300406 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 BATES MJ, 1993, LIBR QUART, V63, P1 Beaulieu M, 1996, J AM SOC INFORM SCI, V47, P85 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BELKIN NJ, 1985, INTERACTION INFORMAT BELKIN NJ, 1983, J INFORM SCI, V5, P153 BELKIN NJ, 1987, INFORM PROCESS MANAG, V23, P395, DOI 10.1016/0306-4573(87)90109-9 BELKIN NJ, 1993, INFORM PROCESS MANAG, V29, P325, DOI 10.1016/0306-4573(93)90059-M Borgman CL, 1996, J AM SOC INFORM SCI, V47, P493, DOI 10.1002/(SICI)1097-4571(199607)47:7<493::AID-ASI3>3.0.CO;2-P BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P387, DOI 10.1002/(SICI)1097-4571(198611)37:6<387::AID-ASI3>3.0.CO;2-8 BOYD R, 1994, INCORPORATING SEMANT, P291 BRAJNIK G, 1988, P ASS COMP MACH SPEC BRAJNIK G, 1986, COOPERATIVE INTERFAC, P259 BROGLIO J, 1995, DOCUMENT RETRIEVAL R, P29 BROOKS HM, 1986, THESIS CIT U BUCKLEY C, 1995, AUTOMATIC ROUTING RE, V31, P315 BUCKLEY C, 1993, AUTOMATIC RETRIEVAL, P59 BUCKLEY C, 1994, AUTOMATIC ROUTING AD, P45 BUCKLEY C, 1995, AUTOMATIC QUERY EXPA, P69 BURKET TG, 1979, USE VOCABULARY FILES, V15, P281 Chen H., 1990, P 13 ANN INT ACM SIG, P115, DOI 10.1145/96749.98013 COOPER WS, 1995, EXPT PROBABILISTIC R, P127 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1990, 13TH P INT C RES DEV, P349 CROUCH C, 1990, P 13 ANN INT ACM SIG, P369, DOI 10.1145/96749.98242 CROUCH CJ, 1994, INT J EXPERT SYST, V7, P193 CROUCH CJ, 1988, P ASS COMP MACH SPEC CUADRA CA, 1967, J DOC, V23, P291, DOI 10.1108/eb026436 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DELIA S, 1985, QUESTQUORUM NEW ONLI DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 DOSZKOCS TE, 1979, INFORMAITON CHOICES, V16 DOSZKOCS TE, 1993, P 3 ASIS SIG CR CLAS, P15 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 DRISCOLL J, 1995, USING DATABASE SCHEM, P373 EFTHIAMIADIS EN, 1992, THESIS CIT U NORTHAM EFTHIAMIADIS EN, 1994, P 15 NAT ONL M 1994, P113 EFTHIMIADIS EN, 1994, P 3 INT C INT SOC KN, P295 EFTHIMIADIS EN, 1993, USER CENTERED EVALUA, P146 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W EFTHIMIADIS EN, 1996, UNPUB INTERACTIVE QU EFTHIMIADIS EN, 1994, QUERY EXPANSION EXPT, P279 EFTHIMIADIS EN, 1990, J DOC, V46, P218, DOI 10.1108/eb026861 EFTHIMIADIS EN, 1992, APPROACHES SEARCH FO EKMEKCIOGLU FC, 1992, J INFORM SCI, V18, P139 ELKALIFA ES, 1991, THESIS U MICROFILMS EVANS DA, 1995, CLARIT TREC EXPT INF, V31, P385 Fenichel C.H., 1980, LIBR RES, V2, P107 FIDEL R, 1991, J AM SOC INFORM SCI, V42, P501, DOI 10.1002/(SICI)1097-4571(199108)42:7<501::AID-ASI5>3.0.CO;2-V FIDEL R, 1995, INFORM PROCESS MANAG, V31, P15, DOI 10.1016/0306-4573(94)E0004-L FIDEL R, 1985, ONLINE REV, V9, P61, DOI 10.1108/eb024176 FIDEL R, 1991, J AM SOC INFORM SCI, V42, P515, DOI 10.1002/(SICI)1097-4571(199108)42:7<515::AID-ASI6>3.0.CO;2-F FIDEL R, 1986, J AM SOC INFORM SCI, V37, P37, DOI 10.1002/asi.4630370107 FOX EA, 1993, DEV MODERN OPAC REVT, P248 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 Fox E. A., 1980, SIGIR Forum, V15 Fox E. A., 1988, Second Conference on Applied Natural Language Processing FRAKES WB, 1984, P 3 JOINT BCS ACM S FRAKES WR, 1992, INFORMATION RETRIEVA Freund G. E., 1982, Information Technology: Research and Development, V1 FRITH AR, 1993, EFFECTIVENESS SIMILA, V1, P155 FROEHLICH TJ, 1994, J AM SOC INFORM SCI, V45, P124, DOI 10.1002/(SICI)1097-4571(199404)45:3<124::AID-ASI2>3.0.CO;2-8 GALLANT SI, 1994, FEEDBACK MIXING EXPT, P101 Gauch S., 1989, Proceedings of the Annual AI Systems in Government Conference (IEEE Cat. No.89CH2715-1), DOI 10.1109/AISIG.1989.47306 GAUCH S, 1991, ACM T INFORM SYST, V9, P249, DOI 10.1145/125187.125193 GAUCH S, 1993, J AM SOC INFORM SCI, V44, P124, DOI 10.1002/(SICI)1097-4571(199304)44:3<124::AID-ASI2>3.0.CO;2-C GOLDSMITH G, 1986, 41 LIBR INF RES GREFENSTETTE G, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P89 GUNTZER U, 1989, INFORM PROCESS MANAG, V25, P265, DOI 10.1016/0306-4573(89)90044-7 HAINES D, 1993, RELEVANCE FEEDBACK I, P2 HANCOCKBEAULIEU M, 1992, J DOC, V48, P406, DOI 10.1108/eb026906 Harbourt A M, 1993, Proc Annu Symp Comput Appl Med Care, P720 Harman D., 1992, 15TH P ANN INT ACM S, P1 Harman D., 1988, 11th International Conference on Research and Development in Information Retrieval HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P HARMAN DK, 1994, 500215 NIST SPEC PUB HARMAN DK, 1993, 500207 NIST SPEC PUB HARMAN DK, 1995, 500225 NIST SPEC PUB HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Harper D.J., 1980, RELEVANCE FEEDBACK D HARTER SP, 1985, ONLINE REV, V9, P407, DOI 10.1108/eb024194 HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q HARTER SP, 1986, ONLINE INFORMAITON R Hartley R. J., 1990, ONLINE SEARCHING PRI HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 HSIEHYEE I, 1993, J AM SOC INFORM SCI, V44, P161, DOI 10.1002/(SICI)1097-4571(199304)44:3<161::AID-ASI5>3.0.CO;2-8 HUMPHREY SM, 1994, CHALLENGES INDEXING, P161 HUMPHREY SM, 1992, ARTIF INTELL MED, V4, P343, DOI 10.1016/0933-3657(92)90020-P Ide E., 1971, SMART RETRIEVAL SYST, P337 INGWERSEN P, 1992, INFORMAITON RETRIEVA INGWERSEN P, 1984, ONLINE REV, V8, P465, DOI 10.1108/eb024167 JACHNA JS, 1993, B MED LIBR ASSOC, V81, P20 JACKSON DM, 1970, INFORM STORAGE RET, V6, P187, DOI 10.1016/0020-0271(70)90061-6 JARVELIN K, 1995, DEDUCTIVE DATA MODEL JING YF, 1994, RIAO 94 CONFERENCE PROCEEDINGS - INTELLIGENT MULTIMEDIA INFORMATION RETRIEVAL SYSTEMS AND MANAGEMENT, P146 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1971, AUTOMATIC KEYWORD CL Jones K. S., 1970, Information Storage and Retrieval, V5, DOI 10.1016/0020-0271(70)90046-X Jones K. S., 1973, Journal of the American Society for Information Sciences, V24, DOI 10.1002/asi.4630240502 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 1973, INFORM STORAGE RET, V9, P339, DOI 10.1016/0020-0271(73)90073-9 JONES KS, 1980, J INFORM SCI, V1, P325, DOI 10.1177/016555157900100603 JONES KS, 1971, J AM SOC INFORM SCI, V22, P166, DOI 10.1002/asi.4630220305 JONES KS, 1977, RES AUTOMATIC INDEXI JONES S, 1993, J INFORM SCI, V19, P167, DOI 10.1177/016555159301900301 JONES S, 1995, J AM SOC INFORM SCI, V46, P52, DOI 10.1002/(SICI)1097-4571(199501)46:1<52::AID-ASI6>3.0.CO;2-1 JUNG GS, 1990, WORK NOT AAAI S TEXT, P123 KANTOR PB, 1994, ANNU REV INFORM SCI, V29, P53 KINGLAND LC, 1993, B MED LIBR ASSOC, V81, P178 KINGSLAND LC, 1992, MEDINFO 92 P 7 WORLD, V1, P382 KOENEMANN J, 1995, NEW TOOLS OLD HABITS, P145 KORFHAGE R, 1993, SIGIR 93 P ASS COMP KRAFT DH, 1995, FUZZINESS DATABASE M, P330 KRISTENSEN J, 1993, INFORM PROCESS MANAG, V29, P733, DOI 10.1016/0306-4573(93)90102-J KRISTENSEN J, 1990, INT CLASSIF, V17, P77 Krovetz R, 1993, VIEWING MORPHOLOGY I, P191 KROVETZ R, 1989, SIGIR 89 P ASS COMP, P127 KWOK KL, 1993, RETRIEVAL EXPT LARGE, P153 LENNON M, 1981, J INFORM SCI, V3, P177, DOI 10.1177/016555158100300403 LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 Lewis D., 1990, P 13 ANN INT ACM SIG, P385, DOI 10.1145/96749.98244 LU X, 1995, QUERY EXPANSION REDU, P231 Markey K., 1981, ONTAP ONLINE TRAININ MARTIN WA, 1982, P 6 INT ONL INF M 19, P67 MCGILL M, 1979, EVALUATION FACTORS A MCMATH CF, 1989, INT J MAN MACH STUD, V31, P121, DOI 10.1016/0020-7373(89)90024-2 MEADOW CT, 1981, BASICS ONLINE SEARCH MEGHINI C, 1993, MODEL INFORMAITON RE, P298 MERZ RB, 1993, P 16 ANN S COMP APPL, P498 Miller G A, 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 Nahl D, 1996, J AM SOC INFORM SCI, V47, P276 Nutter J. T., 1990, Literary & Linguistic Computing, V5, DOI 10.1093/llc/5.2.129 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 OLDROYD BK, 1982, ONLINE REV, V6, P127, DOI 10.1108/eb024094 OLDROYD BK, 1984, ONLINE REV, V8, P233, DOI 10.1108/eb024156 OLDROYD BK, 1977, ONLINE REV, V1, P295, DOI 10.1108/eb023957 PAICE CD, 1991, INFORM PROCESS MANAG, V27, P433, DOI 10.1016/0306-4573(91)90061-P PALMQUIST RA, 1988, ASIS 88 INF TECHN PL, V25 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PETRY FE, 1993, P ASIS ANNU MEET, V30, P122 PIETILAINEN P, 1983, INFORM PROCESS MANAG, V19, P51, DOI 10.1016/0306-4573(83)90035-3 PITERNICK AB, 1984, ONLINE REV, V8, P441, DOI 10.1108/eb024165 POLLITT A, 1981, P 5 INT ONL INF M 19, P25 POLLITT AS, 1994, J INFORM SCI, V20, P413, DOI 10.1177/016555159402000604 POLLITT AS, 1988, 12TH P INT ONL INF M, V2, P445 POLLITT S, 1987, INFORM PROCESS MANAG, V23, P119, DOI 10.1016/0306-4573(87)90053-7 POPOVIC M, 1992, J AM SOC INFORM SCI, V43, P384, DOI 10.1002/(SICI)1097-4571(199206)43:5<384::AID-ASI6>3.0.CO;2-L PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Porter M. F., 1982, Information Technology: Research and Development, V1 PREECE SE, 1980, ONLINE REV, V4, P375, DOI 10.1108/eb024043 QIU YG, 1994, IMPROVING RETRIEVAL QIU YG, 1993, CONCEPT BASED QUERY, P160 RAGHAVAN V, 1989, PHASE DIAGRAMS TER 3, P111 Raghavan V. V., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320071.320081 Rees AM, 1967, FIELD EXPT APPROACH, VI RO JS, 1988, J AM SOC INFORM SCI, V39, P147, DOI 10.1002/(SICI)1097-4571(198805)39:3<147::AID-ASI1>3.0.CO;2-O RO JS, 1988, J AM SOC INFORM SCI, V39, P73, DOI 10.1002/(SICI)1097-4571(198803)39:2<73::AID-ASI1>3.0.CO;2-X Robertson A. M., 1993, Literary & Linguistic Computing, V8, DOI 10.1093/llc/8.3.143 ROBERTSON AM, 1992, P ASS COMPUTING MACH, P256, DOI 10.1145/133160.133208 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1983, FRONT END IR EXPT FI Robertson SE, 1995, OV 3 TEXT RETR C TRE, P109 ROBERTSON SE, 1994, OKAPI TREC2, P21 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROGERS HJ, 1991, J DOC, V47, P333, DOI 10.1108/eb026883 SAGER WKH, 1976, INT FORUM INFORM DOC, V1, P12 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1988, P 11 ANN INT ACM SIG, p147 , DOI 10.1145/62437.62447 SALTON G, 1972, INFORM STORAGE RET, V8, P349, DOI 10.1016/0020-0271(72)90022-8 SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1980, INFORM PROCESS MANAG, V16, P1, DOI 10.1016/0306-4573(80)90002-3 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P177, DOI 10.1002/(SICI)1097-4571(198805)39:3<177::AID-ASI3>3.0.CO;2-F SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 SCHRODER JJ, 1983, ONLINE REV, V7, P229, DOI 10.1108/eb024129 SCHWARTZ C, 1978, INFORMATION AGE PERS, V15, P291 SHOVAL P, 1985, INFORM PROCESS MANAG, V21, P475, DOI 10.1016/0306-4573(85)90002-0 SHOVAL P, 1986, INT J MAN MACH STUD, V24, P125, DOI 10.1016/S0020-7373(86)80044-X SHUTE SJ, 1993, INFORM PROCESS MANAG, V29, P29, DOI 10.1016/0306-4573(93)90021-5 SIEGFRIED S, 1993, J AM SOC INFORM SCI, V44, P273, DOI 10.1002/(SICI)1097-4571(199306)44:5<273::AID-ASI3>3.0.CO;2-X SIEVERT M, 1983, ONLINE REV, V7, P489, DOI 10.1108/eb024144 SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 SMEATON AF, 1982, THESIS U COLL DUBL D SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Smeaton Alan F., 1981, P 4 INT C INF STOR R, P83, DOI 10.1145/511754.511767 Smith J. B., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42027 SMITH PJ, 1989, 12TH P ANN INT ACMSI, P3 Sparck Jones K., 1973, INFORMATION STORAGE, V9, P499, DOI 10.1016/0020-0271(73)90036-3 SPINK A, 1994, SIGIR 94 P ASS COMP STRZALKOWSKI T, 1994, RECENT DEV NATURAL L, P123 STRZALKOWSKI T, 1993, NATURAL LANGUAGE PRO, P173 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 TENOPIR C, 1991, LIBR INFORM SCI RES, V13, P237 Thompson P., 1995, Text REtrieval Conference (TREC-3) (NIST SP 500-225) THOMPSON RH, 1989, INT J MAN MACH STUD, V30, P639, DOI 10.1016/S0020-7373(89)80014-8 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 VICKERY A, 1988, P 12 INT ONL M, V1, P301 VICKERY A, 1987, J DOC, V43, P1, DOI 10.1108/eb026798 VICKERY B, 1992, J DOC, V48, P255, DOI 10.1108/eb026897 Voorhees E. M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval VOORHEES EM, 1994, EXPANDING QUERY VECT, P223 VOORHEES EM, 1993, VECTOR EXPANSION LAR, P343 WADE SJ, 1988, PROGRAM-AUTOM LIBR, V22, P44, DOI 10.1108/eb046986 WALKER S, 1991, OPACS USER P 3 ANGL, P23 WALKER S, 1987, IMPROVING SUBJECT RE Walker S., 1990, Prospects for Intelligent Retrieval. Informatics 10. Proceedings of a Conference WALKER S, 1989, ONLINE CATALOGUE DEV, P84 WALKER S, 1990, IMPROVING SUBJECT RE White H. D., 1989, Online Information 89. 13th International Online Information Meeting Proceedings WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 WILLIAMS PW, 1984, P 8 INT ONL INF M 19, P139 WILLIAMS PW, 1985, P 9 INT ONL INF M 19, P23 WONG SKM, 1993, COMPUTATION TERM ASS, P107 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 WONG SKM, 1993, J AM SOC INFORM SCI, V44, P431, DOI 10.1002/(SICI)1097-4571(199309)44:8<431::AID-ASI1>3.0.CO;2-V WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 YIP MK, 1981, EXPERT SYSTEM DOCUME YU CT, 1993, INFORMATION SYSTEMS, V44, P431 YU CT, 1977, J AM SOC INFORM SCI, V28, P345, DOI 10.1002/asi.4630280607 YU CT, 1974, INFORM STORAGE RET, V10, P243, DOI 10.1016/0020-0271(74)90019-9 YU CT, 1975, J ACM, V22, P17, DOI 10.1145/321864.321867 NR 255 TC 140 Z9 141 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1996 VL 31 BP 121 EP 187 PG 67 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VU798 UT WOS:A1996VU79800007 ER PT J AU Ellis, D AF Ellis, D TI The dilemma of measurement in information retrieval research SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Review ID SUBJECT SEARCHING BEHAVIOR; QUANTITATIVE ASPECTS; COGNITIVE MODELS; LIBRARY CATALOG; ONLINE CATALOG; SYSTEM-DESIGN; SCIENCE; RELEVANCE; FOUNDATIONS; USER AB The problem of measurement in information retrieval research is traced to its source in the first retrieval tests. The problem is seen as presenting a chronic dilemma for the field. This dilemma has taken three forms as the discipline has evolved: (1) The dilemma of measurement in the archetypal approach: Stated relevance versus user relevance; (2) the dilemma of measurement in the probabilistic approach: Realism versus formalism; and (3) the dilemma of measurement in the Information Retrieval-Expert System (IR-ES) approach: Linear measures of relevance versus logarithmic measures of knowledge. It is argued that the dilemma of measurement has remained intractable even given the different assumptions of the different approaches for three connected reasons-the nature of the subject matter of the field; the nature of relevance judgment; and the nature of cognition and knowledge. Finally, it is concluded that the original vision of information retrieval research as a discipline founded on quantification proved restricting for its theoretical and methodological development and that increasing recognition of this is reflected in growing interest in qualitative methods in information retrieval research in relation to the cognitive, behavioral, and affective aspects of the information retrieval interaction. RP Ellis, D (reprint author), UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ALLEN B, 1991, LIBR QUART, V61, P188 ALLEN B, 1990, INFORM PROCESS MANAG, V26, P535, DOI 10.1016/0306-4573(90)90073-B ALLEN BL, 1991, ANNU REV INFORM SCI, V26, P3 BATES MJ, 1990, INFORM PROCESS MANAG, V26, P575, DOI 10.1016/0306-4573(90)90103-9 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 Belkin N. J, 1980, THEORY APPLICATION I, P187 BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 BELKIN NJ, 1984, SOC SCI INFORM STUD, V4, P111, DOI 10.1016/0143-6236(84)90070-X BELKIN NJ, 1979, INFORMATICS, V5, P227 BELKIN NJ, 1990, J INFORM SCI, V16, P11, DOI 10.1177/016555159001600104 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BELKIN NJ, 1978, J DOC, V34, P55, DOI 10.1108/eb026653 BELKIN NJ, 1993, INFORM PROCESS MANAG, V29, P325, DOI 10.1016/0306-4573(93)90059-M Blair D. C., 1990, LANGUAGE REPRESENTAT BORGMAN CL, 1986, INT J MAN MACH STUD, V24, P47, DOI 10.1016/S0020-7373(86)80039-6 Brookes B. C., 1975, INFORMATICS, V2, P42 BROOKES BC, 1981, J INFORM SCI, V3, P3, DOI 10.1177/016555158100300102 BROOKES BC, 1980, J INFORM SCI, V2, P209, DOI 10.1177/016555158000200502 BROOKES BC, 1975, PROBLEMS INFORMATION, P115 BROOKES BC, 1980, J INFORM SCI, V2, P269, DOI 10.1177/016555158000200602 BROOKES BC, 1980, J INFORM SCI, V2, P125, DOI 10.1177/016555158000200302 BROOKES BC, 1977, INT WORKSHOP COGNITI, P195 BROOKES BC, 1981, INFORMATION RETRIEVA, P1 CHEN HC, 1991, INFORM PROCESS MANAG, V27, P405, DOI 10.1016/0306-4573(91)90060-Y CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 Cleverdon C. W., 1962, REPORT TESTING ANAL CLEVERDON CW, 1970, ASLIB P, V22, P538, DOI 10.1108/eb050265 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 CUADRA CA, 1967, J DOC, V23, P291, DOI 10.1108/eb026436 CUADRA CA, 1967, TM352000100 SYST DEV, V1 DANIELS PJ, 1986, J DOC, V42, P272, DOI 10.1108/eb026797 De Mey M, 1982, COGNITIVE PARADIGM DOYLE LB, 1963, 26TH P AM DOC I ANN, P199 ELLIS D, 1994, ENCY LIBRARY INFORMA, V54, P275 ELLIS D, 1990, NEW HORIZONS INFORMA ELLIS D, 1994, ENCY MICROCOMPUTES, V13, P57 ELLIS D, 1984, J INFORM SCI, V8, P25, DOI 10.1177/016555158400800105 ELLIS D, 1989, J INFORM SCI, V15, P237, DOI 10.1177/102831538901500406 ELLIS D, 1984, SOC SCI INFORM STUD, V4, P261, DOI 10.1016/0143-6236(84)90002-4 ELLIS D, 1993, LIBR QUART, V63, P469 ELLIS D, 1993, J DOC, V49, P356, DOI 10.1108/eb026919 ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 FIDEL R, 1984, J AM SOC INFORM SCI, V35, P211, DOI 10.1002/asi.4630350404 FIDEL R, 1985, ONLINE REV, V9, P61, DOI 10.1108/eb024176 FIDEL R, 1993, LIBR INFORM SCI RES, V15, P219 FROHMANN B, 1994, LIBR INFORM SCI RES, V16, P119, DOI 10.1016/0740-8188(94)90004-3 GULL CD, 1956, AM DOC, V7, P320, DOI 10.1002/asi.5090070408 HANCOCK M, 1987, J DOC, V43, P303, DOI 10.1108/eb026813 HANCOCKBEAULIEU M, 1990, J DOC, V46, P318, DOI 10.1108/eb026863 HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q HEINE MD, 1981, INFORMATION RETRIEVA, P179 HJORLAND B, 1988, BEHAV SOC SCI LIBR, V6, P39, DOI 10.1300/J103v06n03_03 HOLLNAGEL E, 1987, KNOWLEDGE ENG INGWERSEN P, 1992, INFORMATION RETRIEVA INGWERSEN P, 1982, J DOC, V38, P165, DOI 10.1108/eb026727 INGWERSEN P, 1984, SOC SCI INFORM STUD, V4, P83, DOI 10.1016/0143-6236(84)90068-1 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 JONES KS, 1975, 5266 U CAMBR COMP LA JONES KS, 1981, INFORMATION RETRIEVA, P256 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 Keen M., 1966, FACTORS DETERMINING, V2 Keen M., 1966, FACTORS DETERMINING, V1 KUHLTHAU CC, 1993, J DOC, V49, P339, DOI 10.1108/eb026918 KUHLTHAU CC, 1988, RQ, V28, P232 KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-# LESK ME, 1968, INFORM STORAGE RET, V4, P343, DOI 10.1016/0020-0271(68)90029-6 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mey de M., 1980, THEORY APPL INFORMAT, P48 Neill S. D., 1992, DILEMMAS STUDY INFOR ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 Oddy R. N., 1977, Journal of Informatics, V1 ODDY RN, 1981, INFORMATION RETRIEVA, P156 PARK TK, 1993, LIBR QUART, V63, P318 PARK TK, 1994, J AM SOC INFORM SCI, V45, P135, DOI 10.1002/(SICI)1097-4571(199404)45:3<135::AID-ASI3>3.0.CO;2-1 PEJTERSEN AM, 1984, SOC SCI INFORM STUD, V4, P167, DOI 10.1016/0143-6236(84)90074-7 Putnam Hilary, 1978, MEANING MORAL SCI ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1979, ANAL MEANING INFORMA, V5, P202 Robertson SE, 1994, 50 YEARS INFORMATION, P119 Robertson S.E., 1981, INFORMATION RETRIEVA, P9 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SARACEVIC T, 1987, INFORM PROCESS MANAG, V23, pR1, DOI 10.1016/0306-4573(87)90108-7 SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C SHARP JR, 1982, J DOC, V38, P238 Sparck Jones K., 1981, INFORM RETRIEVAL EXP, P213 SUTTON SA, 1994, J AM SOC INFORM SCI, V45, P186, DOI 10.1002/(SICI)1097-4571(199404)45:3<186::AID-ASI8>3.0.CO;2-F SWANSON DR, 1986, LIBR QUART, V56, P389 THORNE RG, 1955, J DOC, V11, P130 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORMATION RETRIEVA, P3 WATERWORTH JA, 1989, HYPERMEDIA, V3, P35 WESTBROOK L, 1993, RQ, V32, P541 Wilkins L., 1964, SOCIAL DEVIANCE WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702 Wilson T.D., 1994, 50 YEARS INFORM PROG, P15 Wittgenstein L, 1967, ZETTEL NR 102 TC 44 Z9 42 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1996 VL 47 IS 1 BP 23 EP 36 DI 10.1002/(SICI)1097-4571(199601)47:1<23::AID-ASI3>3.0.CO;2-4 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TK884 UT WOS:A1996TK88400003 ER PT B AU Bloedorn, E Mani, I MacMillan, TR AF Bloedorn, E Mani, I MacMillan, TR GP AMER ASSOC ARTIFICIAL INTELLIGENCE AMER ASSOC ARTIFICIAL INTELLIGENCE TI Machine learning of user profiles: Representational issues SO PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2 LA English DT Proceedings Paper CT 13th National Conference on Artificial Intelligence (AAAI 96) / 8th Conference on Innovative Applications of Artificial Intelligence (IAAI 96) CY AUG 04-08, 1996 CL PORTLAND, OR SP Amer Assoc Artificial Intelligence ID INFORMATION AB As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research described here focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem. C1 Mitre Corp, Artificial Intelligence Tech Ctr, Mclean, VA 22102 USA. RP Bloedorn, E (reprint author), Mitre Corp, Artificial Intelligence Tech Ctr, Z40,11820 Dolley Madison Blvd, Mclean, VA 22102 USA. CR ARMSTRONG R, 1995, P 1995 AAAI SPRING S BELEW R, 1989, ACM SIGIR, P11 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bloedorn E., 1993, Proceedings of the Second International Workshop on Multistrategy Learning (MSL-93) BROGLIO J, 1993, P HUM LANG TECHN WOR BUCKLEY C, 1994, EFFECT ADDING RELEVA EVANS DA, 1991, MED DECIS MAKING, V11, pS108 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 HAINES D, 1993, RELEVANCE FEEDBACK I HARMAN D, 1992, RELEVANCE FEEDBACK R HARMAN D, 1994, OVERVIEW 3 TEXT RETR HEARST M, 1992, P ACL SIGLEX WORKSH JONES S, 1995, J AM SOC INFORM SCI, V46, P52, DOI 10.1002/(SICI)1097-4571(199501)46:1<52::AID-ASI6>3.0.CO;2-1 Lang K., 1995, P 12 INT C MACH LEAR, P331 LASHKARI Y, 1994, P 13 NAT C ART INT LIDDY E, 1992, P AAAI FALL S SER PR LIDDY E, 1992, P 1 TEXT RETR C NATL MAES P, 1994, COMMUN ACM, V37, P31 MANI I, 1995, CORPUS PROCESSING LE MENCZER F, 1994, P CIKM 94 WORKSH INT Miller G A, 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 PAZZANI M, 1995, P TOOLS AI C QUINLAN J, 1995, COMMUNICATION Quinlan J.R., 1992, C4 5 PROGRAMS MACHIN ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1990, J AM SOC INFORM SCI, P88 SALTON G, 1983, INTRO MODERN INFORMA Schutze H., 1995, COMP CLASSIFIERS DOC WNEK J, 1995, ML954 G MAS U MACH L NR 30 TC 6 Z9 6 PU AMER ASSOC ARTIFICIAL INTELLIGENCE PI MENLO PK PA 445 BURGESS DR, MENLO PK, CA 94025 USA BN 0-262-51091-X PY 1996 BP 433 EP 438 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BN59H UT WOS:000082323300065 ER PT J AU KWOK, KL AF KWOK, KL TI A NETWORK APPROACH TO PROBABILISTIC INFORMATION-RETRIEVAL SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE EXPERIMENTATION; ARTIFICIAL NEURAL NETWORKS; DOCUMENT-FOCUSED AND QUERY-FOCUSED RELEVANCE FEEDBACK; INDEXING AND RETRIEVAL; ITEM SELF-LEARNING LEANING; PROBABILISTIC INDEXING; PROBABILISTIC RETRIEVAL; QUERY EXPANSION; TRAINING ID DOCUMENT-RETRIEVAL; RANKING PRINCIPLE; SYSTEM; MODEL; COMPONENTS; FEEDBACK; TERMS AB In this article we show how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with four standard small collections and a large Wall Street Journal collection (173,219 documents) show that performance of feedback improves substantially over no feedback, and further gains are obtained when queries are expanded with terms from the feedback documents. The effect is much more pronounced in small collections than in the large collection. Query expansion may be considered as a tool for both precision and recall enhancement. In particular, small query expansion levels of about 30 terms can achieve most of-the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at-the high-recall low-precision region of a precision recall curve. RP KWOK, KL (reprint author), CUNY QUEENS COLL,DEPT COMP SCI,FLUSHING,NY 11367, USA. CR BEACMAN RJ, 1988, 11TH P INT C RD IR G, P161 BEIN J, 1988, CUCS40588 U COL DEP BOOKSTEIN A, 1981, IR RES BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Brachman R. J., 1988, 11th International Conference on Research and Development in Information Retrieval BRAUEN RL, 1971, SMART SYSTEM EXPT AU, P456 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CATER SC, 1989, INFORM PROCESS MANAG, V25, P15, DOI 10.1016/0306-4573(89)90088-5 COOPER WS, 1991, 14 ANN INT ACM SIGIR, P57 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 GALLANT SI, 1991, JUL AAAI 91 NAT LANG HAINES D, 1993, 16TH P ACM SIGIR ANN, P1 Harman D., 1992, 15TH P ANN INT ACM S, P1 HARMAN DK, 1993, 1ST TEXT RETR C TREC JONES KS, 1972, J DOC, V8, P11 Kohonen T., 1988, SELF ORG ASS MEMORY, V2nd KWOK KL, 1988, INFORM PROCESS MANAG, V24, P405, DOI 10.1016/0306-4573(88)90044-1 KWOK KL, 1989, 12TH P ANN INT ACM S, P21 KWOK KL, 1990, P INT JOINT C NEUR N, V2, P623 KWOK KL, 1993, 1ST TEXT RETR C TREC KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V11, P939, DOI 10.1109/TSE.1985.232827 MOZERMC, 1984, ICS8406 U CAL TECH R RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSE DE, 1991, INT J MAN MACH STUD, V35, P1, DOI 10.1016/S0020-7373(07)80006-X Rumelhart DE, 1986, PARALLEL DISTRIBUTED, V1 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SMITH PJ, 1989, ACM T INFORM SYST, V7, P246, DOI 10.1145/65943.65947 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1989, 12TH ACM SIGIR ANN I, P77 WONG SKM, 1991, J AM SOC INFORM SCI, V42, P723, DOI 10.1002/(SICI)1097-4571(199112)42:10<723::AID-ASI5>3.0.CO;2-U WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 45 TC 19 Z9 19 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 1995 VL 13 IS 3 BP 324 EP 353 DI 10.1145/203052.203067 PG 30 WC Computer Science, Information Systems SC Computer Science GA RM667 UT WOS:A1995RM66700005 ER PT J AU SHAW, WM AF SHAW, WM TI TERM-RELEVANCE COMPUTATIONS AND PERFECT RETRIEVAL PERFORMANCE SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID QUERY EXPANSION; SEARCH TERMS; FEEDBACK; SYSTEM AB Computing formulas for binary independent (BI) term relevance weights are evaluated as a function of query representations and retrieval expectations in the CF database. Query representations consist of the limited set of terms appearing in each query statement and the complete set of terms appearing in the database. Retrieval expectations include comprehensive searches, for which many relevant documents are sought, and specific searches, for which only a few documents have merit. Conventional computing equations, which are known to over estimate term relevance weights, are shown to produce mediocre results for all combinations of query representations and retrieval expectations. Modified computing equations, which do not over estimate relevance weights, produce essentially perfect retrieval results for both comprehensive and specific searches, when the query representation is complete. Probabilistic retrieval, based on BI assumptions and applied to simple subject descriptions of documents and queries, can retrieve all relevant documents and only relevant documents, when term relevance weights are computed accurately. RP SHAW, WM (reprint author), UNIV N CAROLINA,SCH INFORMAT & LIB SCI,MEANNING HALL,CHAPEL HILL,NC 27599, USA. CR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cox D, 1970, ANAL BINARY DATA ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 SHAW WM, 1994, INFORM PROCESS MANAG, V30, P711, DOI 10.1016/0306-4573(94)90079-5 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 14 TC 16 Z9 16 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 1995 VL 31 IS 4 BP 491 EP 498 DI 10.1016/0306-4573(95)00011-5 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RY937 UT WOS:A1995RY93700003 ER PT J AU EFTHIMIADIS, EN AF EFTHIMIADIS, EN TI USER CHOICES - A NEW YARDSTICK FOR THE EVALUATION OF RANKING ALGORITHMS FOR INTERACTIVE QUERY EXPANSION SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID FULL-TEXT RETRIEVAL; CO-OCCURRENCE DATA; SEARCH TERMS; RELEVANCE; APPLICABILITY; INFORMATION; SELECTION; FEEDBACK; IMPROVE AB The performance of eight ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. This study focuses on the identification of algorithms that most effectively take cognizance of user preferences. User choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the eight ranking algorithms. This methodology introduces a user-oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system-oriented approaches. Similarities in the performance of the eight algorithms and the ways that these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, emim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full-text) environments is needed before these results can be generalized beyond the context of the present study. RP EFTHIMIADIS, EN (reprint author), UNIV CALIF LOS ANGELES,GRAD SCH EDUC & INFORMAT STUDIES,405 HILGARD AVE,LOS ANGELES,CA 90024, USA. CR Efthimiadis E., 1992, THESIS CITY U LONDON EFTHIMIADIS EN, UNPUB INTERACTIVE QU EFTHIMIADIS EN, 1993, 16TH P ANN INT ACM S, P146 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 INGWERSEN P, 1984, ONLINE REV, V8, P465, DOI 10.1108/eb024167 Jones K. S., 1988, 11th International Conference on Research and Development in Information Retrieval JONES KS, 1971, AUTOMATIC KEYWORD CL Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 Lehmann E., 1975, NONPARAMETRICS MARTIN WA, 1982, 6 INT ONL M LOND 7 9, P67 MCGILL M, 1979, EVALUATION FACTORS A PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 RO JS, 1988, J AM SOC INFORM SCI, V39, P147, DOI 10.1002/(SICI)1097-4571(198805)39:3<147::AID-ASI1>3.0.CO;2-O RO JS, 1988, J AM SOC INFORM SCI, V39, P73, DOI 10.1002/(SICI)1097-4571(198803)39:2<73::AID-ASI1>3.0.CO;2-X ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGER WKH, 1976, INT FORUM INFORM DOC, V1, P12 SALTON G, 1975, DYNAMIC INFORMATION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 NR 24 TC 21 Z9 22 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 1995 VL 31 IS 4 BP 605 EP 620 DI 10.1016/0306-4573(95)00070-W PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RY937 UT WOS:A1995RY93700011 ER PT J AU ROBERTSON, SE WALKER, S HANCOCKBEAULIEU, MM AF ROBERTSON, SE WALKER, S HANCOCKBEAULIEU, MM TI LARGE TEST COLLECTION EXPERIMENTS ON AN OPERATIONAL, INTERACTIVE SYSTEM - OKAPI AT TREC SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID RELEVANCE AB The Okapi system has been used in a series of experiments on the TREC collections, investigating probabilistic models, relevance feedback, and query expansion, and interaction issues. Some new probabilistic models have been developed, resulting in simple weighting functions that take account of document length and within-document and within-query term frequency. All have been shown to be beneficial. Relevance feedback and query expansion are highly beneficial when based on large quantities of relevance data (as in the routing task). Interaction issues are much more difficult to evaluate in the TREC framework, and no benefits have yet been demonstrated from feedback based on small numbers of ''relevant'' items identified by intermediary searchers. RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,CTR INTERACT SYST RES,DEPT INFORMAT SCI,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. CR Cooper Wm. S., 1993, Text REtrieval Conference (TREC-1) (NIST-SP-500-207) CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARMAN D, 1992, SIGIR 92, P280 HARMAN DK, 1994, 2ND TEXT RETR C TREC HARMAN DK, 1993, 1ST TEXT RETR C TREC HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 Jones K., 1977, REPORT DESIGN STUDY KEEN EM, 1991, J DOC, V47, P1, DOI 10.1108/eb026869 MITEV NN, 1985, LIBRARY INFORMATION, V39 Robertson S., 1994, TEXT RETRIEVAL C, P21 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 Robertson S. E., 1993, Text REtrieval Conference (TREC-1) (NIST-SP-500-207) ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1994, J DOC, V50, P233, DOI 10.1108/eb026932 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WALKER S, 1990, 72 BRIT LIBR RES PAP WALKER S, 1991, 6056 BRIT LIBR RES R Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval WALKER S, 1987, 24 BRIT LIBR RES PAP NR 20 TC 39 Z9 39 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY-JUN PY 1995 VL 31 IS 3 BP 345 EP 360 DI 10.1016/0306-4573(94)00051-4 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QZ920 UT WOS:A1995QZ92000006 ER PT J AU CHEN, HC AF CHEN, HC TI MACHINE LEARNING FOR INFORMATION-RETRIEVAL - NEURAL NETWORKS, SYMBOLIC LEARNING, AND GENETIC ALGORITHMS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Review ID DOCUMENT-RETRIEVAL; ARTIFICIAL-INTELLIGENCE; EXPERT SYSTEMS; CONNECTIONIST; DESIGN; SEARCH; DATABASES; MODELS AB Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to ''intelligent'' information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution-based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques. RP CHEN, HC (reprint author), UNIV ARIZONA,KARL ELLER GRAD SCH MANAGEMENT,DEPT MANAGEMENT INFORMAT SYST,MCCLELLAND HALL 430Z,TUCSON,AZ 85721, USA. CR Dalton J., 1991, IEEE Potentials, V10, DOI 10.1109/45.84097 APPELT D, 1985, USER MODELLING PANEL, P1298 BELEW RK, 1989, 12TH P ANN INT ACM S, P11 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BLOSSEVILLE MJ, 1992, 15TH P ANN INT ACM S, P51 Booker L. B., 1990, MACHINE LEARNING PAR, P235 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BORGIDA A, 1985, 11 INT C VER LARG DA, P72 Brajnik G., 1988, 11th International Conference on Research and Development in Information Retrieval Brauen T., 1971, SMART RETRIEVAL SYST, P456 Breiman L., 1984, CLASSIFICATION REGRE BUCKLAND MK, 1991, J AM SOC INFORM SCI, V42, P635, DOI 10.1002/(SICI)1097-4571(199110)42:9<635::AID-ASI2>3.0.CO;2-L Cai Y., 1991, Knowledge discovery in databases Carbonell J, 1983, MACHINE LEARNING ART, P3 CHEN H, 1993, GANNET INFORMATION R CHEN H, 1994, COMMUN ACM, V37, P56, DOI 10.1145/194313.194322 CHEN H, 1987, 6TH P NAT C ART INT, P285 CHEN H, IN PRESS J AM SOC IN CHEN H, IN PRESS IEEE EXPERT CHEN H, 1994, 27TH P ANN HAW INT C CHEN HC, 1991, INFORM PROCESS MANAG, V27, P405, DOI 10.1016/0306-4573(91)90060-Y CHEN HC, 1990, INT J MAN MACH STUD, V32, P673, DOI 10.1016/S0020-7373(05)80107-5 CHEN HC, 1993, IEEE EXPERT, V8, P25, DOI 10.1109/64.207426 CHEN HC, 1992, IEEE T SYST MAN CYB, V22, P885, DOI 10.1109/21.179830 CHIARAMELLA Y, 1987, INFORM PROCESS MANAG, V23, P285, DOI 10.1016/0306-4573(87)90019-7 COHEN PR, 1987, INFORM PROCESS MANAG, V23, P255, DOI 10.1016/0306-4573(87)90017-3 Crawford S. L., 1992, Statistics and Computing, V2, DOI 10.1007/BF01889586 CRAWFORD SL, 1991, 8 INT WORKSH MACH LE, P245 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 DANIELS PJ, 1986, INTELLIGENT INFORMAT DERTHICK M, 1988, THESIS CARNEGIE MELL DOSZKOCS TE, 1990, ANNU REV INFORM SCI, V25, P209 Everitt B., 1980, CLUSTER ANAL Fisher D.H., 1989, 11TH P INT JOINT C A, P788 FOGEL DB, 1994, IEEE T NEURAL NETWOR, V5, P3, DOI 10.1109/72.265956 Fogel L, 1962, IND RES, V4, P14 FOGEL LJ, 1964, ORG INTELLECT FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FREUND JE, 1971, MATH STATISTICS FRIEDER O, 1991, 14TH P ANN INT ACM S, P230 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 FUHR N, 1990, 8TH P NAT C ART INT, P789 FUHR N, 1994, ACM T INFORM SYST, V12, P92, DOI 10.1145/174608.174612 FUNG RM, 1990, PROCEEDINGS : EIGHTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P762 GALLANT SI, 1988, COMMUN ACM, V31, P152, DOI 10.1145/42372.42377 GOLDBERG DE, 1994, COMMUN ACM, V37, P113, DOI 10.1145/175247.175259 Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P311, DOI 10.1002/(SICI)1097-4571(199106)42:5<311::AID-ASI1>3.0.CO;2-J GREENE DP, 1992, 10TH P NAT C ART INT, P111 HALL LO, 1990, 8TH P NAT C ART INT, P783 HAN JW, 1993, IEEE T KNOWL DATA EN, V5, P29, DOI 10.1109/69.204089 HARP SA, 1989, 3RD P INT C GEN ALG HOLLAND JH, 1975, ADAPTATION NATURAL A HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V78, P2554 HUMPHREYS BL, 1989, 13TH P ANN S COMP AP Ide E., 1971, SMART RETRIEVAL SYST, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 IOANNIDIS YE, 1992, ACM T INFORM SYST, V10, P265 HAYESROTH F, 1994, COMMUN ACM, V37, P27 JONES KS, 1991, J AM SOC INFORM SCI, V42, P558, DOI 10.1002/(SICI)1097-4571(199109)42:8<558::AID-ASI4>3.0.CO;2-4 KITANO H, 1990, 8TH P NAT C AI, P789 KNIGHT K, 1990, COMMUN ACM, V33, P59 Kohonen T, 1989, SELF ORG ASS MEMORY Koza J, 1992, GENETIC PROGRAMMING KWOK KL, 1989, 12TH P ANN INT ACM S, P21 LEBOWITZ M, 1987, MACHINE LEARNING ART, P193 Lewis D., 1992, 15 ANN INT ACM SIGIR, P37 LEWIS DD, 1991, 8TH P INT WORKSH MAC, P235 LI Q, 1989, EXPERT DATABASE SYST, P469 LIN X, 1991, 14TH P ANN INT ACM S, P262 LINDBERG DA, 1990, 14TH P ANN S COMP AP Lippmann R. P., 1987, IEEE ASSP Magazine, V4, DOI 10.1109/MASSP.1987.1165576 MACLEOD KJ, 1991, INFORM PROCESS MANAG, V27, P337, DOI 10.1016/0306-4573(91)90088-4 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARTIN BK, 1987, MED INFORM, V12, P187 MASAND B, 1992, 15 ANN INT ACM SIGIR, P59 Frawley W. J., 1991, Knowledge discovery in databases MCCRAY AT, 1990, 14TH P ANN S COMP AP MICHALEWICZ Z, 1992, GENETIC ALGORITHMS P Michalski R, 1983, MACHINE LEARNING ART, P83 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 MITCHELL TM, 1982, ARTIF INTELL, V18, P203, DOI 10.1016/0004-3702(82)90040-6 Monarch I., 1987, IEEE EXPERT SPR, P39 MONTANA DJ, 1989, 11 INT JOINT C ART I, P762 Montgomery D, 1976, DESIGN ANAL EXPT MOONEY R, 1989, 11TH IJCAI 89 INT JO, P775 PARSAYE K, 1989, INTELLIGENT DATABASE PETRY F, 1993, P ASIS ANNU MEET, P122 PIATETSKYSHAPIR.G, 1989, INT JOINT C ARTIFICI POLLITT S, 1987, INFORM PROCESS MANAG, V23, P119, DOI 10.1016/0306-4573(87)90053-7 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Quinlan J. R., 1979, Expert Systems in the Micro-Electronic Age. Proceedings of the 1979 AISB Summer School Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Quinlan J. R., 1983, MACHINE LEARNING ART, P463 Raghavan V. V., 1987, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms RAU LF, 1991, 14TH P ANN INT ACM S, P337 Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 RICH E, 1979, AUG INT JOINT C ART, P720 RICH E, 1983, INT J MAN MACH STUD, V18, P199, DOI 10.1016/S0020-7373(83)80007-8 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSE DE, 1991, INT J MAN MACH STUD, V35, P1, DOI 10.1016/S0020-7373(07)80006-X RUMELHART DE, 1986, PARALLEL DISTRIBUTED, P45 RUMELHART DE, 1994, COMMUN ACM, V37, P87, DOI 10.1145/175247.175256 Rumelhaxt D. E., 1986, PARALLEL DISTRIBUTED, P318 Salton G., 1989, AUTOMATIC TEXT PROCE SHASTRI L, 1991, PRINCIPLES SEMANTIC, P109 Simon H. A., 1991, IEEE Transactions on Knowledge and Data Engineering, V3, DOI 10.1109/69.87993 Simpson P. K., 1990, ARTIFICIAL NEURAL SY SLEEMAN D, 1985, INT J MAN MACH STUD, V23, P71, DOI 10.1016/S0020-7373(85)80025-0 SMITH PJ, 1989, ACM T INFORM SYST, V7, P246, DOI 10.1145/65943.65947 STEPP RE, 1987, MACHINE LEARNING ART, V2, P472 SWARTUT W, 1985, USER MODELLING PANEL, P1298 TANK DW, 1987, SCI AM, V257, P104 TOURETZKY DS, 1988, COGNITIVE SCI, V12, P423, DOI 10.1207/s15516709cog1203_4 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H. R., 1990, 13TH P INT C RES DEV, P1 Tzeras K., 1993, 16TH P ANN INT ACM S, P22 Utgoff P. E., 1989, Machine Learning, V4, DOI 10.1023/A:1022699900025 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VICKERY A, 1987, INFORM PROCESS MANAG, V23, P99, DOI 10.1016/0306-4573(87)90052-5 WEISS S, 1989, 11TH IJCAI 89 INT JO, P781 Weisss S. M., 1991, COMPUTER SYSTEMS LEA WIDROW B, 1994, COMMUN ACM, V37, P93, DOI 10.1145/175247.175257 WILKINSON R, 1991, 14 ANN INT ACM SIGIR, P202 Wilkinson R., 1992, Library Hi Tech, V10, DOI 10.1108/eb047846 YANG J, 1993, TEXT RETRIEVAL C GAI, P31 YANG JJ, 1993, 2 AN S DOC N INF RET, P271 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZISSOS AY, 1985, INT J MAN MACH STUD, V23, P729, DOI 10.1016/S0020-7373(85)80065-1 NR 131 TC 85 Z9 87 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD APR PY 1995 VL 46 IS 3 BP 194 EP 216 DI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QM562 UT WOS:A1995QM56200003 ER PT J AU FUNG, R DELFAVERO, B AF FUNG, R DELFAVERO, B TI APPLYING BAYESIAN NETWORKS TO INFORMATION-RETRIEVAL SO COMMUNICATIONS OF THE ACM LA English DT Article RP FUNG, R (reprint author), PREVIC INC,2817 ALMERIA ST,DAVIS,CA 95616, USA. CR CHANG KC, 1989, P 11 IJCAI DETR MI, P265 DELFAVERO BA, 1993, 2ND P TEXT RETR C TR, P151 FUNG RM, 1990, 13TH P C RES DEV INF, P455 HARMAN D, 1994, 2 TEXT RETR C TREC 2, P1 HECKERMAN DE, 1989, 5TH P WORKSH UNC ART, P162 Howard RA, 1981, READINGS PRINCIPLES, P721 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Pearl J., 1988, PROBABILISTIC REASON ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H. R., 1990, 13TH P INT C RES DEV, P1 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 13 TC 33 Z9 34 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0001-0782 J9 COMMUN ACM JI Commun. ACM PD MAR PY 1995 VL 38 IS 3 BP 42 EP & DI 10.1145/203330.203340 PG 0 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA QJ741 UT WOS:A1995QJ74100012 ER PT J AU SPINK, A AF SPINK, A TI TERM RELEVANCE FEEDBACK AND MEDIATED DATABASE SEARCHING - IMPLICATIONS FOR INFORMATION-RETRIEVAL PRACTICE AND SYSTEMS-DESIGN SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT ACM SIGIR Conference CY 1994 CL DUBLIN, IRELAND SP ACM SIGIR ID DOCUMENT-RETRIEVAL; CONTROLLED VOCABULARY; QUERY EXPANSION; FREE-TEXT; SELECTION; KEYS AB Research into both the algorithmic and human approaches to information retrieval is required to improve information retrieval system design and database searching effectiveness. This study uses the human approach to examine the sources and effectiveness of search terms selected during mediated interactive information retrieval. The study focuses on determining the retrieval effectiveness of search terms identified by users and intermediaries from retrieved items during term relevance feedback. Results show that terms selected from particular database fields of retrieved items during term relevance feedback (TRF) were more effective than search terms from the intermediary, database thesauri or users' domain knowledge during the interaction, but not as effective as terms from the users' written question statements. Implications for the design and testing of automatic relevance feedback techniques that place greater emphasis on these sources and the practice of database searching are also discussed. RP SPINK, A (reprint author), UNIV N TEXAS,SCH LIB & INFORMAT SCI,POB 13796,DENTON,TX 76203, USA. CR BATES MJ, 1993, LIBR QUART, V63, P1 BELKIN NJ, 1992, INFORMATION PROCESSI, V29, P325 Blair D. C., 1990, LANGUAGE REPRESENTAT BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cleverdon C. W., 1962, REPORT TESTING ANAL CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DUBOIS CPR, 1987, ONLINE REV, V11, P243 Efthimiadis E., 1992, THESIS CITY U LONDON EFTHIMIADIS EN, 1993, 16TH P ANN INT ACM S, V16, P146 FIDEL R, 1991, J AM SOC INFORM SCI, V42, P501, DOI 10.1002/(SICI)1097-4571(199108)42:7<501::AID-ASI5>3.0.CO;2-V FIDEL R, 1991, J AM SOC INFORM SCI, V42, P490, DOI 10.1002/(SICI)1097-4571(199108)42:7<490::AID-ASI4>3.0.CO;2-V FIDEL R, 1991, J AM SOC INFORM SCI, V42, P515, DOI 10.1002/(SICI)1097-4571(199108)42:7<515::AID-ASI6>3.0.CO;2-F HARMAN D, 1988, P ACM SIGIR C RES DE Harman D., 1992, 15TH P ANN INT ACM S, P1 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS JESUS COLLEGE Harter S. P., 1986, ONLINE INFORMATION R Ide E., 1971, SMART RETRIEVAL SYST, P337 JONES KS, 1971, AUTOMATIC KEYWORD CL KEEN EM, 1973, J DOC, V29, P1, DOI 10.1108/eb026547 LANCASTER FW, 1980, NEW TRENDS DOCUMENTA Parker J.E., 1971, PROGRAM, V5, P26, DOI 10.1108/eb046635 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SARACEVIC T, 1991, 12TH P ANN NAT ONL M, V12, P329 SPINK A, 1993, THESIS RUTGERS U SPINK A, 1994, 17TH P INT C RES DEV SPINK A, 1992, P ASIS ANNU MEET, V29, P249 SPINK A, 1993, P ASIS ANNU MEET, V30, P63 SPINK A, 1993, P ASIS ANNU MEET, V30, P115 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WALKER G, 1993, ONLINE RETRIEVAL DIA WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 NR 36 TC 27 Z9 27 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR-APR PY 1995 VL 31 IS 2 BP 161 EP 171 DI 10.1016/0306-4573(94)00043-3 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QL320 UT WOS:A1995QL32000001 ER PT J AU CRESTANI, F VANRIJSBERGEN, CJ AF CRESTANI, F VANRIJSBERGEN, CJ TI INFORMATION-RETRIEVAL BY LOGICAL IMAGING SO JOURNAL OF DOCUMENTATION LA English DT Article AB The evaluation of an implication by Imaging is a logical technique developed in the framework of modal logic. Its interpretation in the context of a 'possible worlds' semantics is very appealing for IR. In 1989, Van Rijsbergen suggested its use for solving one of the fundamental problems of logical models of IR: the evaluation of the implication d --> q (where d and q are respectively a document and a query representation). Since then, others have tried to follow that suggestion proposing models and applications, though without much success. Most of these approaches had as their basic assumption the consideration that 'a document is a possible world'. We propose instead an approach based on a completely different assumption: 'a term is a possible world'. This approach enables the exploitation of term-term relationships which are estimated using an information theoretic measure. RP CRESTANI, F (reprint author), UNIV GLASGOW,DEPT COMP SCI,GLASGOW G12 8RZ,SCOTLAND. CR AMATI G, 1992, 5B04892 FOND UG BORD BRUZA PD, 1993, THESIS KATHOLIEKE U CHIARAMELLA Y, 1992, COMPUT J, V35, P233, DOI 10.1093/comjnl/35.3.233 Cleverdon C.W., 1966, ASLIB CRANFIELD RES David Lewis, 1981, IFS CONDITIONALS BEL, P129 GARDENFORS P, 1982, J PHILOS, V79, P747, DOI 10.2307/2026039 HILLMAN DJ, 1965, 9 LEH U REP Kripke S., 1971, REFERENCE MODALITY, P63 LALMAS M, 1993, 1ST P WORKSH INC UNC, P62 Lewis D., 1986, COUNTERFACTUALS NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 NIE JY, 1988, JUN P ACM SIGIR GREN, P495 QIU Y, 1993, JUNP ACM SIGIR PITTS, P160 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SEMBOK TMT, 1993, UNPUB IMAGING RELEVA STALNAKER R, 1981, IFS, P107 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1989, JUN P ACM SIGIR CAMB NR 21 TC 18 Z9 18 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD MAR PY 1995 VL 51 IS 1 BP 3 EP 17 DI 10.1108/eb026939 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QK637 UT WOS:A1995QK63700002 ER PT J AU YAO, YY AF YAO, YY TI MEASURING RETRIEVAL EFFECTIVENESS BASED ON USER PREFERENCE OF DOCUMENTS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RELEVANCE; PRECISION; JUDGMENTS; RECALL AB The notion of user preference is adopted for the representation, interpretation, and measurement of the relevance or usefulness of documents. User judgments on documents may be formally described by a weak order (i.e., user ranking) and measured using an ordinal scale. Within this framework, a new measure of system performance is suggested based on the distance between user ranking and system ranking. It only uses the relative order of documents and therefore confirms to the valid use of an ordinal scale measuring relevance. It is also applicable to multilevel relevance judgments and ranked system output. The appropriateness of the proposed measure is demonstrated through an axiomatic approach. The inherent relationships between the new measure and many existing measures provide further supporting evidence. RP YAO, YY (reprint author), LAKEHEAD UNIV,DEPT MATH SCI,THUNDER BAY,ON P7B 5E1,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR BOGART KP, 1973, J MATH SOCIOL, V3, P49 Bollmann P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42023 BOLLMANN P, 1992, INFORM PROCESS MANAG, V28, P291, DOI 10.1016/0306-4573(92)90077-D BOLLMANN P, 1981, INFORMATION RETRIEVA, P256 Bollmann P., 1988, P 11 ANN INT ACM SIG, P245, DOI 10.1145/62437.62457 BOLLMANN P, 1983, UNPUB NORMALIZED REC Bollmann P., 1977, Journal of Informatics, V1 BOLLMANN P, 1986, LEISTUNGSBEWERTUNG I, P183 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEIN A, 1989, INFORM PROCESS MANAG, V25, P465, DOI 10.1016/0306-4573(89)90018-6 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 CHERNIAVSKY VS, 1971, AUTOMATIC DOCUMENTAT, V4, P9 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CUADRA CA, 1967, TM3520 SYST DEV CORP EISENBERG M, 1988, J AM SOC INFORM SCI, V39, P293, DOI 10.1002/(SICI)1097-4571(198809)39:5<293::AID-ASI1>3.0.CO;2-I EISENBERG MB, 1988, INFORM PROCESS MANAG, V24, P373, DOI 10.1016/0306-4573(88)90042-8 Fishburn PC, 1970, UTILITY THEORY DECIS FREI HP, 1991, INFORM PROCESS MANAG, V27, P153, DOI 10.1016/0306-4573(91)90046-O French S., 1986, DECISION THEORY INTR FUHR N, 1989, ACM T INFORM SYST, V3, P183 GOFFMAN W, 1966, INFORM STORAGE RET, V3, P19, DOI 10.1016/0020-0271(66)90006-4 HARPER DJ, 1977, J DOC, V34, P189 HEINE MH, 1973, INFORM STORAGE RET, V9, P181, DOI 10.1016/0020-0271(73)90066-1 JANES JW, 1991, J AM SOC INFORM SCI, V42, P754, DOI 10.1002/(SICI)1097-4571(199112)42:10<754::AID-ASI9>3.0.CO;2-C JONES KS, 1981, INFORMATION RETRIEVA KEEN EM, 1971, SMART RETRIEVAL SYST, P74 Kemeny J., 1962, MATH MODELS SOCIAL S Kendall M., 1955, RANK CORRELATION MET KING DW, 1968, ANNU REV INFORM SCI, V3, P61 KOCHEN M, 1974, PRINCIPLES INFORMATI LESK ME, 1968, INFORMATION RETRIEVA, V4, P317 Marczewski E, 1958, C MATH, V6, P319 MARON ME, 1970, INTRO INFORM SCI, P295 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 Rees A., 1967, FIELD EXPT APPROACH Roberts F.S., 1976, DISCRETE MATH MODELS Roberts F.S., 1979, MEASUREMENT THEORY ROBERTSO.SE, 1969, J DOC, V25, P93, DOI 10.1108/eb026466 ROBERTSO.SE, 1969, J DOC, V25, P1, DOI 10.1108/eb026462 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, SMART RETRIEVAL SYST, P57 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1992, INFORM PROCESS MANAG, V28, P441, DOI 10.1016/0306-4573(92)90002-H SARACEVIC T, 1987, J AM SOC INFORM SCI, V39, P161 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Saracevic T., 1970, INTRO INFORMATION SC, P111 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1974, J DOC, V30, P365 Wong S. K. M., 1988, 11th International Conference on Research and Development in Information Retrieval WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 Wong S. K. M., 1991, International Journal of General Systems, V19, DOI 10.1080/03081079108935166 NR 54 TC 71 Z9 75 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAR PY 1995 VL 46 IS 2 BP 133 EP 145 DI 10.1002/(SICI)1097-4571(199503)46:2<133::AID-ASI6>3.0.CO;2-Z PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QH352 UT WOS:A1995QH35200006 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI ON MODELING INFORMATION-RETRIEVAL WITH PROBABILISTIC INFERENCE SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE BOOLEAN MODEL; DOCUMENT REPRESENTATION; FUZZY SET MODEL; MAXIMUM AND MINIMUM ENTROPY PRINCIPLES; PROBABILISTIC INFERENCE; PROBABILISTIC MODEL; RELEVANCE; SIMILARITY MEASURES; SUBJECTIVE PROBABILITY; UNCERTAIN IMPLICATIONS; VECTOR SPACE MODEL ID EXPERT OPINION APPROACH; RELEVANCE; LOGIC; COMBINATION; FRAMEWORK AB This article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic inference. This new approach provides a common conceptual and mathematical basis for many retrieval models, such as the Boolean, fuzzy set, vector space, and conventional probabilistic models. Within this framework, the underlying assumptions employed by each model are identified, and the inherent relationships between these models are analyzed. Although this article is mainly a theoretical analysis of probabilistic inference for information retrieval, practical methods for estimating the required probabilisties are provided by simple examples. C1 LAKEHEAD UNIV,DEPT MATH SCI,THUNDER BAY,ON P7B 5E1,CANADA. RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA,SK S4S 0A2,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Adams E., 1975, LOGIC CONDITIONALS Bellman RE, 1970, MANAGE SCI, V17, P141, DOI DOI 10.1287/MNSC.17.4.B141 BOOKSTEIN A, 1985, P 8 ANN INT ACM SIGI, P11, DOI 10.1145/253495.253505 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 CARNAP R, 1962, LOGICAL F PROBABILIT Carnap Rudolf, 1971, STUDIES INDUCTIVE LO, VI, P5 CHIARAMELLA Y, 1992, COMPUT J, V35, P233, DOI 10.1093/comjnl/35.3.233 CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Fine T., 1973, THEORIES PROBABILITY Finetti B. De, 1974, THEORY PROBABILITY FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1986, P 9 SIGIR, P249, DOI 10.1145/253168.253221 GILES R, 1976, INT J MAN MACH STUD, V8, P313, DOI 10.1016/S0020-7373(76)80003-X GOOD IJ, 1983, GOOD THINKING F PROB GORDON M, 1989, J AM SOC INFORM SCI, V40, P145, DOI 10.1002/(SICI)1097-4571(198905)40:3<145::AID-ASI1>3.0.CO;2-I Hacking Ian, 1975, EMERGENCE PROBABILIT Harrison M. A., 1965, INTRO SWITCHING AUTO JAYNES E. T., 1979, MAXIMUM ENTROPY FORM Klir G J, 1988, FUZZY SETS UNCERTAIN MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Marr D., 1982, VISION NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 NIE JY, 1992, 15TH P ANN INT ACM S, P140 PAWLAK Z, 1988, INT J MAN MACH STUD, V29, P81, DOI 10.1016/S0020-7373(88)80032-4 Pearl J., 1988, PROBABILISTIC REASON RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SAVAGE LJ, 1972, F STATISTICS Schauble P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42034 Shafer G., 1987, STAT SCI, V2, P3, DOI 10.1214/ss/1177013426 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 THOMPSON P, 1988, J DOC, V44, P119, DOI 10.1108/eb026821 THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P383, DOI 10.1016/0306-4573(90)90098-M THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P371, DOI 10.1016/0306-4573(90)90097-L Turtle H. R., 1990, 13TH P INT C RES DEV, P1 TURTLE HR, 1992, COMPUT J, V35, P279, DOI 10.1093/comjnl/35.3.279 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1989, P SIGIR 89, P77 Watanabe S., 1985, PATTERN RECOGNITION WONG SKM, 1991, INFORM SYST, V16, P301, DOI 10.1016/0306-4379(91)90003-R WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 WONG SKM, 1991, INT J GEN SYST, V19, P301 YAO YY, 1992, INT J MAN MACH STUD, V37, P793, DOI 10.1016/0020-7373(92)90069-W YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 60 TC 68 Z9 69 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN PY 1995 VL 13 IS 1 BP 38 EP 68 DI 10.1145/195705.195713 PG 31 WC Computer Science, Information Systems SC Computer Science GA QQ670 UT WOS:A1995QQ67000002 ER PT J AU COOPER, WS AF COOPER, WS TI SOME INCONSISTENCIES AND MISIDENTIFIED MODELING ASSUMPTIONS IN PROBABILISTIC INFORMATION-RETRIEVAL SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE ASSUMPTIONS; BIBLIOGRAPHIC SEARCHING; CONSISTENCY; DOCUMENT RETRIEVAL; INDEPENDENCE; LOGIC; MODELING ID MAXIMUM-ENTROPY; SYSTEMS; DESIGN AB Research in the probabilistic theory of information retrieval involves the construction of mathematical models based on statistical assumptions. One of the hazards inherent in this kind of theory construction is that the assumptions laid down may be inconsistent in unanticipated ways with the data to which they are applied. Another hazard is that the stated assumptions may not be those on which the derived modeling equations or resulting experiments are actually based. Both kinds of mistakes have been made in past research on probabilistic information retrieval. One consequence of these errors is that the statistical character of certain probabilistic IR models, including the so-called Binary Independence model, has been seriously misapprehended. RP COOPER, WS (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 Eells E., 1982, RATIONAL DECISION CA FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 FUHR N, 1991, 13TH P INT C RES DEV, P345 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 LEE JJ, 1991, J AM SOC INFORM SCI, V42, P166, DOI 10.1002/(SICI)1097-4571(199104)42:3<166::AID-ASI2>3.0.CO;2-A MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 ROBERTSON S, 1982, 5739 BRIT LIBR RES D Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTSO.SE, 1974, J DOC, V30, P41 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 18 TC 26 Z9 26 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN PY 1995 VL 13 IS 1 BP 100 EP 111 DI 10.1145/195705.195735 PG 12 WC Computer Science, Information Systems SC Computer Science GA QQ670 UT WOS:A1995QQ67000004 ER PT J AU HEINE, MH AF HEINE, MH TI AN INVESTIGATION OF THE RELATIVE INFLUENCES OF DATABASE INFORMATIVENESS, QUERY SIZE AND QUERY TERM SPECIFICITY ON THE EFFECTIVENESS OF MEDLINE SEARCHING SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID RETRIEVAL AB An investigation of search effectiveness in Medline used regression analysis to compare the relative influences on search performance (characterised by Recall, Precision and the MZ-metric) of the three variables: database informativeness, and size and mean specificity of the query. (A 'query' is defined as an unstructured set of search terms.) A reduction in random and/or systematic error was sought by means of three experimental devices: (i) error arising from the subjectivity of third-party relevance judges was minimised by using behaviourally-generated target sets of records; (ii) errors associated with the definitions of queries were avoided (or at least standardised) by using algorithmically derived queries, and (iii) error associated with uncontrolled variation in the logical structure of the search expression was minimised by using search expressions that had been optimised separately against each of the three performance criteria referred to. It was found that the dominant variable influencing search performance was the number of query terms. Database informativeness had a statistically significant, but small, influence on search performance in almost all the data-sets, and this influence could be in either direction, depending on the data-set. The influence of term specificity was in general not statistically significant. Overall, the three predicting variables were able to explain up to 50% of the variation in search performance. RP HEINE, MH (reprint author), NORTHUMBRIA UNIV,DEPT INFORMAT & LIB MANAGEMENT,NEWCASTLE TYNE NE1 8ST,TYNE & WEAR,ENGLAND. CR BARR D, 1971, PROBABILITY BOLLMANN P, 1981, INFORMATION RETRIEVA, P256 HEAPS HS, 1978, INFORMATION RETRIEVA HEINE MH, 1991, J AM SOC INFORM SCI, V42, P267, DOI 10.1002/(SICI)1097-4571(199105)42:4<267::AID-ASI3>3.0.CO;2-Y HEINE MH, 1973, INFORM STORAGE RET, V9, P181, DOI 10.1016/0020-0271(73)90066-1 HEINE MH, 1984, INFORM TECHNOL R & D, V3, P95 HEINE MH, 1980, THEORY APPLICATION I, P137 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1979, INFORMATION PROCESSI, V15, P135 KOCHEN M, 1974, PRINCIPLES INFORMATI MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 MONK A, 1991, EXPLORING STATISTICS Peck E, 1982, INTRO LINEAR REGRESS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1972, J AM SOC INFORM SCI, V23, P11, DOI 10.1002/asi.4630230105 SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 SINCHICH T, 1991, COURSE MODERN BUSINE TAGUESUTCLIFFE J, 1992, INFORM PROCESS MANAG, V28, P467, DOI 10.1016/0306-4573(92)90005-K JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 23 TC 2 Z9 2 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 1352-7460 J9 J INFORM SCI JI J. Inf. Sci. PY 1995 VL 21 IS 3 BP 173 EP 185 DI 10.1177/016555159502100303 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RF704 UT WOS:A1995RF70400003 ER PT J AU WADE, S BRAEKEVELT, P AF WADE, S BRAEKEVELT, P TI IR FRAMEWORK - AN OBJECT-ORIENTED FRAMEWORK FOR DEVELOPING INFORMATION-RETRIEVAL SYSTEMS SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article ID ALGORITHM AB This paper discusses progress on the development of IR}Framework an extensible object oriented framework for information retrieval currently under development at the University of Huddersfield. When completed, IR}Framework will include the data structures and algorithms required to support the development of any information retrieval system in which it is necessary to store, automatically index and retrieve objects on the basis of their text content. A primary objective of the research is to make the framework as extensible as possible so that the information retrieval functionality can be extended to the specific requirements of a wide range of applications. We are therefore fully exploiting the benefits of working in an object oriented development environment. The paper includes a brief review of earlier information retrieval research on which the current work is based; describes the object oriented design of IR}Framework; discusses the way in which IR}Framework has been used in the development of a reference management system called Papers; and discusses the way in which IR}Framework will be further developed to support both teaching and further research. RP WADE, S (reprint author), UNIV HUDDERSFIELD,SCH COMP & MATH,HUDDERSFIELD HD1 3DH,ENGLAND. CR BOYER RS, 1977, COMMUN ACM, V20, P762, DOI 10.1145/359842.359859 FALOUTSOS C, 1992, INFORMATION RETRIEVA FRAKES WR, 1992, INFORMATION RETRIEVA HARPER DJ, 1992, COMPUT J, V35, P256, DOI 10.1093/comjnl/35.3.256 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rumbaugh J, 1991, OBJECT ORIENTED MODE SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 WADE S, 1994, IR FRAMEWORK CLASS L WADE SJ, 1989, J INFORM SCI, V19, P11 WADE SJ, 1988, PROGRAM-AUTOM LIBR, V22, P44, DOI 10.1108/eb046986 WALTON G, 1989, AUTOMATIC TEXT PROCE NR 12 TC 2 Z9 2 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JAN PY 1995 VL 29 IS 1 BP 15 EP 29 DI 10.1108/eb047184 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QC757 UT WOS:A1995QC75700002 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI QUERY-DOCUMENT SYMMETRY AND DUAL MODELS SO JOURNAL OF DOCUMENTATION LA English DT Note AB The idea that there is some natural symmetry between queries and documents is explored. If symmetry can be assumed, then it leads to a conception of 'dual' models in information retrieval (given a model, we can construct a dual model in which the roles of documents and queries are reversed). But symmetry breaks down in various ways, which may invalidate this construction. If we can construct a dual, it is not obvious that it can be combined with the original. RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,CTR INTERACT SYST RES,DEPT INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1991, 2ND TEXT RETRIEVAL C, P21 ROBERTSON SE, 1994, SIRIG 94 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 6 TC 10 Z9 10 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD SEP PY 1994 VL 50 IS 3 BP 233 EP 238 DI 10.1108/eb026932 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PF550 UT WOS:A1994PF55000004 ER PT J AU SAVOY, J AF SAVOY, J TI A LEARNING SCHEME FOR INFORMATION-RETRIEVAL IN HYPERTEXT SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID DOCUMENT-RETRIEVAL; SYSTEMS; TERMS AB In proposing a searching strategy well suited to the hypertext environment, we have considered four criteria: (1) the retrieval scheme should be integrated into a large hypertext environment; (2) the retrieval process should be operable with an unrestricted text collection; (3) the processing time should be reasonable; and (4) the system should be capable of learning in order to improve its retrieval effectiveness. To satisfy these four criteria, we have designed and implemented a search strategy for hypertext systems based on an extended Boolean model (the p-norm scheme) and supplemented it with links to improve the ranking of the retrieved items in a sequence most likely to fulfill the intent of the user. These links, representing additional information about document content, are established according to the requests and relevance judgments. Using a fully automatic procedure, our retrieval scheme can be applied to most existing systems. Based on the CACM test collection, which includes 3,204 documents and the CISI corpus (1,460 documents), we have built a hypertext and evaluated our proposed strategy. The retrieval effectiveness of our solution presents encouraging results. C1 UNIV MONTREAL,DEPT INFORMAT & RECH OPERAT,MONTREAL H3C 3J7,QUEBEC,CANADA. CR ALSCHULER L, 1989, SOC TEXT HYPERTEXT H, P343 ARON JD, 1983, PROGRAM DEV PROCES 2 Blair D. C., 1990, LANGUAGE REPRESENTAT Bush V., 1945, ATLANTIC MONTHLY, P101 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Fox E. A., 1988, 11th International Conference on Research and Development in Information Retrieval FOX EA, 1990, SIGIR FORUM, V24, P19 FUHR N, 1987, 10TH P INT SIGIR C N, P13 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 HALASZ FG, 1988, COMMUN ACM, V31, P836, DOI 10.1145/48511.48514 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 KWOK KL, 1990, P INT JOINT C NEUR N, V2, P623 KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SAVOY J, 1993, IN PRESS HYPERMEDIA, V5 SAVOY J, 1993, RANKING SCHEMES HYBR, P46 SAVOY J, 1992, INFORM PROCESS MANAG, V28, P389, DOI 10.1016/0306-4573(92)90082-B Siegel S., 1956, NONPARAMETRIC STATIS SMITH ME, 1990, ASPECTS PNORM MODEL SPARCK JK, 1977, RES AUTOMATIC INDEXI SPARCK JK, 1971, AUTOMATIC KEYWORD CL SPARCK JK, 1981, INFORMATION RETRIEVA, P213 SPARCKJONES K, 1972, J DOC, V28, P11 TURTLE H, 1990, COINS9092 TECHN REP VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILLIAMS DH, 1971, SPEKTROSKOPISCHE MET, P12 NR 32 TC 10 Z9 10 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL-AUG PY 1994 VL 30 IS 4 BP 515 EP 533 DI 10.1016/0306-4573(94)90037-X PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA NK607 UT WOS:A1994NK60700004 ER PT J AU FUHR, N PFEIFER, U AF FUHR, N PFEIFER, U TI PROBABILISTIC INFORMATION-RETRIEVAL AS A COMBINATION OF ABSTRACTION, INDUCTIVE LEARNING, AND PROBABILISTIC ASSUMPTIONS SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE EXPERIMENTATION; THEORY; CONTROLLED VOCABULARY; LOGISTIC REGRESSION; PROBABILISTIC INDEXING; PROBABILISTIC RETRIEVAL AB We show that former approaches in probabilistic information retrieval are based on one or two of the three concepts abstraction, inductive learning, and probabilistic assumptions, and we propose a new approach which combines all three concepts. This approach is illustrated for the case of indexing with a controlled vocabulary. For this purpose, we describe a new probabilistic model first, which is then combined with logistic regression, thus yielding a generalization of the original model. Experimental results for the pure theoretical model as well as for heuristic variants are given. Furthermore, linear and logistic regression are compared. RP FUHR, N (reprint author), UNIV DORTMUND,LEHRSTUHL INFORMAT 6,D-44221 DORTMUND,GERMANY. CR Biebricher P., 1988, 11th International Conference on Research and Development in Information Retrieval COOPER WS, 1991, 14 ANN INT ACM SIGIR, P57 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 Fienberg SE, 1980, ANAL CROSS CLASSIFIE, V2nd Fox E. A., 1988, 11th International Conference on Research and Development in Information Retrieval FREEMAN DH, 1987, APPLIED CATEGORIAL D FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1991, 14 INT ACM SIGIR C R, P46 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Fuhr N., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium FUHR N, 1985, P RIAO85 1 INT C REC, P207 FUHR N, 1991, APR P RIAO 91 BARC, P606 KNORZ G, 1986, AUTOMATISCHE INDEXIE, P98 KNORZ G, 1983, DVII833 TH DARMST FB MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 PFEIFER U, 1990, THESIS TH DARMSTADT PFEIFER U, 1991, INFORMATION RETRIEVA, P23 Quinlan J.R., 1986, MACHINE LEARNING ART, P149 ROBERTSON S, 1982, 5739 BRIT LIBR REP Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1984, J DOC, V40, P264, DOI 10.1108/eb026768 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 YU CT, 1986, 1986 P ACM C RES DEV, P265 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CT, 1989, ACM T DATABASE SYST, V14, P147, DOI 10.1145/63500.63519 NR 26 TC 18 Z9 18 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN PY 1994 VL 12 IS 1 BP 92 EP 115 DI 10.1145/174608.174612 PG 24 WC Computer Science, Information Systems SC Computer Science GA MW999 UT WOS:A1994MW99900004 ER PT J AU FRENCH, JC AF FRENCH, JC TI DIRE - AN APPROACH TO IMPROVING INFORMAL SCIENTIFIC COMMUNICATION SO INFORMATION AND DECISION TECHNOLOGIES LA English DT Article ID FREE-TEXT; SYSTEMS; RETRIEVAL; PERFORMANCE; ACCESS AB In 1963 the President's Science Committee prepared a report [1], declaring that there was 'a ''crisis'' in scientific and technical information'. This crisis is due to the phenomenal growth in the scientific literature. Almost 30 years later on the eve of a new information explosion this crisis still persists and shows no signs of abating. This paper discusses a prototype system, DIRE, that seeks to attack this problem by interconnecting interested parties into an informal distributed information storage and retrieval system. After initially expressing areas of interest, users of this system will be able to participate rather passively while DIRE constantly monitors its holdings for items of interest. User notifications will be automatically generated when interesting items appear in the information system. This approach offers an economical, unobtrusive means for increasing awareness of new intellectual developments both within and across scientific research communities. RP FRENCH, JC (reprint author), UNIV VIRGINIA,DEPT COMP SCI,CHARLOTTESVILLE,VA 22903, USA. CR BELKIN NJ, 1987, INFORM PROCESS MANAG, V23, P395, DOI 10.1016/0306-4573(87)90109-9 BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 Bush V., 1945, ATLANTIC MONTHLY, P101 CONNOR JH, 1967, LIBR QUART, V37, P373 CRANE D, 1972, INVISIBLE COLLEGES DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEPPISCH U, 1986, 1986 P ACM C RES DEV, P77 EVANS NH, 1989, 1 CARN U MERC TECH R FALOUTSOS C, 1985, COMPUT SURV, V17, P49 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FOX EA, 1990, PSYCHOL SCI, V1, P355, DOI 10.1111/j.1467-9280.1990.tb00240.x FRENCH JC, 1992, CS9242 U VIRG DEP CO FRENCH JC, 1992, INT SPACE YEAR C EAR FRENCH JC, 1992, CS9226 U VIRG DEP CO FRENCH JC, 1985, IDAM FILE ORG FURTH SE, 1968, J CHEM DOC, V8, P202, DOI 10.1021/c160031a006 Garfield E., 1979, CITATION INDEXING GATHEGI JN, 1992, J AM SOC INFORM SCI, V43, P323, DOI 10.1002/(SICI)1097-4571(199205)43:4<323::AID-ASI8>3.0.CO;2-S HOUSMAN EM, 1970, IEEE T PROF COMMUN, VEW13, P78, DOI 10.1109/TEWS.1970.4322444 INOSE H, 1984, INFORMATION TECHNOLO KAHLE B, 1991, UNPUB INFORMATION SY KAHN RE, 1988, DIGITAL LIBRARY PROJ, V1 LANCASTER FW, 1978, PAPERLESS INFORMATIO LANCASTER FW, 1979, INFORMATION RETRIEVA LAY JO, 1989, INFORM PROCESS MANAG, V25, P703 LEDERBERG J, INVITATIONAL WORKSHO LEDERBERG J, 1978, P IEEE, V66, P1314, DOI 10.1109/PROC.1978.11142 LEGGATE P, 1975, J DOC, V31, P93, DOI 10.1108/eb026596 LICKLIDER JCR, 1965, LIBRARIES FUTURE Licklider J.C.R., 1960, Institute of Radio Engineers Transactions on Human Factors in Electronics, VHFE-1, DOI 10.1109/THFE2.1960.4503259 LICLIDER JCR, 1966, AM PSYCHOL, V21, P1044 LIEVROUW LA, 1989, COMMUN RES, V16, P615, DOI 10.1177/009365089016005004 LUHN HP, 1958, IBM J RES DEV, V2, P314 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1961, AM DOC, V12, P131, DOI 10.1002/asi.5090120209 LYNCH CA, 1990, ANNU REV INFORM SCI, V25, P263 MALONE TW, 1987, COMMUN ACM, V30, P390, DOI 10.1145/22899.22903 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 Miller S.P., 1988, KERBEROS AUTHENTICAT Naisbitt J., 1982, MEGATRENDS NELSON TH, 1987, LITERARY MACHINES PACKER KH, 1979, J AM SOC INFORM SCI, V30, P125, DOI 10.1002/asi.4630300303 PRICE DJD, 1966, AM PSYCHOL, V21, P1011 RAMAKRISHNA MV, 1989, COMMUN ACM, V32, P1237, DOI 10.1145/67933.67941 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1975, THEORY INDEXING Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1988, AUTOMATC TEXT PROCES SCHATZ BR, 1987, P IEEE GLOB 87, P1181 SCHWARZ C, 1990, J AM SOC INFORM SCI, V41, P408, DOI 10.1002/(SICI)1097-4571(199009)41:6<408::AID-ASI2>3.0.CO;2-S SINHA A, 1992, COMMUN ACM, V35, P77, DOI 10.1145/129902.129908 STORY GA, 1992, COMPUTER, V25, P17, DOI 10.1109/2.156379 TERRY DB, 1990, CSL9012 TECH REP Toffler Alvin, 1970, FUTURE SHOCK TOMER C, 1992, J AM SOC INFORM SCI, V43, P566, DOI 10.1002/(SICI)1097-4571(199209)43:8<566::AID-ASI8>3.0.CO;2-U WAH BW, 1992, CRHC9226 U ILL URB C WEINBERG AM, 1963, INT SCI TECHNOL APR, P65 WESTLAND JC, 1990, ACM T DATABASE SYST, V15, P341, DOI 10.1145/88636.87943 WIEDERHOLD G, 1992, COMPUTER, V25, P38, DOI 10.1109/2.121508 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 WYLE MF, 1989, 12TH P INT ACM SIGIR, P108 1988, ANSINISO Z39501988 1965, SCI INFORMATION ACTI 1963, SCI GOVT INFORMATION 1989, INFORMATION TECHNOLO 1975, SYSTEMS ANAL SCI TEC NR 68 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0923-0408 J9 INFORM DECIS TECHNOL PY 1994 VL 19 IS 6 BP 527 EP 541 PG 15 WC Operations Research & Management Science SC Operations Research & Management Science GA PM170 UT WOS:A1994PM17000005 ER PT J AU YANG, JJ KORFHAGE, RR AF YANG, JJ KORFHAGE, RR TI QUERY MODIFICATION USING GENETIC ALGORITHMS IN VECTOR-SPACE MODELS SO INTERNATIONAL JOURNAL OF EXPERT SYSTEMS LA English DT Article ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; TEXT RETRIEVAL; FEEDBACK AB The ability of information retrieval systems to tune the system parameters on the basis of user's relevance feedback for improving the retrieval effectiveness has become an important and also an active research area. One such adjustment is to modify the query which reflects the user's information request via the judgment of the previously retrieved results. In this paper we present our idea, based on an adaptive technique of ''genetic algorithms,'' for modifying a user's query to improve the retrieval results. The effectiveness of query modification has been tested on the Cranfield Collection, a classical set of documents, queries and known relevance responses to each query. Results of this study show that the method is highly effective, achieving higher precision levels than those without query modification and those that have been conducted by other research based on relevance feedback. C1 UNIV PITTSBURGH,DEPT INFORMAT SCI,PITTSBURGH,PA 15260. CR Baker J.E., 1987, 2ND P INT C GEN ALG, P14 BHUYAN J, 1990, THESIS U SW LOUISIAN BRAUEN TL, 1971, SMART RETRIEVAL SYST, pCH24 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 Davis L., 1991, HDB GENETIC ALGORITH Davis L., 1987, GENETIC ALGORITHMS S, P1 Fox C., 1990, SIGIR Forum, V24 Frakes W., 1992, INFORMATION RETRIEVA, P131 FRIEDER O, 1991, 14TH P ANN INT ACM S, P230 FRIEDMAN SR, 1971, SMART RETRIEVAL SYST, pCH23 GARZA HC, 1982, THESIS SO METHODIST GARZA HC, 1982, 6TH P COMPSAC 82 INT, P470 GIULIANO VE, 1963, VISTAS INFORMATION H, V1 GLOVER DE, 1987, GENETIC ALGORITHMS S, P12 Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GORDON MD, 1988, INFORM PROCESS MANAG, V24, P339, DOI 10.1016/0306-4573(88)90100-8 Grefenstette J.J., 1987, GENETIC ALGORITHMS S, P42 GREFENSTETTE JJ, 1986, IEEE T SYST MAN CYB, V16, P122, DOI 10.1109/TSMC.1986.289288 Harman D., 1992, 15TH P ANN INT ACM S, P1 Hilliard M. R., 1987, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms HOLLAND JH, 1975, ADAPTATION NATURAL A IDE E, 1971, SMART RETRIEVAL SYST, pCH16 KEEN E, 1971, SMART RETRIEVAL SYST, pCH5 Keen E. M., 1966, ASLIB CRANFIELD RES, V1 KORFHAGE RR, 1988, LIS001IS88001 U PITT KORFHAGE RR, 1991, LIS034IS91002 U PITT KORFHAGE RR, 1991, SIGIR FORUM, V25, P104 LESK ME, 1969, INFORMATION STORAGE, V4, P343 MARON ME, 1988, INFORM PROCESS MANAG, V24, P249, DOI 10.1016/0306-4573(88)90092-1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MYAENG SH, 1990, INFORM PROCESS MANAG, V26, P719, DOI 10.1016/0306-4573(90)90048-7 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 Raghavan V. V., 1987, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms Robertson G., 1987, GENETIC ALGORITHMS S, P129 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, SMART RETRIEVAL SYST, pCH14 SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 Salton G., 1971, SMART RETRIEVAL SYST Syswerda G., 1989, 3RD P INT C GEN ALG, P2 YANG JJ, 1992, P S DOC AN INF RETR, P134 NR 49 TC 17 Z9 17 PU JAI PRESS INC PI GREENWICH PA 55 OLD POST RD-#2, PO BOX 1678, GREENWICH, CT 06836-1678 SN 0894-9077 J9 INT J EXPERT SYST JI Int. J. Expert Syst. PY 1994 VL 7 IS 2 BP 165 EP 191 PG 27 WC Computer Science, Artificial Intelligence SC Computer Science GA PL827 UT WOS:A1994PL82700005 ER PT J AU SCHOCKEN, S HUMMEL, RA AF SCHOCKEN, S HUMMEL, RA TI ON THE USE OF THE DEMPSTER-SHAFER MODEL IN INFORMATION INDEXING AND RETRIEVAL APPLICATIONS SO INTERNATIONAL JOURNAL OF MAN-MACHINE STUDIES LA English DT Article ID UNCERTAINTY; RELEVANCE; LANGUAGE; SYSTEMS C1 NYU,COURANT INST MATH SCI,NEW YORK,NY 10012. RP SCHOCKEN, S (reprint author), NYU,STERN SCH BUSINESS,NEW YORK,NY 10003, USA. CR BARON J, 1987, THEOR DECIS, V23, P25, DOI 10.1007/BF00127335 BISWAS G, 1987, J AM SOC INFORM SCI, V38, P83, DOI 10.1002/(SICI)1097-4571(198703)38:2<83::AID-ASI2>3.0.CO;2-X BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BUXTON R, 1989, INT J MAN MACH STUD, V31, P415, DOI 10.1016/0020-7373(89)90004-7 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEMPSTER AP, 1967, ANN MATH STAT, V38, P325, DOI 10.1214/aoms/1177698950 DEMPSTER AP, 1967, BIOMETRIKA, V54, P515, DOI 10.2307/2335042 GORDON J, 1985, ARTIF INTELL, V26, P323, DOI 10.1016/0004-3702(85)90064-5 HARTER PS, 1975, J ASIS 1, V26, P197 HUMMEL RA, 1988, IEEE T PATTERN ANAL, V10, P235, DOI 10.1109/34.3885 JACOBY J, 1962, INDEXING CONSISTENCY KYBURG HE, 1987, ARTIF INTELL, V31, P271, DOI 10.1016/0004-3702(87)90068-3 Lindley D.V., 1987, STAT SCI, V12, P17, DOI 10.1214/ss/1177013427 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1982, J AM SOC INFORM SCI, V43, P308 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P219, DOI 10.1016/0306-4573(88)90089-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SCHOCKEN S, 1989, IEEE T SYST MAN CYB, V19, P1106, DOI 10.1109/21.44027 SCHOCKEN S, 1990, 23RD P HAW INT C SYS, P544 Shafer G, 1976, MATH THEORY EVIDENCE Shafer G., 1987, STAT SCI, V2, P3, DOI 10.1214/ss/1177013426 STEVENS ME, 1965, AUTOMATIC INDEXING S THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P371, DOI 10.1016/0306-4573(90)90097-L TONG RM, 1985, INT J MAN MACH STUD, V22, P265, DOI 10.1016/S0020-7373(85)80003-1 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 YEN J, 1989, COMMUN ACM, V32, P573, DOI 10.1145/63485.63488 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZADEH LA, 1986, AI MAG, P85 NR 33 TC 18 Z9 18 PU ACADEMIC PRESS LTD PI LONDON PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX SN 0020-7373 J9 INT J MAN MACH STUD PD NOV PY 1993 VL 39 IS 5 BP 843 EP 879 DI 10.1006/imms.1993.1086 PG 37 WC Computer Science, Cybernetics; Ergonomics; Psychology SC Computer Science; Engineering; Psychology GA MK908 UT WOS:A1993MK90800007 ER PT J AU DATRI, A TARANTINO, L DISTEFANO, G AF DATRI, A TARANTINO, L DISTEFANO, G TI ON THE REPRESENTATION AND MANAGEMENT OF MEDICAL RECORDS IN A KNOWLEDGE-BASED SYSTEM SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article ID DATABASES AB The need for adapting Hospital Information Systems (HISs) to new applications (aimed at clinical data management) and to the ever growing user population requires an evolution from both the representation and the usability points of view. The goal of this paper is to discuss the interdependencies of the advanced data representation models necessary to the new HISs and the interaction requirements. In particular, we present the technical approach taken in the MILORD (Multimedia Interaction with Large Object-oriented Radiological and clinical Databases) project to implement a departmental environment which integrates different kinds of clinical data in a user-friendly and homogeneous way. The implementation platform of the MILORD system is based on results obtained in the KIWIS project, which produced an advanced knowledge-base environment for large database systems. The interaction with a HIS is analyzed identifying the HIS-specific interaction tasks, the typical phases of the interaction with an information system, and the necessary interaction paradigms. In particular we describe the approach adopted in KIWIS for overcoming the problem of querying object-oriented databases and we discuss the new requirements that multimediality poses on the interaction paradigms. C1 UNIV ROMA LA SAPIENZA,I-00185 ROME,ITALY. RP DATRI, A (reprint author), UNIV AQUILA,DIPARTIMENTO INGN ELETTR,POGGIO ROIO,I-67040 LAQUILA,ITALY. CR AHLSEN M, 1991, P CAISE 91 TRONDHEIM, P79 ALHAWAMDEH S, 1991, MIS91 AUSIELLO G, 1986, J COMPUT SYST SCI, V33, P179, DOI 10.1016/0022-0000(86)90018-8 BACHILHON F, 1990, 2NT P INT WORKSH DAT BANARJEE K, 1988, 4TH P IEEE INT C DAT, P97 BANCILHON F, 1988, 7TH P ACM SIGMOD POD, P5 BERTINO E, 1991, COMPUTER, V24, P33, DOI 10.1109/2.76261 BLUM BI, 1986, CLIN INFORMATION SYS BRACAGLIA G, 1988, 6 P IASTED INT S APP, P130 BUDD JR, 1988, COMPUT BIOMED RES, V21, P419, DOI 10.1016/0010-4809(88)90002-X CAMPBELL JR, 1986, 10TH ANN S COMP APPL CHRISTODOULAKIS S, 1986, ACM T INFORM SYST, V4, P345, DOI 10.1145/9760.9764 CUPPENS F, 1988, 2ND P INT C EXP DAT, P333 CURRO V, 1988, 10TH IEEE ENG MED BI, P1341 CURRO V, 1990, P COMPUTERS MED HLTH, P206 CURRO V, 1989, 11TH P IEEE EMBS, P1847 DATRI A, 1989, DATA ENG, V12, P46 DATRI A, 1901, DESIGNING USING HUMA, V4, P44 DIFELICE P, 1988, 10TH IEEE ENG MED BI, P1343 DIFELICE P, 1989, COMPUT METH PROG BIO, V28, P225, DOI 10.1016/0169-2607(89)90155-7 EHRENREICH SL, 1981, HUM FACTORS, V23, P709 HOCK AY, 1991, INT C MULTIMEDIA INF KIM W, 1989, 15TH P INT C MULT IN KIM W, 1989, 1KTH P INT C VER LAR KUHN IM, 1982, NTIS PUBLICATION, P178 LAENENS E, 1990, P WORKSHOP LOGIC PRO, P62 MAIER D, 1986, ADV COMPUTING RES, V3, P213 MEGHINI C, 1991, COMPUTER, V24, P23, DOI 10.1109/2.97248 MOTRO A, 1986, ACM T INFORM SYST, V4, P312, DOI 10.1145/9760.9762 MOTRO A, 1986, ACM T INFORM SYST, V4, P164, DOI 10.1145/6168.6172 MOTRO A, 1984, P ACM SIGMOD C, P197 MOTRO A, 1988, 2ND P INT C EXP DAT, P107 NARASIMHALU AD, 1991, COMPUTER, V24, P6 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 NORMAN DA, 1984, INT J MAN MACH STUD, V21, P365, DOI 10.1016/S0020-7373(84)80054-1 ORPHANOUDAKIS SC, 1991, MED IMAGE DATABASE S PAU LF, 1986, PATTERN RECOGN LETT, V4, P477, DOI 10.1016/0167-8655(86)90047-4 RECTOR AL, 1987, MANAGING DOCTOR COMP ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROWE L, 1987, 13TH P INT C VER LAR Shneiderman B., 1989, Designing and Using Human-Computer Interfaces and Knowledge Based Systems. Proceedings of the Third International Conference on Human Computer Interaction. Vol.II STAES F, 1991, 13TH P ANN INT C IEE, P210 STAES F, 1990, 1ITH P ANN INT C IEE, P1355, DOI 10.1109/IEMBS.1990.691786 STAES F, 1991, P IFIPWG8 1 INT C OB, P193 STAES F, 1991, P INT C DATABASE EXP STAES F, 1991, P IEEE INT WORKSHOP, P53 STONEBRAKER M, 1982, 8TH P INT C VER LARG, P1 TABUCHI M, 1991, HYPERBOOK MULTIMEDIA VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILLIAMS MD, 1984, INT J MAN MACH STUD, V21, P333, DOI 10.1016/S0020-7373(84)80052-8 ZLOOF MM, 1977, IBM SYST J, V16, P324 NR 51 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD OCT-DEC PY 1993 VL 6 IS 4 BP 469 EP 482 DI 10.1016/0957-4174(93)90039-9 PG 14 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA LX543 UT WOS:A1993LX54300009 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI A PROBABILISTIC METHOD FOR COMPUTING TERM-BY-TERM RELATIONSHIPS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID EXPERT OPINION APPROACH; INFORMATION-RETRIEVAL; MODEL; COMBINATION AB This article suggests a probabilistic method to compute the term relationships from relevance information, which complements the studies on a nonprobabilistic technique called pseudo-classification. A quadratic ranking function (i.e., a bilinear function) on the components of document and query vectors is derived by incorporating the term-by-term relationships. The conventional probabilistic indexing model, the probabilistic retrieval model, and our earlier generalized model are special cases of the proposed model. By exploring the different views of probability, procedures for estimating the required parameters are provided. RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR COOPER WS, 1991, 14 ANN INT ACM SIGIR, P57 Fine T., 1973, THEORIES PROBABILITY FISHBURN PC, 1974, J MATH PSYCHOL, V11, P294, DOI 10.1016/0022-2496(74)90024-8 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GIULIANO VE, 1963, VISTAS INFORMATION H Hacking Ian, 1975, EMERGENCE PROBABILIT HARPER DJ, 1977, J DOC, V34, P189 Heckerman D., 1986, UNCERTAINTY ARTIFICI, P167 JACKSON DM, 1970, INFORM STORAGE RET, V6, P187, DOI 10.1016/0020-0271(70)90061-6 JONES KS, 1971, AUTOMATI KEYWORD CLA LARSON RR, 1991, JASIS, V43, P34 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 Raghavan V. V., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320071.320081 RAGHAVAN VV, 1989, 4TH P INT S METH INT, P111 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1984, J DOC, V40, P264, DOI 10.1108/eb026768 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1980, INFORMATION PROCESS, V15, P1 SALTON G, 1989, AUTOMATIC TEST PROCE SCHAUBLE P, 1989, 113 DEP INF TECHN RE THOMPSON P, 1988, J DOC, V44, P119, DOI 10.1108/eb026821 THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P383, DOI 10.1016/0306-4573(90)90098-M THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P371, DOI 10.1016/0306-4573(90)90097-L VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Wong S. K. M., 1988, 11th International Conference on Research and Development in Information Retrieval WONG SKM, 1991, INFORM SYST, V16, P301, DOI 10.1016/0306-4379(91)90003-R WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, IN PRESS 16TH P ANN WONG SKM, 1990, J AM SOC INFORM SCI, V41, P342 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 WONG SKM, 1989, TR89988 CORN U DEP C YU CT, 1977, J AM SOC INFORM SCI, V28, P345, DOI 10.1002/asi.4630280607 YU CT, 1974, INFORM STORAGE RET, V10, P243, DOI 10.1016/0020-0271(74)90019-9 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1975, J ACM, V22, P17, DOI 10.1145/321864.321867 NR 41 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD SEP PY 1993 VL 44 IS 8 BP 431 EP 439 DI 10.1002/(SICI)1097-4571(199309)44:8<431::AID-ASI1>3.0.CO;2-V PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LU342 UT WOS:A1993LU34200001 ER PT J AU PAIJMANS, H AF PAIJMANS, H TI COMPARING THE DOCUMENT REPRESENTATIONS OF 2 IR-SYSTEMS - CLARIT AND TOPIC SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article AB The TOPIC and CLARIT information retrieval systems are discussed in terms of assigned vs. derived and precoordinate vs. postcoordinate indexing. The document representations of the two systems are compared. A test was done on a small sample of Wall Street Journal articles. The positive results found for CLARIT in earlier tests on medical documents were not observed in this general database. RP PAIJMANS, H (reprint author), TILBURG UNIV,INST LANGUAGE TECHNOL & ARTIFICIAL INTELLIGENCE,POB 90153,5000 LE TILBURG,NETHERLANDS. CR AJIFERUKE I, 1989, ONLINE REV, V13, P1 APPELBAUM LA, 1988, RIAO88 *BAS INF DIM INC, 1990, CLAR CLAIMS VEIT COR BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BUCKLEY C, 1988, J AM SOC INFORM SCI, V24, P513 CLEVERDEN CW, 1966, ASLIBCRANFIELD RES P Cleverdon C. W., 1991, SIGIR Forum Cleverdon C., 1984, Information Services & Use, V4 EVANS D, 1991, CARLIT PROJECT EVANS DA, 1991, RIAO 91, P624 FOSKETT AC, 1969, SUBJECT APPROACH INF FUNG RM, 1990, SIGIR, P455 HAHN U, 1988, 1987 IFIP C P AMST *IBM CORP, 1976, IBM PROGR PROD 5740 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V11, P939, DOI 10.1109/TSE.1985.232827 PAIJMANS H, 1992, WORKSHOP ARTIFICIAL ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROES H, 1992, WORKSHOP ARTIFICIAL SALTON G, 1983, INTRO MODERN INFORMA SWANSON DR, 1971, LIBRARY Q, V41, P229 WHITE HD, 1987, INFORM PROCESS MANAG, V23, P211, DOI 10.1016/0306-4573(87)90005-7 NR 21 TC 8 Z9 8 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD AUG PY 1993 VL 44 IS 7 BP 383 EP 392 DI 10.1002/(SICI)1097-4571(199308)44:7<383::AID-ASI3>3.0.CO;2-L PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LN644 UT WOS:A1993LN64400003 ER PT J AU WILBUR, WJ AF WILBUR, WJ TI RETRIEVAL TESTING WITH HYPERGEOMETRIC DOCUMENT MODELS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; SYSTEM; TERMS AB If one could identify the source subject areas of documents and could compute the probability that any given document came from a given source, one could apply Baye's theorem to compute the probability that a query document and any other document came from the same subject area (i.e., were related). Even correct prior probabilities could be assigned under this hypothesis by examining the whole database to obtain the probabilities with which different sources occur. While we do not know how to carry out this scheme in such a way as to account for all the information contained in documents, we show here how it may be realized in a limited way. A method of modeling the sources of documents is described which accounts for the information in global term weights. The methodology is based on the hypergeometric probability distribution. Such a source model may be fit closely to a real database and may be used to convert the real database to an abstract database in which document sources are known and model retrieval is the best retrieval possible based on model document content. We have constructed such an abstract model corresponding to a database of MEDLINE records. Tests of vector retrieval methods on the abstract model indicate they are near optimal but suggest minor improvement with correct parameter choices. Preliminary results based on a test set (human judged) from the real database support these results. RP WILBUR, WJ (reprint author), NIH,NATL LIB MED,NATL CTR BIOTECHNOL INFORMAT,BLDG 38A,ROOM 8S806,8600 ROCKVILLE PIKE,BETHESDA,MD 20894, USA. CR BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Cheeseman P, 1988, 5TH P INT C MACH LEA, P54 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B FORD N, 1991, J AM SOC INFORM SCI, V42, P72, DOI 10.1002/(SICI)1097-4571(199101)42:1<72::AID-ASI8>3.0.CO;2-9 GORDON MD, 1990, J AM SOC INFORM SCI, V41, P313, DOI 10.1002/(SICI)1097-4571(199007)41:5<313::AID-ASI1>3.0.CO;2-G GRIFFITHS JM, 1978, THESIS U COLLEGE LON Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Larson H, 1982, INTRO PROBABILITY TH Lenat D., 1986, AI MAG, V6, P65 LEWIS DD, 1991, MACHINE LEARNING, P235 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 NELSON MJ, 1985, J AM SOC INFORM SCI, V36, P283, DOI 10.1002/asi.4630360502 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1991, P ASIS ANNU MEET, V28, P82 SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q SWANSON DR, 1988, J AM SOC INFORM SCI, V39, P92, DOI 10.1002/(SICI)1097-4571(198803)39:2<92::AID-ASI4>3.0.CO;2-P TAGUE J, 1984, CAN J INFORM SCI, V9, P37 TAGUE J, 1981, INFORMATION RETRIEVA, P236 TAGUE JM, 1981, ACM SIGIR FORUM, P66 TAGUE JM, 1982, RES DEV INFORMATION, P194 WILBUR WJ, 1992, INFORM SYST, V17, P283, DOI 10.1016/0306-4379(92)90019-J WILBUR WJ, 1992, J AM SOC INFORM SCI, V43, P358, DOI 10.1002/(SICI)1097-4571(199206)43:5<358::AID-ASI4>3.0.CO;2-I Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium NR 28 TC 2 Z9 2 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUL PY 1993 VL 44 IS 6 BP 340 EP 351 DI 10.1002/(SICI)1097-4571(199307)44:6<340::AID-ASI4>3.0.CO;2-Z PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LJ307 UT WOS:A1993LJ30700004 ER PT J AU MOHAN, KC AF MOHAN, KC TI FREE-TEXT RETRIEVAL-SYSTEMS - RESEARCH-AND-DEVELOPMENT IN INFORMATION-RETRIEVAL SO JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH LA English DT Article ID CO-OCCURRENCE DATA; DOCUMENT-RETRIEVAL; PROBABILISTIC MODELS; MATHEMATICAL-MODEL; TEACHING PACKAGE; REPRESENTATION; PRINCIPLE; INSTRUCT; PERILS; VALUES AB A majority of text retrieval systems which exist today are based upon the Boolean logic: AND, OR, and NOT. Despite its commercial popularity Boolean retrieval has several obvious limitations. The alternative NN searching method overcomes some of these problems, for example, the difficulty of query formulation, but it also has some disadvantages. Accordingly, there have been several attempts to merge the two approaches into hybrid retrieval models. However, these methods are theoretically unsound and researchers are now increasingly considering the use of Multi-Strategy retrieval systems in which a range of techniques are available. Thus, if one strategy failed to give an optimum performance for a particular query the alternative strategies could be used. Experiments were conducted in an operational environment with the aim of designing a Multi-Strategy retrieval system. Unlike the other studies, here the two chosen search strategies were based on entirely different concepts in that one produces ranked output and the other produces unranked output. The series of tests carried out to establish the difference in retrieval effectiveness of these two different search systems, and to see whether it is statistically significant, yielded no significant results. In general, the statistical criteria considered produced no reliable mark to reveal the true nature of relationship, if there was any, of query characteristics on the retrieval performance. Although it was not possible to derive a more continuous measure of relative effectiveness in operational systems, because of the non-availability of information on the total number of relevant items in the collection, it could be concluded that largely the findings were in agreement with the previous conclusions. Unlike the retrieval mechanisms considered here, most of the techniques retrieve different sets of documents on varied document representations despite the comparable performance. So, it might be possible to choose automatically any two retrieval mechanisms and provide the user retrieval output of both together, excluding the common items, but the cost factor would be increased. It is unlikely to satisfy the users otherwise without sacrificing the cost factor, while considering different levels of search satisfaction, and vice versa. RP MOHAN, KC (reprint author), INDIAN INST HORT RES,HESSARAGHATTA LAKE POST,BANGALORE 560089,INDIA. CR ALHAWAMDEH S, 1988, P INT ONLINE INFORMA BELKIN NJ, 1986, ACM C RES DEV INFORM, P11 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1985, 1985 RIAO C P GREN, P373 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1981, INFORMATION RETRIEVA, P23 BOOKSTEIN A, 1985, 8TH P ANN INT ACM SI, P11 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P249, DOI 10.1016/0306-4573(81)90019-4 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1984, RES DEV INFORMATION, P45 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 CROFT WB, 1981, ACM SIGIR FORUM, V16, P40, DOI 10.1145/1013228.511760 DEOGUN JS, 1985, 1985 RIAO C P GREN, P627 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P382, DOI 10.1108/eb046949 Ide E., 1971, SMART RETRIEVAL SYST, P373 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KRAFT DH, 1985, ADV COMPUT, V24, P277, DOI 10.1016/S0065-2458(08)60369-1 KRAFT DH, 1983, INT J MAN MACH STUD, V19, P45, DOI 10.1016/S0020-7373(83)80041-8 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MCCALL FM, 1986, INT J MAN MACH STUD, V25, P317, DOI 10.1016/S0020-7373(86)80063-3 MCGILL MJ, 1979, EVALUATON FACTORS AF MINKER J, 1977, ACM SIGIR FORUM, V12, P1 MOHAN KC, 1985, THESIS U SHEFFIELD S MOHAN KC, 1987, THESIS U SHEFFELD SH MOHAN KC, 1985, J INFORM SCI, V11, P31, DOI 10.1177/016555158501100105 NOREAULT T, 1981, INFORMATION RETRIEVA, P57 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P33 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Porter M. F., 1982, Information Technology: Research and Development, V1 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P8, DOI 10.1002/asi.4630330104 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1985, 1985 RIAO C P GREN, P1 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, RES DEV INFORMATION, P151 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1983, INF P MAN, V19, P755 SALTON G, 1980, COMPUTER, V13, P41 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 STIBIC V, 1980, ONLINE REV, V4, P273, DOI 10.1108/eb024034 TAGUE J, 1983, RES DEV INFORMATION, P194 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, 1986 ACM C RES DEV I, P194 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WONG SKM, 1986, ACM C PISA, P175 WONG SKM, 1985, 7TH P INT C INF STOR, P118 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 76 TC 0 Z9 0 PU COUNCIL SCIENTIFIC INDUSTRIAL RESEARCH PI NEW DELHI PA PUBL & INFO DIRECTORATE, NEW DELHI 110012, INDIA SN 0022-4456 J9 J SCI IND RES INDIA JI J. Sci. Ind. Res. PD MAY PY 1993 VL 52 IS 5 BP 338 EP 349 PG 12 WC Engineering, Multidisciplinary SC Engineering GA LC877 UT WOS:A1993LC87700002 ER PT J AU HANCOCKBEAULIEU, M WALKER, S AF HANCOCKBEAULIEU, M WALKER, S TI AN EVALUATION OF AUTOMATIC QUERY EXPANSION IN AN ONLINE LIBRARY CATALOG SO JOURNAL OF DOCUMENTATION LA English DT Article ID RETRIEVAL AB An automatic query expansion (AQE) facility in an online catalogue was evaluated in an operational library setting. The Okapi experimental system had other features including: ranked output 'best match' keyword searching, automatic stemming, spelling normalisation and cross referencing as well as relevance feedback. A combination of transaction log analysis, search replays, questionnaires and interviews was used for data collection. Findings show that contrary to previous results, AQE was beneficial in a substantial number of searches. User intentions, the effectiveness of the 'best match' search and user interaction were identified as the main factors affecting the take-up of the query expansion facility. RP HANCOCKBEAULIEU, M (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,CTR INTERACT SYST RES,LONDON EC1V 0HB,ENGLAND. CR BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P387, DOI 10.1002/(SICI)1097-4571(198611)37:6<387::AID-ASI3>3.0.CO;2-8 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 EFTHIMIADIS EN, 1989, PERSPECTIVES INFORMA, P257 HANCOCKBEAULIEU M, 1990, J DOC, V46, P318, DOI 10.1108/eb026863 MITEV NN, 1985, 39 LIBR INF RES REP PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 WALKER S, 1990, 6056 BRIT LIBR RES R WALKER S, 1990, INFORMATICS 10, P119 WALKER S, 1987, 24 BRIT LIBR RES PAP NR 11 TC 22 Z9 22 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD DEC PY 1992 VL 48 IS 4 BP 406 EP 421 DI 10.1108/eb026906 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KA882 UT WOS:A1992KA88200003 ER PT J AU EICHMANN, D AF EICHMANN, D TI ASSESSING REPOSITORY TECHNOLOGY - WHERE DO WE GO FROM HERE SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING LA English DT Article AB Three sample information retrieval systems, archie, autoLib, and WAIS, are compared as to their expressiveness and usefulness-first, in the general context of information retrieval, and then as prospective software reuse repositories. While the representational capabilities of these systems are limited, they provide a useful foundation for future repository efforts, particularly from the perspective of repository distribution and coherent user interface design. C1 W VIRGINIA UNIV,DEPT STAT & COMP SCI,MORGANTOWN,WV 26506. CR CONN R, 1988, TUTORIAL SOFTWARE RE, P238 CONN R, 1987, 5TH P ANN JOINT C AD, P45 EMTAGE A, 1992, JAN P USENIX WINT C, P93 KAHLE B, 1989, WIDE AREA INFORMATIO NEUMAN C, 1989, VIRTUAL SYSTEM MODEL ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHWARTZ MF, 1990, CUCS48290 U COL TECH Stonebraker M., 1990, IEEE Transactions on Knowledge and Data Engineering, V2, DOI 10.1109/69.50912 Tracz W., 1988, ACM SIGSOFT SOFTWARE, V13, P17, DOI 10.1145/43857.43859 WEGNER P, 1984, IEEE SOFTWARE, V1, P7 1990, AUTOLIB AUTOMATED ON NR 11 TC 1 Z9 1 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA JOURNAL DEPT PO BOX 128 FARRER ROAD, SINGAPORE 9128, SINGAPORE SN 0218-1940 J9 INT J SOFTW ENG KNOW JI Int. J. Softw. Eng. Knowl. Eng. PD SEP PY 1992 VL 2 IS 3 BP 467 EP 481 DI 10.1142/S0218194092000221 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA JW222 UT WOS:A1992JW22200008 ER PT J AU AGOSTI, M MARCHETTI, PG AF AGOSTI, M MARCHETTI, PG TI USER NAVIGATION IN THE IRS CONCEPTUAL STRUCTURE THROUGH A SEMATIC ASSOCIATION FUNCTION SO COMPUTER JOURNAL LA English DT Article ID INFORMATION; RETRIEVAL AB This paper addresses the methodological aspects of designing a semantic association function in a hypertext information retrieval environment and model. Through this function a part of an associative information retrieval model is designed and implemented. Initially the paper provides reference to previous efforts in associative information retrieval and automatic search formulation and re-formulation operations. The semantic association function is a function which belongs to a new functional hypertext information retrieval model that has been previously justified and presented. For this reason, the meaning and aim of the semantic association function are also recalled and reported. The central part of the paper presents motivations and justification for the design and implementation of the semantic association function in an experimental hypertext information retrieval interface as opposed to a traditional online information retrieval system. C1 EUROPEAN SPACE AGCY, INFORMAT RETRIEVAL SERV, I-00044 FRASCATI, ITALY. RP AGOSTI, M (reprint author), UNIV PADUA, DIPARTIMENTO ELETTRON & INFORMAT, VIA GRADENIGO 6-U, I-35131 PADUA, ITALY. CR AGOSTI M, 1991, P RIAO 91 BARCELONA, P2 AGOSTI M, 1992, INFORMATION PROCESSI, V28 AGOSTI M, 1989, 3RD P INT C LOG INF, P1 AGOSTI M, 1991, ADV HUM FACT ERGON, V18, P317 AGOSTI M, 1988, 12TH P INT ONL INF M, V1, P57 AITCHISON J, 1987, THESAURUS CONSTRUCTI BRUZA PD, 1990, 1ST P EUR C HYP INRI, P109 Bruza P. D., 1990, SIGIR Forum, V24 Bush V., 1945, ATLANTIC MONTHLY, P101 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 de Finetti B., 1975, THEORY PROBABILITY, V1-2 De Finetti B., 1975, THEORY PROBABILITY, V2 DOSZKOCS TE, 1978, 39TH P FID C EDINB, P295 EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 FRISSE ME, 1988, COMMUN ACM, V31, P880, DOI 10.1145/48511.48518 FRISSE ME, 1989, HYPERTEXT 89 PROCEEDINGS, P199 INGWERSEN P, 1986, 9TH P ACM SIGIR C RE, P68 JONES PE, 1968, NASA CR1020 TECHN RE LUCARELLA D, 1990, 1ST P EUR C HYP INRI, P81 LUHN HP, 1958, IBM J RES DEV, V2, P159 MARCHETTI PG, 1991, NATIONAL ONLINE M NW MARCHETTI PG, 1981, ESA B, V66, P115 MARTIN WA, 1986, 7TH P NAT ONL M NEW Nielsen J., 1990, Communications of the ACM, V33, DOI 10.1145/77481.77483 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1988, 11th International Conference on Research and Development in Information Retrieval SAVAGE LJ, 1972, F STATISTICS SHNEIDERMAN B, 1989, HYPERTEXT HANDS INTR STREITZ N, 1990, 1ST P EUR C HYP INRI VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, 1986 ACM C RES DEV I, P194 WHITE HD, 1989, 13 INT ONL INF M P L, P33 NR 35 TC 8 Z9 8 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0010-4620 J9 COMPUT J JI Comput. J. PD JUN PY 1992 VL 35 IS 3 BP 194 EP 199 DI 10.1093/comjnl/35.3.194 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA JA889 UT WOS:A1992JA88900002 ER PT J AU HARPER, DJ WALKER, ADM AF HARPER, DJ WALKER, ADM TI ECLAIR - AN EXTENSIBLE CLASS LIBRARY FOR INFORMATION-RETRIEVAL SO COMPUTER JOURNAL LA English DT Article ID SYSTEMS AB This paper describes ECLAIR, an extensible object-oriented class library, which can be used to construct information retrieval systems and applications. ECLAIR is implemented in C++ and uses an object-oriented database management system for persistent object storage. The library provides data structures and operations necessary to allow the efficient indexing, storage and retrieval of data items based on their text content using automatic indexing and best-match retrieval techniques. By exploiting the object-oriented paradigm, we have been able to develop a library which is usable by any application requiring information retrieval functionality, and further, is extensible such that many of the functions provided by the library can be changed as required by an application. Additionally, applications can exploit the features of the underlying object-oriented database management system, which include support for persistence of data (objects), modelling complex objects, concurrent access to data, and reliable processing of data in the presence of system failures. In the paper we consider how to deliver information retrieval functionality to the developers of application systems and we evaluate current approaches. The design and implementation of ECLAIR is described in detail. A number of experiments applying ECLAIR are reported. RP HARPER, DJ (reprint author), UNIV GLASGOW,DEPT COMP SCI,GLASGOW G12 8QQ,SCOTLAND. CR ASHFORD J, 1984, PROGRAM-AUTOM LIBR, V18, P124, DOI 10.1108/eb046874 Ashford J., 1988, TEXT RETRIEVAL DOCUM ATKINSON M, 1989, ACM COMPUT SURV, V12, P105 BANCILHON F, 1988, 7TH P ACM PODS, P152 BANCILHON F, 1988, LECTURE NOTES COMPUT, V334 BRETT R, 1988, OBJECT ORIENTED CONC CAREY M, DATABASE ENG, V10, P287 Cattell R., 1991, OBJECT DATA MANAGEME CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DUNLOP MD, 1991, P RIAO 91 BARCELONA HARPER DJ, 1991, ASPECTS DATABASES HARPER DJ, 1986, INFORM PROCESS MANAG, V22, P83, DOI 10.1016/0306-4573(86)90117-2 MORRISON R, 1989, 7789 U GLAS U ST AND MORRISSEY JM, 1986, INFORM PROCESS MANAG, V22, P121, DOI 10.1016/0306-4573(86)90119-6 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PORTER MF, 1982, INFORMATION TECHNOLO, V2, P131 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTSO.SE, 1974, J DOC, V30, P41 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1980, INFORMATION RETRIEVA WALKER A, UNPUB ECLAIR REFEREN WILKINSON R, 1991, P SIGAR 91 CHICAGO, V6 WILLET P, 1988, DOCUMENT RETRIEVAL S 1989, ONTOS OBJECT DATABAS 1990, DOCUMENTATION BASIS 1991, COMANDOS GUIDE NR 29 TC 9 Z9 9 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH STREET, NEW YORK, NY 10011-4211 SN 0010-4620 J9 COMPUT J JI Comput. J. PD JUN PY 1992 VL 35 IS 3 BP 256 EP 267 DI 10.1093/comjnl/35.3.256 PG 12 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA JA889 UT WOS:A1992JA88900008 ER PT J AU TURTLE, HR CROFT, WB AF TURTLE, HR CROFT, WB TI A COMPARISON OF TEXT RETRIEVAL MODELS SO COMPUTER JOURNAL LA English DT Article ID BOOLEAN QUERIES; FEEDBACK AB Many retrieval models have been proposed as the basis of text retrieval systems. The three main classes that have been investigated are the exact-match, vector space and probabilistic models. The retrieval effectiveness of strategies based on these models has been evaluated experimentally, but there has been little in the way of comparison in terms of their formal properties. In this paper we introduce a recent form of the probabilistic model based on inference networks, and show how the rector space and exact-match models can be described in this framework. Differences between these models can be explained as differences in the estimation of probabilities, both in the initial search and during relevance feedback. C1 UNIV MASSACHUSETTS,DEPT COMP SCI,AMHERST,MA 01002. RP TURTLE, HR (reprint author), W PUBLISHING CO,ST PAUL,MN 55164, USA. CR BOLLMANN P, 1991, NSF WORKSH FUT DIR T, P16 BOOKSTEIN A, 1989, INFORM PROCESS MANAG, V25, P465, DOI 10.1016/0306-4573(89)90018-6 BOOKSTEIN A, 1983, 6TH P ANN INT C RES, P118 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1991, 14TH P INT C RES DEV, P32 CROFT WB, 1984, INFORM TECHNOL R & D, V3, P113 CROFT WB, 1990, 13TH P INT C RES DEV, P349 FRISSE ME, 1989, HYPERTEXT 89 PROCEEDINGS, P199 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Fuhr N, 1990, 13TH P INT C RES DEV, P45 GARCIAMOLINA H, 1990, P EDBT, P60 Harman D., 1988, 11th International Conference on Research and Development in Information Retrieval LEWIS D, 1991, THESIS U MASSACHUSET LOSEE RM, 1988, INFORM PROCESS MANAG, V24, P315, DOI 10.1016/0306-4573(88)90097-0 MOTRO A, 1988, ACM T INFORM SYST, V6, P187, DOI 10.1145/45945.48027 Neapolitan R. E., 1990, PROBABILISTIC REASON Pearl J., 1988, PROBABILISTIC REASON RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P281, DOI 10.1016/0306-4573(88)90095-7 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSS S, 1988, 1ST COURSE PROBABILI SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1980, INFORMATION PROCESSI, V24, P269 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 STIRLING KH, 1975, P AM SOC INFORM SCI, V12, P105 TURTLE, 1991, RIA091 C P, P644 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 TURTLE H, COINS9092 TECHN REP Turtle H. R., 1990, 13TH P INT C RES DEV, P1 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG SKM, 1986, 9TH P INT C RES DEV, P175 Yu C. T., 1988, 11th International Conference on Research and Development in Information Retrieval Zdonik S., 1990, READINGS OBJECT ORIE NR 38 TC 36 Z9 36 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH STREET, NEW YORK, NY 10011-4211 SN 0010-4620 J9 COMPUT J JI Comput. J. PD JUN PY 1992 VL 35 IS 3 BP 279 EP 290 DI 10.1093/comjnl/35.3.279 PG 12 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA JA889 UT WOS:A1992JA88900010 ER PT J AU VANRIJSBERGEN, CJ AF VANRIJSBERGEN, CJ TI PROBABILISTIC RETRIEVAL REVISITED SO COMPUTER JOURNAL LA English DT Article ID INFORMATION; PRINCIPLE AB The well-known probabilistic model for information retrieval based on Bayesian conditioning of probability functions is examined. It is extended to allow conditioning based on evidence derived from the 'passage of experience' which may be non-propositional in nature. This latter form of conditioning is derived from Jeffrey's work on probability kinematics and it is compared with the Dempster-Shafer approach to revising belief functions whilst motivating its appropriateness for adaptive information retrieval. This new form of conditioning is combined with a non-classical logic to define a new probabilistic model for information retrieval. RP VANRIJSBERGEN, CJ (reprint author), UNIV GLASGOW,DEPT COMP SCI,GLASGOW G12 8QQ,SCOTLAND. CR Boole G., 1854, INVESTIGATION LAWS T COHEN LJ, 1975, ANN PHILOS LECTURE Cohen L.J., 1977, PROBABLE PROVABLE DIACONIS P, 1982, J AM STAT ASSOC, V77, P822, DOI 10.2307/2287313 Donkin W. F., 1851, PHILOS MAG, V1, P353 Donkin W. F., 1851, PHILOS MAG, V1, P458 FIELD H, 1978, PHILOS SCI, V45, P362 FUHR N, 1992, COMPUTER J, V35 GARBER D, 1986, PHILOS SCI, V47, P142 Goodman I.R., 1991, CONDITIONAL INFERENC Jeffrey R., 1983, LOGIC DECISION, V2nd Keynes J.M., 1929, TREATISE PROBABILITY LEWIS D, 1976, PHILOS REV, V85, P297, DOI 10.2307/2184045 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Pearl J., 1988, NETWORKS PLAUSIBLE I ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SHAFER G, 1981, SYNTHESE, V48, P1, DOI 10.1007/BF01064627 Shafer G, 1976, MATH THEORY EVIDENCE SHAFER G, 1981, PHILOS SCI, V48, P337, DOI 10.1086/289004 SIBSON S, 1969, Z WAHRSCHEINLICKKEIT, V14, P149 VANFRAASEN B, 1976, FDN PROBABILITY THEO, P261 VANFRAASSEN BC, 1980, PHILOS SCI, V47, P165 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1989, 12TH ACM SIGIR ANN I, P77 WILLIAMS PM, 1980, BRIT J PHILOS SCI, V31, P131, DOI 10.1093/bjps/31.2.131 WONG SKM, 1991, INFORM SYST, V16, P301, DOI 10.1016/0306-4379(91)90003-R NR 28 TC 22 Z9 22 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH STREET, NEW YORK, NY 10011-4211 SN 0010-4620 J9 COMPUT J JI Comput. J. PD JUN PY 1992 VL 35 IS 3 BP 291 EP 298 DI 10.1093/comjnl/35.3.291 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA JA889 UT WOS:A1992JA88900012 ER PT J AU WILBUR, WJ AF WILBUR, WJ TI RETRIEVAL TESTING BY THE COMPARISON OF STATISTICALLY INDEPENDENT RETRIEVAL METHODS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; RELEVANCE AB A procedure for retrieval testing is developed which is based on the comparison of statistically independent methods of retrieval applied to the same database. This is, in essence, a form of relative retrieval testing in which, due to the independence assumption, the conclusions drawn may be taken as absolute. The methodology is illustrated on a large database of MEDLINE records where documents are themselves used as queries. Test results in this setting indicate that, among the several methods tested, a form of cosine coefficient retrieval will give the best performance for the purpose of clustering documents or producing nearest neighbors of documents. RP WILBUR, WJ (reprint author), NIH,NATL LIB MED,NATL CTR BIOTECHNOL INFORMAT,8600 ROCKVILLE PIKE,BETHESDA,MD 20894, USA. CR AYER M, 1955, ANN MATH STAT, V26, P641, DOI 10.1214/aoms/1177728423 BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 COOPER WS, 1976, P AM SOC INFORM SCI, V13, P44 Halmos P.R., 1950, MEASURE THEORY Larson H, 1982, INTRO PROBABILITY TH LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 MARON ME, 1976, INFORMATION POLITICS, V13, P46 MARON ME, 1965, STATISTICAL ASS METH, P9 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 REGAZZI JJ, 1988, J AM SOC INFORM SCI, V39, P235, DOI 10.1002/(SICI)1097-4571(198807)39:4<235::AID-ASI3>3.0.CO;2-H ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1989, AUTOMATIC TEXT PROCE SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SWANSON DR, 1988, J AM SOC INFORM SCI, V39, P92, DOI 10.1002/(SICI)1097-4571(198803)39:2<92::AID-ASI4>3.0.CO;2-P Tague J. M., 1981, INFORMATION RETRIEVA, P59 WILBUR WJ, UNPUB RETRIEVAL TEST YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 19 TC 6 Z9 6 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUN PY 1992 VL 43 IS 5 BP 358 EP 370 DI 10.1002/(SICI)1097-4571(199206)43:5<358::AID-ASI4>3.0.CO;2-I PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA HV360 UT WOS:A1992HV36000003 ER PT J AU BURGIN, R DILLON, M AF BURGIN, R DILLON, M TI IMPROVING DISAMBIGUATION IN FASIT SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RETRIEVAL; SYSTEMS; TERMS; TEXT AB An improved disambiguation mechanism, based on a recursive transition network, was used to enhance the automatic indexing system FASIT. FASIT with both the recursive transition network and alone showed improvement over single-term indexing in retrieval experiment with a medical test collection. However, FASIT with the enhanced disambiguation mechanism did not provide any noticeable improvement over FASIT alone. The failure of FASIT with the recursive transition network to improve upon the retrieval results of FASIT alone appeared to be due partly to the stepwise reduction of the effectiveness of further disambiguation in the FASIT environment. This factor was examined in a second experiment, which tested the effects of a perfect diambiguation mechanism on retrieval based on indexing representations provided by FASIT. Again, the difference in retrieval performance between FASIT alone and with a perfect disambiguation was minimal. The results corroborate the findings of the earlier experiment, which showed that retrieval performance is not noticeably affected by improvements to FASIT's disambiguation process. This experiment provides evidence that little improvement in retrieval performance can be expected, regardless of the degree of which FASIT's disambiguation mechanism alone is enhanced. Future research with FASIT should focus on other areas, such as refinements in concept selection and concept grouping and the incorporation of semantic information. C1 ONLINE COMP LIB CTR INC,OFF RES,DUBLIN,OH 43017. RP BURGIN, R (reprint author), N CAROLINA CENT UNIV,SCH LIB & INFORMAT SCI,DURHAM,NC 27707, USA. CR Bates M., 1978, Natural language communication with computers BRUCE B, 1975, ARTIF INTELL, V6, P327, DOI 10.1016/0004-3702(75)90020-X BUCKLEY C, 1985, 85686 CORN U TECHN R BURGIN R, 1990, THESIS U N CAROLINA DILLON M, 1982, J AM SOC INFORM SCI, V33, P294 DILLON M, 1983, J DOC, V39, P135, DOI 10.1108/eb026744 DILLON M, 1984, J AM SOC INFORM SCI, V35, P3 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Fagan J., 1987, 10TH P ASS COMP MACH, P91 FAGAN JL, 1987, 87868 CORN U TECHN R HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1971, SMART RETRIEVAL SYST SAMPSON G, 1987, COMPUTATIONAL ANAL E, P165 SMEATON AF, 1986, 1986 P ACM C RES DEV, P103 SMEATON AF, 1988, CSC88R1 U GLASG DEP Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 Winograd T., 1983, LANGUAGE COGNITIVE P, VI WOOD JB, 1989, 8902 U N CAR TECHN R WOOD JB, 1987, 1987 NAT ONL M P MED, P493 WOODS WA, 1970, COMMUN ACM, V13, P591, DOI 10.1145/355598.362773 NR 26 TC 15 Z9 15 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAR PY 1992 VL 43 IS 2 BP 101 EP 114 DI 10.1002/(SICI)1097-4571(199203)43:2<101::AID-ASI1>3.0.CO;2-Y PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA HE951 UT WOS:A1992HE95100001 ER PT J AU HARMAN, D AF HARMAN, D TI USER-FRIENDLY SYSTEMS INSTEAD OF USER-FRIENDLY FRONT-ENDS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID ONLINE CATALOG; SEARCH; RETRIEVAL; TEXT AB Most commercial online retrieval systems are not designed to service end users and, therefore, have often built "front-ends" to their systems specifically to serve the end-user market. These front-ends have not been well accepted, mostly because the underlying systems are still difficult for end users to use successfully in searching. New techniques, based on statistical methods, that allow natural language input and return lists of records in order of likely relevance, have long been available from research laboratories. This article presents four prototype implementations of these statistical retrieval systems that demonstrate their potential as powerful and easily used retrieval systems able to service all users. RP HARMAN, D (reprint author), NIST,GAITHERSBURG,MD 20899, USA. CR BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BOOKSTEIN A, 1985, ANN REV INFORMAT NOV, P117 CAMPBELL I, 1990, NEWS RETRIEVAL TOOL CLEVERDON C, 1990, ONLINE REV, V14, P35 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 DOSZKOCS TE, 1982, RES DEV INFORM RET, P251 DOSZKOCS TE, 1979, 42ND P ANN M AM SOC DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 HARMAN D, INFORMATION PROCESSI, V27, P449 HARMAN D, 1986, 1986 P ACM C RES DEV HARMAN D, 1990, J AM SOC INFORM SCI, V41, P581, DOI 10.1002/(SICI)1097-4571(199012)41:8<581::AID-ASI4>3.0.CO;2-U HARPER DJ, 1980, THESIS JESUS COLLEGE Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KATZER J, 1982, INFORMATION TECHNOLO, V2, P261 Miller N, 1988, Med Ref Serv Q, V7, P1 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SANDERSON M, 1991, NRT NEWS RETRIEVAL T SIEGEL ER, 1984, INFORM TECHNOL LIBR, V3, P35 SUMMIT RK, 1989, ONLINE REV, V13, P485, DOI 10.1108/eb024325 ULMSCHNEIDER JE, 1983, ONLINE REV, V7, P301, DOI 10.1108/eb024132 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 26 TC 12 Z9 12 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAR PY 1992 VL 43 IS 2 BP 164 EP 174 DI 10.1002/(SICI)1097-4571(199203)43:2<164::AID-ASI9>3.0.CO;2-W PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA HE951 UT WOS:A1992HE95100009 ER PT J AU PRICE, R CHUA, TS ALHAWAMDEH, S AF PRICE, R CHUA, TS ALHAWAMDEH, S TI APPLYING RELEVANCE FEEDBACK TO A PHOTO ARCHIVAL SYSTEM SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID RETRIEVAL AB Relevance feedback techniques have been successfully applied to document retrieval systems (DRS) to refine queries and document representations. This paper represents a first attempt to apply and optimise relevance feedback techniques to improve retrieval in a text-based image archival system. The design exploits the rapid assessment possible with image data to facilitate query refinement and collect large amounts of relevance feedback data. This data can then be used to extend incomplete image descriptions, thus ameliorating problems associated with text annotation of images. Visually oriented relevance feedback and query modification is implemented using direct manipulation of icons. An algorithm designed for dynamic modification of image descriptions based on relevance feedback is proposed, implemented, and experimentally tested. Initial experiments show significant improvements and demonstrate the potential of using these techniques for image retrieval applications. C1 NATL UNIV SINGAPORE,DEPT INFORMAT SYST & COMP SCI,LOWER KENT RIDGE RD,KENT RIDGE,SINGAPORE 0511,SINGAPORE. CR ALHAWAMDEH S, 1991, P RIAO 91 C BARCELON, P916 ALHAWAMDEH S, 1991, TR91540 NAT U SING I ALHAWAMDEH S, 1991, P ACM INT C MULTIMED, P17 ALLMENDINGER L, 1987, 1987 P C PLANN DES M, P66 ASSMANN K, 1986, VISUAL LANGUAGES, P261 BELEW RK, 1989, 12TH P ANN INT ACM S, P11 Bender A., 1988, Library Hi Tech, V6, DOI 10.1108/eb047734 BERTINO E, 1988, ACM T INFORM SYST, V6, P1, DOI 10.1145/42279.42281 BORODIN A, 1971, SMART RETRIEVAL SYST, P394 Brauen T., 1971, SMART RETRIEVAL SYST, P456 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chang S.-K., 1989, PRINCIPLES PICTORIAL FRIEDMAN SR, 1971, SMART RETRIEVAL SYST, P447 Fuhr N, 1990, 13TH P INT C RES DEV, P45 Ide E., 1971, SMART RETRIEVAL SYST, P373 KUNII T, 1974, 8519 U TOKY TECHN RE KWOK KL, 1989, 12TH P ANN INT ACM S, P21 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 PRICE R, 1991, THESIS NATIONAL U SI Rijsbergen CJV, 1979, INFORMATION RETRIEVA, V2nd ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1972, COMMUN ACM, V15, P658, DOI 10.1145/361454.361509 SHELTER T, 1990, BYTE, V15, P221 NR 29 TC 8 Z9 8 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1992 VL 18 IS 3 BP 203 EP 215 DI 10.1177/016555159201800305 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA HZ719 UT WOS:A1992HZ71900005 ER PT J AU GORDON, MD LENK, P AF GORDON, MD LENK, P TI WHEN IS THE PROBABILITY RANKING PRINCIPLE SUBOPTIMAL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RELEVANCE; RETRIEVAL AB The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. Gordon and Lenk (1991) demonstrated that this principal is optimal within a signal detection-decision theory framework, and it maximizes the inquirer's expected utility for relevant documents. These results hold under three conditions: calibration, independent assessment of relevance by the inquirer, and certainty about the computed probabilities of relevance. We demonstrate that the probability ranking principle can be suboptimal with respect to expected utility when one of these conditions fails to hold. C1 UNIV MICHIGAN,SCH BUSINESS ADM,ANN ARBOR,MI 48109. CR Berger JO, 1985, STATISTICAL DECISION BICKEL PJ, 1977, MATH STATISTICS BOOKSTEIN A, 1977, J AM SOC INFORM SCI, V13, P377 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 Brier GW, 1950, MONTHLY WEATHER REVI, V78, P1, DOI DOI 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 COOPER WS, 1972, UNPUB INADEQUACY PRO Cox D, 1970, ANAL BINARY DATA DeGroot M. H., 1982, STATISTICAL DECISION, V1, P291 DEGROOT MH, 1983, STATISTICIAN, V32, P12, DOI 10.2307/2987588 EISENBERG M, 1988, J AM SOC INFORM SCI, V39, P293, DOI 10.1002/(SICI)1097-4571(198809)39:5<293::AID-ASI1>3.0.CO;2-I FUHR N, 1989, INFORM PROCESS MANAG, V25, P493, DOI 10.1016/0306-4573(89)90020-4 GORDON MD, 1991, INFORM PROCESS MANAG, V27, P27, DOI 10.1016/0306-4573(91)90029-L GORDON MD, 1991, J AM SOC INFORM SCI, V42, P703, DOI 10.1002/(SICI)1097-4571(199112)42:10<703::AID-ASI3>3.0.CO;2-1 KOCHEN M, 1974, PRINCIPLES INFORMATI LENK PJ, 1988, MANAGE SCI, V34, P1450, DOI 10.1287/mnsc.34.12.1450 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAVAGE JL, 1971, J AM STAT ASSOC, V77, P783 STIRLING KH, 1977, THESIS U CALIFORNIA TAGUE JM, 1973, INFORM STORAGE RET, V9, P129, DOI 10.1016/0020-0271(73)90063-6 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 23 TC 9 Z9 9 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1992 VL 43 IS 1 BP 1 EP 14 DI 10.1002/(SICI)1097-4571(199201)43:1<1::AID-ASI1>3.0.CO;2-5 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GX839 UT WOS:A1992GX83900001 ER PT J AU LARSON, RR AF LARSON, RR TI EVALUATION OF ADVANCED RETRIEVAL TECHNIQUES IN AN EXPERIMENTAL ONLINE CATALOG SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; SUBJECT ACCESS; DOCUMENT-RETRIEVAL; CLASSIFICATION; LIBRARY; MODEL; SYSTEMS; PROBABILITY; PERFORMANCE; USERS AB Research on the use and users of online catalogs conducted in the early 1980s found that subject searches were the most common form of online catalog search. At the same time, many of the problems experienced by online catalog users have been traced to difficulties with the subject access mechanisms of the online catalog. Numerous proposals have been made for methods intended to improve subject access to online catalog records. These commonly involve enhancing the catalog' s bibliographic records with additional terms, or incorporating subject authority files or additional thesauri in the database. Another stream of research has concentrated on applying retrieval techniques derived from information retrieval (IR) research to replace the Boolean search methods of conventional online catalog systems. This study describes the results of retrieval tests using a variety of these search methods in the CHESHIRE experimental online catalog system. RP LARSON, RR (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BATES MJ, 1989, LIBR RESOUR TECH SER, V33, P400 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BJORKLUND L, 1989, MANAGING INFORMATION, P129 BLACKSHAW L, 1988, J AM SOC INFORM SCI, V39, P369, DOI 10.1002/(SICI)1097-4571(198811)39:6<369::AID-ASI1>3.0.CO;2-2 BLAZEK R, 1988, RQ, V28, P169 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P387, DOI 10.1002/(SICI)1097-4571(198611)37:6<387::AID-ASI3>3.0.CO;2-8 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 1985, RES DEV INFORMATION BYRNE A, 1988, COLL RES LIBR, V49, P432 CHAN LM, 1986, INFORM TECHNOL LIBR, V5, P181 CHAN LM, 1989, RQ, V28, P530 Cochrane P. A, 1983, LIBRARY INFORMATION, V5, P337 COCHRANE PA, 1985, INFORM TECHNOL LIBR, V4, P91 COOPER WS, 1988, INFORMATION PROCESSI, V23, P243 COPPER WS, 1983, J AM SOC INFORM SCI, V34, P31 CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Dickson J., 1984, Cataloging & Classification Quarterly, V4, DOI 10.1300/J104v04n03_02 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 GARLAND K, 1983, INFORM PROCESS MANAG, V19, P113, DOI 10.1016/0306-4573(83)90064-X HANCOCK M, 1987, J DOC, V43, P303, DOI 10.1108/eb026813 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 HARTER SP, 1985, ONLINE REV, V9, P407, DOI 10.1108/eb024194 HARTER SP, 1990, J AM SOC INFORM SCI, V41, P132, DOI 10.1002/(SICI)1097-4571(199003)41:2<132::AID-ASI6>3.0.CO;2-L Henty M., 1986, LASIE (Bulletin of LASIE Australia Company Limited), V17 Hildreth C., 1984, ONLINE CATALOGS ONLI, P31 HILDRETH CR, 1985, ANNU REV INFORM SCI, V20, P233 HILDRETH CR, 1987, LIBR TRENDS, V35, P647 HILDRETH CR, 1989, ONLINE CATALOGUE DEV, P1 HILDRETH CR, 1989, INTELLIGENT INTERFAC HJERPPE R, 1986, INTELLIGENT INFORMAT, P211 HJERPPE R, 1989, ONLINE CATALOGUE DEV, P177 KERNSIMIRENKO C, 1983, LIB HI TECH, V1, P27, DOI 10.1108/eb047508 LARSON RR, 1991, LIBR QUART, V61, P133 Larson Ray, 1991, ADV LIBRARIANSHIP, V15, P175, DOI 10.1108/S0065-2830(1991)0000015008 LARSON RR, 1989, MANAGING INFORMATION, P129 LARSON RR, 1983, ED231401 Lawrence G. S., 1983, USING ONLINE CATALOG MARKEY K, 1988, INFORM TECHNOL LIBR, V7, P299 MARKEY K, 1983, OCLCOPRRR833 RES REP, V2 Markey K., 1987, Cataloging & Classification Quarterly, V7, DOI 10.1300/J104v07n03_05 MARKEY K, 1989, ONLINE CATALOGUE DEV, P61 MARKEY K, 1986, OCLCOPRRR861 RES REP MARKEY K, 1986, IMPACT ONLINE CATALO, P35 MARKEY K, 1985, LIBR RESOUR TECH SER, V29, P34 MARKEY K, 1984, SUBJECT SEARCHING LI MARON ME, 1988, INFORM PROCESS MANAG, V24, P249, DOI 10.1016/0306-4573(88)90092-1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MITEV NN, 1985, 39 LIBR INF RES REP NOERR PL, 1985, INFORM PROCESS MANAG, V21, P205, DOI 10.1016/0306-4573(85)90105-0 ONEILL ET, 1979, OCLCRDDRR791 REP PEJTERSEN AM, 1989, SIGIR FORUM, V23, P40, DOI 10.1145/75334.75340 PETERS TA, 1989, J ACAD LIBR, V15, P267 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 POTTER WG, 1989, INFORM TECHNOL LIBR, V8, P99 PRABHA C, 1989, OPACS RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1984, SIGMOD Record, V14 Salton G., 1989, AUTOMATIC TEXT PROCE Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SIEGEL ER, 1984, INFORM TECHNOL LIBR, V3, P35 SVENONIUS E, 1983, LIBR RESOUR TECH SER, V27, P76 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WALKER S, 1987, 24 BRIT LIBR RES DEV WALKER S, 1987, LIBR TRENDS, V35, P631 WALKER S, 1989, ONLINE CATALOGUE DEV, P84 WALKER S, 1988, PROGRAM-AUTOM LIBR, V22, P21, DOI 10.1108/eb046984 WIBERLEY SE, 1988, COLL RES LIBR, V49, P149 Williamson Nancy, 1986, INT CATALOGUING, V15, P45 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P324, DOI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 NR 80 TC 24 Z9 24 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1992 VL 43 IS 1 BP 34 EP 53 DI 10.1002/(SICI)1097-4571(199201)43:1<34::AID-ASI4>3.0.CO;2-F PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GX839 UT WOS:A1992GX83900004 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI AN INFORMATION-THEORETIC MEASURE OF TERM SPECIFICITY SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID DOCUMENT REPRESENTATION; PROBABILISTIC MODELS; RETRIEVAL AB The inverse document frequency (IDF) and signal-noise ratio (S/N) approaches are two well known term weighting schemes based on term specificity. However, the existing justifications for these methods are still somewhat inconclusive and sometimes even based on incompatible assumptions. Although both methods are related to term specificity, their relationship has not been thoroughly investigated. An information-theoretic measure for term specificity is introduced in this study. It is explicitly shown that the IDF weighting scheme can be derived from the proposed approach by assuming that the frequency of occurrence of each index term is uniform within the set of documents containing the term. The information-theoretic interpretation of term specificity also establishes the relationship between the IDF and S/N methods. RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BROOKES BC, 1972, J DOC, V28, P160, DOI 10.1108/eb026537 CHEN Z, 1988, 1988 IEEE INT S INF, P107 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 DENNIS SF, 1964, MISCELLANEOUS PUBLIC, V269 GUIASU S, 1977, INFORMATION THEORY A HARTER PS, 1975, J ASIS 1, V26, P197 Hartley RVL, 1928, BELL SYST TECH J, V7, P535 Hwang C. L., 1981, MULTIPLE ATTRIBUTE D Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Klir J., 1988, FUZZY SETS UNCERTAIN MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 RAGHAVAN VV, 1983, 6TH P ACM SIGIR C RE, P88 RAO CR, 1982, THEOR POPUL BIOL, V21, P24, DOI 10.1016/0040-5809(82)90004-1 ROBERTSON SE, 1981, ACM SIGIR FORUM, V16, P22, DOI 10.1145/1013228.511758 ROBERTSO.SE, 1972, J DOC, V28, P164 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBERTSO.SE, 1974, J DOC, V30, P41 Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 SRINIVASAN P, 1990, INFORM PROCESS MANAG, V26, P269, DOI 10.1016/0306-4573(90)90030-6 SRINIVASAN P, 1990, J AM SOC INFORM SCI, V41, P61, DOI 10.1002/(SICI)1097-4571(199001)41:1<61::AID-ASI5>3.0.CO;2-Q VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Wong S. K. M., 1988, 11th International Conference on Research and Development in Information Retrieval WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, 1989, TR89985 CORN U DEP C Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 Zeleny M., 1974, LINEAR MULTIOBJECTIV NR 34 TC 13 Z9 14 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1992 VL 43 IS 1 BP 54 EP 61 DI 10.1002/(SICI)1097-4571(199201)43:1<54::AID-ASI5>3.0.CO;2-A PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GX839 UT WOS:A1992GX83900005 ER PT J AU WILBUR, WJ AF WILBUR, WJ TI A RETRIEVAL-SYSTEM BASED ON AUTOMATIC RELEVANCE WEIGHTING OF SEARCH TERMS SO PROCEEDINGS OF THE ASIS ANNUAL MEETING LA English DT Article; Proceedings Paper CT 55TH ANNUAL MEETING OF THE AMERICAN SOC FOR INFORMATION SCIENCE CY OCT 26-29, 1992 CL PITTSBURGH, PA SP AMER SOC INFORMAT SCI ID DOCUMENT-RETRIEVAL AB We have developed a retrieval methodology based on relevance weighting of search terms with an automatic implementation. It is founded on the familiar Bayesian formulation of the probability of relevance as a function of term occurrence where the contribution from individual terms is assumed to be independent. However our formulation departs from the usual in that it is based on considering pairs of documents, and terms contribute to the score (log odds of relevance) in a symmetric manner. Terms which appear in both documents in a pair contribute positively to the score and terms that occur in only one document contribute negatively. This allows the length of a document to become a negative factor in the scoring much as it is in the cosine formula commonly used in vector retrieval. As a consequence each term has two weights associated with it, one positive which is its contribution when it occurs in both documents of a pair, and one negative which is its contribution when it occurs in only one document of a pair. A method is found to incorporate local term weights based on within document term frequencies, into this retrieval scheme. We term the result the relevance pairs(RP) model. We obtain the necessary statistics to estimate the weights in the model by substituting the set of all highly rated document pairs identified by a successful retrieval method for the set of all relevant pairs of documents. For the successful retrieval method we use the vector cosine (VC) model. In our test environment we find improved retrieval by the RP model when compared with the VC model. RP WILBUR, WJ (reprint author), NATL LIB MED,NCBI,BETHESDA,MD 20209, USA. CR BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUCKLEY C, 1985, 85686 CORN U DEP COM COOPER WS, 1991, 54TH P ANN M AM SOC, V28, P366 CROFT WB, 1982, COINS8221 U MASS TEC FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B LEWIS DD, 1991, MACHINE LEARNING, P235 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILBUR WJ, IN PRESS INFORMATION Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 16 TC 0 Z9 0 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0044-7870 J9 P ASIS ANNU MEET JI Proc. ASIS Annu. Meet. PY 1992 VL 29 BP 216 EP 220 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA JV325 UT WOS:A1992JV32500030 ER PT J AU WONG, SKM YAO, YY SALTON, G BUCKLEY, C AF WONG, SKM YAO, YY SALTON, G BUCKLEY, C TI EVALUATION OF AN ADAPTIVE LINEAR-MODEL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; TEXT SEARCH; FEEDBACK AB This article reports on the experimental evaluation of an adaptive linear model that constructs improved query vectors from the user preference judgments on a sample set of documents. The performance of this method is compared with that of the standard relevance feedback techniques. The experimental results seem to demonstrate the effectiveness of the adaptive method. C1 CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853. RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Bollmann P., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42023 Chang Y, 1971, SMART RETRIEVAL SYST, P355 DILLON N, 1980, J DOC, V36, P197 Duda R., 1973, PATTERN CLASSIFICATI FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1971, SMART RETRIEVAL SYST, P357 Minsky M. L., 1969, PERCEPTRONS Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 SALTON G, 1988, 88898 CORN U DEP COM SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SCHAUBLE P, 1989, 113 ETH ZURICH DEP I STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WAN SJ, 1990, UNPUB SAMPLE SELECTI WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, 1989, INFORM SYST, V14, P47, DOI 10.1016/0306-4379(89)90024-0 WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CT, 1989, ACM T DATABASE SYST, V14, P147, DOI 10.1145/63500.63519 NR 28 TC 9 Z9 9 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD DEC PY 1991 VL 42 IS 10 BP 723 EP 730 DI 10.1002/(SICI)1097-4571(199112)42:10<723::AID-ASI5>3.0.CO;2-U PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GQ890 UT WOS:A1991GQ89000005 ER PT J AU SALTON, G AF SALTON, G TI DEVELOPMENTS IN AUTOMATIC TEXT RETRIEVAL SO SCIENCE LA English DT Article ID INTELLIGENT INFORMATION-RETRIEVAL; VECTOR-SPACE MODEL; DOCUMENT-RETRIEVAL; SYSTEM; RELEVANCE; SEARCH; HYPERTEXT; INTERMEDIARY; PROBABILITY; FEEDBACK AB Recent developments in the storage, retrieval, and manipulation of large text files are described. The text analysis problem is examined, and modern approaches leading to the identification and retrieval of selected text items in response to search requests are discussed. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR AMSLER RA, 1984, ANNU REV INFORM SCI, V19, P161 BARHILLEL Y, 1964, LANGUAGE INFORMATION, P330 Biebricher P., 1988, 11th International Conference on Research and Development in Information Retrieval BISWAS G, 1987, J AM SOC INFORM SCI, V38, P83, DOI 10.1002/(SICI)1097-4571(198703)38:2<83::AID-ASI2>3.0.CO;2-X BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BOOKSTEIN A, 1983, LECT NOTES COMPUT SC, V146, P118 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Brachman R. J., 1985, READINGS KNOWLEDGE R BRAJNIK G, 1987, INFORM PROCESS MANAG, V23, P305, DOI 10.1016/0306-4573(87)90020-3 Brauen T., 1971, SMART RETRIEVAL SYST, P456 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 CHIARAMELLA Y, 1987, INFORM PROCESS MANAG, V23, P285, DOI 10.1016/0306-4573(87)90019-7 Cleverdon C., 1984, Information Services & Use, V4 COHEN PR, 1987, INFORM PROCESS MANAG, V23, P255, DOI 10.1016/0306-4573(87)90017-3 CONKLIN J, 1987, COMPUTER, V20, P17 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 CROFT WB, 1989, 1989 P HYP 89 PITTSB, P213 CROUCH DC, 1986, 9TH P ANN INT C RES, P58 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 DOCZKOCS TE, 1986, 9TH P ANN INT C RES, P49 Eco U., 1976, THEORY SEMIOTICS FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Findler N. V., 1979, ASS NETWORKS REPRESE Fox E. A., 1988, Second Conference on Applied Natural Language Processing FRISSE ME, 1988, COMMUN ACM, V31, P880, DOI 10.1145/48511.48518 Fuhr N, 1990, 13TH P INT C RES DEV, P45 FURUTA R, 1989, COMPUT J, V32, P493 GAY LS, 1990, INFORM PROCESS MANAG, V26, P21, DOI 10.1016/0306-4573(90)90007-O GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 GUIDA G, 1983, AUTOMATICA, V19, P759, DOI 10.1016/0005-1098(83)90044-4 Ide E., 1971, SMART RETRIEVAL SYST, P337 INGWERSEN P, 1986, 9TH P ACM SIGIR C RE, P68 Jacobs P. S., 1988, 11th International Conference on Research and Development in Information Retrieval LANCASTER FW, 1979, INFORMATION RETRIEVA Lenat D., 1986, AI MAG, V6, P65 Lovins J., 1968, MECH TRANSLATION COM, V11, P11 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MURTAGH F, 1982, COMPUT J, V26, P354 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 POLLITT AS, 1984, ASLIB PROC, V36, P229, DOI 10.1108/eb050928 Quillian M. R., 1968, SEMANTIC INFORMATION, P216 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RAU LF, 1987, INFORM PROCESS MANAG, V23, P269, DOI 10.1016/0306-4573(87)90018-5 RAYMOND DR, 1988, COMMUN ACM, V31, P871, DOI 10.1145/48511.48517 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 Salton G., 1975, REGIONAL C SERIES AP, V18 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1990, 901083 CORN U COMP S SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 SALTON G, 1990, 901125 CORN U COMP S Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1990, TR901113 CORN U COMP SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1990, INFORM PROCESS MANAG, V26, P73, DOI 10.1016/0306-4573(90)90010-Y SALTON G, 1981, ACM SIGIR FORUM, V16, P22, DOI 10.1145/1095425.1095427 Schank R. C., 1975, CONCEPTUAL INFORMATI SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q Smeaton A. F., 1988, 11th International Conference on Research and Development in Information Retrieval SPARCKJONES K, 1971, KEYWORD CLASSIFICATI SPARCKJONES K, 1972, J DOC, V28, P11 SPARCKJONES K, 1983, INTELLIGENT INFORMAT, V7, P136 SPARCK-JONES K, 1984, J DOC, V40, P50 STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 Stanfill C., 1986, Communications of the ACM, V29, DOI 10.1145/7902.7906 STANFILL C, 1990, 13TH P INT C RES DEV, P413 TONG RM, 1987, 10TH P ANN INT ACM S, P247 Turtle H. R., 1990, 13TH P INT C RES DEV, P1 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees E.M., 1985, 8TH P ANN INT C RES, P188 VOORHEES EM, 1986, 9TH P ANN INT ACM SI, P164 Walker D., 1986, ANAL LANGUAGE RESTRI Wittgenstein L., 1953, PHILOS INVESTIGATION WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, 1986, 9TH P INT C RES DEV, P175 WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 NR 97 TC 247 Z9 272 PU AMER ASSOC ADVANCEMENT SCIENCE PI WASHINGTON PA 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 SN 0036-8075 J9 SCIENCE JI Science PD AUG 30 PY 1991 VL 253 IS 5023 BP 974 EP 980 DI 10.1126/science.253.5023.974 PG 7 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA GC982 UT WOS:A1991GC98200033 PM 17775340 ER PT J AU FUHR, N BUCKLEY, C AF FUHR, N BUCKLEY, C TI A PROBABILISTIC LEARNING APPROACH FOR DOCUMENT INDEXING SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE EXPERIMENTATION; THEORY; COMPLEX DOCUMENT REPRESENTATION; LINEAR INDEXING FUNCTIONS; LINEAR RETRIEVAL FUNCTIONS; PROBABILISTIC INDEXING; PROBABILISTIC RETRIEVAL; RELEVANCE DESCRIPTIONS ID INFORMATION-RETRIEVAL; SYSTEM; MODELS; REPRESENTATION AB We describe a method for probabilistic document indexing using relevance feedback data that has been collected from a set of queries. Our approach is based on three new concepts: (1) ion from specific terms and documents, which overcomes the restriction of limited relevance information for parameter estimation. (2) Flexibility of the representation, which allows the integration of new text analysis and knowledge-based methods in our approach as well as the consideration of document structures or different types of terms. (3) Probabilistic learning or classification methods for the estimation of the indexing weights making better use of the available relevance information. Our approach can be applied under restrictions that hold for real applications. We give experimental results for five test collections which show improvements over other methods. C1 TH DARMSTADT,W-6100 DARMSTADT,GERMANY. CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853. RP FUHR, N (reprint author), UNIV DORTMUND,POSTFACH 500500,W-4600 DORTMUND,GERMANY. CR BEINKEGEISER U, 1986, AUTOMATISCHE INDEXIE, P73 Biebricher P., 1988, 11th International Conference on Research and Development in Information Retrieval BIEBRICHER P, 1988, NACHR DOK, V39, P135 CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Fagan J., 1987, 10TH P ASS COMP MACH, P91 FREEMAN DH, 1987, APPLIED CATEGORIAL D FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1988, THESIS TH DARMSTADT GAISST S, 1990, THESIS TH DARMSTADT GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 IKNORZ G, 1983, AUTOMATISCHES INDEXI KWOK KL, 1988, INFORM PROCESS MANAG, V24, P405, DOI 10.1016/0306-4573(88)90044-1 KWOK KL, 1986, 1986 P ACM SIGIR INT, P275 KWOK KL, 1989, 12TH P ANN INT ACM S, P21 MARON ME, 1983, RES DEV INFORMATION, P98 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 PFEIFER U, 1990, THESIS TH DARMSTADT Quinlan J.R., 1986, MACHINE LEARNING ART, P149 Robertson S. E., 1982, Information Technology: Research and Development, V1 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q SMEATON AF, 1986, 1986 P ACM C RES DEV, P103 TIETZE A, 1989, THESIS TH DARMSTADT Turtle H. R., 1990, THESIS U MASSACHUSET VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 WONG AKC, 1987, IEEE T PATTERN ANAL, V9, P796 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 Yu C. T., 1988, 11th International Conference on Research and Development in Information Retrieval YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 37 TC 83 Z9 85 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 1991 VL 9 IS 3 BP 223 EP 248 DI 10.1145/125187.125189 PG 26 WC Computer Science, Information Systems SC Computer Science GA GU977 UT WOS:A1991GU97700003 ER PT J AU PEAT, HJ WILLETT, P AF PEAT, HJ WILLETT, P TI THE LIMITATIONS OF TERM COOCCURRENCE DATA FOR QUERY EXPANSION IN DOCUMENT-RETRIEVAL SYSTEMS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RELEVANCE INFORMATION; SEARCH TERMS; FEEDBACK; MODEL AB Term cooccurrence data has been extensively used in document retrieval systems for the identification of indexing terms that are similar to those that have been specified in a user query: these similar terms can then be used to augment the original query statement. Despite the plausibility of this approach to query expansion, the retrieval effectiveness of the expanded queries is often no greater than, or even less than, the effectiveness of the unexpanded queries. This article demonstrates that the similar terms identified by cooccurrence data in a query expansion system tend to occur very frequently in the database that is being searched. Unfortunately, frequent terms tend to discriminate poorly between relevant and nonrelevant documents, and the general effect of query expansion is thus to add terms that do little or nothing to improve the discriminatory power of the original query. C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. RI Peat, Helen/E-9666-2015 CR BIRU T, 1989, J DOC, V45, P85, DOI 10.1108/eb026840 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1971, AUTOMATIC KEYWORD CL LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 PEAT HJ, 1989, THESIS U SHEFFIELD ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1975, THEORY INDEXING SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SMEATON AF, 1982, THESIS U COLLEGE DUB STEVENS ME, 1965, NBS OCCASIONAL PUBLI, V269 STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 24 TC 99 Z9 111 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUN PY 1991 VL 42 IS 5 BP 378 EP 383 DI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA FN780 UT WOS:A1991FN78000008 ER PT J AU ALHAWAMDEH, S DEVERE, R SMITH, G WILLETT, P AF ALHAWAMDEH, S DEVERE, R SMITH, G WILLETT, P TI USING NEAREST-NEIGHBOR SEARCHING TECHNIQUES TO ACCESS FULL-TEXT DOCUMENTS SO ONLINE REVIEW LA English DT Article; Proceedings Paper CT TEXT RETRIEVAL CONF OF THE INST OF INFORMATION SCIENTISTS CY OCT, 1990 CL LONDON, ENGLAND SP INST INFORMAT SCIENTISTS ID INFORMATION-RETRIEVAL; TEACHING PACKAGE; COMPUTATIONAL ASPECTS; RANKING ALGORITHMS; INSTRUCT; HYPERTEXT; SYSTEM; APPLICABILITY; KNOWLEDGE; DATABASES AB Full-text documents are usually searched by means of a Boolean retrieval algorithm that requires the user to specify the logical relationships between the terms of a query. In this paper, we summarise the results to date of a continuing programme of research at the University of Sheffield to investigate the use of nearest-neighbour retrieval algorithms for full-text searching. Given a natural-language query statement, our methods result in a ranking of the paragraphs comprising a full-text document in order of decreasing similarity with the query, where the similarity for each paragraph is determined by the number of keyword stems that it has in common with the query. A full-text document test collection has been created to allow systematic tests of retrieval effectiveness to be carried out. Experiments with this collection demonstrate that nearest-neighbour searching provides a means for paragraph-based access to full-text documents that is of comparable effectiveness to both Boolean and hypertext searching and that index term weighting schemes which have been developed for the searching of bibliographical databases can also be used to improve the effectiveness of retrieval from full-text databases. A current project is investigating the extent to which a paragraph-based full-text retrieval system can be used to augment the explication facilities of an expert system on welding. RP WILLETT, P (reprint author), UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR AGOSTI M, 1988, ONLINE INFORMATION 88, PROCEEDINGS VOLS 1-2, P57 ALHAWAMDEH S, 1988, ONLINE INFORMATION 88, PROCEEDINGS VOLS 1-2, P761 ALHAWAMDEH S, 1989, INT J INFORMATION LI, V1, P116 ALHAWAMDEH S, 1991, PROGRAM-AUTOM LIBR, V25, P119, DOI 10.1108/eb047080 Al-hawamdeh S., 1989, Electronic Publishing: Origination, Dissemination and Design, V2 Ashford J., 1988, TEXT RETRIEVAL DOCUM BERNSTEIN LM, 1984, J AM SOC INFORM SCI, V35, P235, DOI 10.1002/asi.4630350407 BLAIR DC, 1986, INT CLASSIF, V13, P18 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOVEY JD, 1987, J DOC, V43, P125, DOI 10.1108/eb026805 COHEN SM, 1980, J CHEM INF COMP SCI, V20, P247, DOI 10.1021/ci60024a011 CONKLIN J, 1987, COMPUTER, V20, P17 COVE JF, 1988, INFORM PROCESS MANAG, V24, P31, DOI 10.1016/0306-4573(88)90075-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DABNEY DP, 1986, LAW LIBR J, V78, P5 FRANKLIN C, 1990, DATABASE, V13, P24 FRISSE M, 1987, COMMUN ACM, V31, P819 GAUCH S, 1990, 13 ACM SIGIR INT C R, P255 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P382, DOI 10.1108/eb046949 KINNELL S, 1988, ONLINE, V12, P34 MARCHIONINI G, 1988, COMPUTER, V21, P70, DOI 10.1109/2.222119 MCALEESE R, 1989, HYPERTEXT THEORY PRA MCKININ EJ, 1989, ONLINE REV, V13, P369, DOI 10.1108/eb024317 OCONNOR J, 1981, J AM SOC INFORM SCI, V31, P227 OCONNOR J, 1975, INFORM PROCESS MANAG, V11, P155, DOI 10.1016/0306-4573(75)90004-7 RO JS, 1988, J AM SOC INFORM SCI, V39, P147, DOI 10.1002/(SICI)1097-4571(198805)39:3<147::AID-ASI1>3.0.CO;2-O RO JS, 1988, J AM SOC INFORM SCI, V39, P73, DOI 10.1002/(SICI)1097-4571(198803)39:2<73::AID-ASI1>3.0.CO;2-X ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROWLEY JE, 1988, ASLIB PROC, V40, P311, DOI 10.1108/eb051116 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SHNEIDERMAN B, 1989, J AM SOC INFORM SCI, V40, P172, DOI 10.1002/(SICI)1097-4571(198905)40:3<172::AID-ASI8>3.0.CO;2-3 Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 TENOPIR C, 1988, P ASIS ANNU MEET, V25, P80 TENOPIR C, 1985, ONLINE REV, V9, P149, DOI 10.1108/eb024180 TENOPIR C, 1984, ANNU REV INFORM SCI, V19, P215 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WADE SJ, 1988, PROGRAM-AUTOM LIBR, V22, P44, DOI 10.1108/eb046986 Wagers R., 1986, 10th International Online Information Meeting WATTERS CR, 1985, ONLINE REV, V9, P391, DOI 10.1108/eb024193 WILLETT P, 1988, DOCUMENT RETRIEVAL S 1988, GUIDE VERSION 2 0 US NR 44 TC 5 Z9 5 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0309-314X J9 ONLINE REV PD JUN-AUG PY 1991 VL 15 IS 3-4 BP 173 EP 191 DI 10.1108/eb024372 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GY117 UT WOS:A1991GY11700005 ER PT J AU LARSON, RR AF LARSON, RR TI CLASSIFICATION CLUSTERING, PROBABILISTIC INFORMATION-RETRIEVAL, AND THE ONLINE CATALOG SO LIBRARY QUARTERLY LA English DT Review ID SUBJECT ACCESS; DOCUMENT-RETRIEVAL; RELEVANCE INFORMATION; AUTHORITY CONTROL; LIBRARY; SYSTEMS; MODEL; OPAC; REFERENCES; SUCCESS RP LARSON, RR (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR ATHERTON P, 1978, ED156131 BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BATES MJ, 1989, LIBR RESOUR TECH SER, V33, P400 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P280 BATES MJ, 1977, J AM SOC INFORM SCI, V28, P161, DOI 10.1002/asi.4630280304 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P205, DOI 10.1002/asi.4630300406 BELKIN NJ, 1987, KANN REV INFORMATION, V22, P109 BLACKSHAW L, 1988, J AM SOC INFORM SCI, V39, P369, DOI 10.1002/(SICI)1097-4571(198811)39:6<369::AID-ASI1>3.0.CO;2-2 BLAIR DC, 1980, J AM SOC INFORM SCI, V31, P271, DOI 10.1002/asi.4630310407 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BLAZEK R, 1988, RQ, V28, P169 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P387, DOI 10.1002/(SICI)1097-4571(198611)37:6<387::AID-ASI3>3.0.CO;2-8 BUCKLEY C, 1987, IMPLEMENTATION SMART SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BYRNE A, 1988, COLL RES LIBR, V49, P432 CHAN LM, 1986, INFORM TECHNOL LIBR, V5, P181 CHAN LM, 1989, RQ, V28, P530 COCHRANE PA, 1985, INFORM TECHNOL LIBR, V4, P91 COOPER WS, 1988, INFORM PROCESS MANAG, V24, P243, DOI 10.1016/0306-4573(88)90091-X COOPER WS, 1983, J AM SOC INFORM SCI, V434, P31 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Dickson J., 1984, Cataloging & Classification Quarterly, V4, DOI 10.1300/J104v04n03_02 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 GARLAND K, 1983, INFORM PROCESS MANAG, V19, P113, DOI 10.1016/0306-4573(83)90064-X GERHAN DR, 1989, J ACAD LIBR, V15, P83 GOUKE MN, 1982, J ACAD LIBR, V8, P137 HANCOCK M, 1987, LJ DOCUMENTATION, V43, P303 HARTER SP, 1985, ONLINE REV, V9, P407, DOI 10.1108/eb024194 HARTER SP, 1990, J AM SOC INFORM SCI, V41, P132, DOI 10.1002/(SICI)1097-4571(199003)41:2<132::AID-ASI6>3.0.CO;2-L HILDRETH C, 1989, ONLINE CATALOGUE DEV HILDRETH CR, 1985, ANNU REV INFORM SCI, V20, P233 HILDRETH CR, 1984, ONLINE CATALOGS ONLI HILDRETH CR, 1989, INTELLIGENT INTERFAC HJERRPE R, 1986, INTELLIGENT INFORMAT JAMIESON AJ, 1986, J ACAD LIBR, V12, P277 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KERNSIMIRENKO C, 1983, LIB HI TECH, V1, P27, DOI 10.1108/eb047508 LARSON RR, 1991, IN PRESS ADV LIBRARI LARSON RR, EVALUATION ADV RETRI LARSON RR, 1986, THESIS U CALIFORNIA LARSON RR, 1989, MANAGING INFORMATION LARSON RR, IN PRESS J AM SOC IN, V42 LARSON RR, 1983, ED231401 Lawrence G. S., 1983, USING ONLINE CATALOG LAWRENCE GS, 1985, LIBR RESOUR TECH SER, V29, P16 Lenat D. B., 1990, BUILDING LARGE KNOWL LYNCH CA, 1989, OPACS P JOINT M BRIT MANDEL CA, 1985, LIBR RESOUR TECH SER, V29, P5 MANDEL CA, 1987, ED291390 MANDELL CA, 1981, SUBJECT ACCESS ONLIN MARKEY K, 1988, INFORM TECHNOL LIBR, V7, P299 MARKEY K, 1986, IMPACT ONLINE CATALO Markey K., 1987, Cataloging & Classification Quarterly, V7, DOI 10.1300/J104v07n03_05 MARKEY K, 1983, OCLCOPRRR831 ONL COM MARKEY K, 1989, LIBR RESOUR TECH SER, V33, P37 MARKEY K, 1985, LIBR RESOUR TECH SER, V29, P34 MARKEY K, 1984, SUBJECT SEARCHING LI MARON ME, 1988, INFORM PROCESS MANAG, V24, P249, DOI 10.1016/0306-4573(88)90092-1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MATTHEWS JR, 1984, INFORM TECHNOL LIBR, V3, P354 MISCHO WH, 1987, ANNU REV INFORM SCI, V22, P227 MITEV N, 1985, 39 BRIT LIBR LIBR IN PETERS TA, 1989, J ACAD LIBR, V15, P267 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 POTTER WG, 1989, INFORM TECHNOL LIBR, V8, P99 PRABHA C, 1989, OPACS PRABHA C, 1989, ANN REV OCLC RES JUL RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1984, SIGMOD Record, V14 Salton G., 1989, AUTOMATIC TEXT PROCE SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1971, SMART RETRIEVAL SYST STEINBERG D, 1984, COLL RES LIBR, V45, P66 SVENONIUS E, 1983, LIBR RESOUR TECH SER, V27, P76 SVENONIUS E, 1986, J AM SOC INFORM SCI, V37, P331, DOI 10.1002/asi.4630370509 SWANSON DR, 1965, LIBR QUART, V35, P1 VANPULIS N, 1988, COLL RES LIBR, V49, P523 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WALKER S, 1987, LIBR TRENDS, V35, P631 WALKER S, 1988, PROGRAM-AUTOM LIBR, V22, P21, DOI 10.1108/eb046984 *WALKER S, 1987, 24 BRIT LIBR RES DEV WIBERLEY SE, 1988, COLL RES LIBR, V49, P149 Williamson Nancy, 1986, INT CATALOGUING, V15, P45 WILLIAMSON NJ, 1985, INT CATALOGUING, V14, P29 WILLIAMSON NJ, 1984, ADV LIBRARIANSHIP, V13, P49 Witty F. J., 1973, Indexer, V8 NR 93 TC 32 Z9 32 PU UNIV CHICAGO PRESS PI CHICAGO PA 5720 S WOODLAWN AVE, CHICAGO, IL 60637 SN 0024-2519 J9 LIBR QUART JI Libr. Q. PD APR PY 1991 VL 61 IS 2 BP 133 EP 173 PG 41 WC Information Science & Library Science SC Information Science & Library Science GA FF319 UT WOS:A1991FF31900001 ER PT J AU ODDY, RN BALAKRISHNAN, B AF ODDY, RN BALAKRISHNAN, B TI PTHOMAS - AN ADAPTIVE INFORMATION-RETRIEVAL SYSTEM ON THE CONNECTION MACHINE SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID DISTRIBUTED ARRAY PROCESSOR; SPREADING ACTIVATION; TEXT SEARCH; NETWORKS; DIALOG AB This paper reports the state of development of PThomas, a network based document retrieval system implemented on a massively parallel fine-grained computer, the Connection Machine. The program is written in C*, an enhancement of the C programming language which exploits the parallelism of the Connection Machine. The system is based on Oddy's original Thomas program, which was highly parallel in concept, and makes use of the Connection Machine's single instruction multiple data (SIMD) processing capabilities. After an introduction to systems like Thomas, and their relationship to spreading activation and neural network models, the current state of PThomas is described, including details about the network representation and the parallel operations that are executed during a typical PThomas session. RP ODDY, RN (reprint author), SYRACUSE UNIV,SCH INFORMAT STUDIES,SYRACUSE,NY 13210, USA. CR ALTON G, 1988, 11TH P INT C RES DEV, P147 BELEW RK, 1986, THESIS U MICHIGAN AN BELEW RK, 1989, 12TH P ANN INT ACM S, P11 Bush V., 1945, ATLANTIC MONTHLY, P101 CARROLL DM, 1988, J AM SOC INFORM SCI, V39, P390, DOI 10.1002/(SICI)1097-4571(198811)39:6<390::AID-ASI2>3.0.CO;2-N COHEN PR, 1987, INFORM PROCESS MANAG, V23, P255, DOI 10.1016/0306-4573(87)90017-3 COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 Croft W. B., 1988, 11th International Conference on Research and Development in Information Retrieval DILLON M, 1980, J DOC, V42, P197 DOYLE LB, 1961, J ACM, V8, P553, DOI 10.1145/321088.321095 FELDMAN JA, 1988, COMMUN ACM, V31, P170, DOI 10.1145/42372.42378 HOLLNAGEL E, 1979, P INFORMATICS, V5, P135 KWOK KL, 1989, 12TH P ANN INT ACM S, P21 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 Oddy R. N., 1977, Journal of Informatics, V1 ODDY RN, 1975, THESIS U NEWCASTLE U ODDY RN, 1988, P RIAO C BOSTON OFORIDWUMFUO GO, 1982, THESIS U ASTON BIRMI OFORIDWUMFUO GO, 1982, J INFORM SCI, V4, P105, DOI 10.1177/016555158200400206 OFORIDWUMFUO GO, 1984, J INFORM SCI, V9, P19, DOI 10.1177/016555158400900103 PALAY AJ, 1981, INFORMATION RETRIEVA, P310 POGUE CA, 1989, PARALLEL COMPUT, V4, P259 PREECE SE, 1981, THESIS U ILLINOIS RASMUSSEN EM, 1989, J DOC, V45, P1, DOI 10.1108/eb026836 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ross Quillian M., 1968, SEMANTIC INFORMATION, P227 Rumelhart D, 1986, PARALLEL DISTRIBUTED SALTON G, 1971, SMART RETRIEVAL SYST, pCH15 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 STANFILL C, 1989, 12TH P ANN INT ACMSI, P88 STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 STONE HS, 1987, COMPUTER, V20, P11 *THINK MACH CORP, 1988, CONN MACH PROGR C VE VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 1989, J ASIS, V38, P158 NR 37 TC 10 Z9 10 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1991 VL 27 IS 4 BP 317 EP 335 DI 10.1016/0306-4573(91)90087-3 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA FV879 UT WOS:A1991FV87900005 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI A PROBABILISTIC INFERENCE MODEL FOR INFORMATION-RETRIEVAL SO INFORMATION SYSTEMS LA English DT Article DE INFORMATION RETRIEVAL; INFERENCE; VECTOR SPACE MODEL; PROBABILISTIC MODEL; RELEVANCE FEEDBACK; INDEXING; QUERY FORMULATION ID EXPERT OPINION APPROACH; RELEVANCE; COMBINATION; LOGIC AB Based on the epistemological view of probability, a probabilistic inference model is proposed in this paper. It is argued that some of the problems presented in the conventional probabilistic models may be resolved if one takes the epistemological view instead of the alcatory view of probability. The new model also recognizes the close connections between probabilistic and vector-based approaches. It is explicitly shown that both the standard and generalized vector space models are special cases of the proposed probabilistic inference model. Our method is applicable to any basic concepts chosen from an indexing scheme. Furthermore, it provides a theoretical basis for the vector-based relevance feedback models, and has the additional advantage that no ad hoc parameters are needed. The new approach gives a plausible explanation for adopting a linear measure (discriminant function) for decision making in general, although our discussion is mainly focussed on information retrieval. RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Adams E., 1975, LOGIC CONDITIONALS BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CAN F, 1985, P ACM SIGIR C RES DE, P204, DOI 10.1145/253495.253526 Carnap R., 1971, STUDIES INDUCTIVE LO, VI CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1988, P ACM C RES DEV INF, P481, DOI 10.1145/62437.62491 CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 CROFT WB, 1989, HYPERTEXT 89 PROCEEDINGS, P213 CUADRA CA, 1967, J DOC, V23, P291, DOI 10.1108/eb026436 Duda R., 1973, PATTERN CLASSIFICATI GOOD IJ, 1983, GOOD THINKING F PROB Hacking Ian, 1975, EMERGENCE PROBABILIT Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LEWIS D, 1981, IFS Minsky M. L., 1969, PERCEPTRONS Nie J., 1988, P ACM SIGIR GREN FRA, P495, DOI 10.1145/62437.62493 NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 Pearce Glenn, 1981, IFS Pearl J., 1988, PROBABILISTIC REASON Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1977, SMART RETRIEVAL SYST SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Shafer G, 1976, MATH THEORY EVIDENCE THOMPSON P, 1988, J DOC, V44, P119, DOI 10.1108/eb026821 THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P383, DOI 10.1016/0306-4573(90)90098-M THOMPSON P, 1990, INFORM PROCESS MANAG, V26, P371, DOI 10.1016/0306-4573(90)90097-L Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, 1986 ACM C RES DEV I, P194 WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X Wong S. K. M., 1986, P 1986 ACM SIGIR C I, P228, DOI 10.1145/253168.253217 WONG SKM, 1989, ACM T DATABASE SYST, V12, P299 WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, IN PRESS J AM SOC IN WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 48 TC 14 Z9 15 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PY 1991 VL 16 IS 3 BP 301 EP 321 DI 10.1016/0306-4379(91)90003-R PG 21 WC Computer Science, Information Systems SC Computer Science GA FW514 UT WOS:A1991FW51400003 ER PT J AU GOKER, A MCCLUSKEY, TL AF GOKER, A MCCLUSKEY, TL TI TOWARDS AN ADAPTIVE INFORMATION-RETRIEVAL SYSTEM SO LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article AB Standard Information Retrieval Systems (IRS) can be used to retrieve information in response to specific requests, but they have no powers of adaption to particular users over repeated sessions. This paper describes a learning system which uses relevance feedback from a probabilistic IRS to incrementally evolve a context for a user, over a number of online sessions. We demonstrate the learning implementation with an example, and argue that it can help an IRS adapt to a user's specific needs, by using this context to influence document display and selection. C1 CITY UNIV LONDON,DEPT COMP SCI,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. RP GOKER, A (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. CR CROFT WB, 1979, J DOCUMENTATION, V35 MICHALSKI R. S., 1983, MACHINE LEARNING ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WALKER S, 1990, 72 BRIT LIBR RES DEV 1991, IN PRESS BRIT LIBRAR NR 7 TC 2 Z9 2 PU SPRINGER VERLAG PI NEW YORK PA 175 FIFTH AVE, NEW YORK, NY 10010 J9 LECT NOTES ARTIF INT JI Lect. Notes Artif. Intell. PY 1991 VL 542 BP 348 EP 357 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA KV083 UT WOS:A1991KV08300036 ER PT J AU KHARIN, NP AF KHARIN, NP TI CONTEXT-MORPHOLOGICAL METHOD OF ITERATION RETRIEVAL OF THE TEXT INFORMATION SO NAUCHNO-TEKHNICHESKAYA INFORMATSIYA SERIYA 2-INFORMATSIONNYE PROTSESSY I SISTEMY LA Russian DT Article CR BEREZKIN BS, 1987, PRIKLADNAYA INFORMAT, P29 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SOLTON D, 1979, DINAMICNESKIE BIBLIO VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 1986, DIALOGOVAYA INFORM 7 NR 9 TC 0 Z9 0 PU VSESOYUZNYI INST NAUCHNO- TEKHNICHESKOI INFORM PI MOSCOW PA BALTIISKAYA UL 14, 1-19 MOSCOW, RUSSIA SN 0548-0027 J9 NAUCH-TEKHN INFORM 2 JI Nauchn-Tekhnicheskaya Informatsiya Seriya 2-Informatsionnye Protsessy I Sistemy PY 1991 IS 11 BP 14 EP 18 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GX291 UT WOS:A1991GX29100003 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI ON TERM SELECTION FOR QUERY EXPANSION SO JOURNAL OF DOCUMENTATION LA English DT Note AB In the framework of a relevance feedback system, term values or term weights may be used to (a) select new terms for inclusion in a query, and/or (b) weight the terms for retrieval purposes once selected. It has sometimes been assumed that the same weighting formula should be used for both purposes. This paper sketches a quantitative argument which suggests that the two purposes require different weighting formulae. RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,CTR INTERACT SYST RES,DEPT INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR BROOKES BC, 1968, J DOC, V24, P41, DOI 10.1108/eb026442 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 5 TC 99 Z9 102 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD DEC PY 1990 VL 46 IS 4 BP 359 EP 364 DI 10.1108/eb026866 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ET107 UT WOS:A1990ET10700005 ER PT J AU ORMEROD, A WILLET, P BAWDEN, D AF ORMEROD, A WILLET, P BAWDEN, D TI FURTHER COMPARATIVE-STUDIES OF FRAGMENT WEIGHTING SCHEMES FOR SUBSTRUCTURAL ANALYSIS SO QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS LA English DT Article DE CHEMICAL FRAGMENTS; FRAGMENT WEIGHTING SCHEMES; ACTIVITY WEIGHTING SCHEMES; SUBSTRUCTURAL ANALYSIS; COMPARISON OF WEIGHTING SCHEMES; CHEMICAL STRUCTURE DATABASES ID ASSISTED STRUCTURE-ACTIVITY; PATTERN-RECOGNITION; CHEMICAL CARCINOGENS; DRUG DESIGN; SELECTION; MUTAGENICITY; SCREENS; FILES AB This paper considers the use of fragment weighting schemes for substructural analysis studies. Experiments are reported using 2-D and 3-D substructural descriptors with both small and large datasets for which qualitative activity data are available. The results support previous work in suggesting the potential of probabilistic models of weighting first used in studies of text retrieval. C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. PFIZER LTD,CENT RES,SANDWICH CT13 9NJ,KENT,ENGLAND. CR Ash J., 1985, COMMUNICATION STORAG AVIDON VV, 1978, KHIM FARM ZH+, V12, P88 BAWDEN D, 1983, J CHEM INF COMP SCI, V23, P14, DOI 10.1021/ci00037a003 BAWDEN D, 1988, CHEM STRUCTURES INT, P63 CHASTRETTE M, 1986, EUR J MED CHEM, V21, P505 CRAMER RD, 1988, QUANT STRUCT-ACT REL, V7, P18, DOI 10.1002/qsar.19880070105 CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 DEFLORA S, 1985, TOXICOL ENVIRON CHEM, V10, P157, DOI 10.1080/02772248509357099 DITTMAR PG, 1983, J CHEM INF COMP SCI, V23, P93, DOI 10.1021/ci00039a002 FRANKE R, 1984, THEORETICAL DRUG DES HENRY DR, 1982, J MED CHEM, V25, P899, DOI 10.1021/jm00350a004 Hodes L., 1979, ACS SYM SER, V112, P583 HODES L, 1977, J MED CHEM, V20, P469, DOI 10.1021/jm00214a002 JAKES SE, 1986, J MOL GRAPHICS, V4, P12 JAKES SE, 1987, J MOL GRAPHICS, V5, P41 JURS PC, 1979, J MED CHEM, V22, P476, DOI 10.1021/jm00191a004 JURS PC, 1985, J CHEM INF COMP SCI, V25, P296, DOI 10.1021/ci00047a028 MARTIN YC, 1990, REV COMPUTATIONAL CH ORMEROD A, 1990, THESIS U SHEFFIELD ORMEROD A, 1989, QUANT STRUCT-ACT REL, V8, P115, DOI 10.1002/qsar.19890080207 REDL G, 1974, CHEM SOC REV, V3, P273, DOI 10.1039/cs9740300273 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE SL, 1982, J MED CHEM, V25, P769, DOI 10.1021/jm00349a002 Siegel S., 1956, NONPARAMETRIC STATIS STOUCH TR, 1986, J MED CHEM, V29, P2125, DOI 10.1021/jm00161a001 STUPER AJ, 1978, J PHARM SCI, V67, P745, DOI 10.1002/jps.2600670604 TOPLISS JG, 1979, J MED CHEM, V22, P1238, DOI 10.1021/jm00196a017 WALSH DB, 1987, MUTAT RES, V182, P55, DOI 10.1016/0165-1161(87)90054-9 WILLETT O, 1988, DOCUMENT RETRIEVAL S Willett P, 1987, J CHEMOMETR, V1, P139, DOI 10.1002/cem.1180010303 YUTA K, 1981, J MED CHEM, V24, P241, DOI 10.1021/jm00135a003 NR 31 TC 6 Z9 6 PU VCH PUBLISHERS INC PI DEERFIELD BEACH PA 303 NW 12TH AVE, DEERFIELD BEACH, FL 33442-1788 SN 0931-8771 J9 QUANT STRUCT-ACT REL JI Quant. Struct.-Act. Relat. PD DEC PY 1990 VL 9 IS 4 BP 302 EP 312 DI 10.1002/qsar.19900090403 PG 11 WC Chemistry, Medicinal; Pharmacology & Pharmacy SC Pharmacology & Pharmacy GA EW227 UT WOS:A1990EW22700002 ER PT J AU KWOK, KL AF KWOK, KL TI EXPERIMENTS WITH A COMPONENT THEORY OF PROBABILISTIC INFORMATION-RETRIEVAL BASED ON SINGLE TERMS AS DOCUMENT COMPONENTS SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE EXPERIMENTATION THEORY; DOCUMENT-FOCUSED AND QUERY-FOCUSED RELEVANCE FEEDBACK, INDEXING AND RETRIEVAL; INVERSE COLLECTION TERM FREQUENCY WEIGHTING; INVERSE DOCUMENT FREQUENCY WEIGHTING; PROBABILISTIC INDEXING; PROBABILISTIC RETRIEVAL; RANKING AND WEIGHTING OF COMPOSITE OBJECTS ID CO-OCCURRENCE DATA; RELEVANCE; SYSTEM; FRAMEWORK; FEEDBACK; DESIGN; MODELS; ASK AB A component theory of information retrieval using single content terms as component for queries and documents was reviewed and experimented with. The theory has the advantages of being able to (1) bootstrap itself, that is, define initial term weights naturally based on the fact that items are self-relevant; (2) make use of within-item term frequencies; (3) account for query-focused and document-focused indexing and retrieval strategies cooperatively; and (4) allow for component-specific feedback if such information is available. Retrieval results with four collections support the effectiveness of all the first three aspects, except for predictive retrieval. At the initial indexing stage, the retrieval theory performed much more consistently across collections than Croft's model and provided results comparable to Salton's tf*idf approach. An inverse collection term frequency (ICTF) formula was also tested that performed much better than the inverse document frequency (IDF). With full feedback retrospective retrieval, the component theory performed substantially better than Croft's, because of the highly specific nature of document-focused feedback. Repetitive retrieval results with partial relevance feedback mirrored those for the retrospective. However, for the important case of predictive retrieval using residual ranking, results were not unequivocal. RP KWOK, KL (reprint author), CUNY QUEENS COLL,DEPT COMP SCI,FLUSHING,NY 11367, USA. CR BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 CROFT WB, 1989, INFORM PROCESS MANAG, V25, P599, DOI 10.1016/0306-4573(89)90095-2 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Duda R., 1973, PATTERN CLASSIFICATI FAGAN J, THESIS CORNELL U ITH FEINMAN RD, 1973, J AM SOC INFORM SCI, V24, P382, DOI 10.1002/asi.4630240510 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 Fox E. A., 1988, 11th International Conference on Research and Development in Information Retrieval FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1989, ACM T INF SYST, V7, P193 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER PS, 1975, J ASIS 2, V26, P280 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Katzer J., 1982, Information Technology: Research and Development, V1 KEMP DA, 1974, INFORM STORAGE RET, V10, P37, DOI 10.1016/0020-0271(74)90002-3 KWOK KL, 1988, INFORM PROCESS MANAG, V24, P405, DOI 10.1016/0306-4573(88)90044-1 KWOK KL, 1986, 1986 P ACM SIGIR INT, P275 KWOK KL, 1989, 12TH P ANN INT ACM S, P21 KWOK KL, 1990, 1990 P INT JOINT C N, P623 KWOK KL, 1985, J AM SOC INFORM SCI, V36, P342, DOI 10.1002/asi.4630360510 LEWIS DD, UNPUB SIGIR 90 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1981, IR RES ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CT, 1989, ACM T DATABASE SYST, V14, P147, DOI 10.1145/63500.63519 NR 42 TC 17 Z9 17 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD OCT PY 1990 VL 8 IS 4 BP 363 EP 386 DI 10.1145/102675.102677 PG 24 WC Computer Science, Information Systems SC Computer Science GA FN624 UT WOS:A1990FN62400002 ER PT J AU EFTHIMIADIS, EN AF EFTHIMIADIS, EN TI ONLINE SEARCHING AIDS - A REVIEW OF FRONT ENDS, GATEWAYS AND OTHER INTERFACES SO JOURNAL OF DOCUMENTATION LA English DT Review RP EFTHIMIADIS, EN (reprint author), CITY UNIV LONDON,CTR INTERACT SYST RES,DEPT INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR Amsler R. A., 1988, Fourth Annual Conference of the UW Centre for the New Oxford English Dictionary: Information in Text. Proceedings of the Conference BAECKER R., 1987, READINGS HUMAN COMPU BARNARD JM, 1989, PERSPECTIVES INFORMA, P133 BATES MJ, 1987, ONLINE, V11, P47 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P205, DOI 10.1002/asi.4630300406 BELKIN NJ, 1985, 35 BRIT LIBR LIBR IN BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BELKIN NJ, 1987, INFORM PROCESS MANAG, V23, P395, DOI 10.1016/0306-4573(87)90109-9 BENYON D, 1988, COMPUT J, V31, P465, DOI 10.1093/comjnl/31.5.465 BOLC L, 1986, COOPERATIVE INTERFAC BONHAM MD, 1988, B MED LIBR ASSOC, V76, P22 BORGMAN CL, 1984, ANNU REV INFORM SCI, V19, P33 BORGMAN CL, 1985, 48TH ASIS 85 P ASIS, P324 BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 BOVEY JD, 1987, J DOC, V43, P125, DOI 10.1108/eb026805 BRAJNIK G, 1986, COOPERATIVE INTERFAC, P259 BROOKS HM, 1987, INFORM PROCESS MANAG, V23, P367, DOI 10.1016/0306-4573(87)90023-9 BROOKS HM, 1986, J INFORM SCI, V12, P37, DOI 10.1177/016555158601200107 BROOKS HM, 1986, THESIS CITY U LONDON BURKET TG, 1979, INFORM PROCESS MANAG, V15, P281, DOI 10.1016/0306-4573(79)90052-9 BURTON HD, 1989, INFORM PROCESS MANAG, V25, P509, DOI 10.1016/0306-4573(89)90021-6 Burton M., 1989, Computer Systems Europe CAPLINGER M, 1987, SIGPLAN NOTICES, V22, P126 Card S.K., 1983, PSYCHOL HUMAN COMPUT CHAMBERLIN DD, 1987, COMPUT GRAPH, V11, P343, DOI 10.1016/0097-8493(87)90051-3 CHAMIS AY, 1988, J AM SOC INFORM SCI, V39, P217, DOI 10.1002/(SICI)1097-4571(198805)39:3<217::AID-ASI5>3.0.CO;2-C CHIARAMELLA Y, 1987, INFORM PROCESS MANAG, V23, P285, DOI 10.1016/0306-4573(87)90019-7 CHORAFAS D, 1989, MACHINE LEARNING PRI, P238 CLARK PM, 1987, DATABASE, V10, P47 COCKSHOTT P, 1989, 1989 IEE C PAR TECHN COMER DE, 1988, INTERNETWORKING TCP, P382 Constantopoulos P., 1987, ESPRIT '86: Results and Achievements COTTER GA, 1986, 2ND P C COMP INT INT CRAWFORD RG, 1985, 48TH P ASIS ANN M LA, P236 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROPLEY J, 1989, PERSPECTIVES INFORMA, P273 DANIELS PJ, 1986, J DOC, V42, P272, DOI 10.1108/eb026797 DETEMPLE W, 1988, 12TH P INT ONL M LON, P271 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X Doszkocs TE., 1983, RES DEV INFORM RETRI, P251 EFTHIMIADIS EN, 1989, PERSPECTIVES INFORMA, P257 EFTHIMIADIS EN, 1989, ONLINE SEARCHING AID Fenichel C. H., 1985, Microcomputers for Information Management, V2 Fenichel C. H., 1985, Microcomputers for Information Management, V2 FIDEL R, 1985, ONLINE REV, V9, P61, DOI 10.1108/eb024176 FIDEL R, 1986, J AM SOC INFORM SCI, V37, P37, DOI 10.1002/asi.4630370107 FLORIAN D, 1987, 11TH P INT ONL INF M, P423 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FUHRMAN S, 1989, IES NEWS, P3 GARFIELD E, 1985, CURR CONTENTS, P3 GAUCH S, 1989, 1989 P ANN AI SYST G, P65, DOI 10.1109/AISIG.1989.47306 GETTYS J, 1989, DR DOBBS J, V14, P42 GETTYS J, 1989, DR DOBBS J SOFTWARE, V14, P49 GETTYS J, 1989, DR DOBBS J SOFTWARE, V14, P44 GETTYS J, 1989, DR DOBBS J SOFTWARE, V14, P53 GOLDSTEIN CM, 1985, 1985 P C LIBR JAM, P40 Goldstein C. M., 1989, National Online Meeting Proceedings - 1989 GOOD MD, 1984, COMMUN ACM, V27, P1032, DOI 10.1145/358274.358284 GRAY R, 1985, PROGRAM-AUTOM LIBR, V19, P311, DOI 10.1108/eb046916 GREENBERG S, 1985, BEHAV INFORM TECHNOL, V4, P31 HARTER SP, 1985, ONLINE REV, V9, P407, DOI 10.1108/eb024194 Hartson HR, 1989, ACM COMPUT SURV, V21, P5, DOI 10.1145/62029.62031 HARTSON HR, 1989, INT J MAN MACH STUD, V31, P477, DOI 10.1016/0020-7373(89)90005-9 HAWKINS DT, 1988, INFORM PROCESS MANAG, V24, P459, DOI 10.1016/0306-4573(88)90048-9 HAWKINS DT, 1988, ONLINE, V12, P31 HAWKINS DT, 1985, ONLINE, V9, P30 Hickey T. B., 1989, National Online Meeting Proceedings - 1989 HILDRETH CR, 1982, ONLINE PUBLIC ACCESS, P263 HOROWITZ GL, 1983, JAMA-J AM MED ASSOC, V250, P2494, DOI 10.1001/jama.250.18.2494 HUSHON JM, 1986, 2ND P C COMP INT INT, P181 INGWERSEN P, 1984, ONLINE REV, V8, P465, DOI 10.1108/eb024167 JANKE RV, 1983, ONLINE, V7, P12 KEHOE CA, 1985, ONLINE REV, V9, P485 Kosmin L. J., 1986, National Online Meeting Proceedings - 1986 KUHN AD, 1986, 49TH P ASIS ANN M CH, P150 LAENENS E, 1989, COMPUT J, V32, P333, DOI 10.1093/comjnl/32.4.333 LAMB MR, 1985, 48TH ASIS 8K P ASIS, P229 LANCASTER FW, 1979, INFORMATION RETRIEVA Landau H. B., 1987, Information Services & Use, V7 LARSEN G, 1987, ELECTRON LIBR, V5, P146, DOI 10.1108/eb044747 LEBOWITZ M, 1987, 1987 EL 87 MIN MICR, P1 LEIGH W, 1986, INFORM TECHNOL LIBR, V5, P345 LEVY LR, 1986, ONLINE, V10, P33 LEVY LR, 1984, ONLINE, V8, P67 MAHON B, COMMUNICATION MARCUS RS, 1985, 48TH P ASIS ANN M LA, P289 MARCUS RS, 1983, 46TH P AM SOC INF SC, P19 MARCUS RS, 1981, J AM SOC INFORM SCI, V32, P287, DOI 10.1002/asi.4630320409 MARCUS RS, 1986, 49TH P ANN M AM SOC, P185 MARCUS RS, 1984, 1ST C COMP INT INT I MARCUS RS, 1981, J AM SOC INFORM SCI, V32, P304, DOI 10.1002/asi.4630320410 MCALPINE G, 1989, 12TH P INT ACMSIGIR, P48 MCCRACKEN DL, 1984, INT J MAN MACH STUD, V21, P293, DOI 10.1016/S0020-7373(84)80050-4 MCMATH CF, 1989, INT J MAN MACH STUD, V31, P121, DOI 10.1016/0020-7373(89)90024-2 MEADOW CT, 1981, BASICS ONLINE SEARCH MEADOW CT, 1982, J AM SOC INFORM SCI, V33, P325 MEADOW CT, 1989, J AM SOC INFORM SCI, V40, P86, DOI 10.1002/(SICI)1097-4571(198903)40:2<86::AID-ASI3>3.0.CO;2-K MEADOW CT, 1982, J AM SOC INFORM SCI, V33, P357, DOI 10.1002/asi.4630330602 MEADOW CT, 1988, INFORM PROCESS MANAG, V24, P449, DOI 10.1016/0306-4573(88)90047-7 Mecchia A., 1989, ESPRIT '89. Proceedings of the 6th Annual ESPRIT Conference (EUR 12512) Meyer B., 1988, OBJECT ORIENTED SOFT MISCHO B, 1986, 2ND P C COMP INT INT, P241 MISCHO WH, 1987, ANNU REV INFORM SCI, V22, P227 MOULINOUX C, 1983, RES DEV INFORMATION, P51 NEGUS AE, 1981, 5TH P INT ONL INF M, P425 NEWLIN B, 1985, ANSWERS ONLINE YOUR NIEHOFF RT, 1984, FINAL REPORT EVALUAT ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 ODDY RN, 1988, RIAO 88, V2, P1020 OLEARY M, 1988, ONLINE, V12, P22 PACHE JE, 1987, TEXT RETRIEVAL CONTE, P97 PARTRIDGE D, 1989, MACHINE LEARNING PRI, P207 PEDERSON GS, 1987, ESPRIT 87 ACHIEVEMEN, P688 PENNIMAN DW, 1982, MODELING EVALUATION POLLITT AS, 1988, 12TH P INT ONL M LON, P445 POLLITT AS, 1981, 5TH P INT ON LIN INF, P25 PRICE LA, 1987, 4TH P INT C TEXT PRO, P118 QUINT B, 1986, ONLINE, V10, P17 RADA R, 1989, MACHINE LEARNING PRI, P187 Rayner D., 1980, Command Language Directions. Proceedings of the IFIP TC 2.7 Working Conference on Command Languages ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1983, SIG569 BRIT LIBR RES ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ross E., 1988, Electronic Imaging '88. International Electronic Imaging Exposition and Conference. Advance Printing of Paper Summaries SAARI DS, 1989, DATABASE, V12, P22 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1988, COMMUN ACM, V31, P205 Schatz B. R., 1987, GLOBECOM Tokyo '87. IEEE/IECE Global Telecommunications Conference 1987. Conference Record (Cat. No.87CH2520-5) Sheppard M., 1988, New Directions for UNIX. Proceedings of the Autumn 1988 EUUG Conference SHNEIDERMAN B, 1982, DIRECTIONS HUMAN COM Shneiderman B, 1987, DESIGNING USER INTER SHOVAL P, 1985, INFORM PROCESS MANAG, V21, P475, DOI 10.1016/0306-4573(85)90002-0 SIEVERTS EG, 1988, 12TH P INT ONL M LON, P581 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SMITH C, 1987, 4TH P INT C TEXT PRO, P139 SMITH JB, 1987, 10TH P ANN INT ACM S, P187 Smithies T., 1986, Information Online 86. Proceedings of the First Australian Online Information Conference SNYDER H, 1986, ONLINE, V10, P5 SPIGAI F, 1984, INFORMATION TOD 0201, P6 STOUT C, 1983, ONLINE, V7, P112 TENOPIR C, 1988, LIBR J, V1, P142 THOMPSON RH, 1989, INT J MAN MACH STUD, V30, P639, DOI 10.1016/S0020-7373(89)80014-8 TOLIVER D, 1981, INFORM PROCESS MANAG, V17, P61, DOI 10.1016/0306-4573(81)90027-3 TOLIVER D, 1987, CAN J INFORM SCI, V12, P150 TOLIVER DE, 1986, 2ND P C COMP INT INT, P225 TOLIVER DE, 1982, INFORM TECHNOL LIBR, V1, P317 Trautman R., 1989, Reference Librarian Treleaven P. C., 1986, CONPAR 86. Conference on Algorithms and Hardware for Parallel Processing. Proceedings TRENNER L, 1989, INFORM PROCESS MANAG, V25, P119, DOI 10.1016/0306-4573(89)90001-0 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VICKERY A, 1988, 12TH P INT ONL M, V1, P301 VICKERY A, 1987, ONLINE REV, V11, P149, DOI 10.1108/eb024244 WALKER S, 1987, IMPROVING SUBJECT RE, V1 WALKER S, 1989, IMPROVING SUBJECT RE, V2 WALTZ D, 1987, 1987 AFIPS C P, V56, P159 WEINBERG BH, 1985, 1985 P C LIBR JAM, P135 Wiesner S. J., 1988, SIGOIS Bulletin, V9 WILLIAMS ME, 1985, SCIENCE, V228, P445, DOI 10.1126/science.3885395 WILLIAMS ME, 1986, J AM SOC INFORM SCI, V37, P204 Williams P. W., 1985, 9th International Online Information Meeting WILLINGHAM MC, 1985, ATLAS IMMUNOFLUORESC, P1 WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 YIP MK, 1981, EXPERT SYSTEM DOCUME NR 164 TC 16 Z9 16 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD SEP PY 1990 VL 46 IS 3 BP 218 EP 262 DI 10.1108/eb026861 PG 45 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DY720 UT WOS:A1990DY72000003 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI A GENERALIZED BINARY PROBABILISTIC INDEPENDENCE MODEL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Berge C, 1973, GRAPHS HYPERGRAPHS CROFT WB, 1977, J DOC, V35, P106 DeGroot M. H., 1970, OPTIMAL STATISTICAL FUHR N, 1986, P 9 SIGIR, P249, DOI 10.1145/253168.253221 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHMITT SA, 1969, MEASURE UNCERTAINTY VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG SKM, 1987, FUZZY SET SYST, V21, P357, DOI 10.1016/0165-0114(87)90135-7 Yu C.T., 1985, P 8 ANN INT ACM SIGI, P197, DOI 10.1145/253495.253525 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 15 TC 13 Z9 13 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUL PY 1990 VL 41 IS 5 BP 324 EP 329 DI 10.1002/(SICI)1097-4571(199007)41:5<324::AID-ASI2>3.0.CO;2-9 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA EK532 UT WOS:A1990EK53200002 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI QUERY FORMULATION IN LINEAR RETRIEVAL MODELS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR BOLLMANN P, 1987, P ACM SIGIR C RES DE BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CROFT WB, 1977, J DOC, V35, P106 Duda R., 1973, PATTERN CLASSIFICATI Fishburn PC, 1970, UTILITY THEORY DECIS Minsky M. L., 1969, PERCEPTRONS Nilsson NJ, 1965, LEARNING MACHINES RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 ROBERTS FS, 1976, MEASUREMENT THEORY ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Wong S. K. M., 1986, P 1986 ACM SIGIR C I, P228, DOI 10.1145/253168.253217 WONG SKM, 1987, P ACM SIGIR C RES DE WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 20 TC 33 Z9 35 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUL PY 1990 VL 41 IS 5 BP 334 EP 341 DI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA EK532 UT WOS:A1990EK53200004 ER PT J AU THOMPSON, P AF THOMPSON, P TI A SENSITIVITY ANALYSIS OF A PROBABILISTIC INFORMATION-RETRIEVAL SYSTEM SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 DREXEL UNIV,COLL INFORMAT STUDIES,PHILADELPHIA,PA 19104. CR Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 DIROCCAFERRERA GMF, 1962, OPERATIONS RES MODEL KAHNEMAN D, 1979, TIMS STUDIES MANAGEM, V12, P312 Kendall M. G., 1970, RANK CORRELATION MET Knuth D. E., 1981, ART COMPUTER PROGRAM LINDLEY DV, 1979, J ROY STAT SOC A STA, V142, P146, DOI 10.2307/2345078 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Scheffe H., 1959, ANAL VARIANCE SWANSON DR, 1965, LIBR QUART, V35, P1 TAGUE JM, 1983, LECTURE NOTES COMPUT TAGUE JM, 1973, INFORM STORAGE RET, V9, P129, DOI 10.1016/0020-0271(73)90063-6 THOMPSON P, 1988, J DOC, V44, P119, DOI 10.1108/eb026821 YOUNG HD, 1962, STATISTICAL TREATMEN NR 17 TC 0 Z9 0 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUL PY 1990 VL 41 IS 5 BP 348 EP 358 DI 10.1002/(SICI)1097-4571(199007)41:5<348::AID-ASI6>3.0.CO;2-K PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA EK532 UT WOS:A1990EK53200006 ER PT J AU SALTON, G BUCKLEY, C AF SALTON, G BUCKLEY, C TI IMPROVING RETRIEVAL PERFORMANCE BY RELEVANCE FEEDBACK SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR Chang Y, 1971, SMART RETRIEVAL SYST, P355 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 FOX EA, 1983, THESIS CORNELL U HARPER DJ, 1980, THESIS U CAMBRIDGE E Ide E., 1971, SMART RETRIEVAL SYST, P337 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCCHIO JJ, 1966, ISR10 NAT SCI F HARV SALTON G, 1971, SMART SYSTEM EXPT AU, P324 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1987, 87828 CORN U DEP COM SALTON G, 87881 CORN U DEP COM SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 WALTZ DL, 1987, COMPUTER, V20, P85 WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 NR 25 TC 485 Z9 502 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUN PY 1990 VL 41 IS 4 BP 288 EP 297 DI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DH010 UT WOS:A1990DH01000008 ER PT J AU FUHR, N AF FUHR, N TI BRIDGING THE GAP BETWEEN RETRIEVAL RESEARCH AND PRACTICE SO NACHRICHTEN FUR DOKUMENTATION LA German DT Article RP FUHR, N (reprint author), TH DARMSTADT,FACHBEREICH INFORMAT,ALEXANDERSTR 6,W-6100 DARMSTADT,GERMANY. CR BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 BRAJNIK G, 11TH INT C RES DEV I, P387 BRAND A, 1988, VERGLEICH BOOLESCHER BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1988, ACM T INFORM SYST, V6, P42, DOI 10.1145/42279.45947 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FUHR N, 1989, INFORM PROCESS MANAG, V25, P493, DOI 10.1016/0306-4573(89)90020-4 FUHR N, 1990, GRUNDLAGEN PRAKTISCH, V2 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1988, PROBABILISTISCHES IN FUHR N, 1986, DTSCH DOKUMENTARTAG, P222 FUHR N, 1987, 10TH P INT SIGIR C N, P13 FUHR N, 1989, DV II893 TH DARMST F GLOECKNERRIST A, 1986, NACHR DOK, V40, P151 HARMANN D, 11TH INT C RES DEV I, P321 JONES KAS, 11TH INT C RES DEV I, P13 Krause J, 1987, INHALTSERSCHLIESSUNG KUHLEN R, 1977, EXPT MORPHOLOGIE INF LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W LOVINS JB, 1968, MECH TRANSL, V11, P22 LUSTIG G, 1986, AUTOMATISCHE INDEXIE METZLER DP, 1989, 12TH P ACM SIGIR C C, P117 MORRISSEY J, 1983, RES DEV INFORMATION, P38 PEJTERSEN AM, 1989, 12TH P ANN INT ACMSI, P40 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAECKI T, 1988, INFORMATION PROCESSI, V24, P219 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSTEK L, 1981, BESCHREIBUNG PROGRAM SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1988, INFORM PROCESS MANAG, V24, P269, DOI 10.1016/0306-4573(88)90094-5 SALTON G, 1987, IMPROVING RETRIEVAL Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 SALTON G, 1989, 12TH P ANN INT ACMSI, P137 SMITH LC, 1987, ANNU REV INFORM SCI, V22, P41 STUBBS J, 1985, 8TH P INT ACM SIGIR, P78 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 THIEL U, 1987, 10TH P ANN INT ACM S, P45 THURMAIR G, 1986, 186 P ACM C RES DEV, P138 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZIMMERMANN HH, 1983, STUDIEN KLASSIFIKATI, V13, P14 NR 50 TC 2 Z9 2 PU VERLAG HOPPENSTEDT & CO PI DARMSTADT 1 PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY SN 0027-7436 J9 NACHR DOK JI Nachr. Dok. PD FEB PY 1990 VL 41 IS 1 BP 3 EP 7 PG 5 WC Information Science & Library Science SC Information Science & Library Science GA CV065 UT WOS:A1990CV06500001 ER PT J AU THOMPSON, P AF THOMPSON, P TI A COMBINATION OF EXPERT OPINION APPROACH TO PROBABILISTIC INFORMATION-RETRIEVAL .1. THE CONCEPTUAL-MODEL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP THOMPSON, P (reprint author), DREXEL UNIV,COLL INFORMAT STUDIES,PHILADELPHIA,PA 19104, USA. CR BELKIN NJ, 1983, J INFORM SCI, V5, P153 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEIN A, 1985, P 8 ANN INT ACM SIGI, P11, DOI 10.1145/253495.253505 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BUNN DW, 1984, APPLIED DECISION ANA CLEMEN RT, 1986, J BUS ECON STAT, V4, P39, DOI 10.2307/1391385 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 GAVASAKAR U, 1988, MANAGE SCI, V34, P784, DOI 10.1287/mnsc.34.6.784 GENEST C, 1986, STATISTICAL SCI, V1, P39 GUSTAFSO.DH, 1973, COMPUT BIOMED RES, V6, P355, DOI 10.1016/0010-4809(73)90071-2 GUSTAFSON DH, 1969, ORGAN BEHAV HUM PERF, V4, P20, DOI 10.1016/0030-5073(69)90029-4 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 HOFFMAN PJ, 1966, FORMAL REPRESENTATIO HUSEBY AB, 1985, UNPUB CONSENSUS PROB Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 Kahneman D., 1982, JUDGMENT UNCERTAINTY KERLINGER FN, 1973, F BEHAVIORAL RES LENK PJ, 1985, 8597 NEW YORK U GRAD LINDLEY D, 1983, OPER RES, V31, P866, DOI 10.1287/opre.31.5.866 LINDLEY DV, 1986, MANAGE SCI, V32, P303, DOI 10.1287/mnsc.32.3.303 LINDLEY DV, 1979, J ROY STAT SOC A STA, V142, P146, DOI 10.2307/2345078 LOSEE RM, 1988, INFORM PROCESS MANAG, V24, P315, DOI 10.1016/0306-4573(88)90097-0 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MORRIS PA, 1977, MANAGE SCI, V23, P679, DOI 10.1287/mnsc.23.7.679 MORRIS PA, 1986, MANAGE SCI, V32, P321, DOI 10.1287/mnsc.32.3.321 MORRIS PA, 1983, MANAGE SCI, V29, P24, DOI 10.1287/mnsc.29.1.24 MORRIS PA, 1974, MANAGE SCI, V20, P1233, DOI 10.1287/mnsc.20.9.1233 PETERSON CR, 1967, PSYCHOL BULL, V68, P29, DOI 10.1037/h0024722 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P219, DOI 10.1016/0306-4573(88)90089-1 Raiffa H, 1961, APPLIED STATISTICAL Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1988, COMMUNICATION ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHAEFER RE, 1973, ACTA PSYCHOL, V37, P117, DOI 10.1016/0001-6918(73)90025-5 SMEATON AF, 1983, COMPUT J, V25, P239 SMITH LC, 1983, LIPOLYTIC ENZYMES, P263 SOERGEL D, 1976, J AM SOC INFORM SCI, V27, P256, DOI 10.1002/asi.4630270411 Spiegelhalter D.J., 1986, ARTIF INTELL, P17 TAGUE J, 1983, LECTURE NOTES COMPUT, P194 TAGUE JM, 1973, INFORM STORAGE RET, V9, P129, DOI 10.1016/0020-0271(73)90063-6 THOMPSON P, 1986, THESIS U CALIFORNIA VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X WINKLER RL, 1968, MANAGE SCI, V15, pB61 WINKLER RL, 1981, MANAGE SCI, V27, P479, DOI 10.1287/mnsc.27.4.479 WINKLER RL, 1986, MANAGE SCI, V32, P298, DOI 10.1287/mnsc.32.3.298 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 55 TC 25 Z9 25 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1990 VL 26 IS 3 BP 371 EP 382 DI 10.1016/0306-4573(90)90097-L PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DL925 UT WOS:A1990DL92500003 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI ON SAMPLE SIZES FOR NON-MATCHED-PAIR IR EXPERIMENTS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,CTR INTERACT SYST RES,NORTHAMPTON SQ,LONDON EC1V 0HB,ENGLAND. CR BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 Cleverdon C. W., 1962, REPORT TESTING ANAL GILBERT H, 1979, BLRD5481 U CAMBR COM JAMIESON SH, 1979, UNPUB IMPLEMENTATION JONES KS, 1977, BLRD5428 U CAMBR COM KEEN EM, 1972, REPORT INFORMATION S Keen M., 1966, FACTORS DETERMINING, V2 ROBERTSO.SE, 1969, J DOC, V25, P93, DOI 10.1108/eb026466 ROBERTSON SE, 1990, INFORMATICS, V10, P153 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 Robertson S.E., 1981, INFORMATION RETRIEVA, P9 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Siegel S., 1956, NONPARAMETRIC STATIS NR 14 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1990 VL 26 IS 6 BP 739 EP 753 DI 10.1016/0306-4573(90)90049-8 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA EK779 UT WOS:A1990EK77900004 ER PT J AU OBRIEN, A AF OBRIEN, A TI RELEVANCE AS AN AID TO EVALUATION IN OPACS SO JOURNAL OF INFORMATION SCIENCE LA English DT Article RP OBRIEN, A (reprint author), LOUGHBOROUGH UNIV TECHNOL,DEPT LIB & INFORMAT STUDIES,LOUGHBOROUGH LE11 3TU,LEICS,ENGLAND. CR BASCH R, 1986, 7TH NAT ONL M, P19 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BORGMAN CL, 1984, ANNU REV INFORM SCI, V19, P33 BORGMAN CL, 1989, INFORM PROCESS MANAG, V25, P237, DOI 10.1016/0306-4573(89)90042-3 BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P387, DOI 10.1002/(SICI)1097-4571(198611)37:6<387::AID-ASI3>3.0.CO;2-8 DAY J, 1988, ED LIBRARIES B, V31, P1 DERVIN B, 1986, ANNU REV INFORM SCI, V21, P3 DOSZKOCS TE, 1986, J AM SOC INFORM SCI, V37, P191, DOI 10.1002/(SICI)1097-4571(198607)37:4<191::AID-ASI3>3.0.CO;2-Y EISENBERG M, 1986, P AM SOC INFORM SCI, V23, P80 EISENBERG MB, 1988, INFORM PROCESS MANAG, V24, P373, DOI 10.1016/0306-4573(88)90042-8 FENICHEL CH, 1981, J AM SOC INFORM SCI, V32, P23, DOI 10.1002/asi.4630320104 HALL HJ, 1981, J AM SOC INFORM SCI, V32, P103, DOI 10.1002/asi.4630320205 HANCOCKBEAULIEU M, 1989, ONLINE CATALOGUE DEV, P25 HILDRETH CR, 1989, ONLINE CATALOGUE DEV, P22 KEMP DA, 1974, INFORM STORAGE RET, V10, P37, DOI 10.1016/0020-0271(74)90002-3 Lawrence G. S., 1983, USING ONLINE CATALOG MCCARTHY C, 1986, COLL RES LIBR, V47, P48 MISCHO WH, 1989, ONLINE CATALOGUE DEV, P107 MITEV NN, 1986, PROGRAM-AUTOM LIBR, V20, P111, DOI 10.1108/eb046930 POTTER WG, 1989, INFORM TECHNOL LIBR, V8, P99 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P161, DOI 10.1002/(SICI)1097-4571(198805)39:3<161::AID-ASI2>3.0.CO;2-0 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SLATER M, 1988, J DOC, V44, P226, DOI 10.1108/eb026827 TAYLOR RS, 1968, COLL RES LIBR, V29, P178 VICKERY A, 1988, EXPERT SYSTEMS REFER WALKER S, 1989, ONLINE CATALOGUE DEV, P84 NR 27 TC 6 Z9 6 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1990 VL 16 IS 4 BP 265 EP 271 DI 10.1177/016555159001600407 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DV900 UT WOS:A1990DV90000007 ER PT J AU SMEATON, AF AF SMEATON, AF TI RETRIEV - AN INFORMATION-RETRIEVAL SYSTEM IMPLEMENTED ON TOP OF A RELATIONAL DATABASE SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article RP SMEATON, AF (reprint author), DUBLIN CITY UNIV,SCH COMP APPL,GLASNEVIN,DUBLIN 9,IRELAND. CR ASHFORD JH, 1987, PROGRAM-AUTOM LIBR, V21, P108, DOI 10.1108/eb046963 BROOKS TA, 1988, J AM SOC INFORM SCI, V39, P2, DOI 10.1002/(SICI)1097-4571(198801)39:1<2::AID-ASI2>3.0.CO;2-G CRAWFORD RG, 1988, TR88226 QUEENS U DEP CRAWFORD RG, 1988, TR88227 QUEENS U DEP DAVIDSON L, 1962, COMMUN ACM, V5, P169, DOI 10.1145/366862.366913 DEOGUN JS, 1988, INFORM PROCESS MANAG, V24, P303, DOI 10.1016/0306-4573(88)90096-9 GARCIAMOLINA H, 1988, CSTR14788 PRINC U DE GREEN KE, 1988, PROGRAM-AUTOM LIBR, V22, P163, DOI 10.1108/eb046995 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 LYNCH CA, 1988, UCBERL M8818 U CAL M MITEV NN, 1985, 39 LIBR INF RES REP PAVLOVICLAZETIC G, 1986, UCBERL M8621 U CAL M PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Russell R. C., 1918, Russell Index U.S. Patent, Patent No. [1,261,167, 1261167] Russell R. C., 1922, Russell Index U.S. Patent, Patent No. [1,435,663, 1435663] SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 SMEATON AF, 1988, CA0788 DUBL CIT U SC USZYNSKI M, 1987, UCBCSD87333 U CAL BE NR 20 TC 0 Z9 0 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JAN PY 1990 VL 24 IS 1 BP 21 EP 32 DI 10.1108/eb047046 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CP473 UT WOS:A1990CP47300002 ER PT J AU ASHFORD, J AF ASHFORD, J TI DOCUMENT-RETRIEVAL SYSTEMS - WILLETT,P SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Book Review RP ASHFORD, J (reprint author), ASHFORD ASSOCIATES LTD,MAIDENHEAD,ENGLAND. CR PAPE DL, 1988, PROGRAM-AUTOM LIBR, V22, P32, DOI 10.1108/eb046985 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WILLETT P, 1988, DOCUMENT RETRIEVAL S NR 3 TC 0 Z9 0 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JUL PY 1989 VL 23 IS 3 BP 359 EP 360 PG 2 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AF801 UT WOS:A1989AF80100017 ER PT J AU YU, CT MENG, W PARK, S AF YU, CT MENG, W PARK, S TI A FRAMEWORK FOR EFFECTIVE RETRIEVAL SO ACM TRANSACTIONS ON DATABASE SYSTEMS LA English DT Article RP YU, CT (reprint author), UNIV ILLINOIS,DEPT ELECT ENGN & COMP SCI,CHICAGO,IL 60680, USA. CR BOOKSTEIN A, 1980, J AM SOC INFORM SCI, P240 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1986, ACM SIGIR C, P244 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 CHENG Y, 1985, IEEE T PATTERN ANAL, P592 CHOW D, 1982, J ACM, P127 CROFT W, 1979, J DOC, P285 DEERWESTER S, COMMUNICATION Feller W, 1968, INTRO PROBABILITY TH GAGUE J, 1983, RES DEV INFORMATION HARPER D, 1978, J DOC SEP, P189 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KOLL M, 1978, ACM SIGIR C KRAFT D, 1986, ACM SIGIR FORUM LAM K, 1982, ACM T DATABASE SYST, P500 LOSEE R, 1986, THESIS U CHICAGO LOSEE RM, 1986, ACM C RES DEV INFORM, P258 MARON M, 1960, J ACM, P216 MCCUNE B, 1985, IEEE T SOFTWARE ENG, P939 RADECKI T, 1979, INFORM PROCESS MANAG, P247 RAGHAVAN VV, 1983, ACM ANN C RES DEV IN, P88 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1982, INF TECHNOL, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, P33 SALTON G, 1975, DYNAMIC INFORMATION TONG R, 1985, 1985 ACM SIGIR C, P243 TSICHRITZIS D, 1985, OFFICE AUTOMATION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG M, 1985, 1985 ACM SIGIR C, P18 WONG MH, COMMUNICATION WU H, 1981, J DOC, P194 YU CT, 1976, J ACM, P273 YU CT, 1979, INFORM SYST, P205 YU CT, 1985, 1985 ACM SIGIR C, P131 YU CT, 1983, INF TECH, P129 YU CT, 1986, 1986 P ACM C RES DEV, P265 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CT, 1985, ACM T DATABASE SYST, V10, P180, DOI 10.1145/3857.3861 Zadeh L D, 1965, INFORM CONTR, P338 NR 47 TC 11 Z9 11 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0362-5915 J9 ACM T DATABASE SYST JI ACM Trans. Database Syst. PD JUN PY 1989 VL 14 IS 2 BP 147 EP 167 DI 10.1145/63500.63519 PG 21 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA U8679 UT WOS:A1989U867900001 ER PT J AU BIRU, T ELHAMDOUCHI, A REES, RS WILLETT, P AF BIRU, T ELHAMDOUCHI, A REES, RS WILLETT, P TI INCLUSION OF RELEVANCE INFORMATION IN THE TERM DISCRIMINATION MODEL SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BIRU T, 1987, INCORPORATION RELEVA CAN F, 1987, J AM SOC INFORM SCI, V38, P171, DOI 10.1002/(SICI)1097-4571(198705)38:3<171::AID-ASI6>3.0.CO;2-S CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROUCH CJ, 1988, INFORM PROCESS MANAG, V24, P5, DOI 10.1016/0306-4573(88)90072-6 ELHAMDOUCHI A, 1988, INFORM PROCESS MANAG, V24, P17, DOI 10.1016/0306-4573(88)90073-8 ELHAMDOUCHI A, 1987, J INFORM SCI, V13, P361, DOI 10.1177/016555158701300607 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1975, THEORY INDEXING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 NR 22 TC 7 Z9 7 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD JUN PY 1989 VL 45 IS 2 BP 85 EP 109 DI 10.1108/eb026840 PG 25 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AF775 UT WOS:A1989AF77500001 ER PT J AU ORMEROD, A WILLETT, P BAWDEN, D AF ORMEROD, A WILLETT, P BAWDEN, D TI COMPARISON OF FRAGMENT WEIGHTING SCHEMES FOR SUBSTRUCTURAL ANALYSIS SO QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT SYST,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. PFIZER LTD,CENT RES,SANDWICH CT13 9NJ,KENT,ENGLAND. CR ADAMSON GW, 1972, J CHEM SOC PERK T 1, P2428, DOI 10.1039/p19720002428 Ash J., 1985, COMMUNICATION STORAG AVIDON VV, 1978, KHIM FARM ZH+, V12, P88 BAWDEN D, 1983, J CHEM INF COMP SCI, V23, P14, DOI 10.1021/ci00037a003 CARHART RE, 1985, J CHEM INF COMP SCI, V25, P64, DOI 10.1021/ci00046a002 CHASTRETTE M, 1986, EUR J MED CHEM, V21, P505 CRAMER RD, 1974, J MED CHEM, V17, P533, DOI 10.1021/jm00251a014 DEFLORA S, 1985, TOXICOL ENVIRON CHEM, V10, P157, DOI 10.1080/02772248509357099 DITTMAR PG, 1983, J CHEM INF COMP SCI, V23, P93, DOI 10.1021/ci00039a002 FRANKE R, 1984, THEORETICAL DRUG DES HENRY DR, 1982, J MED CHEM, V25, P899, DOI 10.1021/jm00350a004 HODES L, 1977, J MED CHEM, V20, P469, DOI 10.1021/jm00214a002 HODES L, 1979, COMPUTER ASSISTED DR, P583 HOPFINGER AJ, 1985, J MED CHEM, V28, P1133, DOI 10.1021/jm00147a001 JURS PC, 1979, J MED CHEM, V22, P476, DOI 10.1021/jm00191a004 JURS PC, 1985, J CHEM INF COMP SCI, V25, P296, DOI 10.1021/ci00047a028 MEYER E, 1988, CHEM STRUCTURES INT, P235 MEYER E, 1987, 8TH INT C COMP CHEM OLSEN EC, 1979, COMPUTER ASSISTED DR ORMEROD A, THESIS REDL G, 1974, CHEM SOC REV, V3, P273, DOI 10.1039/cs9740300273 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE SL, 1982, J MED CHEM, V25, P769, DOI 10.1021/jm00349a002 Siegel S., 1956, NONPARAMETRIC STATIS STOUCH TR, 1986, J MED CHEM, V29, P2125, DOI 10.1021/jm00161a001 STREICH WJ, 1985, QUANT STRUCT-ACT REL, V4, P13, DOI 10.1002/qsar.19850040104 STUPER AJ, 1978, J PHARM SCI, V67, P745, DOI 10.1002/jps.2600670604 Topliss J., 1983, QUANTITATIVE STRUCTU WALSH DB, 1987, MUTAT RES, V182, P55, DOI 10.1016/0165-1161(87)90054-9 Willett P, 1987, J CHEMOMETR, V1, P139, DOI 10.1002/cem.1180010303 YUTA K, 1981, J MED CHEM, V24, P241, DOI 10.1021/jm00135a003 NR 31 TC 48 Z9 49 PU VCH PUBLISHERS INC PI DEERFIELD BEACH PA 303 NW 12TH AVE, DEERFIELD BEACH, FL 33442-1788 SN 0931-8771 J9 QUANT STRUCT-ACT REL JI Quant. Struct.-Act. Relat. PD JUN PY 1989 VL 8 IS 2 BP 115 EP 129 DI 10.1002/qsar.19890080207 PG 15 WC Chemistry, Medicinal; Pharmacology & Pharmacy SC Pharmacology & Pharmacy GA AH855 UT WOS:A1989AH85500006 ER PT J AU WONG, SKM YAO, YY AF WONG, SKM YAO, YY TI A PROBABILITY-DISTRIBUTION MODEL FOR INFORMATION-RETRIEVAL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP WONG, SKM (reprint author), UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RI Yao, Yiyu/B-2926-2008 OI Yao, Yiyu/0000-0001-6502-6226 CR Aczel J., 1975, MATH SCI ENG, V115 BOLLMANN P, 1987, P ACM SIGIR C RES DE CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 Fishburn PC, 1970, UTILITY THEORY DECIS Jardine N, 1971, MATH TAXONOMY KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Raghavan V. V., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320071.320081 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RAO CR, 1982, THEOR POPUL BIOL, V21, P24, DOI 10.1016/0040-5809(82)90004-1 RAO CR, 1985, IEEE T INFORM THEORY, V31, P589, DOI 10.1109/TIT.1985.1057082 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1980, INFORMATION RETRIEVA WONG AKC, 1985, IEEE T PATTERN ANAL, V7, P599 WONG SKM, 1987, P ACM SIGIR C RES DE WONG SKM, 1986, P 9 ANN INT ACM SIGI, P175, DOI 10.1145/253168.253205 WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1975, J ACM, V22, P17, DOI 10.1145/321864.321867 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 22 TC 16 Z9 16 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1989 VL 25 IS 1 BP 39 EP 53 DI 10.1016/0306-4573(89)90090-3 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA T5764 UT WOS:A1989T576400005 ER PT J AU FUHR, N AF FUHR, N TI MODELS FOR RETRIEVAL WITH PROBABILISTIC INDEXING SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP FUHR, N (reprint author), TH DARMSTADT,FACHBEREICH INFORMAT,KAROLINENPL 5,D-6100 DARMSTADT,FED REP GER. CR BOLLMANN P, 1986, LEISTUNGSBEWERTUNG I, P183 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 FUHR H, 1986, THESIS TH DARMSTADT Fuhr N., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium FUHR N, 1985, P RIAO85 1 INT C REC, P207 FUHR N, 1987, JUN ACM C RES DEV IN HARMAN D, 1987, JUN ACM C RES DEV IN HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 KNORZ G, 1982, RES DEV INFORMATION, P174 KNORZ G, 1983, AUTOMATISCHES INDEXI KNORZ G, 1983, DVII833 TH DARMST IN, P2 LOSEE R, 1986, SEP ACM C RES DEV IN LUSTIG G, 1986, AUTOMATISCHE INDEXIE MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1982, RES DEV INFORMATION, P98 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1984, J DOC, V40, P264, DOI 10.1108/eb026768 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V36, P33 SALTON G, 1976, J ASSOC COMPUT MACH, V23, P76 SALTON G, 1986, SEP ACM C RES DEV IN VANRIJSBERGEN CJ, 1978, J DOC, V34, P294 NR 29 TC 73 Z9 73 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1989 VL 25 IS 1 BP 55 EP 72 DI 10.1016/0306-4573(89)90091-5 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA T5764 UT WOS:A1989T576400006 ER PT J AU BOOKSTEIN, A AF BOOKSTEIN, A TI SET-ORIENTED RETRIEVAL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP BOOKSTEIN, A (reprint author), UNIV CHICAGO,CTR INFORMAT STUDIES,CHICAGO,IL 60637, USA. CR Birkhoff G., 1953, SURVEY MODERN ALGEBR BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P31 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1985, ANN REV INFORMATION, V20, P118 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 Fishburn PC, 1970, UTILITY THEORY DECIS GOFFMAN W, 1968, INFORM STORAGE RET, V4, P361, DOI 10.1016/0020-0271(68)90030-2 Kaufmann A., 1975, INTRO THEORY FUZZY S, V1 LANCASTER FW, 1979, INFORMATION RETREIVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1983, INTRO MODERN INFORMA SWANSON D, J AM SOC INFORM SCI, V38, P228 SWANSON DR, 1986, LIBR QUART, V56, P103 NR 14 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1989 VL 25 IS 5 BP 465 EP 475 DI 10.1016/0306-4573(89)90018-6 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CA147 UT WOS:A1989CA14700001 ER PT J AU FUHR, N HUTHER, H AF FUHR, N HUTHER, H TI OPTIMUM PROBABILITY ESTIMATION FROM EMPIRICAL DISTRIBUTIONS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP FUHR, N (reprint author), TH DARMSTADT,FACHBEREICH INFORMAT,KAROLINENPL 5,D-6100 DARMSTADT,FED REP GER. CR Biebricher P., 1988, 11th International Conference on Research and Development in Information Retrieval BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 CHEESEMAN P, 1986, METHOD COMPUTING GEN, P198 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 Cox D, 1970, ANAL BINARY DATA FUHR N, 1986, 1986 ACM C RES DEV I, P249 Fuhr N., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium FUHR N, 1985, P RIAO85 1 INT C REC, P207 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 HUTHER H, 1986, THESIS TH DARMSTADT HUTHER H, 1983, DTSCH GESELLSCHAFT D, P139 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 KANTOR PB, 1985, 8TH P INT JOINT C AR, P269 KANTOR PB, 1986 P ACM C RES DEV LAM K, 1982, ACM T DATABASE SYST, V7, P500, DOI 10.1145/319732.319756 LOSEE R, 1987, 1987 P ACM C RES DEV, P167 LOSEE RM, 1988, J AM SOC INFORM SCI, V39, P8, DOI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 RAGHAVAN V, 1983, 1983 P ACM C RES DEV, P88 ROBERTSON S, 1982, 5739 TECHN REP Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, RES DEV INFORMATION, P151 SALTON G, 1976, J ASSOC COMPUT MACH, V23, P76 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 WOMSERHACKER C, 1986, THESIS U REGENSBURG YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 31 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1989 VL 25 IS 5 BP 493 EP 507 DI 10.1016/0306-4573(89)90020-4 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CA147 UT WOS:A1989CA14700003 ER PT J AU WONG, SKM ZIARKO, W RAGHAVAN, VV WONG, PCN AF WONG, SKM ZIARKO, W RAGHAVAN, VV WONG, PCN TI EXTENDED BOOLEAN QUERY-PROCESSING IN THE GENERALIZED VECTOR-SPACE MODEL SO INFORMATION SYSTEMS LA English DT Article C1 UNIV SW LOUISIANA,CTR ADV COMP STUDIES,POB 44330,LAFAYETTE,LA 70504. UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. CR ANGIONE PV, 1975, J AM SOC INFORM SCI, V26, P112, DOI 10.1002/asi.4630260207 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V31, P275 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P249, DOI 10.1016/0306-4573(81)90019-4 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X FOX EA, 1983, TR83561 CORN U DEP C Gersting J.L., 1982, MATH STRUCTURES COMP HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 KRAFT DH, 1973, J AM SOC INFORM SCI, V24, P368, DOI 10.1002/asi.4630240508 LANCASTER FW, 1973, INFORMATION RETRIEVA PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 RICKMAN JT, 1971, P NATIONAL M ACM NEW, P478 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, SMART RETRIEVAL SYST, pCH14 SALTON G, 1983, INTRO MODERN INFORMA, V4 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SALTON G, 1983, TR83539 CORN U DEP C Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, 9TH P IFIP WORLD COM, P755 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VERHOEFF J, 1981, COMMUN ACM, V24, P557 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WONG PCN, 1985, THESIS U REGINA REGI Wong S. K. M., 1987, Fundamenta Informaticae, V10 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 NR 33 TC 10 Z9 10 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PY 1989 VL 14 IS 1 BP 47 EP 63 DI 10.1016/0306-4379(89)90024-0 PG 17 WC Computer Science, Information Systems SC Computer Science GA U2058 UT WOS:A1989U205800004 ER PT J AU GORDON, M AF GORDON, M TI PROBABILISTIC AND GENETIC ALGORITHMS FOR DOCUMENT-RETRIEVAL SO COMMUNICATIONS OF THE ACM LA English DT Article RP GORDON, M (reprint author), UNIV MICHIGAN,GRAD SCH BUSINESS ADM,ANN ARBOR,MI 48109, USA. CR BLAIR DC, 1986, INFORM PROCESS MANAG, V22, P229, DOI 10.1016/0306-4573(86)90055-5 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 BRAUEN T, 1975, SMART RETRIEVAL SYST COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 FURNAS GW, 1985, 1985 P ACM SIGCHI C, P131 GORDON MD, IN PRESS J AM SOC IN HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HOLLAND JH, 1975, ADAPTATION NATURAL A HOLLAND JH, 1986, MACHINE LEARNING, V2 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 TAGUE J, 1984, CAN J INFORM SCI, V9, P37 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA ZUNDE P, 1969, AM DOC, V20, P259, DOI 10.1002/asi.4630200313 NR 22 TC 85 Z9 85 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0001-0782 J9 COMMUN ACM JI Commun. ACM PD OCT PY 1988 VL 31 IS 10 BP 1208 EP 1218 DI 10.1145/63039.63044 PG 11 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA Q4225 UT WOS:A1988Q422500005 ER PT J AU WADE, S WILLETT, P ROBINSON, B VICKERY, B VICKERY, A AF WADE, S WILLETT, P ROBINSON, B VICKERY, B VICKERY, A TI A COMPARISON OF KNOWLEDGE-BASED AND STATISTICALLY-BASED TECHNIQUES FOR REFERENCE RETRIEVAL SO ONLINE REVIEW LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,PHD PROGRAMME,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. TOME ASSOCIATES,LONDON W13 9SJ,ENGLAND. CR ASHFORD J, 1984, PROGRAM-AUTOM LIBR, V18, P124, DOI 10.1108/eb046874 Belkin N.J., 1981, INFORMATION RETRIEVA, P44 BOBROW DG, 1986, COMMUN ACM, V29, P880, DOI 10.1145/6592.6597 CERCONE N, 1986, ADV COMPUT, V25, P1, DOI 10.1016/S0065-2458(08)60473-8 Cleverdon C., 1984, Information Services & Use, V4 Cleverdon C. W., 1966, FACTORS DETERMINING COREY EJ, 1985, SCIENCE, V228, P408, DOI 10.1126/science.3838594 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, IN PRESS J AM SOC IN DAVIES R, 1986, INTELLIGENT INFORMAT DOYLE LB, 1963, 26TH P AM DOC I ANN, P199 ELLIS D, 1987, J INFORM SCI, V8, P25 Freund G. E., 1982, Information Technology: Research and Development, V1 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P382, DOI 10.1108/eb046949 KEYHOE CA, 1985, ONLINE REV, V9, P489 KIMBERLEY R, 1980, ONLINE REV, V4, P273 MARCUS RS, 1981, P AM SOC INFORM SCI, V18, P270 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 POLLITT AS, 1984, ASLIB PROC, V36, P229, DOI 10.1108/eb050928 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RICH E, 1984, COMPUTER, V17, P39 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1983, INTRO MODERN INFORMA SMEATON AF, 1986, 9TH P INT C RES DEV, P103 TENNANT H, 1984, AFIPS CONF P, V53, P629 VICKERY A, 1987, ONLINE REV, V11, P149, DOI 10.1108/eb024244 VICKERY A, 1987, J DOC, V43, P1, DOI 10.1108/eb026798 VICKERY A, 1987, INFORMATION PROCESSI, V23 WADE SJ, 1988, PROGRAM-AUTOM LIBR, V22, P44, DOI 10.1108/eb046986 Willett P., 1985, 9th International Online Information Meeting NR 35 TC 7 Z9 7 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0309-314X J9 ONLINE REV PD APR PY 1988 VL 12 IS 2 BP 91 EP 108 DI 10.1108/eb024270 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA T3442 UT WOS:A1988T344200001 ER PT J AU RADECKI, T AF RADECKI, T TI TRENDS IN RESEARCH ON INFORMATION-RETRIEVAL - THE POTENTIAL FOR IMPROVEMENTS IN CONVENTIONAL BOOLEAN RETRIEVAL-SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP RADECKI, T (reprint author), UNIV NEBRASKA,DEPT COMP SCI,LINCOLN,NE 68588, USA. CR ARNOLD BH, 1962, LOGIC BOOLEAN ALGEBR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V32, P275, DOI 10.1002/asi.4630320407 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEIN A, 1981, INFORMATION RETRIEVA, P23 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P249, DOI 10.1016/0306-4573(81)90019-4 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 Heine M. H., 1982, Information Technology: Research and Development, V1 KANTOR PB, 1981, IEEE T SYST MAN CYB, V11, P816 KRAFT DH, 1985, ADV COMPUT, V24, P277, DOI 10.1016/S0065-2458(08)60369-1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NEGOITA CV, 1976, INT J MAN MACH STUD, V8, P711, DOI 10.1016/S0020-7373(76)80032-6 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1985, INFORMATION PROCESSI, V21, P473 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 TAHANI V, 1977, INFORM PROCESS MANAG, V13, P289, DOI 10.1016/0306-4573(77)90018-8 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WONG SKM, 1986, 1986 ACM C RES DEV I, P175 YAGER RR, 1980, IEEE T SYST MAN CYB, V10, P51 NR 45 TC 17 Z9 17 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 219 EP 227 DI 10.1016/0306-4573(88)90089-1 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200002 ER PT J AU COOPER, WS AF COOPER, WS TI GETTING BEYOND BOOLE SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP COOPER, WS (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR BARHILLEL Y, 1957, AM DOCUMENTATION, V3, P103 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 COOPER WS, 1976, SUBOPTIMALITY RETRIE COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 LANCASTER FW, 1979, INFORMATION RETRIEVA MARON ME, 1984, PROGR COMMUNICATION, V5, P145 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1983, 5807 CITY U DEP INF ROBERTSON SE, 1977, J DOCUMENTATION, V33, P292 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA, pCH6 VANRIJSBERGEN CJ, 1980, 5587 U CAMBR COMP LA NR 20 TC 40 Z9 40 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 243 EP 248 DI 10.1016/0306-4573(88)90091-X PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200004 ER PT J AU MARON, ME AF MARON, ME TI PROBABILISTIC DESIGN PRINCIPLES FOR CONVENTIONAL AND FULL-TEXT RETRIEVAL-SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP MARON, ME (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1975, J AM SOC INFORMATION, V26, P5 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BOOKSTEIN A, 1977, J ASS COMPUTING MACH, V24, P294 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1980, INFORMATION RETRIEVA, P199 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 DABNEY DP, 1986, LAW LIBR J, V78, P5 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE C HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HERTHER NK, 1985, ONLINE, V9, P17 LENK PJ, 1985, 8597 NEW YORK U GRAD MARON ME, 1983, RES DEV INFORMATION, P98 MARON ME, 1984, PROGR COMMUNICATION, V5, P145 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1983, RES DEV INFORMATION, P108 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SWANSON DR, 1977, LIBR QUART, V47, P128 TAGUE JM, 1973, INFORM STORAGE RET, V9, P129, DOI 10.1016/0020-0271(73)90063-6 THOMPSON CL, IN PRESS INFORMATION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA, pCH6 VANRIJSBERGEN CJ, 1980, INFORMATION RETRIEVA, P32 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 38 TC 9 Z9 9 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 249 EP 255 DI 10.1016/0306-4573(88)90092-1 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200005 ER PT J AU FOX, EA KOLL, MB AF FOX, EA KOLL, MB TI PRACTICAL ENHANCED BOOLEAN RETRIEVAL - EXPERIENCES WITH THE SMART AND SIRE SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article C1 GEORGE MASON UNIV,DEPT INFORMAT SYST & SYST ENGN,FAIRFAX,VA 22034. RP FOX, EA (reprint author), VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061, USA. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BELKIN NJ, 1984, INFORM TECHNOL R & D, V3, P122 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 Borko H., 1978, INDEXING CONCEPTS ME BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 Buckley C., 1985, TR85686 CORN U DEP C BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUELL DA, 1985, J AM SOC INFORM SCI, V36, P398, DOI 10.1002/asi.4630360609 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 Cheeseman P., 1985, P 9 INT JOINT C ART, P1002 CHOEKA Y, 1980, COMPUTERS HUMANITIES, V14, P153 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DOSZKOCS TE, 1983, 1982 RES DEV INF RET, P38 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FOX EA, 1986, TR861 VIRG TECH DEP FOX EA, 1983, THESIS CORNELL U FOX EA, 1984, FICHE, V5, P253 FOX EA, 1986, CD ROM NEW PAPYRUS, P143 FOX EA, 1983, TR83560 CORN U DEP C Fox E. A., 1986, Microcomputers for Information Management, V3 FOX EA, 1985, 8TH ANN INT ACM SIGI, P42 Fox E. A., 1987, International Journal of Approximate Reasoning, V1, DOI 10.1016/0888-613X(87)90012-0 FOX EA, 1986, 2ND P C COMP INT INT Fox P. J., 1981, SEA, V7, P119 Kandel A, 1986, FUZZY MATH TECHNIQUE Katzer J., 1982, Information Technology: Research and Development, V1 KERRE EE, 1986, J AM SOC INFORM SCI, V37, P341, DOI 10.1002/(SICI)1097-4571(198609)37:5<341::AID-ASI9>3.0.CO;2-A KOLL M, IN PRESS J AM SOC IN KOLL M, 1984, APR P NAT ONL M NEW, P165 Lovins J., 1968, MECH TRANSLATION COM, V11, P11 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V11, P939, DOI 10.1109/TSE.1985.232827 MCGILL M, 1976, ACM SIGIR FORUM, V10, P37, DOI 10.1145/1095286.1095300 MCGILL M, 1979, EVALUATION FACTORS A MEADOW CT, 1981, BASICS ONLINE SEARCH MORRISSEY J, 1983, 1982 RES DEV INF RET, P38 NEUFELD ML, 1986, J AM SOC INFORM SCI, V37, P183, DOI 10.1002/(SICI)1097-4571(198607)37:4<183::AID-ASI2>3.0.CO;2-W Noreault T., 1982, Information Technology: Research and Development, V1 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 NOREAULT T, 1981, INFORMATION RETRIEVA PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 PORTER MF, 1982, INFORMATION TECHNOLO, V1 RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P281, DOI 10.1016/0306-4573(88)90095-7 RADECKI T, 1977, INFORM PROCESS MANAG, V13, P109, DOI 10.1016/0306-4573(77)90044-9 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 Rescher N., 1969, MANY VALUED LOGIC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1980, ENCY LIB INFORM SCI, P1 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1983, Information Processing 83. Proceedings of the IFIP 9th World Computer Congress SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafer G, 1976, MATH THEORY EVIDENCE TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 TENOPIR C, 1984, ANNU REV INFORM SCI, V19, P215 TONG RM, 1983, P NATIONAL C ARTIFIC, P411 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 69 TC 15 Z9 15 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 257 EP 267 DI 10.1016/0306-4573(88)90093-3 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200006 ER PT J AU SALTON, G AF SALTON, G TI A SIMPLE BLUEPRINT FOR AUTOMATIC BOOLEAN QUERY-PROCESSING SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 CATER SC, 1987, 10 ACM SIGIR C RES D, P171 Fox E., 1983, THESIS CORNELL U ITH Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PAICE CD, 1983, INFORMATION TECHNOLO, V3, P343 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1982, LECTURE NOTES COMPUT, V146, P133 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 Salton G., 1981, SIGIR Forum, V16 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 WONG SKM, 1986, EXTENDED BOOLEAN QUE WU H, 1981, THESIS CORNELL U ITH NR 27 TC 17 Z9 17 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 269 EP 280 DI 10.1016/0306-4573(88)90094-5 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200007 ER PT J AU RADECKI, T AF RADECKI, T TI PROBABILISTIC METHODS FOR RANKING OUTPUT DOCUMENTS IN CONVENTIONAL BOOLEAN RETRIEVAL-SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP RADECKI, T (reprint author), UNIV NEBRASKA,DEPT COMP SCI,LINCOLN,NE 68588, USA. CR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 COOK KH, 1975, INFORM PROCESS MANAG, V11, P125, DOI 10.1016/0306-4573(75)90001-1 COOPER WS, 1976, SUBOPTIMALITY RETRIE COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Duda R., 1973, PATTERN CLASSIFICATI GEBHARDT F, 1975, INFORM PROCESS MANAG, V11, P59, DOI 10.1016/0306-4573(75)90034-5 GOFFMAN W, 1964, INFORM STORAGE RET, V2, P73, DOI 10.1016/0020-0271(64)90006-3 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Harrison M. A., 1965, INTRO SWITCHING AUTO HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 JONES KS, 1975, J DOC, V31, P266, DOI 10.1108/eb026606 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 KOCHEN M, 1974, PRINCIPLES INFORMATI KRAFT DH, 1973, J AM SOC INFORM SCI, V24, P368, DOI 10.1002/asi.4630240508 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P77, DOI 10.1002/asi.4630290206 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PAWLAK Z, 1973, CC PAS101 COMP CTR R RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1981, INFORM PROCESS MANAG, V17, P227, DOI 10.1016/0306-4573(81)90017-0 RADECKI T, 1985, 8TH P ANN INT ACM SI, P70 RADECKI T, 1982, J DOC, V38, P14, DOI 10.1108/eb026719 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1981, INFORMATION RETRIEVA, P334 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P8, DOI 10.1002/asi.4630330104 Radecki T., 1980, New Trends in Documentation and Information. Proceedings of the 39th FID Congress Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1976, THESIS U LONDON ROBERTSON SE, 1982, STATISTICAL PROBLEMS ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1981, INFORMATION RETRIEVA, P9 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1980, COMPUTER, V13, P41 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 TAGUE JM, 1973, INFORM STORAGE RET, V9, P129, DOI 10.1016/0020-0271(73)90063-6 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WONG E, 1971, COMMUN ACM, V14, P593, DOI 10.1145/362663.362735 YU CT, 1977, COMMUN ACM, V20, P135, DOI 10.1145/359436.359441 YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1982, J ACM, V29, P152, DOI 10.1145/322290.322300 YU CT, 1978, J ACM, V25, P210, DOI 10.1145/322063.322066 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 64 TC 10 Z9 10 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 281 EP 302 DI 10.1016/0306-4573(88)90095-7 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200008 ER PT J AU DEOGUN, JS RAGHAVAN, VV AF DEOGUN, JS RAGHAVAN, VV TI INTEGRATION OF INFORMATION-RETRIEVAL AND DATABASE-MANAGEMENT SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article C1 UNIV REGINA,DEPT COMP SCI,REGINA S4S 0A2,SASKATCHEWAN,CANADA. RP DEOGUN, JS (reprint author), UNIV NEBRASKA,DEPT COMP SCI,LINCOLN,NE 68588, USA. CR BILLER H, 1982, RES DEV INFORMATION, P80 CRAWFORD RG, 1981, J AM SOC INFORM SCI, V32, P51, DOI 10.1002/asi.4630320107 DEOGUN JS, 1985, ROLE DATA SCI PROGR, P513 FOX EA, 1981, P ACM SIGMOD WORKSHO, P119, DOI 10.1145/800074.802424 *IBM CORP, 1974, SH125400 PUBL Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 JONES KS, 1971, AUTOMATIC KEYWORD CL JONES KS, 1980, J INFORMATION SCI, V2, P325 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KOWARSKI P, 1982, P SIGMOD, P72 LUHN HP, 1957, IBM J RES DEV, V1, P309 MACLEOD IA, 1983, INFORM TECHNOL R & D, V2, P43 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1980, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1982, RES DEV INFORMATION, P157 SCHEK HJ, 1982, P 8 INT C VER LARG D, P197 STONEBRAKER M, 1983, ACM T OFF INF SYST, V1, P143, DOI 10.1145/357431.357433 Ullman J. D., 1982, PRINCIPLES DATABASE VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 25 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 303 EP 313 DI 10.1016/0306-4573(88)90096-9 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200009 ER PT J AU KWOK, KL KUAN, W AF KWOK, KL KUAN, W TI EXPERIMENTS WITH DOCUMENT COMPONENTS FOR INDEXING AND RETRIEVAL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP KWOK, KL (reprint author), CUNY QUEENS COLL,DEPT COMP SCI,FLUSHING,NY 11367, USA. CR BOOKSTEIN A, 1985, 1985 RIAO C P GREN, P373 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 EDMUNDSON HP, 1961, COMMUNICATIONS ACM, P226 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KWOK KL, 1986, 1986 P ACM SIGIR INT, P275 KWOK KL, 1986, 49TH P ANN M ASIS CH, V23, P158 KWOK KL, 1987, 10TH P ANN INT ACMSI, P19 Kwok K. L., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium KWOK KL, 1985, J AM SOC INFORM SCI, V36, P342, DOI 10.1002/asi.4630360510 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 TAGUE J, 1983, RES DEV INFORMATION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 24 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 4 BP 405 EP 417 DI 10.1016/0306-4573(88)90044-1 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA P7217 UT WOS:A1988P721700003 ER PT J AU SALTON, G BUCKLEY, C AF SALTON, G BUCKLEY, C TI TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR AMSLER RA, 1984, ANNU REV INFORM SCI, V19, P161 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 CATER SC, 1987, 10 ACM SIGIR C RES D, P171 Cleverdon C. W., 1966, ASLIB CRANFIELD RES, V2 CLEVERDON CW, 1977, ESA1432 EUR SPAC AG COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1986, 1986 P ACM C RES DEV, P201 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 CROFT WB, 1984, INFORM TECHNOL R & D, V3, P113 DATTOLA RT, 1971, SMART RETRIEVAL SYST, P265 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 FAGAN JL, 1987, 87868 CORN U DEP COM FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FOX EA, 1980, ACM SIGIR FORUM, V15, P5, DOI 10.1145/1095403.1095404 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1971, AUTOMATIC KEYWORD CL JONES KS, 1983, INTELLIGENT INFORMAT, V7, P136 JONES TA, 1984, ACTA CRYSTALLOGR A, V40, P50, DOI 10.1107/S010876738400009X KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P79, DOI 10.1016/0020-0271(73)90020-X KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P477, DOI 10.1016/0020-0271(73)90034-X KUCERA H, 1985, 1ST P U WAT UW CTR N, P7 Lancaster F. W., 1968, EVALUATION MEDLARS D LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 LUHN H, 1955, AM DOC, V4, P14 LUHN HP, 1957, IBM J RES DEV, V1, P309 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 PERRY JW, 1950, AM DOC, V1, P133, DOI 10.1002/asi.5090010303 RADECKI T, 1982, LECTURE NOTES COMPUT, V146, P133 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1986, 49TH ASIS 86 P ASIS, P277 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 SALTON G, 1983, LECT NOTES COMPUT SC, V146, P151 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1972, INFORMATION PROCESSI, V71, P115 SMEATON AF, 1986, 1986 P ACM C RES DEV, P103 SWANSON DR, 1960, SCIENCE, V132, P1099, DOI 10.1126/science.132.3434.1099 Taube M., 1952, AM DOC, V3, P213, DOI 10.1002/asi.5090030404 THOMPSON RH, 1985, P EXPERT SYSTEMS GOV, P448 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WALKER DE, 1987, MACHINE TRANSLATION, P247 WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 WONG SKM, 1986, EXTENDED BOOLEAN QUE Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 WU H, 1981, THESIS CORNELL U ITH YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 55 TC 1971 Z9 2086 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 5 BP 513 EP 523 DI 10.1016/0306-4573(88)90021-0 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA Q4140 UT WOS:A1988Q414000001 ER PT J AU WILLETT, P AF WILLETT, P TI RECENT TRENDS IN HIERARCHIC DOCUMENT CLUSTERING - A CRITICAL-REVIEW SO INFORMATION PROCESSING & MANAGEMENT LA English DT Review RP WILLETT, P (reprint author), UNIV SHEFFIELD,DEPT INFORMAT STUDIES,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ADAMSON GW, 1981, J CHEM INF COMP SCI, V21, P204, DOI 10.1021/ci00032a005 ADAMSON GW, 1975, J CHEM INF COMP SCI, V15, P55, DOI 10.1021/ci60001a016 ALDENDERFER MS, 1977, CONSUMER REPORT CLUS BECKER DS, 1977, CURRENT RES SCI TECH BLASHFIELD RK, 1976, PSYCHOL BULL, V83, P377, DOI 10.1037//0033-2909.83.3.377 BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 CAN F, 1984, J AM SOC INFORM SCI, V35, P268, DOI 10.1002/asi.4630350503 Clifford HT, 1975, INTRO NUMERICAL CLAS CORMACK RM, 1971, J R STAT SOC SER A-G, V134, P321, DOI 10.2307/2344237 CROFT WB, 1979, THESIS U CAMBRIDGE E CROFT WB, 1979, J AM SOC INFORM SCI, V30, P358, DOI 10.1002/asi.4630300612 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1976, INFORM PROCESS MANAG, V12, P327, DOI 10.1016/0306-4573(76)90051-0 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1978, 1ST P INT C RES DEV, P65 CROFT WB, 1984, RES DEV INFORMATION CROFT WB, 1983, COINS8305 U MASS TEC CROFT WB, 1985, INFORM SYST, V10, P377, DOI 10.1016/0306-4379(85)90042-0 CROUCH DB, 1975, INFORM PROCESS MANAG, V11, P11, DOI 10.1016/0306-4573(75)90031-X DEFAYS D, 1977, COMPUTER J, V20, P93 DUBES R, 1980, ADV COMPUT, V19, P113, DOI 10.1016/S0065-2458(08)60034-0 DUBES R, 1979, PATTERN RECOGN, V11, P235, DOI 10.1016/0031-3203(79)90034-7 Duran B. S., 1974, CLUSTER ANAL SURVEY EDELBROCK C, 1979, MULTIVAR BEHAV RES, V14, P367, DOI 10.1207/s15327906mbr1403_6 ELHAMDOUCHI A, IN PRESS COMPUTER J ELHAMDOUCHI A, 1987, THESIS U SHEFFIELD E ELHAMDOUCHI A, 1987, J INFORM SCI, V13, P361, DOI 10.1177/016555158701300607 ELHAMDOUCHI A, 1986, 9TH P ANN INT ACM SI, P149 ENSER PGB, 1985, J DOC, V41, P135, DOI 10.1108/eb026777 Everitt B., 1974, CLUSTER ANAL EVERITT BS, 1979, BIOMETRICS, V35, P169, DOI 10.2307/2529943 FRITSCHE M, 1974, AUTOMATIC CLUSTERING GARLAND K, 1983, INFORM PROCESS MANAG, V19, P113, DOI 10.1016/0306-4573(83)90064-X GOFFMAN W, 1969, INFORMATION STORAGE, V4, P361 Gordon A. D., 1981, CLASSIFICATION Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 GREEN PE, 1969, J MARKETING RES, V6, P359, DOI 10.2307/3150145 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 GRIFFITHS A, 1984, EVALUATION CLUSTERIN GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 HAMILL KA, 1980, J AM SOC INFORM SCI, V31, P396, DOI 10.1002/asi.4630310603 HARDING AF, 1980, J AM SOC INFORM SCI, V31, P298, DOI 10.1002/asi.4630310411 Hartigan J. A., 1975, CLUSTERING ALGORITHM CHEETHAM A H, 1969, Journal of Paleontology, V43, P1130 Jardine N, 1971, MATH TAXONOMY JARVIS RA, 1973, IEEE T COMPUT, VC-22, P1025, DOI 10.1109/T-C.1973.223640 JONES KS, 1971, AUTOMATIC KEYWORD CL KAR G, 1978, INFORM PROCESS MANAG, V14, P57, DOI 10.1016/0306-4573(78)90063-8 Knuth D. E., 1971, Acta Informatica, V1, DOI 10.1007/BF00264289 KUIPER FK, 1975, BIOMETRICS, V31, P777, DOI 10.2307/2529565 LANCE GN, 1967, COMPUT J, V9, P373 Lee RCT, 1981, ADV INFORMATION SYST, V8, P169 LING RF, 1975, J MATH PSYCHOL, V12, P90, DOI 10.1016/0022-2496(75)90051-6 LING RF, 1976, J AM STAT ASSOC, V71, P293, DOI 10.2307/2285300 LITOFSKY B, 1969, THESIS U PENNSYLVANI MANSUR O, 1980, INFORM PROCESS MANAG, V16, P129, DOI 10.1016/0306-4573(80)90016-3 MCCALL FM, 1986, INT J MAN MACH STUD, V25, P317, DOI 10.1016/S0020-7373(86)80063-3 MEZZICH JE, 1978, BIOL PSYCHIAT, V13, P265 MILLIGAN GW, 1981, MULTIVAR BEHAV RES, V16, P379, DOI 10.1207/s15327906mbr1603_7 MILLIGAN GW, 1980, PATTERN RECOGN, V12, P41, DOI 10.1016/0031-3203(80)90001-1 MILLIGAN GW, 1980, PSYCHOMETRIKA, V45, P325, DOI 10.1007/BF02293907 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 MOJENA R, 1977, COMPUT J, V20, P359, DOI 10.1093/comjnl/20.4.359 MOREY LC, 1983, MULTIVAR BEHAV RES, V18, P309, DOI 10.1207/s15327906mbr1803_4 MURRAY DM, 1972, THESIS CORNELL U ITH MURTAGH F, 1983, NEW APPROACH COMP HI Murtagh F., 1984, COMPUTATIONAL STATIS, V1, P101 MURTAGH F, 1984, INFORM PROCESS MANAG, V20, P611, DOI 10.1016/0306-4573(84)90077-3 MURTAGH F, 1983, COMPUT J, V26, P354 Nievergelt J., 1974, Computing Surveys, V6 Noreault T., 1982, Information Technology: Research and Development, V1 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 PREECE SE, 1980, ONLINE REV, V4, P375, DOI 10.1108/eb024043 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rohlf F., 1982, HDB STATISTICS, V2, P267, DOI 10.1016/S0169-7161(82)02015-X SOKAL ROBERT R., 1962, TAXON, V11, P33, DOI 10.2307/1217208 Rohlf F.J., 1974, Annual Rev Ecol Syst, V5, P101, DOI 10.1146/annurev.es.05.110174.000533 ROWLANDS I, 1983, THESIS SHEFFIELD U E SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SHAW WM, 1986, INFORM PROCESS MANAG, V22, P19, DOI 10.1016/0306-4573(86)90006-3 SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30 SMALL H, 1985, SCIENTOMETRICS, V7, P391, DOI 10.1007/BF02017157 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Sneath P.H., 1973, NUMERICAL TAXONOMY Sparck Jones K., 1973, INFORMATION STORAGE, V9, P499, DOI 10.1016/0020-0271(73)90036-3 Spath H., 1980, CLUSTER ANAL ALGORIT JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSB.CJ, 1971, COMPUT J, V14, P407, DOI 10.1093/comjnl/14.4.407 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 VANRIJSBERGEN CJ, 1978, DREXEL LIBR QUART, V14, P75 VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X VOORHEES EM, 1985, 9TH P INT C RES DEV, P164 VOORHEES EM, 1985, THESIS CORNELL U ITH VOORHEES EM, 1985, TR85658 CORN U TECHN WADE SJ, 1988, PROGRAM-AUTOM LIBR, V22, P44, DOI 10.1108/eb046986 WILLETT P, 1982, ANAL CHIM ACTA, V136, P29, DOI 10.1016/S0003-2670(01)95360-7 WILLETT P, 1985, INT FORUM INFORM DOC, V10, P28 WILLETT P, 1983, INT CLASSIF, V10, P138 WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 WILLETT P, 1986, J CHEM INF COMP SCI, V26, P109, DOI 10.1021/ci00051a005 WILLETT P, 1980, J INFORM SCI, V2, P223, DOI 10.1177/016555158000200503 WILLIAMS W T, 1971, Taxon, V20, P519, DOI 10.2307/1218253 WISHART D, 1978, CLUSTAN 1C USER MANU Wishart David, 1969, NUMERICAL TAXONOMY WONG YCA, 1978, THESIS CORNELL U Yu C. T., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320241.320245 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1977, J ACM, V24, P607, DOI 10.1145/322033.322039 NR 119 TC 277 Z9 285 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 5 BP 577 EP 597 DI 10.1016/0306-4573(88)90027-1 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA Q4140 UT WOS:A1988Q414000007 ER PT J AU LOSEE, RM AF LOSEE, RM TI PARAMETER-ESTIMATION FOR PROBABILISTIC DOCUMENT-RETRIEVAL MODELS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP LOSEE, RM (reprint author), UNIV N CAROLINA,SCH LIFE SCI,CHAPEL HILL,NC 27514, USA. CR BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1977, J ACM, V24, P410 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 COOPER W, 1981, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Duda R., 1973, PATTERN CLASSIFICATI Harter SP, 1974, THESIS U CHICAGO JAYNES ET, 1968, IEEE T SYST SCI CYB, VSSC4, P227, DOI 10.1109/TSSC.1968.300117 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 LAM K, 1982, ACM T DATABASE SYST, V7, P500, DOI 10.1145/319732.319756 LOSEE R, 1986, THESIS U CHICAGO LOSEE RM, 1986, 9TH P ANN INT ACM SI LOSEE RM, IN PRESS J AM SOC IN LUHN HP, 1958, IBM J RES DEV, V2, P159 Martz H. F., 1982, BAYESIAN RELIABILITY RAGHAVEN VV, 1983, 6TH P ANN INT ACM SI ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X Winkler R., 1972, INTRO BAYESIAN INFER YU CT, 1977, COMMUN ACM, V20, P135, DOI 10.1145/359436.359441 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 27 TC 35 Z9 35 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1988 VL 39 IS 1 BP 8 EP 16 DI 10.1002/(SICI)1097-4571(198801)39:1<8::AID-ASI3>3.0.CO;2-W PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA L7631 UT WOS:A1988L763100002 ER PT J AU PORTER, M GALPIN, V AF PORTER, M GALPIN, V TI RELEVANCE FEEDBACK IN A PUBLIC-ACCESS CATALOG FOR A RESEARCH LIBRARY - MUSCAT AT THE SCOTT-POLAR-RESEARCH-INSTITUTE SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article C1 UNIV CAMBRIDGE,SCOTT POLAR RES INST,CAMBRIDGE CB2 1ER,ENGLAND. CR ARMSTRONG P, 1986, DOMESDAY VIDEODISC U COMER D, 1979, COMPUT SURV, V11, P121 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 MITEV N, 1985, 39 BRIT LIBR LIBR IN PORTER MF, 1987, MUSCAT MANUAL Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 *SCOTT POL RES I, 1986, UN DEC CLASS US POL STIBIC V, 1980, ONLINE REV, V4, P273, DOI 10.1108/eb024034 NR 10 TC 28 Z9 28 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JAN PY 1988 VL 22 IS 1 BP 1 EP 20 DI 10.1108/eb046983 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA L7019 UT WOS:A1988L701900002 ER PT J AU PAPE, DL JONES, RL AF PAPE, DL JONES, RL TI STATUS WITH IQ - ESCAPING FROM THE BOOLEAN STRAITJACKET SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article RP PAPE, DL (reprint author), COMP POWER GRP,DIV APPL RES & DEV,CNR GEILS CRT & DENISON ST,W DEAKIN,ACT 2902,AUSTRALIA. CR BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 HAYNES EM, 1984, PROGRAM-AUTOM LIBR, V18, P209, DOI 10.1108/eb046877 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 NR 6 TC 7 Z9 7 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JAN PY 1988 VL 22 IS 1 BP 32 EP 43 DI 10.1108/eb046985 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA L7019 UT WOS:A1988L701900004 ER PT J AU WADE, SJ WILLETT, P AF WADE, SJ WILLETT, P TI INSTRUCT - A TEACHING PACKAGE FOR EXPERIMENTAL METHODS IN INFORMATION-RETRIEVAL .3. BROWSING, CLUSTERING AND QUERY EXPANSION SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ASHFORD J, 1984, PROGRAM-AUTOM LIBR, V18, P124, DOI 10.1108/eb046874 BAWDEN D, 1986, J INFORM SCI, V12, P203, DOI 10.1177/016555158601200501 BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 Cleverdon C., 1984, Information Services & Use, V4 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DAVIES R, 1986, INTELLIGENT INFORMAT ELHAMDOUCHI A, IN PRESS COMPUTER J FALOUTSOS C, 1984, IEEE T SOFTWARE ENG, V10, P201 Freund G. E., 1982, Information Technology: Research and Development, V1 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P382, DOI 10.1108/eb046949 HENRY IG, 1986, PROGRAM, V20, P245 HILDRETH CR, 1982, P AM SOC INFORM SCI, V19, P127 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 JONES KS, 1971, AUTOMATIC KEYWORD CL JONES KS, 1982, INFORMATION RETRIEVA KIMBERLEY R, 1987, TEXT RETRIEVAL DIREC LENNON M, 1981, J INFORM SCI, V3, P177, DOI 10.1177/016555158100300403 MCCALL FM, 1986, INT J MAN MACH STUD, V25, P317, DOI 10.1016/S0020-7373(86)80063-3 MOHAN KC, 1985, J INFORM SCI, V11, P31, DOI 10.1177/016555158501100105 MURTAGH F, 1983, COMPUT J, V26, P354 NOERR PL, 1985, INFORM PROCESS MANAG, V21, P205, DOI 10.1016/0306-4573(85)90105-0 Noreault T., 1982, Information Technology: Research and Development, V1 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1981, SMART RETRIEVAL SYST SALTON G, 1983, INTRO MODERN INFORMA SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, 1985, THESIS CORNELL U WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 37 TC 14 Z9 14 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JAN PY 1988 VL 22 IS 1 BP 44 EP 61 DI 10.1108/eb046986 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA L7019 UT WOS:A1988L701900005 ER PT J AU LOSEE, RM AF LOSEE, RM TI A DECISION THEORETIC MODEL OF MATERIALS SELECTION FOR ACQUISITION SO LIBRARY QUARTERLY LA English DT Article RP LOSEE, RM (reprint author), UNIV N CAROLINA,SCH INFORMAT & LIB SCI,CHAPEL HILL,NC 27514, USA. CR ATKINSON R, 1984, LIBR RESOUR TECH SER, V28, P109 BERGER J, 1980, STATISTICAL DECISION BLUE RI, 1981, J EDUC LIBRARIANSHIP, V21, P301, DOI 10.2307/40322693 BOOKSTEIN A, 1986, LIBR QUART, V56, P370 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1977, J ACM, V24, P410 BOOKSTEIN A, 1986, 9TH P ANN INT ACM SI BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1985, FUZZY SET SYST, V17, P131, DOI 10.1016/0165-0114(85)90051-X BROADUS RN, 1981, SELECTING MATERIALS CASEY JB, 1985, THESIS CASE W RESERV CHAMPION DJ, 1973, AM J SOCIOL, V78, P1256, DOI 10.1086/225431 Daultrey S., 1976, PRINCIPAL COMPONENTS DEPEW JN, 1975, J AM SOC INFORM SCI, V26, P237, DOI 10.1002/asi.4630260407 EDELMAN H, 1979, LIBR RESOUR TECH SER, V23, P33 GOLD SD, 1975, COLL RES LIBR, V36, P397 GOYAL SK, 1973, COLL RES LIBR, V34, P219 Harter SP, 1974, THESIS U CHICAGO Holsti O. R., 1969, CONTENT ANAL SOCIAL KRAFT DH, 1973, INFORM STORAGE RET, V9, P1, DOI 10.1016/0020-0271(73)90003-X KRAFT DH, 1979, COLLECTION MANAGEMEN, V3, P163 LAM K, 1982, ACM T DATABASE SYST, V7, P500, DOI 10.1145/319732.319756 LOSEE RM, 1986, 9TH P ANN INT ACM SI LOSEE RM, IN PRESS J AM SOC IN LUHN HP, 1958, IBM J RES DEV, V2, P159 MADUS GG, 1965, EDUC PSYCHOL MEAS, V25, P1105 MCGRATH WE, 1967, COLL RES LIBR, V28, P269 MCPHERON W, 1983, COLL RES LIBR, V44, P116 PEASGOOD AN, 1986, J LIBR, V18, P242, DOI 10.1177/096100068601800402 PIERCE TJ, 1978, COLLECTION MANAGEMEN, V2, P39, DOI 10.1300/J105v02n01_04 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUSSELL RE, 1984, 3RD P ACRL NAT C SEA SCHAD JG, 1978, J ACAD LIBR, V3, P328 SHEARER KD, 1983, DREXEL LIBR QUART, V19, P73 STONE DC, 1968, AM DOC, V19, P411, DOI 10.1002/asi.5090190409 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WELWOOD RJ, 1977, CAN LIBR J, V34, P213 WENGER CB, 1979, J AM SOC INFORM SCI, V30, P88, DOI 10.1002/asi.4630300205 WERKING RH, 1981, COLL RES LIBR, V42, P134 Winkler R., 1972, INTRO BAYESIAN INFER YU CT, 1983, INFORM TECHNOL R & D, V2, P129 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 44 TC 9 Z9 9 PU UNIV CHICAGO PRESS PI CHICAGO PA 5720 S WOODLAWN AVE, CHICAGO, IL 60637 SN 0024-2519 J9 LIBR QUART JI Libr. Q. PD JUL PY 1987 VL 57 IS 3 BP 269 EP 283 PG 15 WC Information Science & Library Science SC Information Science & Library Science GA J6540 UT WOS:A1987J654000002 ER PT J AU PANYR, J AF PANYR, J TI INTERACTIVE RETRIEVAL STRATEGIES - RELEVANCE FEEDBACK SO NACHRICHTEN FUR DOKUMENTATION LA German DT Article RP PANYR, J (reprint author), SIEMENS AG,ZENTRALBEREICH FORSCH & TECH,ZENT AUFGABEN INFORMAT TECH,D-8000 MUNICH 83,FED REP GER. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BORODIN A, 1971, SMART RETRIEVAL SYST, P394 Brauen T., 1971, SMART RETRIEVAL SYST, P456 BRAUEN TL, 1968, ISR14 CORN U SCI REP BROWN JS, 1971, SMART RETRIEVAL SYST, P412 CLEVERDON CW, 1970, J DOCUM, V26, P57 CRAWFORD RG, 1968, ISR14 CORN U SCI REP DAVIS MC, 1968, ISR14 CORN U SCI REP FRIEDMAN SR, 1971, SMART RETRIEVAL SYST, P447 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE IDE E, 1969, ISR15 CORN U SCI REP Ide E., 1971, SMART RETRIEVAL SYST, P373 IDE E, 1967, ISR12 CORN U SCI REP JOCHUM F, 1976, THESIS TU BERLIN JONES KS, 1981, INFORMATION RETRIEVA KELLY J, 1971, SMART RETRIEVAL SYST, P403 KERCHNER MD, 1971, ISR19 CORN U SCI REP KONRAD E, 1973, LECTURE NOTES EC MAT, P499 LANCASTER FW, 1979, INFORMATION RETRIEVA PAAVOLA L, 1970, ISR18 CORN U SCI REP PANYR J, 1986, NACHR DOK, V37, P163 PANYR J, 1986, NACHR DOK, V37, P2 PANYR J, 1986, AUTOMATISCHE KLASSIF PANYR J, 1982, INT CLASSIF, V9, P11 PANYR J, 1986, NACHR DOK, V37, P60 PANYR J, 1987, NACHR DOK, V38, P13 PANYR J, 1987, HDB MODERNEN DATENVE, V133, P15 RIDDLE W, 1966, IRS11 CORN U SCI REP ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965, ISR9 HARV U SCI REP SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1968, ISR14 CORN U SCI REP SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 Salton G., 1971, SMART RETRIEVAL SYST *TELDOK, 1977, TELDOK BEN JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 YANG CS, 1974, ISR22 CORN U SCI REP YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 NR 45 TC 1 Z9 1 PU VERLAG HOPPENSTEDT & CO PI DARMSTADT 1 PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY SN 0027-7436 J9 NACHR DOK JI Nachr. Dok. PD JUN PY 1987 VL 38 IS 3 BP 145 EP 152 PG 8 WC Information Science & Library Science SC Information Science & Library Science GA J2548 UT WOS:A1987J254800005 ER PT J AU BELKIN, NJ CROFT, WB AF BELKIN, NJ CROFT, WB TI RETRIEVAL TECHNIQUES SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review C1 UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003. RP BELKIN, NJ (reprint author), RUTGERS STATE UNIV,SCH COMMUN INFORMAT & LIB STUDIES,4 HUNTINGTON ST,NEW BRUNSWICK,NJ 08903, USA. CR APPELRATH HJ, 1985, NACHR DOK, V36, P13 ARNOW DM, 1985, ASS COMPUTING MACHIN, P111 *ASS COMP MACH SPE, 1985, RES DEV INF RET BADRAN OM, 1985, ASIS 85, P137 Barbi E., 1984, SIGOA Bulletin, V5 Barr A., 1981, HDB ARTIFICIAL INTEL, V1 BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BELKIN NJ, 1983, J INFORM SCI, V5, P153 BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BELKIN NJ, 1986, 1986 P ACM C RES DEV, P11 BELL DA, 1985, INFORMATI, V8, P240 BILLER H, 1983, LECT NOTES COMPUT SC, V146, P80 BISWAS G, 1985, 1985 P INT C CYB SOC, P126 BISWAS G, 1985, ENG KNOWLEDGE BASED, P445 BIVINS KT, 1982, INFORM PROCESS MANAG, V18, P111, DOI 10.1016/0306-4573(82)90034-6 BLAIR DC, 1986, INFORM PROCESS MANAG, V22, P229, DOI 10.1016/0306-4573(86)90055-5 BLAIR DC, 1986, INT CLASSIF, V13, P18 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BLAIR DC, 1984, COMMUN ACM, V27, P369, DOI 10.1145/358027.358049 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BOOKSTEIN A, 1983, LECT NOTES COMPUT SC, V146, P118 BOOKSTEIN A, 1982, 1982 SEM ROYAL I TEC BOSE PK, 1985, ENG KNOWLEDGE BASED, P467 BROOKS HM, 1983, INFORMATIC S, V7, P65 Buckley C., 1985, TR85686 CORN U DEP C BUCKLEY C, 1985, RES DEV INFORMATION, P97 BUELL DA, 1985, J AM SOC INFORM SCI, V36, P398, DOI 10.1002/asi.4630360609 CARROLL DM, 1987, IN PRESS J AM SOC IN Charniak E., 1985, INTRO ARTIFICIAL INT Christodoulakis S., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium CLEVELAND DB, 1984, J AM SOC INFORM SCI, V35, P19, DOI 10.1002/asi.4630350104 COHEN PR, 1987, IN PRESS INFORMATION Cohen P.R., 1982, HDB ARTIFICIAL INTEL, V3 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROFT WB, 1983, SIGIR FORUM, V17 Croft W. B., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium CROFT WB, 1985, COINS8505 U MASS DEP Croft W. B., 1982, Information Technology: Research and Development, V1 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1987, IN PRESS J AM SOC IN CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, 10 ANN INT ACM SIGIR, P26 CROFT WB, 1984, INFORM TECHNOL R & D, V3, P113 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 CROFT WB, 1985, INFORM SYST, V10, P377, DOI 10.1016/0306-4379(85)90042-0 DEERWESTER S, 1987, IN PRESS INDEXING LA ELHAMDOUCHI A, 1986, 1986 ACM C RES DEV I, P149 ELKERTON J, 1984, HUM FACTORS, V26, P171 ENSER PGB, 1985, J DOC, V41, P135, DOI 10.1108/eb026777 ENSER PGB, 1985, INFORMATICS, V8, P68 Findler N. V., 1980, Data Analysis and Informatics. Proceedings of the Second International Symposium on Data Analysis and Informatics Fox E., 1983, THESIS CORNELL U ITH FOX EA, 1987, IN PRESS INFORMATION, V23 FOX EA, 1987, IN PRESS 1984 P WORK FREI HP, 1983, INFORM TECHNOL R & D, V2, P23 FUHR N, 1986, 1986 ACM C RES DEV I, P249 Fuhr N., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium GOFFMAN W, 1968, INFORM STORAGE RET, V4, P361, DOI 10.1016/0020-0271(68)90030-2 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 GUIDA G, 1983, AUTOMATICA, V19, P759, DOI 10.1016/0005-1098(83)90044-4 HAHN U, 1985, NACHR DOK, V36, P2 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1986, INFORM PROCESS MANAG, V22, P83, DOI 10.1016/0306-4573(86)90117-2 HOBBS JR, 1982, COLING 82, P127 HOEPPNER W, 1986, COOPERATIVE INTERFAC, P189 HOLLAAR LA, 1985, ASIS 85, V22, P369 HOLLAAR LA, 1985, RES DEV INFORMATION, P227 Hollaar L. A., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium HOLLAAR LA, 1979, ANNU REV INFORM SCI, V14, P129 INGWERSEN P, 1984, ONLINE REV, V8, P465, DOI 10.1108/eb024167 JONES KS, 1980, R D5553 U CAMBR COMP KANTOR PB, 1983, P AM SOC INFORM SCI, V20, P28 KOLL MB, 1984, 1984 P NAT ONL M NEW, P165 KOLODNER JL, 1983, ACM T DATABASE SYST, V8, P434, DOI 10.1145/319989.319995 KRAWCZAK DA, 1985, ENG KNOWLEDGE BASED, P455 KROPP D, 1981, INFORM PROCESS MANAG, V17, P363, DOI 10.1016/0306-4573(81)90050-9 KWOK KL, 1985, ASIS 85, V22, P59 KWOK KL, 1985, J AM SOC INFORM SCI, V36, P342, DOI 10.1002/asi.4630360510 LEVINSON RA, 1985, THESIS U TEXAS AUSTI MARCUS RS, 1983, J AM SOC INFORM SCI, V34, P381, DOI 10.1002/asi.4630340605 MARON ME, 1982, J AM SOC INFORM SCI, V33, P308 MARON ME, 1986, IEEE T SYST MAN CYB, V16, P21, DOI 10.1109/TSMC.1986.289278 MCCALL FM, 1986, INT J MAN MACH STUD, V25, P317, DOI 10.1016/S0020-7373(86)80063-3 MCCUNE BP, 1983, COMPSAC 83, P166 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V11, P939, DOI 10.1109/TSE.1985.232827 MCGILL M, 1979, EVALUATION FACTORS A MCGILL MJ, 1979, ANNU REV INFORM SCI, V14, P93 METZLER DP, 1983, 46TH P AM SOC INF SC, P269 MIYAMOTO S, 1986, IEEE T SYST MAN CYB, V16, P278, DOI 10.1109/TSMC.1986.4308950 MOHAN KC, 1985, J INFORM SCI, V11, P31, DOI 10.1177/016555158501100105 Muhlhauser G., 1985, 9th International Online Information Meeting MUKHOPADHYAY U, 1986, J AM SOC INFORM SCI, V37, P123, DOI 10.1002/(SICI)1097-4571(198605)37:3<123::AID-ASI3>3.0.CO;2-3 MURTAGH F, 1985, INFORMATICS, V8, P54 NOREAULT T, 1981, INFORMATION RETRIEVA, P57 Noreault T., 1982, Information Technology: Research and Development, V1 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 PANYR J, 1986, NACHR DOK, V37, P163 PANYR J, 1986, NACHR DOK, V37, P60 POGUE C, 1984, ONLINE REV, V8, P569, DOI 10.1108/eb024172 POGUE CA, 1987, IN PRESS PARALLEL CO POLLITT AS, 1985, 9TH INT ONL INF M LO, P373 POLLITT AS, 1983, SIGIR FORUM, V17 Popov I. I., 1985, Automatic Documentation and Mathematical Linguistics, V19 RABITTI F, 1986, 1986 P ASS COMP MACH RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1985, INFORM PROCESS MANAG, V21, P501, DOI 10.1016/0306-4573(85)90004-4 RADECKI T, 1983, LECT NOTES COMPUT SC, V146, P133 RAU LF, 1987, IN PRESS INFORMATION ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SACCO GM, 1984, 2ND P ASS COMP MACH, P104 SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1985, Second Conference of the European Chapter of the Association for Computational Linguistics. Proceedings of the Conference Salton G., 1985, SIGIR Forum, V18 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1983, LECT NOTES COMPUT SC, V146, P151 SALTON G, 1985, RES DEV INFORMATION, P54 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1981, SIGIR Forum, V16 SARACEVIC T, 1983, P AM SOC INFORM SCI, V20, P22 Schank R. C., 1977, SCRIPTS PLANS GOALS Schank R. C., 1975, CONCEPTUAL INFORMATI Schek H.-J., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium SHAW WM, 1986, INFORM PROCESS MANAG, V22, P19, DOI 10.1016/0306-4573(86)90006-3 SHOVAL P, 1985, INFORM PROCESS MANAG, V21, P475, DOI 10.1016/0306-4573(85)90002-0 SIMMONS RF, 1987, IN PRESS INFORMATION SMEATON AF, 1986, INFORM PROCESS MANAG, V22, P135, DOI 10.1016/0306-4573(86)90120-2 SMEATON AF, 1981, 4TH P INT ACM SIGIR, P83 Sokal R.R., 1973, NUMERICAL TAXONOMY P Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 SUBRAMANIAN V, 1986, OPT ENG, V25, P445, DOI 10.1117/12.7973842 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 TONG RM, 1985, INT J MAN MACH STUD, V22, P265, DOI 10.1016/S0020-7373(85)80003-1 TONG RM, 1985, RES DEV INFORMATION, P243 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1986, INFORMATION PROCESSI, V22 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, 1986 ACM C RES DEV I, P194 VANRIJSBERGEN CJ, 1980, R D5587 U CAMBR COMP VANRIJSBERGEN CJ, 1984, BRIT COMPUTER SOC WO VLADUTZ G, 1984, CHALLENGES INFORMATI, V20, P204 VOORHEES EM, 1986, 1986 P ASS COMP MACH, P164 WALKER DE, 1981, 240 SRI INT ART INT WATTERS CR, 1985, ONLINE REV, V9, P391, DOI 10.1108/eb024193 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 WILLETT P, 1985, INT FORUM INFORM DOC, V10, P28 WILLETT P, 1984, INFORM TECHNOL R & D, V3, P78 Willett P., 1985, 9th International Online Information Meeting WILLETT P, 1986, J CHEM INF COMP SCI, V26, P36, DOI 10.1021/ci00049a008 WILLETT P, 1984, J AM SOC INFORM SCI, V35, P149, DOI 10.1002/asi.4630350303 WINETT SG, 1985, ENG KNOWLEDGE BASED, P230 Wong S. K. M., 1985, Annales Societatis Mathematicae Polonae, Series IV: Fundamenta Informaticae, V8 WONG SKM, 1985, RES DEV INFORMATION, P18 Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1986, 1986 P ACM C RES DEV, P265 Zarri G. P., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium ZARRI GP, 1983, SIGIR FORUM, V17 ZARRI GP, 1985, INFORMATICS, V8, P101 ZENNER RBRC, 1985, FUZZY SET SYST, V17, P9 NR 174 TC 103 Z9 103 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1987 VL 22 BP 109 EP 145 PG 37 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N0501 UT WOS:A1987N050100006 ER PT J AU FOX, EA AF FOX, EA TI DEVELOPMENT OF THE CODER SYSTEM - A TESTBED FOR ARTIFICIAL-INTELLIGENCE METHODS IN INFORMATION-RETRIEVAL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Review RP FOX, EA (reprint author), VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061, USA. CR AHLSWEDE T, 1986, ADV LEXICOLOGY, P65 ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434 AMSLER RA, 1984, ANNU REV INFORM SCI, V19, P161 BABATZ R, 1985, SEP P IFIP WORK GROU BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BATES MJ, 1981, ANNU REV INFORM SCI, V16, P139 BATES MJ, 1979, J AM SOC INFORM SCI, V30, P205, DOI 10.1002/asi.4630300406 BELKIN NJ, 1984, INFORM TECHNOL R & D, V3, P122 BELKIN NJ, 1983, J INFORM SCI, V5, P153 Bennett D. C., 1975, SPATIAL TEMPORAL USE BICHTELER J, 1980, J AM SOC INFORM SCI, V31, P278, DOI 10.1002/asi.4630310408 BISWAS G, 1987, J AM SOC INFORM SCI, V38, P83, DOI 10.1002/(SICI)1097-4571(198703)38:2<83::AID-ASI2>3.0.CO;2-X Biswas G., 1985, Second Conference on Artificial Intelligence Applications: The Engineering of Knowledge-Based Systems (Cat. No.85CH2215-2) BISWAS G, 1987, J AM SOC INFORM SCI, V38, P97, DOI 10.1002/(SICI)1097-4571(198703)38:2<97::AID-ASI3>3.0.CO;2-H BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BORGMAN CL, 1984, ANNU REV INFORM SCI, V19, P33 BORGMAN CL, 1985, 8TH P ANN INT ACM SI, P139 BORGMAN CL, 1986, 49TH P ANN M AM SOC, P32 BOSC P, 1986, 1986 P ACM C RES DEV, P114 BRENNER LP, 1980, LIBR RES, V2, P63 BROOKS HM, 1985, P INFORMATICS, V8, P191 BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BYLANDER T, 1986, AI MAG, V7, P66 Chandrasekaran B., 1986, IEEE Expert, V1, DOI 10.1109/MEX.1986.4306977 CHEESEMAN P, 1985, 4TH P NAT C ART INT, P1002 CHIAMARELLA Y, 1986, 1986 P ACM C RES DEV, P207 CLOCKSIN W., 1984, PROGRAMMING PROLOG Cowie A. P., 1983, OXFORD DICT CURRENT, VII Cowie A.P., 1975, OXFORD DICT CURRENT, V1 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 Croft W. B., 1982, Office Information Systems. Proceedings of the Second International Workshop CROFT WB, 1986, 1986 P ACM C RES DEV, P201 CROFT WB, 1984, 3RD P ACM BCS C RES, P95 CROUCH D, 1986, 1986 P ACM C RES DEV, P58 DANIELS PJ, 1985, P RIAO 85 C GREN FRA, P645 DANIELS PJ, 1986, J DOC, V42, P272, DOI 10.1108/eb026797 DANIELS PJ, 1986, 6TH P INT RES FOR IN, P162 DEFUDE B, 1985, 8TH P ACM SIGIR C MO, P147 DEJONG G, 1982, STRATEGIES NATURAL L, P149 DIBENIGNO G, 1986, 1986 P ACM SIGIR C R, P144 DOSZKOCS TE, 1983, RES DEV INFORMATION, P38 DOSZKOCS TE, 1986, J AM SOC INFORM SCI, V37, P191, DOI 10.1002/(SICI)1097-4571(198607)37:4<191::AID-ASI3>3.0.CO;2-Y ERMAN LD, 1980, COMPUT SURV, V12, P213 EVENS M, 1985, HUMANS MACHINES, P73 EVENS MW, 1979, AM J COMPUTATIONAL L, V83, P1 FENICHEL CH, 1981, J AM SOC INFORM SCI, V32, P23, DOI 10.1002/asi.4630320104 Fenichel C.H., 1980, LIBR RES, V2, P107 FIDEL R, 1986, J AM SOC INFORM SCI, V37, P37, DOI 10.1002/asi.4630370107 FIKES R, 1985, COMMUN ACM, V28, P904, DOI 10.1145/4284.4285 FOX E, 1986, 49TH P ANN M ASIS CH, P88 FOX EA, 1983, THESIS CORNELL U ANN FOX EA, 1983, 83560 CORN U DEP COM FOX EA, 1986, CD ROM NEW PAPYRUS, P143 FOX EA, 1986, 2ND P C COMP INT INT, P135 FOX EA, 1980, ACM SIGIR FORUM, V15, P5, DOI 10.1145/1095403.1095404 FOX EA, 1987, IN PRESS INT J APPRO, V1 FOX EA, 1985, 8TH ANN INT ACM SIGI, P42 FOX EA, 1984, INFRASTRUCTURE INFOR, P449 FOX EA, 1986, TR8623 VIRG POL I ST FOX EA, 1984, IN PRESS JUL P WORKS FOX EA, UNPUB INFORMATION PR FOX EA, 1986, TR861 VIRG POL I STA FOX MS, 1986, EXPERT DATABASE SYST, P161 FRANCE RK, 1986, THESIS VIRGINIA POLY FRANCE RK, 1986, 1986 P IEEE EXP SYST, P135 FREI HP, 1984, REPRESENTATION EXCHA, P383 GALLELLI S, 1986, 1986 P ACM C RES DEV, P31 GUIDA G, 1983, AUTOMATICA, V19, P759, DOI 10.1016/0005-1098(83)90044-4 HAHN U, 1984, REPRESENTATION EXCHA, P143 Hanks P., 1979, COLLINS DICT ENGLISH Harter S. P., 1986, ONLINE INFORMATION R HAYESROTH F, 1985, COMMUN ACM, V28, P921, DOI 10.1145/4284.4286 HELM AR, 1985, P EXPERT SYSTEMS GOV, P284 Hornby A., 1974, OXFORD ADV DICT CURR HOROWITZ GL, 1983, JAMA-J AM MED ASSOC, V250, P2494, DOI 10.1001/jama.250.18.2494 HUMPHREY SM, 1984, 49TH P ANN M AM SOC, P106 INGWERSEN P, 1982, J DOC, V38, P165, DOI 10.1108/eb026727 INGWERSEN P, 1986, 1986 ACM SIGIR C, P68 JONES KS, 1973, LINGUISTICS INFORMAT JONES TA, 1984, ACTA CRYSTALLOGR A, V40, P50, DOI 10.1107/S010876738400009X KATZER J, 1986, 49TH ASIS 86 P ASIS, P118 Katzer J., 1982, Information Technology: Research and Development, V1 KIMURA GD, 1984, 840704 U WASH DEP CO KORFHAGE RR, 1984, 84CSE12 SO METH U DE KORFHAGE RR, 1986, 86CSE4 SO METH U DEP Kowalski R., 1979, LOGIC PROBLEM SOLVIN KUCERA H, 1985, 1ST P U WAT UW CTR N, P7 KWOK KL, 1986, 49TH P ANN M AM SOC, P158 LECOT K, 1986, 1986 P IEEE EXP SYST, P234 LEE NS, 1986, 1986 P IEEE EXP SYST, P243 Levesque H. J., 1985, READINGS KNOWLEDGE R, P41 MARCUS RS, 1986, 49TH P ANN M AM SOC, P185 MARKEY K, 1981, INFORM PROCESS MANAG, V17, P215, DOI 10.1016/0306-4573(81)90016-9 MAULDIN ML, 1986, THESIS CARNEGIEMELLO MAYS E, 1986, EXPERT DATABASE SYST, P559 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V11, P939, DOI 10.1109/TSE.1985.232827 MILLER GA, 1985, 1ST P U WAT UW CTR N, P25 MORRISSEY JM, 1986, INFORM PROCESS MANAG, V22, P121, DOI 10.1016/0306-4573(86)90119-6 MUKHOPADHYAY U, 1986, J AM SOC INFORM SCI, V37, P123, DOI 10.1002/(SICI)1097-4571(198605)37:3<123::AID-ASI3>3.0.CO;2-3 MYER TH, 1983, J TELECOMMUN NETWORK, V2, P413 Naish L., 1985, MU PROLOG 3 2DB REFE Nii H. P., 1986, AI MAG, V7, P82 Nii H. P., 1986, AI MAG, V7, P38 NIYOGI D, 1986, 1986 P EXP SYST GOV, P128 OCONNOR J, 1980, J AM SOC INFORM SCI, V31, P227, DOI 10.1002/asi.4630310402 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 OJALA M, 1986, J AM SOC INFORM SCI, V37, P197, DOI 10.1002/(SICI)1097-4571(198607)37:4<197::AID-ASI4>3.0.CO;2-D PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 Patel-Schneider P. F., 1984, First Conference on Artificial Intelligence Applications (Cat. No. 84CH2107-1) PEREIRA F, 1983, 275 SRI INT TECHN NO POLLITT AS, 1984, ASLIB PROC, V36, P229, DOI 10.1108/eb050928 POLLITT SE, 1983, ACM SIGIR FORUM, V17, P136 QUARTERMAN JS, 1986, COMMUN ACM, V29, P932, DOI 10.1145/6617.6618 RAMAMOHANARO K, 1985, 8517 U MELB COMP SCI ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROUSE WB, 1984, INFORM PROCESS MANAG, V20, P129, DOI 10.1016/0306-4573(84)90044-X SACKSDAVIS R, 1985, INFORM SYST, V10, P391, DOI 10.1016/0306-4379(85)90043-2 Sager Naomi, 1981, NATURAL LANGUAGE INF SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1986, 49TH ASIS 86 P ASIS, P277 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1980, ENCY LIB INFORM SCI, V28, P1 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SCIORE E, 1986, EXPERT DATABASE SYST, P293 SEWELL W, 1986, J AM SOC INFORM SCI, V37, P234, DOI 10.1002/(SICI)1097-4571(198607)37:4<234::AID-ASI9>3.0.CO;2-A SIMMONS RF, 1983, TR8324 U TEX AUST TE SKUCE D, 1985, P EXPERT SYSTEMS GOV, P378 SMEATON AF, 1986, 1986 P ACM C RES DEV, P103 SMEATON AF, 1986, INFORM PROCESS MANAG, V22, P135, DOI 10.1016/0306-4573(86)90120-2 SMITH LC, 1980, ANNU REV INFORM SCI, V15, P67 SOMMERVILLE I, 1985, CSST285 U STRATHCL S Sterling L., 1986, ART PROLOG SVENONIUS E, 1986, J AM SOC INFORM SCI, V37, P331, DOI 10.1002/asi.4630370509 TENOPIR C, 1984, ANNU REV INFORM SCI, V19, P215 THOMPSON RH, 1985, P EXPERT SYSTEMS GOV, P448 TONG RM, 1986, 1986 P IEEE EXP SYST, P106 TRIVISON D, 1986, 49TH ASIS 86 P ANN M, P341 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 VANRIJSBERGEN CJ, 1986, 1986 ACM C RES DEV I, P194 WALKER D, 1985, 1ST P U WAT UW CTR N, P11 WATTERS CR, 1986, LOGIC BASIS INFORMAT WILLIAMS ME, 1986, 49TH P ANN M AM SOC, P365 WILLIAMS PW, 1985, 6TH P NAT ONL M NEW, P495 WOHLWEND RC, 1986, CREATION PROLOG FACT YIP M, 1979, THESIS MIT CAMBRIDGE ZARRI GP, 1984, INT J MAN MACH STUD, V20, P87, DOI 10.1016/S0020-7373(84)80007-3 ZARRI GP, 1983, INFORM TECHNOL R & D, V2, P89 1953, STANDARD HDB PREPOSI NR 149 TC 56 Z9 56 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1987 VL 23 IS 4 BP 341 EP 366 DI 10.1016/0306-4573(87)90022-7 PG 26 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA J4822 UT WOS:A1987J482200007 ER PT J AU LOSEE, RM AF LOSEE, RM TI PREDICTING DOCUMENT-RETRIEVAL SYSTEM PERFORMANCE - AN EXPECTED PRECISION MEASURE SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP LOSEE, RM (reprint author), UNIV N CAROLINA,SCH INFORMAT & LIB STUDIES,CHAPEL HILL,NC 27514, USA. CR ALBERT D, 1978, P AM SOC INFORMATION, P15 BOLLMAN P, 1981, INFORMATION RETRIEVA BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1977, J ACM, V24, P410 BOOKSTEIN A, 1985, 8TH P INT ACM SIGIR BOOKSTEIN A, 1982, 5TH P INT C INF RETR BUCKLEY C, 1985, 8TH P INT ACM SIGIR Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COPPER WS, 1981, INFORMATION TECHNOLO, V1, P99 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARTER SP, 1974, THESIS U CHICAGO CHI KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 KANTOR PB, 1987, J AM SOC INFORM SCI, V38, P211, DOI 10.1002/(SICI)1097-4571(198705)38:3<211::AID-ASI10>3.0.CO;2-U KRAFT DH, 1973, J AM SOC INFORM SCI, V24, P368, DOI 10.1002/asi.4630240508 KRAFT DH, 1981, INFORM PROCESS MANAG, V17, P349, DOI 10.1016/0306-4573(81)90049-2 KRAFT DH, 1979, INFORM PROCESS MANAG, V15, P47, DOI 10.1016/0306-4573(79)90007-4 KWOK KL, 1986, 9TH P INT ACM SIGIR LOSEE R, 1987, J AM SOC INFORM SCI, V38, P239, DOI 10.1002/(SICI)1097-4571(198707)38:4<239::AID-ASI4>3.0.CO;2-6 LOSEE RM, UNPUB INTEGRATING BO LOSEE RM, IN PRESS J AM SOC IN LOSEE RM, 1987, 10TH P ANN INT ACM S, P164 OZKARAHAN E, 1986, 9TH P INT ACM SIGIR RAGHAVEN VV, 1983, 6TH P ANN INT ACM SI ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1986, J INFORM SCI, V12, P71, DOI 10.1177/016555158601200112 ROBERTSON SE, 1981, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1974, J DOCUMENTATION, V30, P363 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 34 TC 6 Z9 6 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1987 VL 23 IS 6 BP 529 EP 537 DI 10.1016/0306-4573(87)90057-4 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA L0662 UT WOS:A1987L066200002 ER PT J AU HENDRY, IG WILLETT, P WOOD, FE AF HENDRY, IG WILLETT, P WOOD, FE TI INSTRUCT - A TEACHING PACKAGE FOR EXPERIMENTAL METHODS IN INFORMATION-RETRIEVAL .2. COMPUTATIONAL ASPECTS SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ADAMSON GW, 1974, INFORM STORAGE RET, V10, P253, DOI 10.1016/0020-0271(74)90020-5 BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 BRZOZOWSKI JP, 1983, J INFORM SCI, V6, P67, DOI 10.1177/016555158300600205 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Freund G. E., 1982, Information Technology: Research and Development, V1 HENDRY IG, 1986, PROGRAM-AUTOM LIBR, V20, P245, DOI 10.1108/eb046940 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 MEADOW CT, 1983, J AM SOC INFORM SCI, V34, P289, DOI 10.1002/asi.4630340409 MORRISSEY J, 1983, LECT NOTES COMPUT SC, V146, P38 MURTAGH F, 1984, COMPST 1984 P COMP S, P143 Noreault T., 1982, Information Technology: Research and Development, V1 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 NR 21 TC 13 Z9 13 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD OCT PY 1986 VL 20 IS 4 BP 382 EP 393 DI 10.1108/eb046949 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA E7893 UT WOS:A1986E789300002 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI ON RELEVANCE WEIGHT ESTIMATION AND QUERY EXPANSION SO JOURNAL OF DOCUMENTATION LA English DT Article RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR Cox D, 1970, ANAL BINARY DATA CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 ROBERTSON S, 1982, 5739 BRIT LIBR RES D ROBERTSON SE, UNPUB J INFORMATION ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 NR 8 TC 39 Z9 40 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD SEP PY 1986 VL 42 IS 3 BP 182 EP 188 DI 10.1108/eb026792 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA E0880 UT WOS:A1986E088000003 ER PT J AU SALTON, G AF SALTON, G TI ANOTHER LOOK AT AUTOMATIC TEXT-RETRIEVAL SYSTEMS SO COMMUNICATIONS OF THE ACM LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,405 UPSON HALL,ITHACA,NY 14853, USA. CR BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Cleverdon C., 1984, Information Services & Use, V4 CLEVERDON CW, ASLIB CRANFIELD RES, V2 CLEVERDON CW, 1977, ESA1432 EUR SPAC AG CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lancaster F. W., 1968, EVALUATION MEDLARS D LANCASTER FW, 1979, INFORMATION RETRIEVA Lovins J., 1968, MECH TRANSLATION COM, V11, P11 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1970, SCIENCE, V168, P335, DOI 10.1126/science.168.3929.335 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1982, ACM SIGIR FORUM, V17, P6, DOI 10.1145/1095479.1095480 Salton G., 1981, SIGIR Forum, V16 SWANSON DR, 1960, SCIENCE, V132, P1099, DOI 10.1126/science.132.3434.1099 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA 1972, STORAGE INFORMATION NR 21 TC 129 Z9 129 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0001-0782 J9 COMMUN ACM JI Commun. ACM PD JUL PY 1986 VL 29 IS 7 BP 648 EP 656 DI 10.1145/6138.6149 PG 9 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA D0557 UT WOS:A1986D055700024 ER PT J AU HENDRY, IG WILLETT, P WOOD, FE AF HENDRY, IG WILLETT, P WOOD, FE TI INSTRUCT - A TEACHING PACKAGE FOR EXPERIMENTAL METHODS IN INFORMATION-RETRIEVAL .1. THE USERS VIEW SO PROGRAM-AUTOMATED LIBRARY AND INFORMATION SYSTEMS LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ANGELL RC, 1983, INFORM PROCESS MANAG, V19, P255, DOI 10.1016/0306-4573(83)90022-5 ASHFORD J, 1984, PROGRAM-AUTOM LIBR, V18, P124, DOI 10.1108/eb046874 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BRZOZOWSKI JP, 1983, J INFORM SCI, V6, P67, DOI 10.1177/016555158300600205 CARUSO E, 1981, ANNU REV INFORM SCI, V16, P317 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Frakes W. B., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium Freund G. E., 1982, Information Technology: Research and Development, V1 HENDRY IG, UNPUB PROGRAM JONES KS, 1982, INFORMATION RETRIEVA Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 JONES KS, 1977, RES AUTOMATIC INDEXI KOLL MB, 1984, 1984 P NAT ONL M LENNON M, 1981, J INFORM SCI, V3, P177, DOI 10.1177/016555158100300403 MARCUS RS, 1983, J AM SOC INFORM SCI, V34, P381, DOI 10.1002/asi.4630340605 MEADOW CT, 1981, BASICS ONLINE SEARCH NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 POLLITT AS, 1984, ASLIB PROC, V36, P229, DOI 10.1108/eb050928 PORTER MF, 1983, INFORM TECHNOL R & D, V2, P169 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Porter M. F., 1982, Information Technology: Research and Development, V1 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST TOLIVER D, 1981, INFORM PROCESS MANAG, V17, P61, DOI 10.1016/0306-4573(81)90027-3 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WALES JL, 1984, PROGRAM-AUTOM LIBR, V18, P247, DOI 10.1108/eb046881 Willett P., 1985, 9th International Online Information Meeting WOOD FE, 1981, ONLINE REV, V5, P487, DOI 10.1108/eb024085 WOOD FE, 1984, J AM SOC INFORM SCI, V35, P53, DOI 10.1002/asi.4630350108 NR 33 TC 20 Z9 20 PU PORTLAND PRESS PI LONDON PA 59 PORTLAND PLACE, LONDON, ENGLAND W1N 3AJ SN 0033-0337 J9 PROGRAM-AUTOM LIBR JI Program-Autom. Libr. Inf. Syst. PD JUL PY 1986 VL 20 IS 3 BP 245 EP 263 DI 10.1108/eb046940 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA D4137 UT WOS:A1986D413700001 ER PT J AU PANYR, J AF PANYR, J TI PROBABILISTIC MODELS IN INFORMATION-RETRIEVAL SYSTEMS SO NACHRICHTEN FUR DOKUMENTATION LA German DT Article RP PANYR, J (reprint author), SIEMENS AG,ZENT BEREICH FORSCHUNG & TECH,ZENT AUFGABEN INFORMAT TECH,D-8000 MUNICH 83,FED REP GER. CR Bauer H., 1978, WAHRSCHEINLICHKEITST BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1973, J ASIS, V25, P413 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1976, INFORM PROCESS MANAG, V12, P327, DOI 10.1016/0306-4573(76)90051-0 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Feller W., 1957, INTRO PROBABILITY TH FREI HP, 1985, ETH62 I INF BER GOFFMAN W, 1969, INFORMATION STORAGE, V4, P361 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 IDE E, 1969, ISR15 CORN U SCI REP Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KWOK KL, 1985, J AM SOC INFORM SCI, V36, P342, DOI 10.1002/asi.4630360510 LAM K, 1982, ACM T DATABASE SYST, V7, P500, DOI 10.1145/319732.319756 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1983, LECT NOTES COMPUT SC, V146, P98 MILLER D, 1982, INFORM PROCESS MANAG, V18, P179, DOI 10.1016/0306-4573(82)90058-9 ODDY R, 1981, INFORMATION RETRIEVA PANYR J, 1986, NACHR DOK, V37, P2 PANYR J, 1986, AUTOMATISCHE KLASSIF Porter M. F., 1982, Information Technology: Research and Development, V1 Richter H., 1956, WAHRSCHEINLICHKEITST Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1983, LECT NOTES COMPUT SC, V146, P108 ROBERTSON SE, 1979, DREXEL LIBRARY Q, V14, P40 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, LECTURE NOTES COMPUT, V146 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 43 TC 5 Z9 5 PU VERLAG HOPPENSTEDT & CO PI DARMSTADT 1 PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY SN 0027-7436 J9 NACHR DOK JI Nachr. Dok. PD APR PY 1986 VL 37 IS 2 BP 60 EP 66 PG 7 WC Information Science & Library Science SC Information Science & Library Science GA C7749 UT WOS:A1986C774900001 ER PT J AU PANYR, J AF PANYR, J TI RELEVANCE PROBLEMS IN INFORMATION-RETRIEVAL SYSTEMS SO NACHRICHTEN FUR DOKUMENTATION LA German DT Article RP PANYR, J (reprint author), SIEMENS AG,ZENT BEREICH FORSCH & TECH,ZENT AUFGABEN INFORMAT TECH,D-8000 MUNICH 83,FED REP GER. CR BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 COOPER WS, 1973, J ASIS, V25, P87 COOPER WS, 1973, J ASIS, V25, P413 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 GEBHARDT F, 1981, DOKUMENTATIONSSYSTEM HARPER DJ, 1980, THESIS U CAMBRIDGE HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 PANYR J, 1986, AUTOMATISCHE KLASSIF RADECKI T, 1977, INFORM PROCESS MANAG, V13, P109, DOI 10.1016/0306-4573(77)90044-9 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGER W, 1975, THESIS U KARLSRUHE SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 TAUBE M, 1965, AM DOC, V16, P69, DOI 10.1002/asi.5090160204 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 21 TC 4 Z9 4 PU VERLAG HOPPENSTEDT & CO PI DARMSTADT 1 PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY SN 0027-7436 J9 NACHR DOK JI Nachr. Dok. PD FEB PY 1986 VL 37 IS 1 BP 2 EP 4 PG 3 WC Information Science & Library Science SC Information Science & Library Science GA A8331 UT WOS:A1986A833100001 ER PT J AU MARON, ME CURRY, S THOMPSON, P AF MARON, ME CURRY, S THOMPSON, P TI AN INDUCTIVE SEARCH SYSTEM - THEORY, DESIGN, AND IMPLEMENTATION SO IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS LA English DT Article C1 UNIV CALIF SAN FRANCISCO,SAN FRANCISCO,CA 94143. RP MARON, ME (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1980, INFORMATION RETRIEVA, P199 COOPER WS, INFO TECH R D, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 JONES KS, 1981, INFORMATION RETRIEVA MARON ME, 1960, J ASS COMP MACH, V7, P244 MARON ME, 1984, PROGR COMMUNICATION, V5, P145 MARON ME, 1983, LECTURE NOTES COMPUT, P98 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1983, LECTURE NOTES COMPUT, P108 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, SIGIR FORUM, V17, P252 SWANSON DR, 1977, LIBR QUART, V47, P128 Tversky A., 1982, JUDGMENT UNCERTAINTY, P84 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA, pCH6 VANRIJSBERGEN CJ, 1980, INFORMATION RETRIEVA, P32 YOUNG HD, 1962, STAT TREATMENT EXPT, pCH13 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 25 TC 20 Z9 22 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017-2394 SN 0018-9472 J9 IEEE T SYST MAN CYB JI IEEE Trans. Syst. Man Cybern. PD JAN-FEB PY 1986 VL 16 IS 1 BP 21 EP 28 DI 10.1109/TSMC.1986.289278 PG 8 WC Computer Science, Cybernetics; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AZL10 UT WOS:A1986AZL1000002 ER PT J AU MORRISSEY, JM HARPER, DJ VANRIJSBERGEN, CJ AF MORRISSEY, JM HARPER, DJ VANRIJSBERGEN, CJ TI INTERACTIVE QUERYING TECHNIQUES FOR AN OFFICE FILING FACILITY SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP MORRISSEY, JM (reprint author), NATL UNIV IRELAND UNIV COLL DUBLIN,DEPT COMP SCI,DUBLIN 4,IRELAND. CR Astrahan M. M., 1976, ACM Transactions on Database Systems, V1, DOI 10.1145/320455.320457 BATES M, 1983, 6TH ACM SIGIR C WASH BILLER H, 1982, 5TH INT C INF STOR R CHAMBERLIN DD, 1974, MAY ACM SIGFIDET WOR, P249 CODD EF, 1978, DATABASES IMPROVING CODD EF, 1978, IBM RJ2144 RES REP CRAWFORD RG, 1981, J AM SOC INFORM SCI, P51 DATTOLA RT, 1978, JASIS, V30 DESCHATELETS G, 1983, CAN J INFORM SCI, V8, P39 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 *IBM WORLD TRAD CO, STOR INF RETR STAIRS Lehnert W.G, 1978, PROCESS QUESTION ANS MACLEOD IA, 1985, RECHERCHE INFORMATIO MACLEOD IA, 1983, INFORM TECHNOL R & D, V2, P43 MACLEOD IA, 1981, INFORM SYST, V6, P131, DOI 10.1016/0306-4379(81)90035-1 MACLEOD IA, 1981, COMPUTER, V24 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 OTT N, 1978, NATURAL COMMUNICATIO Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SCHEK HJ, 1978, LECTURE NOTES COMPUT, V65 SCHEK HJ, 1980, INFORMATION PROCESSI SCHEK HJ, 1982, 8TH P INT C VER LARG, P179 SOPENA L, 1983, 6TH ACM SIGIR C WASH VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 WILLIAMS MD, 1982 ACM C, P83 NR 27 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1986 VL 22 IS 2 BP 121 EP 134 DI 10.1016/0306-4573(86)90119-6 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA A2662 UT WOS:A1986A266200004 ER PT J AU ROBERTSON, SE THOMPSON, CL MACASKILL, MJ BOVEY, JD AF ROBERTSON, SE THOMPSON, CL MACASKILL, MJ BOVEY, JD TI WEIGHTING, RANKING AND RELEVANCE FEEDBACK IN A FRONT END SYSTEM SO JOURNAL OF INFORMATION SCIENCE LA English DT Article C1 UNIV KENT,CANTERBURY CT2 7NZ,KENT,ENGLAND. RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 JAMIESON SH, 1979, UNPUB IMPLEMENTATION Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 1980, J INFORM SCI, V1, P325, DOI 10.1177/016555157900100603 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1983, FRONT END IR EXPT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 THOMPSON RH, 1985, COINS8505 U MASS DEP NR 11 TC 21 Z9 21 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1986 VL 12 IS 1-2 BP 71 EP 75 DI 10.1177/016555158601200112 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA D9357 UT WOS:A1986D935700012 ER PT J AU GRIFFITHS, A LUCKHURST, HC WILLETT, P AF GRIFFITHS, A LUCKHURST, HC WILLETT, P TI USING INTERDOCUMENT SIMILARITY INFORMATION IN DOCUMENT-RETRIEVAL SYSTEMS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ALDENDERFER MS, 1977, CONSUMER REPORT CLUS BRZOZOWSKI JP, 1983, J INFORM SCI, V6, P67, DOI 10.1177/016555158300600205 CROFT WB, 1983, ACM SIGIR FORUM, V17, P178, DOI 10.1145/1013230.511820 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1984, RES DEV INFORMATION CROFT WB, 1981, ACM SIGIR FORUM, V16, P40, DOI 10.1145/1013228.511760 DAVIES A, 1983, THESIS U SHEFFIELD EVANS L, 1975, SEARCH STRATEGY VARI GOFFMAN W, 1969, INFORMATION STORAGE, V4, P363 GOWDA KC, 1978, PATTERN RECOGN, V10, P105 GRIFFITHS A, 1984, EVALUATION CLUSTERIN GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 HARDING P, 1982, AUTOMATIC INDEXING C HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 JARVIS RA, 1973, IEEE T COMPUT, VC-22, P1025, DOI 10.1109/T-C.1973.223640 Katzer J., 1982, Information Technology: Research and Development, V1 LANCE GN, 1967, COMPUT J, V9, P373 LUCKHURST HC, 1984, THESIS U SHEFFIELD MANSUR O, 1980, INFORM PROCESS MANAG, V16, P129, DOI 10.1016/0306-4573(80)90016-3 MIZOGUCHI R, 1980, IEEE T PATTERN ANAL, V2, P292 MURTAGH F, 1984, INFORM PROCESS MANAG, V20, P611, DOI 10.1016/0306-4573(84)90077-3 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILLETT P, 1981, INFORM PROCESS MANAG, V17, P53, DOI 10.1016/0306-4573(81)90026-1 WILLETT P, 1984, J AM SOC INFORM SCI, V35, P149, DOI 10.1002/asi.4630350303 WILLETT P, 1980, J INFORM SCI, V2, P223, DOI 10.1177/016555158000200503 WISHART D, 1978, CLUSTAN 1C USER MANU NR 34 TC 65 Z9 66 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1986 VL 37 IS 1 BP 3 EP 11 DI 10.1002/asi.4630370102 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AYV72 UT WOS:A1986AYV7200001 ER PT J AU KWOK, KL AF KWOK, KL TI THE CONCEPT OF DOCUMENT COMPONENTS FOR PROBABILISTIC INDEXING SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP KWOK, KL (reprint author), CUNY QUEENS COLL,DEPT COMP SCI,FLUSHING,NY 11367, USA. CR BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 HILLMAN DJ, 1968, INFORM STORAGE RET, V4, P219, DOI 10.1016/0020-0271(68)90023-5 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P477, DOI 10.1016/0020-0271(73)90034-X Kwok K. L., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium KWOK KL, 1985, 48TH P ASIS ANN M LA, V22, P59 KWOK KL, 1985, J AM SOC INFORM SCI, V36, P342, DOI 10.1002/asi.4630360510 OCONNOR J, 1982, INFORM PROCESS MANAG, V18, P125, DOI 10.1016/0306-4573(82)90036-X OCONNOR J, 1980, J AM SOC INFORM SCI, V31, P227, DOI 10.1002/asi.4630310402 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 17 TC 0 Z9 0 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1986 VL 23 BP 158 EP 162 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA F4226 UT WOS:A1986F422600028 ER PT J AU SALTON, G AF SALTON, G TI ON THE USE OF KNOWLEDGE-BASED PROCESSING IN AUTOMATIC TEXT RETRIEVAL SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR BALLARD BW, 1984, ACM T OFF INF SYST, V2, P1, DOI 10.1145/357417.357418 BATES M, 1983, 6TH P ANN INT SIGIR, V17, P81 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Cleverdon C. W., 1966, ASLIB CRANFIELD RES, V2 CLEVERDON CW, 1977, ESA1432 NASA DAT BAS Cooper W. S., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium CROFT WB, 1985, MAR P RIAO 85 C GREN, P131 CROFT WB, 1986, USER SPECIFIED DOMAI Defude B., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium DEJONG G, 1983, SIGIR FORUM, V17, P10 FAGAN JL, 1985, AUTOMATIC PHRASE IND Grosz B. J., 1983, Conference on Applied Natural Language Processing. Proceedings of the Conference Guida G., 1983, Conference on Applied Natural Language Processing. Proceedings of the Conference HARRIS LR, 1977, INT J MAN MACH STUD, V9, P697, DOI 10.1016/S0020-7373(77)80037-0 Hendrix G. G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320251.320253 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1983, 7 P INF C ASL, P136 LANCASTER FW, 1979, INFORMATION RETRIEVA MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 PAICE CD, 1985, P RIAO 85 C GRENOBLE, P293 POLLITT SE, 1983, SIGIR FORUM, V17, P136 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 SALTON G, 1966, READINGS AUTOMATIC L SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Shoval P., 1981, SIGIR Forum, V16 SWANSON DR, 1960, SCIENCE, V132, P1099, DOI 10.1126/science.132.3434.1099 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 TAIT JI, 1983, 5793 U CAMBR COMP LA Thompson B. H., 1983, Conference on Applied Natural Language Processing. Proceedings of the Conference TONG RM, 1985, 8TH P ANN INT ACM SI, P243 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 NR 38 TC 2 Z9 2 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1986 VL 23 BP 277 EP 287 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA F4226 UT WOS:A1986F422600048 ER PT J AU KRAFT, DH AF KRAFT, DH TI ADVANCES IN INFORMATION-RETRIEVAL - WHERE IS THAT RECORD SO ADVANCES IN COMPUTERS LA English DT Article RP KRAFT, DH (reprint author), LOUISIANA STATE UNIV, DEPT COMP SCI, BATON ROUGE, LA 70803 USA. CR Addis T. R., 1982, Information Technology: Research and Development, V1 ALBERT D, 1978, MANAGEMENT INFORMATI ANGIONE PV, 1975, J AM SOC INFORM SCI, V26, P112, DOI 10.1002/asi.4630260207 BARSTEIN LM, 1980, ANN INTERN MED, V93, P165 BATES M, 1983, 6TH P INT ACM SIGIR, P81 BILLER H, 1982, 5TH P INT C INF RETR, P80 BOLC L, 1984, COMPUT ARTIF INTELL, V3, P33 BOLLMANN P, 1982, PROGR CYBERNETICS SY BOLLMANN P, 1983, 6TH P INT ACM SIGIR, P122 BOLLMANN P, 1982, 5TH P INT C INF RETR, P13 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V32, P275, DOI 10.1002/asi.4630320407 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 BOOKSTEIN A, 1982, TRITALIB6019 ROYAL I Borko H., 1978, INDEXING CONCEPTS ME BRATLEY P, 1982, INFORM PROCESS MANAG, V18, P257, DOI 10.1016/0306-4573(82)90004-8 BROOKES H, 1983, 7 ASLIBS INF C CAMBR BUCKLES BP, 1981, FUZZY SETS SYST, V6 BUELL DA, 1981, P ASS COMPUTING MACH, P56, DOI 10.1145/511754.511762 BUELL DA, 1981, P AM SOC INFORM SCI, P298 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P249, DOI 10.1016/0306-4573(81)90019-4 BUELL DA, 1982, 5TH P INT C INF RETR, P20 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 CHAN FK, 1973, THESIS U ALBERTA EDM CHIEN RT, 1969, R449 U ILL COORD SCI CHOROS K, 1982, INFORM PROCESS MANAG, V18, P207, DOI 10.1016/0306-4573(82)90060-7 CLARKE A, 1983, J LIBR, V15, P277, DOI 10.1177/096100068301500404 COCHRAN PA, 1983, LIBR INF SCI RES, V5, P37 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COOPER WS, 1970, J AM SOC INFORM SCI, V21, P385, DOI 10.1002/asi.4630210603 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 Croft W. B., 1982, Information Technology: Research and Development, V1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1981, COINS8130 U MASS DEP CRYSTAL MI, 1982, ONLINE WESTON, P27 DEBESSONET CG, 1982, 82002 LOUIS STAT U D DEJONG G, 1983, 6TH P INT ACM SIGIR, P10 DESOPENA L, 1983, 6TH P INT ACM SIGIR, P77 DILLON M, 1983, J DOC, V39, P135, DOI 10.1108/eb026744 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 DOSZKOCS TE, 1980, SCIENCE, V208, P25, DOI 10.1126/science.6987735 DOSZKOCS TE, 1982, 5TH P INT C INF RETR, P251 Findler N. V., 1979, ASS NETWORKS REPRESE Garfield E., 1979, CITATION INDEXING HAFNER CD, 1981, INFORMATION RETRIEVA HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HOLLAAR L, 1983, 6TH P INT ACM SIGIR HOROWITZ GL, 1981, NEW ENGL J MED, V305, P924, DOI 10.1056/NEJM198110153051605 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KP, 1983, J DOC, V39, P1, DOI 10.1108/eb026736 KANTOR PB, 1983, UNPUB MODEL STOPPING KANTOR PB, 1981, IEEE T SYST MAN CYB, V11, P816 KERAVNOU E, 1982, COMPUTER REPRESENTAT KERAVNOU E, 1982, 2ND P WORLD C MATH S KOCHEN M, 1974, PRINCIPLES INFORMATI KOHOUT LJ, 1983, P IFAC S FUZZY INFOR KOHOUT LJ, 1982, FUZZY SETS DECISION KOLL MB, 1978, P INT C INFORMATION KRAFT DH, 1973, J AM SOC INFORM SCI, V24, P368, DOI 10.1002/asi.4630240508 KRAFT DH, 1979, P ASIS ANN M, P174 KRAFT DH, 1981, INFORM PROCESS MANAG, V17, P349, DOI 10.1016/0306-4573(81)90049-2 KRAFT DH, 1979, INFORM PROCESS MANAG, V15, P47, DOI 10.1016/0306-4573(79)90007-4 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P77, DOI 10.1002/asi.4630290206 KRAFT DH, 1977, INFO REPORTS BIBLIO, V6, P2 KRAFT DH, 1983, INT J MAN MACH STUD, V19, P45, DOI 10.1016/S0020-7373(83)80041-8 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 KUKICH K, 1983, 6TH P ANN INT ACM SI, P246 LANCASTER FW, 1973, INFORMATION RETRIEVA LANCASTER FW, 1979, INFORMATION RETRIEVA LEBOWITZ M, 1983, 6TH P INT ACM SIGIR, P25 LEVIEN RE, 1967, COMMUN ACM, V10, P715, DOI 10.1145/363790.363817 Liou J. H., 1977, Information Systems, V2, DOI 10.1016/0306-4379(77)90007-2 MACLEOD IA, 1977, INFORM PROCESS MANAG, V13, P167, DOI 10.1016/0306-4573(77)90035-8 MARCUS RS, 1983, J AM SOC INFORM SCI, V34, P381, DOI 10.1002/asi.4630340605 MARCUS RS, 1981, J AM SOC INFORM SCI, V32, P287, DOI 10.1002/asi.4630320409 MARCUS RS, 1981, INFORMATION COMMUNIT, P270 MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1983, J AM SOC INFORM SCI, V34, P308 MAZUR Z, 1979, INFORM PROCESS MANAG, V15, P227, DOI 10.1016/0306-4573(79)90029-3 MCGILL M, 1976, ACM SIGIR FORUM, V10, P37, DOI 10.1145/1095286.1095300 MEADOW CT, 1981, BASICS ONLINE SEARCH MEADOW CT, 1982, J AM SOC INFORM SCI, V33, P325 MIYAMOTO S, 1983, IEEE T SYST MAN CYB, V13, P62 MOREHEAD DR, 1982, INFORM PROCESS MANAG, V18, P193, DOI 10.1016/0306-4573(82)90059-0 MOULINOUX C, 1982, 5TH P INT C INF RETR, P51 NAKAMURA K, 1982, IEEE T SYST MAN CYB, V12, P193, DOI 10.1109/TSMC.1982.4308803 Negoita C. V., 1973, Studii si Cercetari de Documentare, V15 Negoita C. V., 1973, Kybernetes, V2, DOI 10.1108/eb005334 NEGOITA CV, 1976, INT J MAN MACH STUD, V8, P711, DOI 10.1016/S0020-7373(76)80032-6 NEGOITA CV, 1973, INFORM SCIENCES, V5, P279, DOI 10.1016/0020-0255(73)90019-4 NODTVEDT E, 1980, TR80447 CORN U DEP C NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 POLLITT AS, 1981, ONLINE INFORMATION, P25 POLLITT AS, 1983, 6TH P ANN INT ACM SI, P136 RADECKI T, 1982, 5TH P INT C INF RETR, P133 RADECKI T, 1977, INFORM PROCESS MANAG, V13, P109, DOI 10.1016/0306-4573(77)90044-9 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1982, J DOC, V38, P14, DOI 10.1108/eb026719 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P8, DOI 10.1002/asi.4630330104 RAS ZW, 1982, INT J COMPUT INF SCI, V11, P275, DOI 10.1007/BF00999444 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J AM SOC INFORM SCI, V29, P304, DOI 10.1002/asi.4630290607 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUSH JE, 1982, ADV COMPUT, V21, P333, DOI 10.1016/S0065-2458(08)60572-0 SACHS WM, 1976, J AM SOC INFORM SCI, V27, P75 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, 83539 CORN U DEP COM SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1977, TR77309 CORN U DEP C SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1979, TR79363 CORN U DEP C SALTON G, 1980, TR80439 CORN U DEP C SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1970, SCIENCE, V168, P335, DOI 10.1126/science.168.3929.335 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1982, TR82511 CORN U DEP C SALTON G, 1982, 5TH P INT C INF RETR, P151 SALTON G, 1983, 6TH P ANN INT ACM SI, P252 SALTON G, 1975, REG C SER APPL MATH, V18 SALTON G, 1980, TERM WEIGHTING MODEL SCHWIND CB, 1983, INFORM PROCESS MANAG, V19, P223, DOI 10.1016/0306-4573(83)90019-5 SMITH LC, 1980, ONLINE REV, V4, P383, DOI 10.1108/eb024044 SMITH LC, 1976, INFORM PROCESS MANAG, V12, P189, DOI 10.1016/0306-4573(76)90005-4 SWANSON DR, 1977, LIBR QUART, V47, P128 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 TOLLIVER DE, 1982, INFORM TECHNOL LIBR, P317 TONG RM, 1983, P AAII 83 WASHINGTON TRAVIS IL, 1977, INFORM PROCESS MANAG, V13, P49, DOI 10.1016/0306-4573(77)90030-9 UMANO M, 1983, UNPUB S FUZZY INFORM JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VICKERY BC, 1970, TECHNIQUES INFORMATI VICKERY BC, 1965, RETRIEVAL SYSTEM THE WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WOLF RJ, 1982, THESIS U MASSACHUSET WU H, 1981, THESIS CORNELL U ITH WU H, 1980, TR81457 CORN U DEP C YAGER RR, 1980, IEEE T SYST MAN CYB, V10, P51 YOVITS MC, 1981, J AM SOC INFORM SCI, V32, P187, DOI 10.1002/asi.4630320305 YU CT, 1983, TR83543 CORN U DEP C YU CT, 1971, COMMUNICATIONS ASS C, V20, P135 YU CT, 1982, J ACM, V29, P152, DOI 10.1145/322290.322300 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZADEH LA, 1980, P IEEE, V68, P421, DOI 10.1109/PROC.1980.11659 ZARRI GP, 1983, 6TH P ANN INT ACM SI, P189 ZUNDE P, 1969, AM DOC, V20, P259, DOI 10.1002/asi.4630200313 NR 178 TC 6 Z9 6 PU ACADEMIC PRESS INC JNL-COMP SUBSCRIPTIONS PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 SN 0065-2458 J9 ADV COMPUT PY 1985 VL 24 BP 277 EP 318 DI 10.1016/S0065-2458(08)60369-1 PG 42 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering SC Computer Science GA AML16 UT WOS:A1985AML1600007 ER PT J AU BOOKSTEIN, A AF BOOKSTEIN, A TI PROBABILITY AND FUZZY-SET APPLICATIONS TO INFORMATION-RETRIEVAL SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review RP BOOKSTEIN, A (reprint author), UNIV CHICAGO,SCH GRAD LIB,1100 E 57TH ST,CHICAGO,IL 60637, USA. CR Ash R. B., 1965, INFORMATION THEORY BELLMAN R, 1973, INFORM SCIENCES, V5, P149, DOI 10.1016/0020-0255(73)90009-1 Birkhoff G, 1977, SURVEY MODERN ALGEBR BOOKSTEIN A, 1984, HUM SYST MANAGE, V4, P323 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1983, J AM SOC INFORM SCI, V34, P331, DOI 10.1002/asi.4630340504 BOOKSTEIN A, 1983, RES DEV INFORMATION, P118 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1981, GENERALIZED RETRIEVA, P4 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEIN A, 1979, INFORMATION PROCESSI, V13, P153 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 BOOKSTEIN A, 1974, J DOCUMENTATION, V30, P377 BOOKSTEIN A, 1981, INFORMATION RETRIEVA, P23 BOOKSTEIN A, 1985, UNPUB 1985 P INT S BOOKSTEIN A, 1981, J AM SOC INFORMATION, V32, P257 BOOKSTEIN A, 1982, TRITALIB6019 ROYAL I BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 BUCKLAND M, 1983, INFORMATION PROCESSI, V18, P237 BUELL DA, 1982, 1982 RES DEV INF RET, P20 BUELL DA, 1981, 4TH P INT C INF STOR, P56 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P249, DOI 10.1016/0306-4573(81)90019-4 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 CHOROS K, 1982, INFORM PROCESS MANAG, V18, P207, DOI 10.1016/0306-4573(82)90060-7 CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 COOPER WS, 1976, SUBOPTIMALITY RETRIE COOPER WS, 1972, UNPUB INADEQUACY PRO COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1983, RES DEV INFORMATION, P28 CROFT WB, 1984, INFORM TECHNOL R & D, V3, P113 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 CROUCH CJ, 1981, 4TH P INT C INF STOR Duda R., 1973, PATTERN CLASSIFICATI Feller W, 1968, INTRO PROBABILITY TH Fishburn P.C., 1970, PUBLICATIONS OPERATI, V18 FOX EA, 1985, TR851 VIRG POL I STA FOX EA, 1983, 83561 CORN U DEP COM HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS CAMBRIDGE U HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HARTER SP, 1971, LIBR QUART, V41, P229 KANTOR PB, 1984, INFORM TECHNOL R & D, V3, P88 KANTOR PB, 1981, IEEE T SYST MAN CYB, V11, P816 Kaufmann A, 1975, INTRO THEORY FUZZY S KRAFT DH, 1973, J AM SOC INFORM SCI, V24, P368, DOI 10.1002/asi.4630240508 KRAFT DH, 1983, INT J MAN MACH STUD, V19, P45, DOI 10.1016/S0020-7373(83)80041-8 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 LEVINE RD, 1979, 1978 MAX ENTR FORM P Lewis-II P. M., 1959, INFORM CONTR, V2, P214, DOI 10.1016/S0019-9958(59)90207-4 Luce Robert, 1957, GAMES DECISIONS INTR Maron M. E, 1985, PROGR COMMUNICATION, V5, P145 MARON ME, 1982, J AM SOC INFORM SCI, V33, P308 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1983, 1982 RES DEV INF RET, P98 Negoita C. V., 1973, Kybernetes, V2, DOI 10.1108/eb005334 NEGOITA CV, 1976, INT J MAN MACH STUD, V8, P711, DOI 10.1016/S0020-7373(76)80032-6 NEGOITA CV, 1973, INFORM SCIENCES, V5, P279, DOI 10.1016/0020-0255(73)90019-4 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ODDY R, 1981, INFORMATION RETRIEVA RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1981, FUZZY SET SYST, V5, P159, DOI 10.1016/0165-0114(81)90014-2 RADECKI T, 1981, INT J MAN MACH STUD, V14, P169, DOI 10.1016/S0020-7373(81)80039-9 RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1981, KYBERNETES, V10, P35, DOI 10.1108/eb005574 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RAGHAVAN VV, 1983, RES DEV INFORMATION, P88 Raiffa H, 1961, APPLIED STATISTICAL Raiffa H, 1968, DECISION ANAL INTRO Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1981, TERM FREQUENCY TERM, P22 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1984, J DOCUMENTATION, V40 ROBERTSON SE, 1978, J AM SOC INFORM SCI, V29, P711 ROBERTSON SE, 1983, 5807 CIT U CTR INF S ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1982, 5739 CIT U CTR INF S ROBERTSON SE, 1983, 1982 RES DEV INF RET, P108 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SACHS WM, 1976, J AM SOC INFORM SCI, V27, P85, DOI 10.1002/asi.4630270203 SALTON G, 1981, INFORMATION RETRIEVA, P9 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, IFIP WORLD C SERIES, V9 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1984, 84588 CORN U DEP COM SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1983, RES DEV INFORMATION SALTON G, 1983, 83564 CORN U DEP COM Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, RES DEV INFORMATION, P151 SALTON G, 1975, CBMS NSF REGIONAL C, V18 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1983, 83570 CORN U DEP COM SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 SPARCKJONES K, 1979, J DOC, V35, P30 SPARCK-JONES K, 1979, INFORM PROCESS MANAG, V15, P133 SPARCKJONES K, 1972, J DOC, V28, P11 SPARCK-JONES K, 1976, J DOC, V32, P59 SPARCK-JONES K, 1973, INFORM STORAGE RET, V9, P619 STIRLING KH, 1981, THEORETICAL ISSUES I, P63 SWANSON DR, 1971, LIBR QUART, V41, P223 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 TAGUE J, 1983, RES DEV INFORMATION, P194 TAGUE JM, 1973, INFORM STORAGE RET, V9, P129, DOI 10.1016/0020-0271(73)90063-6 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 TRIBUS M, 1980, IEEE T SYST MAN CYB, V10, P501 Tribus M., 1969, RATIONAL DESCRIPTION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1983, RES DEV INFORMATION, P101 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VASWANI PKT, 1970, NATIONAL PHYSICAL LA WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WU H, 1981, 4TH P INT C INF STOR, P30 YAGER RR, 1980, IEEE T SYST MAN CYB, V10, P51 YU CT, 1977, COMMUN ACM, V20, P135, DOI 10.1145/359436.359441 YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1982, J ACM, V29, P152, DOI 10.1145/322290.322300 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 YU CT, 1981, 4TH P INT C INF STOR, P46 YU CT, 1975, J ASS COMPUTING MACH, V23, P76 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X 1982, INFORMATION TECHNOLO 1945, J DOCUMENTATION 1950, J AM SOC INFORMATION 1963, INFORMATION PROCESSI NR 141 TC 56 Z9 56 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1985 VL 20 BP 117 EP 151 PG 35 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AYY68 UT WOS:A1985AYY6800007 ER PT J AU CROFT, WB PARENTY, TJ AF CROFT, WB PARENTY, TJ TI A COMPARISON OF A NETWORK STRUCTURE AND A DATABASE SYSTEM USED FOR DOCUMENT-RETRIEVAL SO INFORMATION SYSTEMS LA English DT Article RP CROFT, WB (reprint author), UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003, USA. CR CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1984, UNPUB 3RD P ACM BCS CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Date C.J., 1981, INTRO DATABASE SYSTE Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE E Horowitz E., 1976, FUNDAMENTALS DATA ST Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 JONES KS, 1980, 5553 BRIT LIBR RES D JONES KS, 1977, 5464 U CAMBR COMP LA MACLEOD IA, 1983, INFORM TECHNOL R & D, V2, P43 MACLEOD IA, 1981, INFORM SYST, V6, P131, DOI 10.1016/0306-4379(81)90035-1 Murtagh F., 1982, Information Technology: Research and Development, V1 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SCHEK HJ, 1982, 8TH P INT C VER LARG, P197 Smeaton A. F., 1981, SIGIR Forum, V16 STONEBRAKER M, 1983, ACM T OFF INF SYST, V1, P143, DOI 10.1145/357431.357433 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1983, INFORM TECHNOL R & D, V2, P129 1982, VAX 11 DBMS UTILITIE NR 25 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PY 1985 VL 10 IS 4 BP 377 EP 390 DI 10.1016/0306-4379(85)90042-0 PG 14 WC Computer Science, Information Systems SC Computer Science GA AVZ65 UT WOS:A1985AVZ6500002 ER PT J AU SALTON, G FOX, EA VOORHEES, E AF SALTON, G FOX, EA VOORHEES, E TI ADVANCED FEEDBACK METHODS IN INFORMATION-RETRIEVAL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. RI Voorhees, Ellen/D-1755-2013 CR BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V32, P275, DOI 10.1002/asi.4630320407 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 CHANG YK, 1971, SMART SYSTEM EXPT AU, pCH17 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X Fox E., 1983, THESIS CORNELL U ITH HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1971, SMART RETRIEVAL SYST, pCH16 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LANCASTER FW, 1973, INFORMATION RETRIEVA RICKMAN JT, 1971, AUG P NAT M ASS COMP, P478 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, SMART RETRIEVAL SYST, pCH14 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1971, SMART RETRIEVAL SYST, pCH15 SALTON G, 1983, TR83539 CORN U DEP C SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1983, TR83564 CORN U DEP C Soergel D., 1974, INDEXING LANGUAGES T VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 WU H, 1981, THESIS CORNELL U ITH NR 24 TC 56 Z9 56 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1985 VL 36 IS 3 BP 200 EP 210 DI 10.1002/asi.4630360311 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AGC95 UT WOS:A1985AGC9500009 ER PT J AU NELSON, MJ TAGUE, JM AF NELSON, MJ TAGUE, JM TI SPLIT SIZE-RANK MODELS FOR THE DISTRIBUTION OF INDEX TERMS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP NELSON, MJ (reprint author), UNIV WESTERN ONTARIO,SCH LIBR & INFORMAT SCI,LONDON N6G 1H1,ONTARIO,CANADA. CR ASAI I, 1981, J AM SOC INFORM SCI, V32, P113, DOI 10.1002/asi.4630320206 AVRAMESC.A, 1971, J AM SOC INFORM SCI, V22, P85, DOI 10.1002/asi.4630220206 BENNETT JM, 1975, 1975 INT COMP S AMST, P233 BIRD PR, 1974, J DOC, V30, P381, DOI 10.1108/eb026586 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOTH AD, 1967, INFORM CONTROL, V10, P386, DOI 10.1016/S0019-9958(67)90201-X BROOKES BC, 1978, J AM SOC INFORM SCI, V29, P5, DOI 10.1002/asi.4630290104 DOSZKOCS TE, 1976, J CHEM INF COMP SCI, V16, P131, DOI 10.1021/ci60007a003 FEDOROWICZ J, 1982, J AM SOC INFORM SCI, V33, P285 GOOD IJ, BIOMETRIKA, V40, P237 GRIFFITHS JM, 1977, THESIS U COLLEGE LON GRIFFITHS JM, 1975, J DOC, V31, P185, DOI 10.1108/eb026600 HAITUN SD, 1982, SCIENTOMETRICS, V4, P181, DOI 10.1007/BF02021059 HAITUN SD, 1982, SCIENTOMETRICS, V4, P5, DOI 10.1007/BF02098003 HAITUN SD, 1982, SCIENTOMETRICS, V4, P89, DOI 10.1007/BF02018448 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HOUSTON N, 1964, AM DOC, V15, P105, DOI 10.1002/asi.5090150208 HUBERT JJ, 1978, J AM SOC INFORM SCI, V29, P159, DOI 10.1002/asi.4630290311 HUBERT JJ, 1980, SOC INDIC RES, V8, P223, DOI 10.1007/BF00286478 JOHNSON NL, 1969, DISTRIBUTIONS STATIS Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Mandelbrot B., 1953, COMMUN THEORY, P486 NELSON MJ, 1983, CAN J INFORM SCI, V8, P67 RAVER N, 1967, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SCHUEGRAF EJ, 1976, INFORM PROCESS MANAG, V12, P377, DOI 10.1016/0306-4573(76)90035-2 SCHULTZ CK, 1961, AM DOC, V12, P247, DOI 10.1002/asi.5090120402 SICHEL HS, 1975, J AM STAT ASSOC, V70, P542, DOI 10.2307/2285930 SIMON HA, 1955, BIOMETRIKA, V42, P425 TAGUE J, 1983, RES DEV INFORMATION, P194 TAGUE J, 1981, INFORMATION RETRIEVA, P236 Tague J. M., 1981, SIGIR Forum, V16 TOMA E, 1971, J DOC, V27, P267, DOI 10.1108/eb026521 WEINBERG BH, 1982, 45TH P AM SOC INF SC, V19, P324 YABLONSKY AI, 1980, SCIENTOMETRICS, V2, P3, DOI 10.1007/BF02016597 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZIPF GK, 1949, HUMAN BEHAVIOUR PRIN NR 38 TC 31 Z9 31 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1985 VL 36 IS 5 BP 283 EP 296 DI 10.1002/asi.4630360502 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA APW84 UT WOS:A1985APW8400001 ER PT J AU KWOK, KL AF KWOK, KL TI A PROBABILISTIC THEORY OF INDEXING AND SIMILARITY MEASURE BASED ON CITED AND CITING DOCUMENTS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP KWOK, KL (reprint author), CUNY QUEENS COLL,DEPT COMP SCI,FLUSHING,NY 11367, USA. CR CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 CLEVELAND DB, 1984, J AM SOC INFORM SCI, V35, P19, DOI 10.1002/asi.4630350104 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1983, INFORM TECHNOL R & D, V2, P1 Duda R., 1973, PATTERN CLASSIFICATI FOSKETT DJ, 1972, INFORM STORAGE RET, V8, P77, DOI 10.1016/0020-0271(72)90009-5 GARFIELD E, 1965, NBS PUB, V269 GOFFMAN W, 1969, INFORMATION STORAGE, V4, P361 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 KRAFT DH, 1978, J AM SOC INFORMATION, V34, P31 KWOK KL, 1975, INFORM PROCESS MANAG, V11, P201, DOI 10.1016/0306-4573(75)90017-5 KWOK KL, 1984, RES DEV INFORMATION MARON ME, 1960, J ASSOC COMPUT MACH, V25, P67 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SPARCK JK, 1979, INFORMATION PROCESSI, V15, P133 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 24 TC 16 Z9 16 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1985 VL 36 IS 5 BP 342 EP 351 DI 10.1002/asi.4630360510 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA APW84 UT WOS:A1985APW8400009 ER PT J AU KWOK, KL AF KWOK, KL TI A PROBABILISTIC THEORY OF INDEXING USING AUTHOR-PROVIDED RELEVANCE INFORMATION SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP KWOK, KL (reprint author), CUNY QUEENS COLL,DEPT COMP SCI,FLUSHING,NY 11367, USA. CR BLAIR DC, 1984, COMMUN ACM, V27, P369, DOI 10.1145/358027.358049 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, UNPUB SUBOPTIMALITY CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 GARFIELD E, 1965, NBS PUB, V269 GOFFMAN W, 1969, INFORMATION STORAGE, V4, P361 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 KOCHEN M, 1974, PRINCIPLES INFORMATI KWOK KL, 1975, INFORM PROCESS MANAG, V11, P201, DOI 10.1016/0306-4573(75)90017-5 KWOK KL, 1984, RES DEV INFORMATION KWOK KL, UNPUB J AM SOC INFOR KWOK KL, 1985, UNPUB 8TH ACM SIGIR MARON ME, 1960, J ASSOC COMPUT MACH, V25, P67 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 24 TC 1 Z9 1 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1985 VL 22 BP 59 EP 63 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ATM61 UT WOS:A1985ATM6100013 ER PT J AU SALTON, G VOORHEES, E FOX, EA AF SALTON, G VOORHEES, E FOX, EA TI A COMPARISON OF 2 METHODS FOR BOOLEAN QUERY RELEVANCY FEEDBACK SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article C1 VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. RI Voorhees, Ellen/D-1755-2013 CR CHANG YK, 1971, SMART SYSTEM EXPT AU, pCH17 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X FAIRTHORNE RA, 1961, INFORMATION RETRIEVA FOX EA, 1983, THESIS CORNELL U HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1971, SMART RETRIEVAL SYST, pCH16 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 MOOERS CN, 1958, INT C SCI INFORM WAS PORTER MF, 1982, RES DEV, V1, P131 RICKMAN JT, 1971, AUG P NAT M ASS COMP, P478 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965, ISR9 HARV U COMP LAB SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1971, SMART RETRIEVAL SYST, pCH15 SALTON G, 1983, TR83539 CORN U DEP C SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 NR 18 TC 18 Z9 19 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1984 VL 20 IS 5-6 BP 637 EP 651 DI 10.1016/0306-4573(84)90080-3 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TY985 UT WOS:A1984TY98500007 ER PT J AU SMEATON, AF AF SMEATON, AF TI RELEVANCE FEEDBACK AND A FUZZY SET OF SEARCH TERMS IN AN INFORMATION-RETRIEVAL SYSTEM SO INFORMATION TECHNOLOGY-RESEARCH DEVELOPMENT APPLICATIONS LA English DT Article RP SMEATON, AF (reprint author), NATL UNIV IRELAND UNIV COLL DUBLIN,DEPT COMP SCI,DUBLIN,IRELAND. CR BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS CAMBRIDGE U Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1971, SMART RETRIEVAL SYST SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 NR 10 TC 11 Z9 11 PU BUTTERWORTH-HEINEMANN LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB SN 0144-817X J9 INFORM TECHNOL R & D PY 1984 VL 3 IS 1 BP 15 EP 23 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RZ413 UT WOS:A1984RZ41300002 ER PT J AU BOVEY, JD ROBERTSON, SE AF BOVEY, JD ROBERTSON, SE TI AN ALGORITHM FOR WEIGHTED SEARCHING ON A BOOLEAN SYSTEM SO INFORMATION TECHNOLOGY-RESEARCH DEVELOPMENT APPLICATIONS LA English DT Article C1 CITY UNIV LONDON,LONDON EC1V 0HB,ENGLAND. RP BOVEY, JD (reprint author), UNIV KENT,CANTERBURY CT2 7NJ,KENT,ENGLAND. CR CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1980, THESIS U CAMBRIDGE JAMIESON SH, 1979, 2ND P INT C INF STOR, P45 MORRISSEY J, 1981, INTELLIGENT TERMINAL ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WEISS SF, 1981, INFORMATION RETRIEVA, P325 NR 6 TC 9 Z9 9 PU BUTTERWORTH-HEINEMANN LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB SN 0144-817X J9 INFORM TECHNOL R & D PY 1984 VL 3 IS 2 BP 84 EP 87 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TC908 UT WOS:A1984TC90800003 ER PT J AU KANTOR, PB AF KANTOR, PB TI MAXIMUM-ENTROPY AND THE OPTIMAL-DESIGN OF AUTOMATED INFORMATION-RETRIEVAL SYSTEMS SO INFORMATION TECHNOLOGY-RESEARCH DEVELOPMENT APPLICATIONS LA English DT Article RP KANTOR, PB (reprint author), TANTALUS INC,SUITE 218,2140 LEE RD,CLEVELAND,OH 44118, USA. CR CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 GOOD IJ, 1963, ANN MATH STAT, V34, P911, DOI 10.1214/aoms/1177704014 KANTOR FW, 1976, INFORMATION MECHANIC KANTOR PB, 1982, ANNU REV INFORM SCI, V17, P99 KANTOR PB, UNPUB IEEE T INF KHINCHIN AI, 1967, F STATISTICAL MECHAN KITTEL C, 1958, ELEMENTARY STATISTIC Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO VANCAMPENHOUT JM, 1981, IEEE T INFORM THEORY, V27, P483, DOI 10.1109/TIT.1981.1056374 ZANGWILL WI, 1981, PATHWAYS SOLUTIONS E NR 15 TC 19 Z9 19 PU BUTTERWORTH-HEINEMANN LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB SN 0144-817X J9 INFORM TECHNOL R & D PY 1984 VL 3 IS 2 BP 88 EP 94 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TC908 UT WOS:A1984TC90800004 ER PT J AU HEINE, MH AF HEINE, MH TI INFORMATION-RETRIEVAL FROM CLASSICAL DATABASES FROM A SIGNAL-DETECTION STANDPOINT - A REVIEW SO INFORMATION TECHNOLOGY-RESEARCH DEVELOPMENT APPLICATIONS LA English DT Review RP HEINE, MH (reprint author), NEWCASTLE UPON TYNE POLYTECH,SCH LIBRARIANSHIP & INFORMAT STUDIES,NEWCASTLE TYNE NE1 8ST,TYNE & WEAR,ENGLAND. CR ANGIONE PV, 1975, J AM SOC INFORM SCI, V26, P112, DOI 10.1002/asi.4630260207 ATTAR R, 1981, INFORM PROCESS MANAG, V17, P115 BARR D, 1971, PROBABILITY Belkin N.J., 1981, INFORMATION RETRIEVA, P44 BOLLMANN P, 1981, INFORMATION RETRIEVA, P256 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1974, J DOC, V30, P374, DOI 10.1108/eb026585 BOOKSTEIN A, 1983, J DOC, V39, P63, DOI 10.1108/eb026739 BOOKSTEIN A, 1982, J AM SOC INF SCI, V34, P331 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1977, INFORM PROCESS MANAG, V13, P377, DOI 10.1016/0306-4573(77)90057-7 BRANDHORST RT, 1966, AM DOCUMENTATION, V17, P145 BROOKES BC, 1963, ASLIB P, V16, P7 BROOKES BC, 1968, J DOC, V24, P41, DOI 10.1108/eb026442 BUCKLAND MK, 1983, INFORM PROCESS MANAG, V19, P237, DOI 10.1016/0306-4573(83)90020-1 CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 CLEVERDON CW, 1976, J DOC, V32, P159, DOI 10.1108/eb026622 Cleverdon C. W., 1966, FACTORS DETERMINING Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 COOPER WS, 1976, INFORM PROCESS MANAG, V12, P367, DOI 10.1016/0306-4573(76)90034-0 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P413, DOI 10.1002/asi.4630240603 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Cuadra C., 1967, EXPT STUDIES RELEVAN CUADRA CA, 1967, J DOC, V23, P291, DOI 10.1108/eb026436 Dabrowski M., 1975, Information Systems, V1, DOI 10.1016/0306-4379(75)90004-6 DERR RL, 1983, INFORM PROCESS MANAG, V19, P273, DOI 10.1016/0306-4573(83)90001-8 DILLON M, 1983, INFORM PROCESS MANAG, V19, P27, DOI 10.1016/0306-4573(83)90033-X DOSZKOCS TE, 1979, P AM SOC INFORM SCI, V16, P131 EGAN JP, 1975, SIGNAL DETECTION THE EVANS L, 1975, R7523 INSP REP EVANS L, 1973, R7318 INSP REP FARRADAN.J, 1974, J DOC, V30, P195, DOI 10.1108/eb026576 GEBHARDT F, 1975, INFORM PROCESS MANAG, V11, P59, DOI 10.1016/0306-4573(75)90034-5 GUAZZO M, 1977, INFORM PROCESS MANAG, V13, P155, DOI 10.1016/0306-4573(77)90034-6 HEAPS HS, 1978, INFORMATION RETRIEVA HEATHER MJ, 1983, BCS IRSG RES C SHEFF HEINE MD, 1981, INFORMATION RETRIEVA, P179 HEINE MH, 1973, J DOC, V29, P81 HEINE MH, 1974, J AM SOC INFORM SCI, V25, P183, DOI 10.1002/asi.4630250309 HEINE MH, 1977, THEORY APPLICATION I, P137 HEINE MH, 1981, THESIS U NEWCASTLE U HEINE MH, 1982, 1982 S EMP F INF SOF HEINE MH, 1973, INFORM STORAGE RET, V9, P181, DOI 10.1016/0020-0271(73)90066-1 HEINE MH, 1982, INFORMATION TECHNOLO, V2, P247 HEINE MH, 1984, CYBERNETICA, V27, P57 HEINE MH, 1975, J DOC, V31, P283, DOI 10.1108/eb026608 HEINE MH, 1977, INFORM PROCESS MANAG, V13, P35, DOI 10.1016/0306-4573(77)90029-2 Hohn F. E., 1966, APPLIED BOOLEAN ALGE HUTCHINSON TP, 1978, J DOC, V34, P51, DOI 10.1108/eb026652 IDE E, 1971, SMART RETRIEVAL SYST, pCH16 IKER HP, 1967, AM DOC, V18, P47, DOI 10.1002/asi.5090180109 JARDIN N, 1971, INFORMATION STORAGE, V7, P214 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KEEN EM, 1973, J DOC, V29, P1, DOI 10.1108/eb026547 KEEN EM, 1972, REPORT INFORMATION S KING DW, 1971, EVALUATION INFORMATI KOCHEN M, 1974, AM J PSYCHOL, V87, P369, DOI 10.2307/1421379 KRIKELAS J, 1983, DREXEL LIBR QUART, V19, P5 LANCASTE.FW, 1968, J DOC, V24, P16, DOI 10.1108/eb026441 LANDRY BL, 1971, THESIS OHIO STATE U LECLAIRE KA, 1981, EUR RES, V9, P134 Lipski W. Jr., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320083.320088 LIPSKI W, 1979, INFORM SYST, V4, P241, DOI 10.1016/0306-4379(79)90008-5 LUDWIG BM, 1975, J AM SOC INFORM SCI, V26, P51, DOI 10.1002/asi.4630260108 MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1964, AM DOC, V15, P217 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 MINKER J, 1977, ACM SIGIR FORUM, V12, P1 MORSE PM, 1972, OPER RES, V20, P741, DOI 10.1287/opre.20.4.741 NOREAULT T, 1981, INFORMATION RETRIEVA, P57 PARKER LMP, 1983, J AM SOC INFORM SCI, V34, P16, DOI 10.1002/asi.4630340104 PAWLAK Z, 1973, CC PAS101 POL AC SCI PIETILAINEN P, 1983, INFORM PROCESS MANAG, V19, P51, DOI 10.1016/0306-4573(83)90035-3 RADECKI T, 1980, PRACE NAUKOWE POLITE, V4 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 REES AM, 1963, P AM DOC I, P175 REES AM, 1967, ANNU REV INFORM SCI, V2, P63 ROBERTSO.SE, 1969, J DOC, V25, P93, DOI 10.1108/eb026466 ROBERTSO.SE, 1969, J DOC, V25, P1, DOI 10.1108/eb026462 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1980, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965, FAL P AFIPS JOINT 1, V27, P293 SAGER WKH, 1976, INT FORUM INFORM DOC, V1, P12 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1982, TR82524 CORN U DEP C SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1983, TR83539 CORN U DEP C Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1982, TR82511 CORN U DEP C SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Saracevic T., 1970, INTRO INFORMATION SC, P111 SMEATON AF, 1984, INFORM TECHNOL R & D, V3, P15 Sparck Jones K., 1981, INFORM RETRIEVAL EXP, P213 STIRLING KH, 1975, P AM SOC INFORM SCI, V12, P105 Swets J. A., 1967, EFFECTIVENESS INFORM SWETS JA, 1969, SIMULATION COMPORTEM, P253 SWETS JA, 1963, SCIENCE, V241, P245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 SWETS JA, AFCRL670412 AIR FORC TAGUE JM, 1981, INFORM PROCESS MANAG, V17, P149, DOI 10.1016/S0306-4573(81)80006-4 TAYLOR RS, 1968, COLL RES LIBR, V29, P178 TURSKI WM, 1971, INFORM STORAGE RET, V7, P89, DOI 10.1016/0020-0271(71)90042-8 UHLMANN W, 1968, AM DOC, V19, P240, DOI 10.1002/asi.5090190306 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1974, J DOC, V30, P365 VANRIJSBERGEN CJ, 1980, NEW MODELS PROBABILI, pCH3 VANRIJSBERGEN CJ, 1979, PROGR COMMUNICATION, V1, P91 VICKERY BC, 1970, TECHNIQUES INFORMATI VICKERY BC, 1966, FACTORS DETERMINING YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 NR 119 TC 10 Z9 10 PU BUTTERWORTH-HEINEMANN LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB SN 0144-817X J9 INFORM TECHNOL R & D PY 1984 VL 3 IS 2 BP 95 EP 112 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TC908 UT WOS:A1984TC90800005 ER PT J AU ROBERTSON, SE HARDING, P AF ROBERTSON, SE HARDING, P TI PROBABILISTIC AUTOMATIC-INDEXING BY LEARNING FROM HUMAN INDEXERS SO JOURNAL OF DOCUMENTATION LA English DT Article C1 INSPEC,STN HOUSE,HITCHIN SG5 1RJ,HERTS,ENGLAND. RP ROBERTSON, SE (reprint author), CITY UNIV LONDON,DEPT INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR AITCHISON T, 1983, INFORMATION MANAGEME COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 HARDING P, 1982, 5723 BRIT LIBR RES D HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Nilsson NJ, 1965, LEARNING MACHINES Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 11 TC 8 Z9 8 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1984 VL 40 IS 4 BP 264 EP 270 DI 10.1108/eb026768 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ACH74 UT WOS:A1984ACH7400002 ER PT J AU PARKER, LMP AF PARKER, LMP TI AN EXPERIMENT IN DOCUMENT REPRESENTATIVE MODIFICATION SO JOURNAL OF INFORMATION SCIENCE LA English DT Article RP PARKER, LMP (reprint author), VIRGINIA COMMONWEALTH UNIV,DEPT MATH SCI,RICHMOND,VA 23284, USA. CR BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 DAVIS CH, 1979, GUIDE INFORMATION SC, P91 PARKER LMP, 1982, P ASIS ANN M, V19 PARKER LMP, 1983, J AM SOC INFORM SCI, V34, P16, DOI 10.1002/asi.4630340104 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA, P145 SPARCK JK, 1977, RES AUTOMATIC INDEXI, V1, pCH2 NR 7 TC 1 Z9 1 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1984 VL 8 IS 1 BP 1 EP 6 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TA837 UT WOS:A1984TA83700001 ER PT J AU NELSON, MJ AF NELSON, MJ TI THE USE OF TERM CO-OCCURRENCE INFORMATION IN INFORMATION-RETRIEVAL SO CANADIAN JOURNAL OF INFORMATION SCIENCE-REVUE CANADIENNE DES SCIENCES DE L INFORMATION LA English DT Article RP NELSON, MJ (reprint author), UNIV WESTERN ONTARIO,SCH LIB & INFORMAT SCI,LONDON N6G 1H1,ONTARIO,CANADA. CR COOPER MD, 1973, INFORM STORAGE RET, V9, P13, DOI 10.1016/0020-0271(73)90004-1 CRAWFORD RG, 1979, CAN J INFORM SCI, V4, P124 GRIFFITHS JM, 1975, J DOC, V31, P185, DOI 10.1108/eb026600 Jacquesson A., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90021-1 JONES KS, 1971, AUTOMATIC KEYWORD CL MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NELSON MJ, 1982, THESIS U W ONTARIO Noreault T., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSENBER.V, 1971, J AM SOC INFORM SCI, V22, P41, DOI 10.1002/asi.4630220106 SCHULTZ CK, 1961, AM DOC, V12, P247, DOI 10.1002/asi.5090120402 SWITZER P, 1965, STAT ASS METHODS MEC, P163 TAGUE J, 1981, INFORMATION RETRIEVA, P236 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 NR 15 TC 7 Z9 7 PU CANADIAN ASSOC INFORMATION SCIENCE PI OTTAWA PA PO BOX 6174, STATION J, OTTAWA ON K2A 1T2, CANADA SN 0380-9218 J9 CAN J INFORM SCI PY 1983 VL 8 IS JUN BP 67 EP 73 PG 7 WC Information Science & Library Science SC Information Science & Library Science GA AHW77 UT WOS:A1983AHW7700007 ER PT J AU SALTON, G FOX, EA WU, H AF SALTON, G FOX, EA WU, H TI EXTENDED BOOLEAN INFORMATION-RETRIEVAL SO COMMUNICATIONS OF THE ACM LA English DT Article C1 INT INST TROP AGR,IBADAN,NIGERIA. INT TEL & TELEGRAPH CORP,CTR PROGRAMMING TECHNOL,STRATFORD,CT 06497. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR Bartle R. G., 1966, ELEMENTS INTEGRATION BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V32, P275, DOI 10.1002/asi.4630320407 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 IDE E, 1971, SMART SYSTEM EXPT AU Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KATZER J, 1982, STUDY OVERLAP DOCUME LANCASTER FW, 1973, INFORMATION RETRIEVA LANCASTER FW, 1979, INFORMATION RETRIEVA NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ORTEGA JM, 1977, NUMERICAL ANAL ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART SYSTEM EXPT AU Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1969, AM DOC, V20, P61, DOI 10.1002/asi.4630200109 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 SALTON G, 1983, TR83539 CORN U DEP C Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1972, J AM SOC INFORM SCI, V23, P75, DOI 10.1002/asi.4630230202 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X WU H, 1981, THESIS CORNELL U ITH YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 28 TC 293 Z9 305 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0001-0782 J9 COMMUN ACM JI Commun. ACM PY 1983 VL 26 IS 11 BP 1022 EP 1036 DI 10.1145/182.358466 PG 15 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA RS929 UT WOS:A1983RS92900025 ER PT J AU SMEATON, AF VANRIJSBERGEN, CJ AF SMEATON, AF VANRIJSBERGEN, CJ TI THE RETRIEVAL EFFECTS OF QUERY EXPANSION ON A FEEDBACK DOCUMENT-RETRIEVAL SYSTEM SO COMPUTER JOURNAL LA English DT Article C1 NATL UNIV IRELAND UNIV COLL DUBLIN,DEPT COMP SCI,DUBLIN,IRELAND. CR BARKER FH, 1974, UKCIS2 RES REP CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS CAMBRIDGE U JONES KS, 1971, AUTOMATIC KEYWORD CL JONES KS, 1980, 5553 BRIT LIBR REP MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SMEATON AF, 1982, 2 U COLL DUBL DEP CO Sparck Jones K., 1976, J DOC, V32, P59, DOI 10.1108/eb026616 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 NR 17 TC 70 Z9 71 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH STREET, NEW YORK, NY 10011-4211 SN 0010-4620 J9 COMPUT J JI Comput. J. PY 1983 VL 26 IS 3 BP 239 EP 246 DI 10.1093/comjnl/26.3.239 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA RB701 UT WOS:A1983RB70100010 ER PT J AU DILLON, M ULMSCHNEIDER, J DESPER, J AF DILLON, M ULMSCHNEIDER, J DESPER, J TI A PREVALENCE FORMULA FOR AUTOMATIC RELEVANCE FEEDBACK IN BOOLEAN SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP DILLON, M (reprint author), UNIV N CAROLINA,SCH LIFE SCI,CHAPEL HILL,NC 27514, USA. CR DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 RICKMAN JT, 1971, AUG P NAT M ASS COMP, P478 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 SALTON G, 1972, J AM SOC INFORM SCI, V23, P75, DOI 10.1002/asi.4630230202 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 NR 7 TC 17 Z9 17 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1983 VL 19 IS 1 BP 27 EP 36 DI 10.1016/0306-4573(83)90033-X PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QE815 UT WOS:A1983QE81500003 ER PT J AU CROFT, WB AF CROFT, WB TI EXPERIMENTS WITH REPRESENTATION IN A DOCUMENT-RETRIEVAL SYSTEM SO INFORMATION TECHNOLOGY-RESEARCH DEVELOPMENT APPLICATIONS LA English DT Article C1 UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003. CR CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1982, P ACM BCS SIGIR C BE EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE U HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 JONES KS, 1977, 5464 U CAMBR BRIT LI Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 MURRAY DM, 1972, ISR20 NAT SCI F NAT ROBERTSON SE, 1980, P ACM BCS SIGIR C CA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SIEGEL S, 1956, NON PARAMETRIC STATI VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 18 TC 34 Z9 35 PU BUTTERWORTH-HEINEMANN LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB SN 0144-817X J9 INFORM TECHNOL R & D PY 1983 VL 2 IS 1 BP 1 EP 21 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RC371 UT WOS:A1983RC37100001 ER PT J AU YU, CT BUCKLEY, C LAM, K SALTON, G AF YU, CT BUCKLEY, C LAM, K SALTON, G TI A GENERALIZED TERM DEPENDENCE MODEL IN INFORMATION-RETRIEVAL SO INFORMATION TECHNOLOGY-RESEARCH DEVELOPMENT APPLICATIONS LA English DT Article C1 CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853. UNIV HONG KONG,DEPT STAT,HONG KONG,HONG KONG. RP YU, CT (reprint author), UNIV ILLINOIS,DEPT INFORMAT ENGN,CHICAGO,IL 60680, USA. CR Bishop YMM, 1974, DISCRETE MULTIVARIAT BUCKLEY C, 1983, PROBABILITY ESTIMATI CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 CHOW D, 1982, J ACM, V29, P127, DOI 10.1145/322290.322299 Duda R., 1973, PATTERN CLASSIFICATI HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE E Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KENDALL M, 1967, ADV THEORY STATISTIC, V2 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 LAM K, 1982, ACM T DATABASE SYST, V7, P500, DOI 10.1145/319732.319756 Lewis-II P. M., 1959, INFORM CONTR, V2, P214, DOI 10.1016/S0019-9958(59)90207-4 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, 83539 CORN U DEP COM SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1983, LECT NOTES COMPUT SC, V146, P151 SALTON G, 1983, INTRO MODERN INFORMA, pCH3 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 24 TC 55 Z9 55 PU BUTTERWORTH-HEINEMANN LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB SN 0144-817X J9 INFORM TECHNOL R & D PY 1983 VL 2 IS 4 BP 129 EP 154 PG 26 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RQ557 UT WOS:A1983RQ55700001 ER PT J AU WILLETT, P AF WILLETT, P TI SIMILARITY COEFFICIENTS AND WEIGHTING FUNCTIONS FOR AUTOMATIC DOCUMENT CLASSIFICATION - AN EMPIRICAL-COMPARISON SO INTERNATIONAL CLASSIFICATION LA English DT Article RP WILLETT, P (reprint author), UNIV SHEFFIELD,DEPT INFORMAT STUDIES,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BOOKSTEIN A, 1982, LECTURE NOTES COMP S, V146, P118 BURNETT JE, 1979, J DOC, V35, P197, DOI 10.1108/eb026680 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Jardine N, 1971, MATH TAXONOMY Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 KEEN EM, 1967, 13 CORN U DEP COMP S NOREAULT T, 1981, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGER WKH, 1976, INT FORUM INFORM DOC, V1, P12 SALTON G, 1975, DYNAMIC INFORMATION Smeaton A.F., 1981, ACM SIGIR FORUM, V16, P83, DOI 10.1145/1013228.511767 Sokal R.R., 1973, NUMERICAL TAXONOMY P VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 16 TC 8 Z9 8 PU INT SOC KNOWLEDGE ORGANIZATION PI FRANKFURT 50 PA 36A WOOGSTR, D-6000 FRANKFURT 50, GERMANY SN 0340-0050 J9 INT CLASSIF PY 1983 VL 10 IS 3 BP 138 EP 142 PG 5 WC Information Science & Library Science SC Information Science & Library Science GA RY044 UT WOS:A1983RY04400004 ER PT J AU BOOKSTEIN, A AF BOOKSTEIN, A TI OUTLINE OF A GENERAL PROBABILISTIC RETRIEVAL MODEL SO JOURNAL OF DOCUMENTATION LA English DT Article RP BOOKSTEIN, A (reprint author), UNIV CHICAGO,CHICAGO,IL 60637, USA. CR BOOKSTEIN A, 1982, TRITALIB6019 ROY I T BOOKSTEIN A, 1983, J AM SOC INFORMATION, V34 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V32, P275, DOI 10.1002/asi.4630320407 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Luce R. D., 1957, GAMES DECISIONS NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 12 TC 30 Z9 30 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1983 VL 39 IS 2 BP 63 EP 72 DI 10.1108/eb026739 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RC863 UT WOS:A1983RC86300001 ER PT J AU PARKER, LMP AF PARKER, LMP TI TOWARDS A THEORY OF DOCUMENT LEARNING SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP PARKER, LMP (reprint author), VIRGINIA COMMONWEALTH UNIV,DEPT MATH SCI,RICHMOND,VA 23284, USA. CR BAXENDALE PB, 1958, IBM J RES DEV, V2, P354 Cagan C, 1970, J AM SOC INFORM SCI, V21, P330, DOI 10.1002/asi.4630210504 CARVILLE M, 1971, INFORM STORAGE RET, V7, P205, DOI 10.1016/0020-0271(71)90049-0 CLARKE DC, 1965, AFIPS C P, V27, P307 CLEVERDON CW, 1962, ASLIB CRANFIELD RES, P84 HARPER S, 1978, J DOCUMENTATION, V34, P189 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 KEEN M, 1971, SMART RETRIEVAL SYST, P181 LUHN HP, 1958, IBM J RES DEV, V2, P159 MCCARN DB, 1980, J AM SOC INFORM SCI, V31, P181, DOI 10.1002/asi.4630310310 PAICE CD, 1977, INFORMATION RETRIEVA, P33 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1971, SMART RETRIEVAL SYST, P389 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1979, INFORMATION STORAGE, V9, P165 SARACEVIC T, 1975, J AM SOC INFORMATION, V26, P84 Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 *US DEP HEW, 1969, PRINC MEDL VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA, P15 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 20 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1983 VL 34 IS 1 BP 16 EP 21 DI 10.1002/asi.4630340104 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PY574 UT WOS:A1983PY57400002 ER PT J AU COOPER, WS AF COOPER, WS TI EXPLOITING THE MAXIMUM-ENTROPY PRINCIPLE TO INCREASE RETRIEVAL EFFECTIVENESS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP COOPER, WS (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT SCI,BERKELEY,CA 94720, USA. CR BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 COOPER WS, 1976, SUBOPTIMALITY RETRIE COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 GOKHALE DV, 1978, INFORMATION CONTINGE GOOD IJ, 1963, ANN MATH STAT, V34, P911, DOI 10.1214/aoms/1177704014 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 JAYNES E. T., 1979, MAXIMUM ENTROPY FORM LANCASTER FW, 1979, INFORMATION RETRIEVA Lewis-II P. M., 1959, INFORM CONTR, V2, P214, DOI 10.1016/S0019-9958(59)90207-4 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1981, PROBABILITY RELEVANC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 STIRLING KH, 1977, THESIS U CALIFORNIA Tribus M., 1969, RATIONAL DESCRIPTION VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1980, RD5587 U CAMBR COMP NR 23 TC 31 Z9 32 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1983 VL 34 IS 1 BP 31 EP 39 DI 10.1002/asi.4630340106 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PY574 UT WOS:A1983PY57400004 ER PT J AU SALTON, G BUCKLEY, C FOX, EA AF SALTON, G BUCKLEY, C FOX, EA TI AUTOMATIC QUERY FORMULATIONS IN INFORMATION-RETRIEVAL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR BARKER FH, 1972, J DOC, V28, P44, DOI 10.1108/eb026529 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LUFKIN RC, 1968, THESIS MIT CAMBRIDGE PRESCHEL BM, 1972, INDEXER CONSISTENCY ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1982, 82511 CORN U DEP COM Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1983, 83539 CORN U DEP COM SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 Salton G., 1971, SMART RETRIEVAL SYST VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 WU H, 1981, THESIS CORNELL U ITH NR 15 TC 38 Z9 40 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1983 VL 34 IS 4 BP 262 EP 280 DI 10.1002/asi.4630340406 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA QX767 UT WOS:A1983QX76700004 PM 10299297 ER PT J AU BOOKSTEIN, A AF BOOKSTEIN, A TI INFORMATION-RETRIEVAL - A SEQUENTIAL LEARNING-PROCESS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP BOOKSTEIN, A (reprint author), UNIV CHICAGO,GRAD LIB SCH,CHICAGO,IL 60637, USA. CR BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1980, 43RD P ASIS ANN M, V17, P27 BOOKSTEIN A, 1982, UNPUB RES DEV INFORM Cooper W.S., 1968, J AM SOC INFORM SCI, V19, P30 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Raiffa H., 1968, APPLIED STATISTICAL Raiffa H, 1968, DECISION ANAL INTRO ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SWANSON DR, 1977, LIBR QUART, V47, P128 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 NR 12 TC 61 Z9 61 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1983 VL 34 IS 5 BP 331 EP 342 DI 10.1002/asi.4630340504 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RF880 UT WOS:A1983RF88000003 ER PT J AU CROFT, WB RUGGLES, L AF CROFT, WB RUGGLES, L TI THE IMPLEMENTATION OF A DOCUMENT-RETRIEVAL SYSTEM SO LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article RP CROFT, WB (reprint author), UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003, USA. CR CROFT WB, 1981, J AM SOC INFORM SCI, V32, P451, DOI 10.1002/asi.4630320609 Croft W. B., 1982, Information Technology: Research and Development, V1 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARPER DJ, 1980, THESIS U CAMBRIDGE HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 JAMIESON SH, 1979, SIGIR FORUM, V14, P45 JONES KS, 1980, 5553 U CAMBR BRIT LI JONES KS, 1977, RES AUTOMATIC INDEXI ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1981, SIGIR Forum, V16 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1982, J ACM, V29, P152, DOI 10.1145/322290.322300 1976, BRIEF GUIDE DIALOG S NR 16 TC 0 Z9 0 PU EDITURA ACADEMIEI ROMANE PI BUCHAREST PA CALEA VICTORIEI 125, BUCHAREST 79717, ROMANIA SN 0302-9743 J9 LECT NOTES COMPUT SC PY 1983 VL 146 BP 28 EP 37 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA QE003 UT WOS:A1983QE00300004 ER PT J AU BOOKSTEIN, A AF BOOKSTEIN, A TI EXPLANATION AND GENERALIZATION OF VECTOR MODELS IN INFORMATION-RETRIEVAL SO LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article RP BOOKSTEIN, A (reprint author), UNIV CHICAGO,GRAD LIB SCH,CHICAGO,IL 60637, USA. CR ANDERSON EB, 1980, DISCRETE STATISTICAL BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1981, J AM SOC INFORM SCI, V32, P275, DOI 10.1002/asi.4630320407 BOOKSTEIN A, 1979, INFORMATION PROCESSI, V13, P153 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 Duda R., 1973, PATTERN CLASSIFICATI ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1981, INFORMATION RETRIEVA Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 NR 13 TC 6 Z9 6 PU EDITURA ACADEMIEI ROMANE PI BUCHAREST PA CALEA VICTORIEI 125, BUCHAREST 79717, ROMANIA SN 0302-9743 J9 LECT NOTES COMPUT SC PY 1983 VL 146 BP 118 EP 132 PG 15 WC Computer Science, Theory & Methods SC Computer Science GA QE003 UT WOS:A1983QE00300011 ER PT J AU RADECKI, T AF RADECKI, T TI INCORPORATION OF RELEVANCE FEEDBACK INTO BOOLEAN RETRIEVAL-SYSTEMS SO LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article C1 POLYTECH INST WROCLAW,MAIN LIB,PL-50370 WROCLAW,POLAND. POLYTECH INST WROCLAW,CTR SCI INFORMAT,PL-50370 WROCLAW,POLAND. CR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 COOPER WS, 1976, SUBOPTIMALITY RETRIE COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 Duda R., 1973, PATTERN CLASSIFICATI HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Harrison M. A., 1965, INTRO SWITCHING AUTO Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 1980, J INFORM SCI, V1, P325, DOI 10.1177/016555157900100603 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PAWLAK Z, 1973, CC PAS101 POL AC SCI RADECKI T, 1981, INFORM PROCESS MANAG, V17, P227, DOI 10.1016/0306-4573(81)90017-0 RADECKI T, J AM SOC INFORMATION RADECKI T, J DOCUMENTATION RADECKI T, 1981, INFORMATION RETRIEVA, P334 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P8, DOI 10.1002/asi.4630330104 Radecki T., 1980, New Trends in Documentation and Information. Proceedings of the 39th FID Congress Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1976, THESIS U LONDON ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WONG E, 1971, COMMUN ACM, V14, P593, DOI 10.1145/362663.362735 NR 29 TC 1 Z9 1 PU EDITURA ACADEMIEI ROMANE PI BUCHAREST PA CALEA VICTORIEI 125, BUCHAREST 79717, ROMANIA SN 0302-9743 J9 LECT NOTES COMPUT SC PY 1983 VL 146 BP 133 EP 150 PG 18 WC Computer Science, Theory & Methods SC Computer Science GA QE003 UT WOS:A1983QE00300012 ER PT J AU KNORZ, G AF KNORZ, G TI A DECISION-THEORY APPROACH TO OPTIMAL AUTOMATIC-INDEXING SO LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article RP KNORZ, G (reprint author), TH DARMSTADT,FACHBEREICH INFORMAT,FG DATENVERWALTUNGSSYST 2,D-6100 DARMSTADT,FED REP GER. CR BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 COOPER WS, 1968, AM DOC, V9, P30 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 FIELD BJ, 1975, J DOC, V31, P246, DOI 10.1108/eb026605 HARTER SP, 1975, J ASIS, V26, P45 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KNORZ G, 1981, MODELLE STRUKTUREN, P31 KNORZ G, 1982, TH DARMSTADT FB, V20 KNORZ G, 1982, ANGEWANDTE INFORMATI, V1, P1 KNORZ G, UNPUB P COLING 82 LUSTIG G, 1982, DTSCH GESELLSCHAFT D, P584 LUSTIG G, 1880, DIALOGSYSTEME TEXTVE, P1 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1977, J ASIS, V28, P28 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P319, DOI 10.1016/0306-4573(76)90050-9 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1975, THEORY INDEXING SCHANK RC, 1980, AM J COMPUTATIONAL L, V6, P13 SCHURMANN J, 1977, POLYNOMKLASSIFIKATOR SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 31 TC 0 Z9 0 PU EDITURA ACADEMIEI ROMANE PI BUCHAREST PA CALEA VICTORIEI 125, BUCHAREST 79717, ROMANIA SN 0302-9743 J9 LECT NOTES COMPUT SC PY 1983 VL 146 BP 174 EP 193 PG 20 WC Computer Science, Theory & Methods SC Computer Science GA QE003 UT WOS:A1983QE00300014 ER PT J AU KANTOR, PB AF KANTOR, PB TI MINIMAL CONSTRAINT IMPLEMENTATION OF THE MAXIMUM-ENTROPY PRINCIPLE IN THE DESIGN OF TERM-WEIGHTING SYSTEMS SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP KANTOR, PB (reprint author), TANTALUS INC,CLEVELAND,OH 44118, USA. CR Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COOPER WS, 1982, INFORMATION TECHNOLO, V1, P99 KANTOR PB, 1983, UNPUB JASIS KANTOR PB, 1982, ANNU REV INFORM SCI, V17, P99 KANTOR PB, 1983, UNPUB IEEE T INFORMA KHINCHIN AI, 1967, F STATISTICAL MECHAN RAGHAVAN VV, 1983, 6TH P ANN INT ACM SI, V17 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1983, 6TH P ANN INT ACM SI, V17 NR 10 TC 1 Z9 1 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1983 VL 20 BP 28 EP 31 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA RV383 UT WOS:A1983RV38300010 ER PT J AU RADECKI, T AF RADECKI, T TI ON A PROBABILISTIC APPROACH TO DETERMINING THE SIMILARITY BETWEEN BOOLEAN SEARCH REQUEST FORMULATIONS SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UNIV SHEFFIELD,LIBRARIANSHIP & INFORMAT SCI POSTGRAD SCH,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR DATTOLA RT, 1971, SMART RETRIEVAL SYST, pCH12 GILL A, 1976, APPLIED ALGEBRA COMP, P173 Lesser V.R., 1966, ISR11 CORN U DEP COM RADECKI T, 1981, P JOINT BCS ACM S RE RADECKI T, 1981, J AM SOC INFORMATION Radecki T., 1980, New Trends in Documentation and Information. Proceedings of the 39th FID Congress ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1966, ISR10 HARV U COMP LA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, DYNAMIC INFORMATION Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG E, 1971, COMMUN ACM, V14, P593, DOI 10.1145/362663.362735 YU CT, 1974, J AM SOC INFORM SCI, V25, P218, DOI 10.1002/asi.4630250403 NR 16 TC 5 Z9 5 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1982 VL 38 IS 1 BP 14 EP 28 DI 10.1108/eb026719 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA NR142 UT WOS:A1982NR14200002 ER PT J AU BELKIN, NJ ODDY, RN BROOKS, HM AF BELKIN, NJ ODDY, RN BROOKS, HM TI ASK FOR INFORMATION-RETRIEVAL .1. BACKGROUND AND THEORY SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UNIV ASTON,CTR COMP,BIRMINGHAM B4 7ET,W MIDLANDS,ENGLAND. RP BELKIN, NJ (reprint author), CITY UNIV LONDON,CTR INFORMAT SCI,LONDON EC1V 0HB,ENGLAND. CR Belkin N. J, 1980, THEORY APPLICATION I, P187 Belkin N. J., 1977, CC 77 COGNITIVE VIEW, P187 BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 BELKIN NJ, 1977, THESIS U LONDON BELKIN NJ, 1976, J AM SOC INFORM SCI, V27, P197, DOI 10.1002/asi.4630270402 BELKIN NJ, 1979, RD5547 U AST COMP CT BELKNAP ND, 1976, LOGIC QUESTIONS ANSW COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 Croft W. B., 1978, THESIS U CAMBRIDGE Davies C. C., 1978, Journal of Informatics, V2 DeMey M., 1977, CC 77, pxvi HARBO O, 1980, THEORY APPLICATIONS HOLLNAGEL E, 1979, ANAL MEANING INFORMA, V5, P135 HUDSON RA, 1975, LANGUAGE, V51, P1, DOI 10.2307/413148 Lehnert W.G, 1978, PROCESS QUESTION ANS ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 Oddy R. N., 1977, Journal of Informatics, V1 ODDY RN, 1975, THESIS U NEWCASTLE T PAISLEY WJ, 1965, ED INFORMATION SCI, P23 ROBERTSON SE, 1979, DREXEL LIBRARY Q, V14, P40 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 TAYLOR RS, 1968, COLL RES LIBR, V29, P178 WERSIG G, 1971, INFORMATION KOMMUNIK WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702 NR 24 TC 422 Z9 428 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1982 VL 38 IS 2 BP 61 EP 71 DI 10.1108/eb026722 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PC607 UT WOS:A1982PC60700001 ER PT J AU RADECKI, T AF RADECKI, T TI REDUCING THE PERILS OF MERGING BOOLEAN AND WEIGHTED RETRIEVAL-SYSTEMS SO JOURNAL OF DOCUMENTATION LA English DT Note C1 UNIV SHEFFIELD,POSTGRAD SCH LIBRARIANSHIP & INFORMAT SCI,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 GILL A, 1976, APPLIED ALGEBRA COMP, P173 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 RADECKI T, UNPUB PROBABILISTIC RADECKI T, 1981, P JOINT BCS ACM S RE RADECKI T, 1982, J AM SOC INFORM SCI, V33, P8, DOI 10.1002/asi.4630330104 Radecki T., 1980, New Trends in Documentation and Information. Proceedings of the 39th FID Congress ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 WONG E, 1971, COMMUN ACM, V14, P593, DOI 10.1145/362663.362735 NR 11 TC 15 Z9 15 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1982 VL 38 IS 3 BP 207 EP 211 DI 10.1108/eb026729 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PR161 UT WOS:A1982PR16100004 ER PT J AU HUTCHINS, WJ AF HUTCHINS, WJ TI NATURAL-LANGUAGE INFORMATION-PROCESSING - A COMPUTER GRAMMAR OF ENGLISH AND ITS APPLICATIONS - SAGER,N SO JOURNAL OF DOCUMENTATION LA English DT Book Review RP HUTCHINS, WJ (reprint author), UNIV E ANGLIA,NORWICH NR4 7TJ,NORFOLK,ENGLAND. CR HARTER SP, 1978, DREXEL LIBR QUART, V14, P57 JONES KS, 1977, NATURAL LANGUAGE INF, P183 JONES KS, 1973, LINGUISTICS INFORMAT ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAGER N, 1978, ADV COMPUT, V17, P89, DOI 10.1016/S0065-2458(08)60391-5 SAGER N, 1975, J AM SOC INFORM SCI, V26, P10, DOI 10.1002/asi.4630260104 Sager Naomi, 1981, NATURAL LANGUAGE INF ANN REV INFORMATION NR 8 TC 0 Z9 0 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1982 VL 38 IS 4 BP 321 EP 323 PG 3 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PW511 UT WOS:A1982PW51100009 ER PT J AU CHOW, D YU, CT AF CHOW, D YU, CT TI ON THE CONSTRUCTION OF FEEDBACK QUERIES SO JOURNAL OF THE ACM LA English DT Article C1 UNIV ILLINOIS,DEPT INFORMAT ENGN,CHICAGO,IL 60680. CR AFIFI AA, 1972, STATISTICAL ANAL COM BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 BORKO B, 1964, J ACM, V11, P138 Duda R., 1973, PATTERN CLASSIFICATI FUKUNAGE K, 1972, INTRO STATISTICAL PA HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HEINE MH, 1974, J AM SOC INFORM SCI, V25, P183, DOI 10.1002/asi.4630250309 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 Larson H., 1974, INTRO PROBABILITY TH, V2nd Miller KS, 1964, MULTIDIMENSIONAL GAU OSSORIO PG, 1966, MULTIVAR BEHAV RES, V1, P479, DOI 10.1207/s15327906mbr0104_6 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965, AFIPS P 1, V27, P293 Salton G., 1971, SMART RETRIEVAL SYST SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Yu C. T., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320241.320245 YU CT, 1978, J ACM, V25, P210, DOI 10.1145/322063.322066 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 22 TC 16 Z9 16 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0004-5411 J9 J ACM JI J. ACM PY 1982 VL 29 IS 1 BP 127 EP 151 DI 10.1145/322290.322299 PG 25 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA MZ023 UT WOS:A1982MZ02300009 ER PT J AU YU, CT LAM, K SALTON, G AF YU, CT LAM, K SALTON, G TI TERM WEIGHTING IN INFORMATION-RETRIEVAL USING THE TERM PRECISION MODEL SO JOURNAL OF THE ACM LA English DT Article C1 CORNELL UNIV, DEPT COMP SCI, ITHACA, NY 14853 USA. UNIV HONG KONG, DEPT STAT, HONG KONG, HONG KONG. RP YU, CT (reprint author), UNIV ILLINOIS, DEPT INFORMAT ENGN, CHICAGO, IL 60680 USA. CR BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1971, SMART RETRIEVAL SYST, pCH18 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, SMART RETRIEVAL SYST, pCH14 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1974, P IFIP C 74, P584 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 Salton G., 1971, SMART RETRIEVAL SYST VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1973, THESIS CORNELL U ITH YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 21 TC 18 Z9 18 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0004-5411 J9 J ACM JI J. ACM PY 1982 VL 29 IS 1 BP 152 EP 170 DI 10.1145/322290.322300 PG 19 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA MZ023 UT WOS:A1982MZ02300010 ER PT J AU MARON, ME AF MARON, ME TI ASSOCIATIVE SEARCH TECHNIQUES VERSUS PROBABILISTIC RETRIEVAL MODELS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP MARON, ME (reprint author), UNIV CALIF BERKELEY,SCH LIB & INFORMAT STUDIES,BERKELEY,CA 94720, USA. CR MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 STEVENS, 1965, NBS MISCELLANEOUS PU, V269 NR 4 TC 8 Z9 8 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1982 VL 33 IS 5 BP 308 EP 310 PG 3 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PF808 UT WOS:A1982PF80800008 ER PT J AU RADECKI, T AF RADECKI, T TI A PROBABILISTIC APPROACH TO INFORMATION-RETRIEVAL IN SYSTEMS WITH BOOLEAN SEARCH REQUEST FORMULATIONS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 UNIV SHEFFIELD,POSTGRAD SCH LIBRARIANSHIP & INFORMAT SCI,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1981, INFORMATION RETRIEVA, P23 GILL A, 1976, APPLIED ALGEBRA COMP Ide E., 1971, SMART RETRIEVAL SYST, P373 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 RADECKI T, 1981, FUZZY SET SYST, V5, P159, DOI 10.1016/0165-0114(81)90014-2 RADECKI T, 1980, RETRIEVAL SYSTEM MOD RADECKI T, 1979, INFORM PROCESS MANAG, V15, P247, DOI 10.1016/0306-4573(79)90031-1 RADECKI T, 1981, KYBERNETES, V10, P35, DOI 10.1108/eb005574 RADECKI T, 1982, PROGR CYBERNETICS SY, V11, P201 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P8, DOI 10.1002/asi.4630330104 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, DYNAMIC INFORMATION Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1980, COMPUTER, V13, P41 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG E, 1971, COMMUN ACM, V14, P593, DOI 10.1145/362663.362735 NR 26 TC 21 Z9 21 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1982 VL 33 IS 6 BP 365 EP 370 DI 10.1002/asi.4630330603 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA PT302 UT WOS:A1982PT30200002 ER PT J AU DRIYANSKII, VM AF DRIYANSKII, VM TI RETRIEVAL MODELS IN ONLINE DOCUMENTARY INFORMATION-SYSTEMS - AN ANALYTIC REVIEW SO CYBERNETICS LA English DT Review CR ANGIONE PV, 1976, J AM SOC INFORM SCI, V26, P112 APTEKMAN GN, 1979, NAUCHN TEKH INFORM, V2, P23 BARRACLOUGH ED, 1977, J DOC, V33, P220, DOI 10.1108/eb026643 BLEK AV, 1967, NAUCH-TEKHN INFORM 2, P26 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1977, INFORM PROCESS MANAG, V13, P377, DOI 10.1016/0306-4573(77)90057-7 BOURBAKI N, 1966, GENERAL TOPOLOGY CHERNENKO NN, 1976, 7643 AC SCI UKR SSR CHERNYI AI, 1975, INTRO THEORY INFORMA CHISTYAKOV VP, 1978, COURSE PROBABILITY T CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 DAMMERS HF, 1975, J DOC, V31, P38, DOI 10.1108/eb026593 DRIYANSKII VM, 1979, SEMANTIC MODELS SCI, P40 DRIYANSKII VM, 1980, 1ST P ALL UN C DAT B, P133 DRIYANSKII VM, 1979, METHODS EXPERIENCE D, P47 DRIYANSKII VM, 1979, 7925 AC SCI UKR SSR GORKOVA VI, 1975, NAUCHN INFORM SER, V2, P18 GORNOSTAEV YM, 1974, NAUCHN TEKH INFORM S, V2, P28 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 KUROSH AG, 1973, READINGS GENERAL ALG LYNDON RC, 1966, NOTES ON LOGIC MALTSEV AI, 1972, ALGEBRAIC SYSTEMS MARTIN J, 1978, DESIGN DATA BASES CO MARTIROSYAN ZG, 1974, NAUCHN TEKH INFORM, V2, P35 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 NILSON NJ, 1965, LEARNING MACHINES F, P81 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 POKRAS YL, 1979, NTI 1, P22 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RAKITIN YM, 1978, NAUCHN TEKH INFORM 2 RASEVA E, 1972, MATH METAMATHEMATICS ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1978, J DOC, V34, P93, DOI 10.1108/eb026654 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, DYNAMIC INFORMATION SHTEIN VS, 1974, NAUCH-TEKHN INFORM 2, P14 SHTEIN VS, 1977, NAUCHN TEKH INFORM S, V2, P19 SIDOROV IG, 1969, AUTOMAT DOC MATH LIN, V3, P61 SKORNYAKOV LA, 1968, ELEMENTS THEORY STRU SMITH LC, 1975, INFORM PROCESS MANAG, V12, P189 Sparck Jones K., 1976, J DOC, V32, P59, DOI 10.1108/eb026616 SWANSON DR, 1977, LIBR QUART, V47, P128 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 VOISKUNSKII VG, 1974, NAUCHNO TECHNICHESKA, V2, P7 WHITNEY VKM, 1972, COMMUN ACM, V15, P273, DOI 10.1145/361284.361299 YAKOVLEV OE, 1979, NAUCHN TEKH INFORM S, V2, P29 ZADEH LA, 1978, INT J MAN MACH STUD, V10, P395, DOI 10.1016/S0020-7373(78)80003-0 NR 60 TC 0 Z9 0 PU PLENUM PUBL CORP PI NEW YORK PA CONSULTANTS BUREAU, 233 SPRING ST, NEW YORK, NY 10013 SN 0011-4235 J9 CYBERNETICS+ PY 1981 VL 17 IS 2 BP 269 EP 287 DI 10.1007/BF01069646 PG 19 WC Computer Science, Cybernetics SC Computer Science GA PU222 UT WOS:A1981PU22200020 ER PT J AU VANRIJSBERGEN, CJ HARPER, DJ PORTER, MF AF VANRIJSBERGEN, CJ HARPER, DJ PORTER, MF TI THE SELECTION OF GOOD SEARCH TERMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article C1 UNIV CAMBRIDGE,COMP LAB,CAMBRIDGE,ENGLAND. CR HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Harter SP, 1974, THESIS U CHICAGO PRIM RC, 1957, AT&T TECH J, V36, P1389 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WHITNEY VKM, 1972, COMMUN ACM, V15, P273, DOI 10.1145/361284.361299 NR 8 TC 66 Z9 66 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1981 VL 17 IS 2 BP 77 EP 91 DI 10.1016/0306-4573(81)90029-7 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LL330 UT WOS:A1981LL33000004 ER PT J AU TAGUE, JM AF TAGUE, JM TI USER-RESPONSIVE SUBJECT CONTROL IN BIBLIOGRAPHIC RETRIEVAL-SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP TAGUE, JM (reprint author), UNIV WESTERN ONTARIO,LONDON N6A 3K7,ONTARIO,CANADA. CR CRAWFORD RG, 1979, CAN J INFORMATION SC EVENS MW, 1979, AM J COMP LINGUISTIC, V83 Lancaster F. W., 1968, EVALUATION MEDLARS D LYON J, 1977, SEMANTICS, V1, pCH9 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1975, DYNAMIC INFORMATION, pCH8 Tague J., 1978, Journal of Informatics, V2 NR 8 TC 10 Z9 10 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1981 VL 17 IS 3 BP 149 EP 159 DI 10.1016/S0306-4573(81)80006-4 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LU511 UT WOS:A1981LU51100004 ER PT J AU WU, H SALTON, G AF WU, H SALTON, G TI THE ESTIMATION OF TERM RELEVANCE WEIGHTS USING RELEVANCE FEEDBACK SO JOURNAL OF DOCUMENTATION LA English DT Article RP WU, H (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR CHANG YK, 1971, SMART RETRIEVAL SYST CHOW D, UNPUB J ACM CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HALL HA, 1967, ISR12 CORN U DEP COM HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Ide E., 1971, SMART RETRIEVAL SYST Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 JONES KS, 1980, J INFORM SCI, V1, P325, DOI 10.1177/016555157900100603 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1981, INFORMATION RETRIEVA, P9 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 WU H, 1980, THESIS CORNELL U WU H, 1981, 4TH P INT C INF STOR, V16, P30 YU CT, UNPUB J ACM YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 23 TC 12 Z9 12 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1981 VL 37 IS 4 BP 194 EP 214 DI 10.1108/eb026717 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA NG548 UT WOS:A1981NG54800006 ER PT J AU SALTON, G WU, H YU, CT AF SALTON, G WU, H YU, CT TI THE MEASUREMENT OF TERM IMPORTANCE IN AUTOMATIC-INDEXING SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 UNIV ILLINOIS,DEPT INFORMAT ENGN,CHICAGO,IL 60680. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1979, MAY JOINT M OP RES S BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LUHN HP, 1957, IBM J RES DEV, V1, P309 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO J, 1971, SMART RETRIEVAL SYST, pCH14 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1971, SMART RETRIEVAL SYST, pCH15 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1980, JUN S RES DEV INF RE VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1980, OPTIMUM TERM WEIGHTI YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 18 TC 24 Z9 25 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1981 VL 32 IS 3 BP 175 EP 186 DI 10.1002/asi.4630320304 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LM437 UT WOS:A1981LM43700003 ER PT J AU CROFT, WB AF CROFT, WB TI DOCUMENT REPRESENTATION IN PROBABILISTIC MODELS OF INFORMATION-RETRIEVAL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP CROFT, WB (reprint author), UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003, USA. CR CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LUHN HP, 1958, IBM J RES DEV, V2, P159 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1980, P ACM BCS S RES DEV ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CU, UNPUBLISHED NR 16 TC 26 Z9 28 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1981 VL 32 IS 6 BP 451 EP 457 DI 10.1002/asi.4630320609 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA MQ052 UT WOS:A1981MQ05200008 ER PT J AU STOCK, WG AF STOCK, WG TI THE IMPORTANCE OF SCIENTIFIC DOCUMENTS RELATIVE TO THE GIVEN TOPIC SO NACHRICHTEN FUR DOKUMENTATION LA German DT Article CR HENRICHS N, 1975, DTSCH DOKUMENTARTAG, V1, P343 HENRICHS N, 1980, DATENBASENDATENBANKE, V3, P157 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 NR 4 TC 5 Z9 5 PU VERLAG HOPPENSTEDT & CO PI DARMSTADT 1 PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY SN 0027-7436 J9 NACHR DOK JI Nachr. Dok. PY 1981 VL 32 IS 4-5 BP 162 EP 164 PG 3 WC Information Science & Library Science SC Information Science & Library Science GA ML302 UT WOS:A1981ML30200001 ER PT J AU DILLON, M AF DILLON, M TI SERVING THE INFORMATION NEEDS OF SCIENTIFIC-RESEARCH SO SPECIAL LIBRARIES LA English DT Article RP DILLON, M (reprint author), UNIV N CAROLINA,SCH LIB SCI,CHAPEL HILL,NC 27514, USA. CR ATHERTON P, 1978, ONLINE REV, V2, P57, DOI 10.1108/eb023964 BURTON HD, 1973, SPEC LIBR, V64, P7 Bush V., 1945, ATLANTIC MONTHLY, P101 CARMON JL, 1975, MODEL USER INTERFACE CLEVELAND DB, 1976, J AM SOC INFORM SCI, V27, P342, DOI 10.1002/asi.4630270511 DATTOLA RT, 1979, J AM SOC INFORMATION, V30, P10 DAVIDSON D, 1977, J AM SOC INFORM SCI, V28, P273, DOI 10.1002/asi.4630280507 ELCHESEN DR, 1978, J AM SOC INFORM SCI, V29, P56, DOI 10.1002/asi.4630290204 EVANS SM, 1973, J LIBR, V5, P214, DOI 10.1177/096100067300500304 GARVEY WD, 1976, INFORM PROCESS MANAG, V12, P165, DOI 10.1016/0306-4573(76)90003-0 GARVEY WD, 1974, INFORM STORAGE RET, V10, P115, DOI 10.1016/0020-0271(74)90014-X HAVELOCK RG, 1974, IDEAL SYSTEMS RES UT KREUZ E, 1975, INFORMATIK, V22, P51 KUNZ W, 1977, METHODS ANAL EVALUAT RICHTER H, 1976, INFORMATIK, V23, P17 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 RUST RA, 1980, ONLINE, V4, P13 SALTON G, 1975, DYNAMIC INFORMATION SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 VALDEZ M, 1974, ASLIB PROC, V26, P473, DOI 10.1108/eb050485 WANGLER J, 1976, IMPACT ONLINE RETRIE Williams M. E., 1980, Bulletin of the American Society for Information Science, V7 NR 23 TC 5 Z9 5 PU SPECIAL LIBRARIES ASSN PI WASHINGTON PA 1700 EIGHTEENTH ST NW, WASHINGTON, DC 20009-2508 SN 0038-6723 J9 SPEC LIBR JI Spec. Libr. PY 1981 VL 72 IS 3 BP 215 EP 223 PG 9 WC Information Science & Library Science SC Information Science & Library Science GA LW340 UT WOS:A1981LW34000008 ER PT J AU SALTON, G AF SALTON, G TI AUTOMATIC INFORMATION-RETRIEVAL SO COMPUTER LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,ITHACA,NY 14853, USA. CR AHO AV, 1975, COMMUN ACM, V18, P333, DOI 10.1145/360825.360855 ATTAR L, 1977, J ACM, V24, P397 BIRD RM, 1978, 4TH P WORKSH COMP AR, P42 BOYER RS, 1977, COMMUN ACM, V20, P762, DOI 10.1145/359842.359859 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 DATTOLA RT, 1979, J AM SOC INFORM SCI, V30, P9 DAVIS EW, 1974, AFIPS C P, V43, P16 DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 DOYLE LB, 1975, INFORMATION RETRIEVA HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HOLLAAR LA, 1977, AFIPS C P, V46, P697 HOLLAAR LA, 1979, COMPUTER, V12, P40 Hollaar L. A., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320263.320281 IDE E, 1971, SMART RETRIEVAL SYST, pCH16 Knuth D. E., 1977, SIAM Journal on Computing, V6, DOI 10.1137/0206024 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 LANCASTER FW, 1978, INFORMATION RETRIEVA Lin C. S., 1976, ACM T DATABASE SYST, V1, P53, DOI 10.1145/320434.320447 MARCUS RS, 1977, ESLR739 MIT EL SYST MINKER J, 1972, TR195 U MAR COMP SCI NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 OZKARAHAN EA, 1974, CSRG43 U TOR COMP SY ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBINSON AL, 1979, SCIENCE, V203, P156, DOI 10.1126/science.203.4376.156 ROCCHIO J, 1971, SMART RETRIEVAL SYST, pCH14 RUDOLPH JA, 1972, P AFIPS JOINT COMP 1, V41, P229 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 Salton G., 1971, SMART SYSTEM EXPT AU SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1972, COMMUN ACM, V15, P658, DOI 10.1145/361454.361509 SPARCKJONES K, 1979, J DOCUMENTATION, V35, P39 SPARCKJONES K, 1971, AUTOMATIC KEYWORD CL VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 WILLIAMS ME, 1977, INFORM PROCESS MANAG, V13, P95, DOI 10.1016/0306-4573(77)90043-7 WILLIAMS ME, 1972, INFORM STORAGE RET, V8, P57, DOI 10.1016/0020-0271(72)90008-3 Yu C. T., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320241.320245 YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1978, J ACM, V25, P210, DOI 10.1145/322063.322066 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 1978, STAIRS VS TOOL END U 1976, SOFTWARE DEV PACKAGE 1976, SH1254002 IBM CORP R NR 48 TC 11 Z9 11 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 SN 0018-9162 J9 COMPUTER JI Computer PY 1980 VL 13 IS 9 BP 41 EP 56 PG 16 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering SC Computer Science GA KF843 UT WOS:A1980KF84300006 ER PT J AU DILLON, M DESPER, J AF DILLON, M DESPER, J TI THE USE OF AUTOMATIC RELEVANCE FEEDBACK IN BOOLEAN RETRIEVAL-SYSTEMS SO JOURNAL OF DOCUMENTATION LA English DT Article RP DILLON, M (reprint author), UNIV N CAROLINA,SCH LIB SCI,CHAPEL HILL,NC 27514, USA. CR DATTOLA RT, 1979, J AM SOC INFORMATION, V30, P10 DILLON M, 1980, J AM SOC INFORM SCI, V31, P89, DOI 10.1002/asi.4630310205 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 RICKMAN JT, 1972, AUG P ASS COMP MACH, P478 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1972, J AM SOC INFORM SCI, V23, P75, DOI 10.1002/asi.4630230202 VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 NR 11 TC 28 Z9 28 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1980 VL 36 IS 3 BP 197 EP 208 DI 10.1108/eb026696 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KX573 UT WOS:A1980KX57300002 ER PT J AU SPARCK-JONES, K AF SPARCK-JONES, K TI SEARCH TERM RELEVANCE WEIGHTING - SOME RECENT RESULTS SO JOURNAL OF INFORMATION SCIENCE LA English DT Article RP SPARCK-JONES, K (reprint author), UNIV CAMBRIDGE, COMP LAB, CAMBRIDGE, ENGLAND. CR HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 ROBERTSON SE, 1976, THESIS U LONDON ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SPARCKJONES K, 1979, J DOC, V35, P30 SPARCK-JONES K, 1979, INFORM PROCESS MANAG, V15, P133 NR 6 TC 0 Z9 0 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1980 VL 1 IS 6 BP 325 EP 332 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KA821 UT WOS:A1980KA82100003 ER PT J AU HAMILL, KA ZAMORA, A AF HAMILL, KA ZAMORA, A TI THE USE OF TITLES FOR AUTOMATIC DOCUMENT CLASSIFICATION SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 CHEM ABSTRACTS SERV,POB 3012,COLUMBUS,OH 43210. CR BAKER DB, 1971, CHEM ENG NEWS, V49, P37, DOI 10.1021/cen-v049n028.p037 BAKER DB, 1978, CHEM ENGINEERING NEW, V56, P41 BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 DAY AC, 1970, COMMUN ACM, V13, P481, DOI 10.1145/362705.362709 Fu K. S., 1968, SEQUENTIAL METHODS P HAMILL KA, 1979, THESIS OHIO STATE U HAMILL KA, 1978, P AM SOC INFORM SCI, V15, P152 HEAPS HS, 1973, INFORM CONTROL, V22, P268, DOI 10.1016/S0019-9958(73)90310-0 KAR BG, 1975, CISRC757 OH STAT U T MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST WHITE LJ, 1977, RADCTR77211 ROM AIR WHITE LJ, 1975, CISRC755 OH STAT U T 1975, CHEM ABSTR NR 16 TC 13 Z9 13 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1980 VL 31 IS 6 BP 396 EP 402 DI 10.1002/asi.4630310603 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KR501 UT WOS:A1980KR50100002 ER PT J AU WILLIAMS, PW AF WILLIAMS, PW TI THE USE OF MICROELECTRONICS TO ASSIST ONLINE INFORMATION-RETRIEVAL SO ONLINE REVIEW LA English DT Article RP WILLIAMS, PW (reprint author), UNIV MANCHESTER,INST SCI & TECHNOL,DEPT COMP,MANCHESTER M60 1QD,LANCASHIRE,ENGLAND. CR BARWISE P, 1978, NOV ICSU AB HOLMES PL, 1978, 2ND P INT ONL INF M NOYCE RN, 1977, SCI AM, P2 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 WILLIAMS PW, 1979, 3RD P INT ONL INF M NR 5 TC 9 Z9 9 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0309-314X J9 ONLINE REV PY 1980 VL 4 IS 4 BP 393 EP 399 DI 10.1108/eb024045 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KV792 UT WOS:A1980KV79200007 ER PT J AU BOOKSTEIN, A AF BOOKSTEIN, A TI PROBABILISTIC MODELS OF DOCUMENT-RETRIEVAL SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP BOOKSTEIN, A (reprint author), UNIV CHICAGO,GRAD LIB SCH,CHICAGO,IL 60637, USA. CR BELZER J, 1973, J AM SOC INFORM SCI, V24, P300, DOI 10.1002/asi.4630240411 BELZER J, 1971, J AM SOC INFORM SCI, V22, P226, DOI 10.1002/asi.4630220310 BOOKSTEIN A, 1979, J AM SOC INFORM SCI, V30, P269 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1979, INFORMATION PROCESSI, V13, P153 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CURTICE RM, 1967, P AM DOC I, V4, P152 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Shannon C. E., 1949, MATH THEORY COMMUNIC SPARCKJONES K, 1972, J DOC, V28, P11 STONE DC, 1968, AM DOC, V19, P411, DOI 10.1002/asi.5090190409 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 NR 14 TC 0 Z9 0 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1980 VL 17 BP 27 EP 29 PG 3 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KP384 UT WOS:A1980KP38400008 ER PT J AU YU, CT AF YU, CT TI MODELS FOR CONSTRUCTING OPTIMAL QUERIES IN INFORMATION-RETRIEVAL SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP YU, CT (reprint author), UNIV ILLINOIS,DEPT INFORMAT ENGN,CHICAGO,IL 60680, USA. CR CHOW D, 1978, CONSTRUCTION FEEDBAC CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROCCHIO JJ, 1965 P AFIPS FJCC, V27, P293 SALTON G, 1974, P IFIP C 74, P584 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1980, MEASUREMENT TERM IMP SPARCKJONES K, 1972, J DOC, V28, P11 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1979, INFORM SYST, V4, P205, DOI 10.1016/0306-4379(79)90004-8 YU CT, 1976, J ACM, V23, P273, DOI 10.1145/321941.321947 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CT, 1980, OPTIMAL TERM WEIGHTI NR 16 TC 0 Z9 0 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1980 VL 17 BP 30 EP 33 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KP384 UT WOS:A1980KP38400009 ER PT J AU MCGILL, MJ HUITFELDT, J AF MCGILL, MJ HUITFELDT, J TI EXPERIMENTAL-TECHNIQUES OF INFORMATION-RETRIEVAL SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article RP MCGILL, MJ (reprint author), SYRACUSE UNIV,SCH INFORMAT STUDIES,SYRACUSE,NY 13210, USA. CR ALVEY CH, 1978, ONLINE, V2, P21 ARNOVICK GN, 1978, INFORM PROCESS MANAG, V14, P369, DOI 10.1016/0306-4573(78)90002-X ARTANDI S, 1976, J AM SOC INFORM SCI, V27, P235, DOI 10.1002/asi.4630270408 ATHERTON P, 1978, BOOKS ARE USE FINAL ATHERTON P, 1978, ONLINE REV, V2, P57, DOI 10.1108/eb023964 ATHERTON PA, 1977, ONLINE INFORMATION, P131 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BARNES CI, 1978, INFORM PROCESS MANAG, V14, P107, DOI 10.1016/0306-4573(78)90068-7 *BATH U LIBR, 1977, APPL CLUST TECHN CIT BILLER H, 1978, INFORM SYST, V3, P11, DOI 10.1016/0306-4379(78)90012-1 BILLER H, 1978, INFORM SYST, V3, P35, DOI 10.1016/0306-4379(78)90014-5 BIRD RM, 1978, 4TH P WORKSH COMP AR, P42 BOOKSTEIN A, 1978, J AM SOC INFORM SCI, V29, P156, DOI 10.1002/asi.4630290309 BOOKSTEIN A, 1978, J LIBR AUTOMAT, V11, P41 BOOKSTEI.A, 1974, J LIBR AUTOMAT, V7, P97 BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022 BRINER LL, 1978, INFORMATION AGE PERS, V15, P55 BROOKES BC, 1978, J AM SOC INFORM SCI, V29, P5, DOI 10.1002/asi.4630290104 BROOKES BC, 1977, J INFORMATICS CAHN DF, 1978, 4TH INT C CYB SYST A CARROLL JM, 1976, J AM SOC INFORM SCI, V27, P63 CARUSO E, UNPUBLISHED CARUSO E, 1978, ONLINE REV, V2, P251, DOI 10.1108/eb023980 CAWKELL AE, 1977, ONLINE REV, V1, P31, DOI 10.1108/eb023932 CAWKELL AE, 1977, ENDEAVOUR, V1, P57, DOI 10.1016/0160-9327(77)90107-7 CAWKELL AE, 1978, J AM SOC INFORM SCI, V29, P41, DOI 10.1002/asi.4630290107 CHERNIAVSKY V, 1978, INFORM SYST, V3, P5, DOI 10.1016/0306-4379(78)90011-X CHOW D, 1978, CONSTRUCTION FEEDBAC CLEVELAND DB, 1976, J AM SOC INFORM SCI, V27, P342, DOI 10.1002/asi.4630270511 CLEVERDON CW, 1976, J DOC, V32, P159, DOI 10.1108/eb026622 COOPER MD, 1977, J LIBR AUTOMAT, V10, P304 COOPER MD, 1977, J AM SOC INFORM SCI, V28, P153, DOI 10.1002/asi.4630280303 Cooper W. S., 1978, Journal of Informatics, V2 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 COUSINS TR, 1978, INFORMATION AGE PERS, V15 CRAVEN TC, 1978, INFORMATION AGE PERS, V15, P79 CRAWFORD RG, 1978, INFORMATION AGE PERS, V15, P83 CRAWFORD RG, 1977, INFORM PROCESS MANAG, V13, P235, DOI 10.1016/0306-4573(77)90004-8 CROFT WB, 1976, INFORM PROCESS MANAG, V12, P327, DOI 10.1016/0306-4573(76)90051-0 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 Croft W. B., 1978, Journal of Informatics, V2 Davies C. C., 1978, Journal of Informatics, V2 DELUTIS TG, 1978, SIMULATION MODEL INF DELUTIS TG, 1978, MODELING LARGE ONLIN DONATI R, 1977, ONLINE, V1, P48 Donati R., 1978, Online, V2 DOSZKOCS TE, 1978, ONLINE REV, V2, P163, DOI 10.1108/eb023972 DUNHAM GS, 1978, J AM SOC INFORM SCI, V29, P81, DOI 10.1002/asi.4630290207 ELCHESEN DR, 1978, J AM SOC INFORM SCI, V29, P56, DOI 10.1002/asi.4630290204 Enser P. G. B., 1978, Journal of Informatics, V2 FIRSCHEIN O, 1978, ONLINE REV, V2, P41, DOI 10.1108/eb023963 FIRSCHEIN O, 1978, SPEC LIBR, V69, P255 FORD WH, 1978, 1978 P MUMPS US GROU GEBHART F, 1978, J AM SOC INFORMATION, V29, P187 GERSON GM, 1978, J AM SOC INFORM SCI, V29, P125, DOI 10.1002/asi.4630290304 GOLDSTEIN CM, 1978, ONLINE REV, V2, P269, DOI 10.1108/eb023981 GRAITSON M, 1975, CAHIERS LEXICOLOGIE, V26 GRAITSON M, 1977, CAHIERS LEXICOLOGIE, V30 GRISHMAN R, 1978, ARTIF INTELL, V11, P25, DOI 10.1016/0004-3702(78)90011-5 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 HAWKINS DT, 1978, ONLINE REV, V2, P63, DOI 10.1108/eb023965 HAWKINS DT, 1977, ONLINE REV, V1, P59, DOI 10.1108/eb023936 HEAPS HS, 1978, INFORMATION RETRIEVA HERLACH G, 1978, J AM SOC INFORM SCI, V29, P308, DOI 10.1002/asi.4630290608 HICKEY TB, 1978, DERIVED SEARCH KEYS HIRSCHMAN L, 1976, 1976 P AM FED INF PR, V45, P267 HIRSCHMAN L, 1977, MEDINFO77, P247 HJERPPE R, 1974, META MODEL EVALUATIN HJERPPE R, 1976, UTILIZATION ESA RECO HJERPPE R, 1976, 1974 75 SDI COMP INF HULTGREN J, 1975, PASTIME SYSTEM FILE JONES WT, 1976, J AM SOC INFORM SCI, V27, P307, DOI 10.1002/asi.4630270506 KANTOR PB, 1976, J AM SOC INFORM SCI, V27, P311, DOI 10.1002/asi.4630270507 KAR G, 1978, INFORM PROCESS MANAG, V14, P57, DOI 10.1016/0306-4573(78)90063-8 KOENIG MED, 1978, APPLICATION AUTOMATE KOLL MB, 1978, ASS COMPUTING MACHIN KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P77, DOI 10.1002/asi.4630290206 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 KRENTZ DM, 1978, J CHEM INF COMP SCI, V18, P4, DOI 10.1021/ci60013a002 LAM L, 1978, CLUSTERED SEARCH ALG LANGEFORS B, 1978, INFORM SYST, V3, P31, DOI 10.1016/0306-4379(78)90013-3 LARSSON R, 1974, VIRA STRING ORIENTED LINDQUIST MG, 1978, J AM SOC INFORM SCI, V29, P67, DOI 10.1002/asi.4630290205 LINDQUIST MG, 1978, DYNAMICS INFORMATION LOFSTROM M, 1977, 3RIP DATA STRUCTURES MACLEOD IA, 1978, SEQUEL LANGUAGE DOCU MACLEOD IA, 1977, INFORM PROCESS MANAG, V13, P167, DOI 10.1016/0306-4573(77)90035-8 MARCUS RS, 1978, J AM SOC INFORM SCI, V29, P15, DOI 10.1002/asi.4630290105 MARCUS RS, 1977, COMPUTER INTERFACES MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 MASON RM, 1978, INFORM PROCESS MANAG, V14, P71, DOI 10.1016/0306-4573(78)90064-X MCGILL MJ, 1976, J AM SOC INFORM SCI, V27, P205, DOI 10.1002/asi.4630270403 MCINROY JW, 1978, THESIS U N CAROLINA MEADOW CT, 1977, ONLINE INFORMATION MEADOW CT, 1978, INFORMATION AGE PERS, V15, P222 MEADOW CT, 1979, ONLINE, V3, P49 MEINCKE PPM, 1976, J AM SOC INFORM SCI, V27, P18 MINKER J, 1977, SIGIR FORUM ASS COMP, V12, P1 MINKER J, 1978, INFORM SYST, V3, P37, DOI 10.1016/0306-4379(78)90015-7 MOHAN C, 1978, SPECIAL INTEREST GRO, V10, P3 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 OCONNOR J, 1978, ANSWER PASSAGE RETRI OCONNOR J, 1977, J CHEM INF COMP SCI, V17, P181, DOI 10.1021/ci60011a014 OCONNOR J, 1978, INFORMATION AGE PERS, V15, P256 PACAK MG, 1978, METHOD INFORM MED, V17, P95 PAICE CD, 1977, INFORMATION RETRIEVA PAO ML, 1978, J AM SOC INFORM SCI, V29, P121, DOI 10.1002/asi.4630290303 PERSSON O, 1975, EVALUATION COMPUTER PIERCE JC, 1978, INFORM PROCESS MANAG, V14, P85, DOI 10.1016/0306-4573(78)90065-1 RADECKI T, 1978, 1978 INT C CYB SYST RADECKI T, 1977, 6TH CRANF INT C MECH RAGHAVAN VV, 1978, STABILITY ANAL CERTA RAGHAVAN VV, 1978, EXPT DETERMINATION R RASTOGI KB, 1978, RETRIEVAL BEHAVIOR D RICHMOND PA, 1976, J AM SOC INFORMATION, V27, P205 ROBERTS DC, 1978, 4TH P WORKSH COMP AR, P51 ROBERTSON SE, 1978, J AM SOC INFORM SCI, V29, P304, DOI 10.1002/asi.4630290607 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSENBERG AL, 1977, J ACM, V24, P199, DOI 10.1145/322003.322006 SAGER N, 1978, MED REC NEWS, V49, P20 SAGER N, 1977, SPECIAL INTEREST GRO, V61, P38 SAGER N, 1978, ADV COMPUT, V17, P89, DOI 10.1016/S0065-2458(08)60391-5 SAGER WKH, 1976, INT FORUM INFORM DOC, V1, P2 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1974, VECTOR SPACE MODEL A Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, J DOCUMENTATION SCHNEIDER HJ, 1978, INFORM SYST, V3, P1, DOI 10.1016/0306-4379(78)90010-8 SCHWARTZ C, 1978, INFORMATION AGE PERS, V15, P291 SCHWARZ S, 1978, LIBRARY SERVICES TRA SHARP G, 1978, ONLINE, V2, P33 Shneiderman B., 1976, ACM Transactions on Database Systems, V1, DOI 10.1145/320473.320487 SHNEIDERMAN B, 1977, INFORM PROCESS MANAG, V13, P23, DOI 10.1016/0306-4573(77)90028-0 Shneiderman B., 1977, Information Systems, V2, DOI 10.1016/0306-4379(77)90003-5 SHNEIDERMAN B, 1978, IMPROVING HUMAN FACT SHNEIDERMAN B, 1978, COMMUN ACM, V21, P831, DOI 10.1145/359619.359623 SIBLEY EH, 1978, INFORMATION MANAGEME SMITH JD, 1977, J AM SOC INFORMATION, V28, P71 SMITH LC, 1976, 5TH ASIS MID YEAR M SMITH LC, 1976, INFORM PROCESS MANAG, V12, P189, DOI 10.1016/0306-4573(76)90005-4 SPARCKJONES K, 1978, EXPT RELEVANCE WEIGH SPARCK-JONES K, 1976, J DOC, V32, P59 SPARCKJONES K, 1977, RES AUTOMATIC INDEXI, V1 SPARCKJONES K, 1977, REPORT DESIGN STUDY Stevens B. A., 1978, Online, V2 SUMMIT RK, 1978, 1978 MAR LOCKH INF R SUMMIT RK, 1977, 1977 AM ASS ADV SCI SVENONIUS E, 1977, LIBR QUART, V47, P326 SWANSON DR, 1977, LIBR QUART, V47, P128 TAGLIACOZZO R, 1977, J DOC, V33, P251, DOI 10.1108/eb026644 TAGLIACOZZO R, 1978, J AM SOC INFORM SCI, V29, P136, DOI 10.1002/asi.4630290306 TAGUE J, 1978, INFORM PROCESS MANAG, V14, P1, DOI 10.1016/0306-4573(78)90053-5 TEITELBAUM HH, 1978, ONLINE, V2, P16 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1976, J DOC, V32, P294 VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 WILLIAMS ME, 1977, J AM SOC INFORM SCI, V28, P71, DOI 10.1002/asi.4630280203 WILLIAMS PW, 1977, COMPUT J, V20, P257, DOI 10.1093/comjnl/20.3.257 Yu C. T., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320241.320245 YU CT, 1978, 1978 CAN CLASS RES G YU CT, 1977, J AM SOC INFORM SCI, V28, P345, DOI 10.1002/asi.4630280607 YU CT, 1977, J ACM, V24, P607, DOI 10.1145/322033.322039 YU CT, 1978, J ACM, V25, P210, DOI 10.1145/322063.322066 YU CT, 1976, J AM SOC INFORM SCI, V27, P248, DOI 10.1002/asi.4630270410 ZIMMERMAN PJ, 1977, J AM SOC INFORM SCI, V28, P183 1977, INFORMATION MANAGEME NR 168 TC 11 Z9 11 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1979 VL 14 BP 93 EP 127 PG 35 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA JT807 UT WOS:A1979JT80700004 ER PT J AU SPARCK-JONES, K AF SPARCK-JONES, K TI EXPERIMENTS IN RELEVANCE WEIGHTING OF SEARCH TERMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP SPARCK-JONES, K (reprint author), UNIV CAMBRIDGE, COMP LAB, CAMBRIDGE, ENGLAND. CR AITCHISON TM, 1970, COMP EVALUATION IN 2 BARKER FH, 1972, J DOC, V28, P44, DOI 10.1108/eb026529 BARKER FH, 1974, UKCIS2 RES REP CLEVERDON CW, 1966, FACTORS DETERMING PE EVANS L, 1975, 7521 I EL ENG REP MILLER WL, 1970, THESIS U NEWCASTLE MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 ROBERTSON SE, 1976, THESIS U LONDON ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 SPARCK-JONES K, 1975, J DOC, V31, P266 SPARCKJONES L, 1977, RES AUTOMATIC INDEXI YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 13 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1979 VL 15 IS 3 BP 133 EP 144 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA HG261 UT WOS:A1979HG26100002 ER PT J AU SPARCKJONES, K AF SPARCKJONES, K TI SEARCH TERM RELEVANCE WEIGHTING GIVEN LITTLE RELEVANCE INFORMATION SO JOURNAL OF DOCUMENTATION LA English DT Article RP SPARCKJONES, K (reprint author), UNIV CAMBRIDGE, COMP LAB, CAMBRIDGE, ENGLAND. CR BARKER FH, 1974, RETRIEVAL EXPT BASED Cleverdon C. W., 1966, FACTORS DETERMINING COX DR, 1970, ANAL BINARY DATA LON HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1969, ISR15 CORN U DEP COM ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1975, AUTOMATIC AIDS PROFI ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SPARCKJONES K, INFORMATION PROCESSI VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1977, COMMUN ACM, V20, P135, DOI 10.1145/359436.359441 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 13 TC 6 Z9 6 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1979 VL 35 IS 1 BP 30 EP 48 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GZ743 UT WOS:A1979GZ74300002 ER PT J AU CROFT, WB HARPER, DJ AF CROFT, WB HARPER, DJ TI USING PROBABILISTIC MODELS OF DOCUMENT-RETRIEVAL WITHOUT RELEVANCE INFORMATION SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UNIV CAMBRIDGE,COMP LAB,CAMBRIDGE,ENGLAND. RP CROFT, WB (reprint author), UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003, USA. CR CROFT WB, 1979, THESIS U CAMBRIDGE HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 IDE E, 1969, THESIS CORNELL U ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SPARCKJONES K, 1979, J DOC, V35, P30 SPARCKJONES K, 1977, RES AUTOMATIC INDEXI VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 10 TC 175 Z9 176 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1979 VL 35 IS 4 BP 285 EP 295 DI 10.1108/eb026683 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA JJ975 UT WOS:A1979JJ97500002 ER PT J AU SALTON, G WALDSTEIN, RK AF SALTON, G WALDSTEIN, RK TI TERM RELEVANCE WEIGHTS IN ONLINE INFORMATION-RETRIEVAL SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV, DEPT COMP SCI, ITHACA, NY 14853 USA. CR Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lancaster Frederick Wilfrid, 1973, INFORM RETRIEVAL LESK ME, 1971, SMART RETRIEVAL SYST, pCH25 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1971, SMART RETRIEVAL SYST, pCH15 SALTON G, 1975, DYNAMIC INFORMATION VERNIMB CO, 1973, NUCL ENG DES, V25, P325, DOI 10.1016/0029-5493(73)90029-0 WALDSTEIN RH, 1977, THESIS YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CT, 1977, ACM COMMUN, V20, P135 YU CU, UNPUBLISHED NR 12 TC 23 Z9 23 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1978 VL 14 IS 1 BP 29 EP 35 DI 10.1016/0306-4573(78)90055-9 PG 7 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA EQ762 UT WOS:A1978EQ76200003 ER PT J AU ROBERTSON, SE BELKIN, NJ AF ROBERTSON, SE BELKIN, NJ TI RANKING IN PRINCIPLE SO JOURNAL OF DOCUMENTATION LA English DT Article C1 CITY UNIV LONDON, CTR INFORMAT SCI, LONDON EC1V 4PB, ENGLAND. RP ROBERTSON, SE (reprint author), UCL, SCH LIB ARCHIVE & INFORMAT STUDIES, LONDON WC1E 6BT, ENGLAND. CR BELKIN NJ, 1977, THESIS U LONDON BELKIN NJ, 1977, 2ND SIRE INT RES FOR BRANDHOR.WT, 1972, ANNU REV INFORM SCI, V7, P379 COOK KH, 1975, INFORM PROCESS MANAG, V11, P125, DOI 10.1016/0306-4573(75)90001-1 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COOPER WE, UNPUBLISHED COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P413, DOI 10.1002/asi.4630240603 DOYLE LB, 1975, INFORMATION RETRIEVA GEBHARDT F, 1975, INFORM PROCESS MANAG, V11, P59, DOI 10.1016/0306-4573(75)90034-5 LESK ME, 1971, SMART RETRIEVAL SYST, P506 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P319, DOI 10.1016/0306-4573(76)90050-9 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074 STIRLING KH, 1975, P AM SOC INFORM SCI, V12, P105 STOKOLOVA NA, 1977, INFORM PROCESS MANAG, V13, P227, DOI 10.1016/0306-4573(77)90003-6 NR 21 TC 21 Z9 21 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PY 1978 VL 34 IS 2 BP 93 EP 100 DI 10.1108/eb026654 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA FN038 UT WOS:A1978FN03800001 ER PT J AU HARPER, DJ VANRIJSBERGEN, CJ AF HARPER, DJ VANRIJSBERGEN, CJ TI EVALUATION OF FEEDBACK IN DOCUMENT-RETRIEVAL USING CO-OCCURRENCE DATA SO JOURNAL OF DOCUMENTATION LA English DT Article RP HARPER, DJ (reprint author), UNIV CAMBRIDGE,COMP LAB,CAMBRIDGE,ENGLAND. CR AITCHISON TM, 1970, COMP EVALUATION IN 2 BARRACLOUGH ED, 1975, 78 U NEWC COMP LAB T CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 Cleverdon C. W., 1966, FACTORS DETERMINING Duda R., 1973, PATTERN CLASSIFICATI HOLMES PL, 1977, EXPT NONMEDICAL INFO, V1 Ide E., 1971, SMART RETRIEVAL SYST IDE E, 1969, THESIS CORNELL U IDE E, 1969, ISR15 CORN U NAT SCI KEEN EM, 1972, REPORT INFORMATION S OLDROYD BK, 1976, COMMUNICATION DEC ROBERTSON SE, 1977, J DOC, V34, P294 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SPARCKJONES K, 1977, RES AUTOMATIC INDEXI VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA, P103 VANRIJSBERGEN CJ, INFORMATION RETRIEVA, pCH6 YU CT, 1975, J ASS COMPUTING MACH, V23, P76 NR 19 TC 82 Z9 85 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1978 VL 34 IS 3 BP 189 EP 216 DI 10.1108/eb026659 PG 28 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA FW139 UT WOS:A1978FW13900002 ER PT J AU YU, CT SALTON, G SIU, MK AF YU, CT SALTON, G SIU, MK TI EFFECTIVE AUTOMATIC-INDEXING USING TERM ADDITION AND DELETION SO JOURNAL OF THE ACM LA English DT Article C1 CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853. UNIV HONG KONG,DEPT MATH,HONG KONG,HONG KONG. RP YU, CT (reprint author), UNIV ALBERTA,DEPT COMP SCI,EDMONTON T6G 2M7,ALBERTA,CANADA. CR Keen M., 1966, FACTORS DETERMINING, V2 KOCHEN M, 1974, PRINCIPLES INFORMATI, pCH8 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1977, TR77299 CORN U COMPT SPARCKJONES K, 1972, J DOC, V28, P11 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CU, UNPUBLISHED NR 9 TC 11 Z9 11 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0004-5411 J9 J ACM JI J. ACM PY 1978 VL 25 IS 2 BP 210 EP 225 DI 10.1145/322063.322066 PG 16 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA FA206 UT WOS:A1978FA20600003 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI NATURE OF FUZZ - DIATRIBE SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 UCL, SCH LIB ARCHIVE & INFORMAT STUDIES, LONDON WC1E 6BT, ENGLAND. CR JONES WT, 1976, J AM SOC INFORM SCI, V27, P307, DOI 10.1002/asi.4630270506 RADECKI T, 1977, INFORM PROCESS MANAG, V13, P109, DOI 10.1016/0306-4573(77)90044-9 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P319, DOI 10.1016/0306-4573(76)90050-9 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SACHS WM, 1976, J AM SOC INFORM SCI, V27, P85, DOI 10.1002/asi.4630270203 TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 9 TC 22 Z9 22 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PY 1978 VL 29 IS 6 BP 304 EP 307 DI 10.1002/asi.4630290607 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA FZ323 UT WOS:A1978FZ32300006 ER PT J AU LISTON, DM HOWDER, ML AF LISTON, DM HOWDER, ML TI SUBJECT ANALYSIS SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review C1 OPERAT RES INC,DIV INFORMAT SYST,BETHESDA,MD 20014. CR ADAMS MD, 1975, LIBR RESOUR TECH SER, V19, P35 ANTHONY E, 1975, RELC J, V6, P21, DOI 10.1177/003368827500600105 AUSTIN D, 1974, J DOC, V30, P47, DOI 10.1108/eb026570 AUSTIN D, 1976, LIBRI, V26, P181 AUSTIN D, 1976, LIBRARY QUART, V46, P77 AUSTIN DW, 1976, LIBRI, V26, P108 Bakewell K. G. B., 1976, Indexer, V10 BALNAVES J, 1974, LASIE INFORMATION B, V5, P3 BENES J, 1974, CHEM PRUM, V24, P475 BERUL LH, 1974, CHANGING PATTERNS IN, P80 BHATTACH.K, 1974, J DOC, V30, P235, DOI 10.1108/eb026580 BONNER RE, 1964, IBM J RES DEV, V8, P22 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOREHAM J, 1976, INFORM PROCESS MANAG, V12, P125, DOI 10.1016/0306-4573(76)90043-1 BORKO H, 1975, ABSTRACTING CONCEPTS, P161 BOYCE B, 1975, B MED LIBR ASSOC, V63, P378 BRAUN S, 1976, INFORM PROCESS MANAG, V12, P147, DOI 10.1016/0306-4573(76)90046-7 BRISNER O, 1975, NSI7504 NORSK SENT I BRUNING R, 1975, 1975 ANN CONV AM ED BUKHALEVA EI, 1974, NAUCHNO TECHNICHES 2, V12, P26 BYFORS I, 1975, PB251937 NTIS BYRNE JR, 1975, J AM SOC INFORM SCI, V26, P223, DOI 10.1002/asi.4630260405 BYRON LS, 1974, THESIS U WISCONSIN CAMPEY LH, 1975, INFORMATION STORAGE, V10, P293 CAMPOS A, 1975, REV BIBLIOTECONOMIA, V3, P23 CARROLL JM, 1975, J AM SOC INFORM SCI, V26, P261, DOI 10.1002/asi.4630260411 CAWKELL AE, 1976, INFORM SCIENTIST, V10, P3 CAWKELL AE, 1974, J AM SOC INFORM SCI, V25, P123, DOI 10.1002/asi.4630250208 CAWKELL AE, 1976, J DOC, V32, P53, DOI 10.1108/eb026615 CLEMENT TY, 1976, J ASSOC COMPUTING MA, V23, P76 CLEMENT TY, 1974, INFORMATION STORAGE, V10, P243 CLEMENT TY, 1975, NATIONAL SCI F NOV CLEMENT TY, 1974, J AM SOC INFORMATION, V25, P218 COHEN SM, 1976, J CHEM INF COMP SCI, V16, P93, DOI 10.1021/ci60006a013 CRAWFORD WC, 1976, J LIBR AUTOMAT, V9, P34 CUSTER BA, 1975, CATALOGUE INDEX ENGL, V39, P1 DAHLBERG I, 1974, INFORMATION UTILITIE, V11, P69 DATTOLA RT, 1968, ISR14 CORN U DEP COM DAVIS CH, 1976, J AM SOC INFORM SCI, V27, P174, DOI 10.1002/asi.4630270307 DAYTON DL, 1976, 172ND NAT M AM CHEM DAYTON DL, 1975, CAS REPORT, V4, P3 DEHEER T, 1974, INFORM STORAGE RET, V10, P133, DOI 10.1016/0020-0271(74)90015-1 DEACON JE, 1976, DSISTM375DIP DEF SCI DEREGT WF, 1974, THESAURI EDUCATIONAL DITTMAR PG, 1976, J CHEM INF COMP SCI, V16, P111, DOI 10.1021/ci60006a016 DOWNING JC, 1976, WILSON LIBRARY B, V50, P799 EBE T, 1976, J CHEM INF COMP SCI, V16, P33, DOI 10.1021/ci60005a011 FANGMEYER H, 1974, AD776642 FIELD BJ, 1975, J DOC, V31, P246, DOI 10.1108/eb026605 FILLIATRE B, 1975, INFORM PROCESS MANAG, V11, P107, DOI 10.1016/0306-4573(75)90012-6 FONTALINA MN, 1975, SOVETSKAIA BIBLIOGRA, V2, P88 FREEMAN FH, 1976, SPEC LIBR, V67, P220 FUGMANN R, 1974, J AM SOC INFORM SCI, V25, P287, DOI 10.1002/asi.4630250503 FUGMANN R, 1975, J CHEM INF COMP SCI, V15, P52, DOI 10.1021/ci60001a015 GAGNE RM, 1976, INSTR SCI, V5, P1, DOI 10.1007/BF00120228 GARDINER GL, 1975, COMPUTER ASSISTED IN GLICKERT P, 1974, J AM SOC INFORM SCI, V25, P308, DOI 10.1002/asi.4630250504 GNANY R, 1975, INT ASSOC AGRICULTUR, V20, P128 GOGUEN JA, 1974, INT J MAN MACH STUD, V6, P513, DOI 10.1016/S0020-7373(74)80017-9 GOLD DL, 1975, INT J TRANSLATION, V21, P69 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HARTSON HR, 1974, J AM SOC INFORM SCI, V25, P162 HARVEY JM, 1976, SPECIALISED INFORMAT, P35 HERMAN E, 1976, FINAL REPORT CATALOG HERSEY DF, 1975, 3RD P INT C CLASS RE HEYM DR, 1976, J CHEM INF COMP SCI, V16, P171, DOI 10.1021/ci60007a013 HICKEY DJ, 1976, LIBR TRENDS, V25, P273 HICKS CE, 1976, ENCYCLOPEDIA COMPUTE HOHNE H, 1976, ZBL BIBLIOTHEKSWESEN, V90, P61 HORSNELL V, 1974, INTERMEDIATE LEXICON HUFFENBERGER MA, 1976, DESIGN DATA ELEMENTS HUNT BL, 1975, J AM SOC INFORM SCI, V26, P230, DOI 10.1002/asi.4630260406 IRWIN J, 1974, FLAGSTAFF COOPERATIV JAHODA G, 1974, J AM SOC INFORM SCI, V25, P139, DOI 10.1002/asi.4630250303 JANOS J, 1975, INFORM PROCESS MANAG, V11, P115, DOI 10.1016/0306-4573(75)90013-8 Janos J., 1975, Ceskoslovenska Informatika, Teorie a Praxe, V17 JONES KP, 1976, J DOC, V32, P118, DOI 10.1108/eb026618 JONES KP, 1974, J AM SOC INFORM SCI, V25, P44, DOI 10.1002/asi.4630250106 KARASEV SA, 1974, NAUCHNO TECHNICHES 2, V12, P14 KAUFFMAN D, 1974, EMPIRICAL DERIVATION KLOVSTAD JW, 1975, IEEE T ACOUST SPEECH, VAS23, P118, DOI 10.1109/TASSP.1975.1162637 KOSA GA, 1975, LASIE INFORMATION B, P6 KUCH TDC, 1974, INFORMATION UTILITIE, V11, P63 KUCH TDC, 1974, COMPUTER SUPPORTED I, P16 KUMAR PSG, 1975, HERALD LIBRARY SCI I, V14, P8 KWOK KL, 1975, INFORM PROCESS MANAG, V11, P201, DOI 10.1016/0306-4573(75)90017-5 KWOK KL, 1974, INFORMATION UTILITIE, V11, P56 LAMBERT G, 1976, PRECIS MULTILINGUA 4 LANCASTER FW, 1974, LIBRARY QUART, V44, P256 LANCASTER FW, 1976, SPECIAL LIBRARIES, V67, P175 LANCASTER FW, 1975, B AM SOC INFORMATION, V2, P23 Leonard LE, 1975, THESIS U ILLINOIS LINEBAUGH RM, 1975, THESAURUS EDU DIVING LUSTIG G, 1974, NACHRICHTEN DOKUMENT, V25, P269 LYON CC, 1975, NSWCDLTR3117 NAV SUR MARSHAKOVA IV, 1974, NAUCHNO TECHNICHES 2, V5, P3 MARTINEZ SJ, 1973, INNOVATIVE DEVELOPME, V10, P137 McClure C., 1976, J ACAD LIBR, V2, P9 MCGILL MJ, 1976, J AM SOC INFORM SCI, V27, P205, DOI 10.1002/asi.4630270403 MEINCKE PPM, 1976, J AM SOC INFORM SCI, V27, P18 MEKHTIEV DM, 1974, NAUCHNO TEKHICHESK 1, V10, P22 MOLL JK, 1975, THESIS RUTGERS U MOSS R, 1975, ASLIB PROC, V27, P273, DOI 10.1108/eb050514 MOWERY RL, 1976, LIBR RESOUR TECH SER, V20, P154 NEELAMEGHAN A, 1976, LIBR SCI SLANT DOC, V13, P24 Neicu S. D., 1975, Probleme de Informare si Documentare, V9 NELSON RD, 1975, J CHEM INF COMP SCI, V15, P85, DOI 10.1021/ci60002a004 NEUFELD ML, 1974, INFORM STORAGE RET, V10, P403, DOI 10.1016/0020-0271(74)90047-3 NIEHOFF RT, 1976, INFORMATION POLITI 2, V13 NIEHOFF RT, 1976, J AM SOC INFORM SCI, V27, P3 NIEHOFF RT, 1976, 39TH ANN M AM SOC IN NIEHOFF RT, 1974, PB2404234ST NTIS FIN NOEL J, 1975, REV LANGUES VIVANTES, V41, P237 NORWOOD JW, 1975, DDCTR751 DEF DOC CTR *OP SYST INC, 1975, COMP EV STRUCT FREE PAINTER AF, 1974, DREXEL LIBRARY QUART, V10 PEPINSKY HB, 1974, J AM SOC INFORM SCI, V25, P59, DOI 10.1002/asi.4630250108 PIETERS DL, 1975, SPEC LIBR, V66, P541 POLLOCK JJ, 1975, J CHEM INF COMP SCI, V15, P226, DOI 10.1021/ci60004a008 RAMDSDEN MJ, 1975, AUSTRALIAN LIBRARY J, V24, P10 REISINGER L, 1974, 1974 P S COMPUT STAT, P119 RIGBY M, 1974, COMPUTERS UDC DECADE ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 ROCCHIO JJ, 1966, ISR10 CORN U DEP COM ROFES WL, 1976, AM ARCHIVIST, V39, P200 SACHS WM, 1976, J AM SOC INFORM SCI, V27, P85, DOI 10.1002/asi.4630270203 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1974, INFORMATION STORAGE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 SAYERS WB, 1975, SAYERS MANUAL CLASSI SCHENK HR, 1976, J CHEM INF COMP SCI, V16, P153, DOI 10.1021/ci60007a010 SCHULTHEIS J, 1975, BUCH BIBEL, V27, P68 SEDELOW SY, 1974, ADA002463 NTIS FIN R SEETHARAMA S, 1976, LIBR SCI SLANT DOC, V13, P67 SMALLEY NS, 1974, J EXP PSYCHOL, V102, P56, DOI 10.1037/h0035686 SMITH JD, 1976, RELATIONSHIP AUTHOR SMITH LC, 1974, J AM SOC INFORM SCI, V25, P343, DOI 10.1002/asi.4630250602 SPARCK-JONES K, 1974, J DOC, V30, P393 STALCUP WS, 1975, 38TH P ANN M AM SOC, V12 STAMM RP, 1975, SR7501 NTIS NAT TECH STEINACK.I, 1974, J AM SOC INFORM SCI, V25, P237, DOI 10.1002/asi.4630250406 STEINER RJ, 1975, INT J TRANSLATION, V21, P123 STOBERSKI Z, 1975, INT J TRANSLATION, V21, P16 STRONG SM, 1974, J AM SOC INFORM SCI, V25, P10, DOI 10.1002/asi.4630250103 TARR D, 1974, INFORMATION UTILITIE, V11, P50 TATE FA, 1976, RETRIEVAL GEOSCIENCE, P2 TAUBER MF, 1974, DREXEL LIBRARY Q, V10, P56 TROTTER RR, 1975, LIBR RESOUR TECH SER, V19, P41 *US DEP HOUS URB D, 1975, NTISPB205604 REP *US EN RES DEV ADM, 1976, NTISTID7000 *US NAT AFR SPAC A, 1976, SP7050V1 REP *US NAT AFR SPAC A, 1976, SP7050V2 REP *US NAT LIBR MED, 1977, MED7605 REP *US NAT LIBR MED, 1976, PB2559334ST REP *US NAT TECH INF S, 1976, PB2548006ST VANDERSTOUW GG, 1975, J CHEM INF COMP SCI, V15, P232, DOI 10.1021/ci60004a009 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 WALL E, 1975, J AM SOC INFORM SCI, V26, P71, DOI 10.1002/asi.4630260202 WATERS ST, 1975, J AM SOC INFORM SCI, V26, P126 WEINBERG BH, 1975, 38TH P ANN M AM SOC, V12 WESSEL A, 1975, COMPUTER AIDED INFOR WIEZELL RJ, 1975, HISPANIA, V58, P131, DOI 10.2307/338899 WIGINGTON RL, 1975, CODATA B, V15, P4 WILKINSON J, 1975, COLL RES LIBR, V36, P351 WILKS Y, 1975, COMMUN ACM, V18, P264, DOI 10.1145/360762.360770 WILLETTS M, 1975, J DOC, V31, P158, DOI 10.1108/eb026599 WILLIAMS EB, 1975, INT J T, V21, P60 WILLIAMS EB, 1975, INT J T, V21, P126 ZAMORA A, 1976, J CHEM INF COMP SCI, V16, P36, DOI 10.1021/ci60005a012 ZAMORA A, 1976, J CHEM INF COMP SCI, V16, P40, DOI 10.1021/ci60005a013 1975, ADAO13552 NTIS FIN R 1976, THESAURUS AGRICULTUR 1974, SOERGEL DAGOBERT NR 178 TC 9 Z9 9 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1977 VL 12 BP 81 EP 118 PG 38 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DV788 UT WOS:A1977DV78800006 ER PT J AU VANRIJSBERGEN, CJ AF VANRIJSBERGEN, CJ TI THEORETICAL BASIS FOR USE OF CO-OCCURRENCE DATA IN INFORMATION-RETRIEVAL SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UNIV CAMBRIDGE,COMP LAB,CAMBRIDGE,ENGLAND. CR ASH R, 1965, INFORMATION THEORY, P21 BENTLEY JL, 1975, STANCS75529 STANF RE BOX GEP, 1973, BAYESIAN INFERENCE S, P304 Calvert T., 1974, CLASSIFICATION ESTIM CHOW CK, 1968, IEEE T INFORM THEORY, V14, P462, DOI 10.1109/TIT.1968.1054142 Cox D. R., 1972, Applied Statistics, V21, DOI 10.2307/2346482 Duda R., 1973, PATTERN CLASSIFICATI Ferguson T., 1967, MATHEMATICAL STATIST Good I. J., 1965, ESTIMATION PROBABILI Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 Hastings NAJ, 1975, STATISTICAL DISTRIBU HUGHES GF, 1968, IEEE T INFORM THEORY, V14, P55, DOI 10.1109/TIT.1968.1054102 Ivie E. L, 1966, THESIS MIT IVIE EL, 1966, MACTR29 REP JONES KS, 1970, AUTOMATIC KEYWORD CL KASHYAP RL, 1974, INFORM SCIENCES, V7, P341, DOI 10.1016/0020-0255(74)90021-8 KU HH, 1969, IEEE T INFORM THEORY, V15, P444, DOI 10.1109/TIT.1969.1054336 Kullback S., 1968, INFORM THEORY STAT, P6 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 Negoita C. V., 1973, Studii si Cercetari de Documentare, V15 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 STEINHAUS H, 1957, ANN MATH STAT, V28, P633, DOI 10.1214/aoms/1177706876 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA WHITNEY VKM, 1972, COMMUN ACM, V15, P273, DOI 10.1145/361284.361299 YU CT, 1975, J ASS COMPUTING MACH, V23, P76 NR 26 TC 185 Z9 185 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1977 VL 33 IS 2 BP 106 EP 119 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DQ650 UT WOS:A1977DQ65000001 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI THEORIES AND MODELS IN INFORMATION-RETRIEVAL SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UCL, SCH LIB ARCHIVE & INFORMAT STUDIES, LONDON WC1E 6BT, ENGLAND. CR BELKIN NJ, 1974, J ASIS, V27, P197 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 BOOKSTEIN A, 1974, J DOC, V30, P374, DOI 10.1108/eb026585 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BROOKES BC, 1972, J DOC, V28, P160, DOI 10.1108/eb026537 BROOKES BC, 1968, J DOC, V24, P41, DOI 10.1108/eb026442 CLEVERDO.CW, 1972, J DOC, V28, P195, DOI 10.1108/eb026538 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WE, UNPUBLISHED COOPER WE, IN PRESS COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 COOPER WS, 1970, J ASIS, V21, P384 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P413, DOI 10.1002/asi.4630240603 GOFFMAN W, 1969, INFORMATION STORAGE, V4, P373 GOFFMAN W, 1968, INFORM STORAGE RET, V4, P361, DOI 10.1016/0020-0271(68)90030-2 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HEINE MH, 1973, J DOC, V29, P81 HEINE MH, 1974, J AM SOC INFORM SCI, V25, P183, DOI 10.1002/asi.4630250309 HEINE MH, 1975, J DOC, V31, P283, DOI 10.1108/eb026608 HILLMAN DJ, 1965, CLASSIFICATION RESEA Keen M., 1966, FACTORS DETERMINING, V2 LAMING D, 1973, MATHEMATICAL PSYCHOL LESLIE P, 1967, PRACTICAL PROBLEMS L, P7 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MCGILL MJ, 1976, J AM SOC INFORM SCI, V27, P205, DOI 10.1002/asi.4630270403 MCGILL MJ, 1975, P AM SOC INFORM SCI, V12, P138 MEINCKE PPM, 1976, J AM SOC INFORM SCI, V27, P18 ROBERTSO.SE, 1969, J DOC, V25, P1, DOI 10.1108/eb026462 ROBERTSON SDNF, UNPUBLISHED ROBERTSON SE, 1976, THESIS U LONDON ROBERTSON SE, 1975, J AM SOC INFORM SCI, V26, P214, DOI 10.1002/asi.4630260404 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1975, THEORY INDEXING SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SOERGEL D, 1967, INFORM STORAGE RET, V3, P129, DOI 10.1016/0020-0271(67)90006-X SPARCKJONES K, 1972, J DOC, V28, P11 SPARCK-JONES K, 1974, J DOC, V30, P393 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 TAYLOR RS, 1968, COLL RES LIBR, V29, P178 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1974, J DOC, V30, P365 VICKERY BC, 1971, J DOC, V27, P69, DOI 10.1108/eb026509 VICKERY BC, 1965, RETRIEVAL SYSTEM THE WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 YU CU, UNPUBLISHED ZUNDE P, 1967, AM DOC, V18, P104, DOI 10.1002/asi.5090180209 NR 53 TC 50 Z9 50 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PY 1977 VL 33 IS 2 BP 126 EP 148 DI 10.1108/eb026639 PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DQ650 UT WOS:A1977DQ65000003 ER PT J AU ROBERTSON, SE AF ROBERTSON, SE TI PROBABILITY RANKING PRINCIPLE IN IR SO JOURNAL OF DOCUMENTATION LA English DT Article RP ROBERTSON, SE (reprint author), UCL, SCH LIB ARCHIVE & INFORMAT STUDIES, LONDON WC1E 6BT, ENGLAND. CR [Anonymous], COMMUNICATION Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P413, DOI 10.1002/asi.4630240603 CROFT WB, 1976, INFORM PROCESS MANAG, V12, P327, DOI 10.1016/0306-4573(76)90051-0 GOFFMAN W, 1969, INFORMATION STORAGE, V4, P361 KEEN EM, 1972, REPORT INFORMATION S MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 ROBERTSON SE, 1971, INFORMATION PROCESSI, V13 ROBERTSON SE, 1976, THESIS U LONDON ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 STIRLING KH, 1975, P AM SOC INFORM SCI, V12, P105 VANRIJSB.CJ, 1973, J DOC, V29, P251 NR 13 TC 229 Z9 232 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0022-0418 J9 J DOC JI J. Doc. PY 1977 VL 33 IS 4 BP 294 EP 304 DI 10.1108/eb026647 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ET135 UT WOS:A1977ET13500004 ER EF