FN Thomson Reuters Web of Science™
VR 1.0
PT J
AU Lu, K
Mao, J
AF Lu, Kun
Mao, Jin
TI An Automatic Approach to Weighted Subject Indexing-An Empirical Study in
the Biomedical Domain
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
DE subject indexing; text mining; weighting
ID INFORMATION-RETRIEVAL; QUERY EXPANSION; ORGANIZATION; ASSIGNMENT;
VOCABULARY; ABOUTNESS; RELEVANCE; FIELD; MESH
AB Subject indexing is an intellectually intensive process that has many inherent uncertainties. Existing manual subject indexing systems generally produce binary outcomes for whether or not to assign an indexing term. This does not sufficiently reflect the extent to which the indexing terms are associated with the documents. On the other hand, the idea of probabilistic or weighted indexing was proposed a long time ago and has seen success in capturing uncertainties in the automatic indexing process. One hurdle to overcome in implementing weighted indexing in manual subject indexing systems is the practical burden that could be added to the already intensive indexing process. This study proposes a method to infer automatically the associations between subject terms and documents through text mining. By uncovering the connections between MeSH descriptors and document text, we are able to derive the weights of MeSH descriptors manually assigned to documents. Our initial results suggest that the inference method is feasible and promising. The study has practical implications for improving subject indexing practice and providing better support for information retrieval.
C1 [Lu, Kun] Univ Oklahoma, Sch Lib & Informat Studies, Norman, OK 73019 USA.
[Mao, Jin] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Hunan, Peoples R China.
RP Lu, K (reprint author), Univ Oklahoma, Sch Lib & Informat Studies, 401 West Brooks, Norman, OK 73019 USA.
EM kunlu@ou.edu; danveno@163.com
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NR 46
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 SEP
PY 2015
VL 66
IS 9
BP 1776
EP 1784
DI 10.1002/asi.23290
PG 9
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA CO2XU
UT WOS:000359021200003
ER
PT J
AU Choi, D
Lee, Y
Park, Y
Kim, S
AF Choi, Daeseon
Lee, Younho
Park, Yongsu
Kim, Seokhyun
TI Estimating Korean Residence Registration Numbers from Public Information
on SNS
SO IEICE TRANSACTIONS ON COMMUNICATIONS
LA English
DT Article
DE personal data security; SNS security; Korean residence registration
number; data estimation; security
ID SOCIAL NETWORKS; SECURITY; PRIVACY
AB People expose their personal information on social network services (SNSs). This paper warns of the dangers of this practice by way of an example. We show that the residence registration numbers (RRNs) of many Koreans, which are very important and confidential personal information analogous to social security numbers in the United States, can be estimated solely from the information that they have made open to the public. In our study, we utilized machine learning algorithms to infer information that was then used to extract a part of the RRNs. Consequently, we were able to extract 45.5% of SNS users' RRNs using a machine learning algorithm and brute-force search that did not consume exorbitant amounts of resources.
C1 [Choi, Daeseon; Kim, Seokhyun] ETRI, Taejon, South Korea.
[Lee, Younho] SeoulTech, Dept Ind & Syst Engn, Seoul, South Korea.
[Park, Yongsu] Hanyang Univ, Div Comp Sci & Engn, Seoul 133791, South Korea.
RP Lee, Y (reprint author), SeoulTech, Dept Ind & Syst Engn, Seoul, South Korea.
EM sunchoi@etri.re.kr; younholee@seoultech.ac.kr
FU IT R&D program of MSIP/IITP [10047212]; Basic Science Research Program
through the National Research Foundation of Korea (NRF) - Ministry of
Education [2013R1A1A2011754]
FX This work was supported by the IT R&D program of MSIP/IITP [No.
10047212, Development of homomorphic encryption supporting arithmetics
on ciphertexts of size less than 1 kB and its applications]. Also, This
research was supported by Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of
Education (2013R1A1A2011754).
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NR 23
TC 0
Z9 0
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 1745-1345
J9 IEICE T COMMUN
JI IEICE Trans. Commun.
PD APR
PY 2015
VL E98B
IS 4
BP 565
EP 574
DI 10.1587/transcom.E98.B.565
PG 10
WC Engineering, Electrical & Electronic; Telecommunications
SC Engineering; Telecommunications
GA CO9KZ
UT WOS:000359496000006
ER
PT J
AU Zong, N
Lee, S
Kim, HG
AF Zong, Nansu
Lee, Sungin
Kim, Hong-Gee
TI Discovering expansion entities for keyword-based entity search in linked
data
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Keyword-based entity search; linked data; query expansion
ID SEMANTIC QUERY EXPANSION; WEB DATA; RETRIEVAL; ONTOLOGIES; RELEVANCE
AB There is an inherent rift between the characteristics of Web of documents and the Web of data - the latter is enriched with semantic properties that are not present in the former. This creates a formidable challenge for entity search in the era of Linked Data, requiring a new method that leverages on such features. Query expansion, used in keyword-based search, improves search quality by enhancing a query with terms. Existing query-expansion methods, statistical- and lexical-based approaches, are inadequate for linked data in two ways: (a) term-to-term co-occurrence, used in the statistical-based approach, cannot find satisfactory expansions in internal corpus (SPO triples) or external corpus (Web of documents); and (b) lexical incomparability between ontologies (or thesauri) as reference knowledge and linked data renders tenuous the possibility of creating lexically sound expanded queries. The study introduces a framework to expand keyword queries with expansion entities for keyword-based entity search in linked data. The framework offers two structures, star-shaped and multi-shaped RDF graphs (documents), to model the entities in linked data for indexing and search, and an algorithm called PFC for expansion entities by which to expand a given query. PFC obtains expansion entities by measuring a global importance (PageRank and entity-document Frequency) and a local importance (Centrality) of the candidates extracted from the returned RDF documents of the entity search. Our experiments illustrate that PFC improves search results by approximately 7%. This study also includes suggestions on how to glean important link types for extracting candidate expansion entities, as well as identifying properties of these entities by which to expand the query.
C1 [Zong, Nansu; Lee, Sungin; Kim, Hong-Gee] Seoul Natl Univ, Biomed Knowledge Engn Lab, Seoul 151, South Korea.
[Zong, Nansu; Kim, Hong-Gee] Seoul Natl Univ, Dent Res Inst, Seoul 151, South Korea.
[Kim, Hong-Gee] Seoul Natl Univ, Inst Human Environm Interface Biol, Seoul 151, South Korea.
RP Kim, HG (reprint author), Seoul Natl Univ, Dent Res Inst, Seoul 151, South Korea.
EM hgkim@snu.ac.kr
FU Industrial Strategic Technology Development Program - Ministry of
Science, ICT & Future Planning (MSIP, Korea) [10044494]; Professor
Overseas Research Support Enterprise of LG Yonam Cultural Foundation
FX This work was supported by the Industrial Strategic Technology
Development Program (10044494, WiseKB: Big data based self-evolving
knowledge base and reasoning platform) funded by the Ministry of
Science, ICT & Future Planning (MSIP, Korea), and in part by Professor
Overseas Research Support Enterprise of LG Yonam Cultural Foundation.
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NR 55
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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 APR
PY 2015
VL 41
IS 2
BP 209
EP 227
DI 10.1177/0165551514562704
PG 19
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA CD4RS
UT WOS:000351071800006
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.
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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 Megler, VM
Maier, D
AF Megler, V. M.
Maier, David
TI Are Data Sets Like Documents?: Evaluating Similarity-Based Ranked Search
over Scientific Data
SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
LA English
DT Article
DE Scientific databases; information retrieval and relevance; similarity
search
ID RETRIEVAL EFFECTIVENESS; RELEVANCE; INFORMATION; PRECISION; METADATA
AB The past decade has seen a dramatic increase in the amount of data captured and made available to scientists for research. This increase amplifies the difficulty scientists face in finding the data most relevant to their information needs. In prior work, we hypothesized that Information Retrieval-style ranked search can be applied to data sets to help a scientist discover the most relevant data amongst the thousands of data sets in many formats, much like text-based ranked search helps users make sense of the vast number of Internet documents. To test this hypothesis, we explored the use of ranked search for scientific data using an existing multi-terabyte observational archive as our test-bed. In this paper, we investigate whether the concept of varying relevance, and therefore ranked search, applies to numeric data-that is, are data sets are enough like documents for Information Retrieval techniques and evaluation measures to apply? We present a user study that demonstrates that data set similarity resonates with users as a basis for relevance and, therefore, for ranked search. We evaluate a prototype implementation of ranked search over data sets with a second user study and demonstrate that ranked search improves a scientist's ability to find needed data.
C1 [Megler, V. M.; Maier, David] Portland State Univ, Dept Comp Sci, Portland, OR 97201 USA.
RP Megler, VM (reprint author), Portland State Univ, Dept Comp Sci, Portland, OR 97201 USA.
EM vmegler@cs.pdx.edu; maier@cs.pdx.edu
FU US National Science Foundation [OCE-0424602]
FX The authors thank Alistair Moffat for his detailed review and input.
They also thank the staff of CMOP for their support. This work was
supported by US National Science Foundation award OCE-0424602.
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NR 31
TC 0
Z9 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1041-4347
EI 1558-2191
J9 IEEE T KNOWL DATA EN
JI IEEE Trans. Knowl. Data Eng.
PD JAN
PY 2015
VL 27
IS 1
BP 32
EP 45
DI 10.1109/TKDE.2014.2320737
PG 14
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA AW3EF
UT WOS:000346167800004
ER
PT J
AU Ruocco, M
Ramampiaro, H
AF Ruocco, Massimiliano
Ramampiaro, Heri
TI Geo-temporal distribution of tag terms for event-related image retrieval
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Information retrieval; Spatial profile; Tag relatedness; Query
expansion; Event retrieval; Social media retrieval
AB Media sharing applications, such as Flickr and Panoramio, contain a large amount of pictures related to real life events. For this reason, the development of effective methods to retrieve these pictures is important, but still a challenging task. Recognizing this importance, and to improve the retrieval effectiveness of tag-based event retrieval systems, we propose a new method to extract a set of geographical tag features from raw geo-spatial profiles of user tags. The main idea is to use these features to select the best expansion terms in a machine learning-based query expansion approach. Specifically, we apply rigorous statistical exploratory analysis of spatial point patterns to extract the geo-spatial features. We use the features both to summarize the spatial characteristics of the spatial distribution of a single term, and to determine the similarity between the spatial profiles of two terms - i.e., term-to-term spatial similarity. To further improve our approach, we investigate the effect of combining our geo-spatial features with temporal features on choosing the expansion terms. To evaluate our method, we perform several experiments, including well-known feature analyzes. Such analyzes show how much our proposed geo-spatial features contribute to improve the overall retrieval performance. The results from our experiments demonstrate the effectiveness and viability of our method. (C) 2014 Elsevier Ltd. All rights reserved.
C1 [Ruocco, Massimiliano; Ramampiaro, Heri] Norwegian Univ Sci & Technol, Dept Comp & Informat Sci, N-7034 Trondheim, Norway.
RP Ruocco, M (reprint author), Norwegian Univ Sci & Technol, Dept Comp & Informat Sci, N-7034 Trondheim, Norway.
EM ruocco@idi.ntnu.no; heri@idi.ntnu.no
FU Research Council of Norway, VERDIKT Program [176858]
FX We acknowledge the anonymous reviewers comments, which have been
invaluable in improving the quality of this manuscript. This work is
supported by the Research Council of Norway, VERDIKT Program Grant No.
176858.
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NR 46
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 JAN
PY 2015
VL 51
IS 1
BP 92
EP 110
DI 10.1016/j.ipm.2014.09.001
PG 19
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA AU3DC
UT WOS:000345491900006
ER
PT J
AU Gupta, Y
Saini, A
Saxena, AK
AF Gupta, Yogesh
Saini, Ashish
Saxena, A. K.
TI Fuzzy logic-based approach to develop hybrid similarity measure for
efficient information retrieval
SO JOURNAL OF INFORMATION SCIENCE
LA English
DT Article
DE Fuzzy inference system; information retrieval; precision; recall;
similarity measure; vector space model
ID RANKING; MODEL; SETS
AB A similarity measure is used in information retrieval systems to retrieve and rank the relevant documents. In this paper, a new fuzzy-based approach to develop hybrid similarity measure is proposed and implemented. The proposed approach overcomes the limitations of extensively used similarity measures such as Cosine, Jaccard, Euclidean and Okapi-BM25 along with Genetic Algorithm-based hybrid similarity measures proposed by researchers. This approach uses fuzzy rules to infer the weights of different similarity measures. In this paper, the experiments are performed on CACM and CISI benchmark data collections. The performance of the proposed approach is evaluated in terms of precision, recall and average precision and average recall of retrieved relevant documents. The results are compared with different similarity measures available in literature. The results show the marked improvement in performance of information retrieval systems using the proposed fuzzy logic-based hybrid similarity measure.
C1 [Gupta, Yogesh; Saini, Ashish; Saxena, A. K.] Dayalbagh Educ Inst, Agra, Uttar Pradesh, India.
RP Saini, A (reprint author), Dayalbagh Educ Inst, Dept Elect Engn, Fac Engn, Agra, Uttar Pradesh, India.
EM ashish7119@gmail.com
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NR 33
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 DEC
PY 2014
VL 40
IS 6
BP 846
EP 857
DI 10.1177/0165551514548989
PG 12
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA AW3JK
UT WOS:000346182400011
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.
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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 Bielza, C
Larranaga, P
AF Bielza, Concha
Larranaga, Pedro
TI Bayesian networks in neuroscience: a survey
SO FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
LA English
DT Review
DE Bayesian networks; probabilistic inference; learning from data;
supervised classification; association discovery; neuroimaging;
connectivity analysis
ID FMRI DATA; ALZHEIMERS-DISEASE; PC-ALGORITHM; NAIVE BAYES; PROBABILISTIC
INFERENCE; TRUNCATED EXPONENTIALS; CONSTRUCTIVE INDUCTION; COGNITIVE
IMPAIRMENT; GRAPHICAL STRUCTURES; EQUIVALENCE CLASSES
AB Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind -morphological, electrophysiological, -omics and neuroimaging-, there by broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.
C1 [Bielza, Concha; Larranaga, Pedro] Univ Politecn Madrid, Dept Inteligencia Artificial, E-28660 Madrid, Spain.
RP Bielza, C (reprint author), Univ Politecn Madrid, Dept Inteligencia Artificial, Campus Montegancedo, E-28660 Madrid, Spain.
EM mcbielza@fi.upm.es
RI Larranaga, Pedro/F-9293-2013; Bielza, Concha/F-9277-2013
OI Bielza, Concha/0000-0002-7813-1188
FU Spanish Ministry of Economy and Competitiveness [TIN2013-41592-P]; Cajal
Blue Brain Project; European Union [604102]
FX Research partially supported by the Spanish Ministry of Economy and
Competitiveness (grant TIN2013-41592-P), the Cajal Blue Brain Project
(Spanish partner of the Blue Brain Project initiative from EPFL) and the
European Union Seventh Framework Programme (FP7/2007-2013) under grant
agreement no. 604102 (Human Brain Project).
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NR 157
TC 1
Z9 1
PU FRONTIERS RESEARCH FOUNDATION
PI LAUSANNE
PA PO BOX 110, LAUSANNE, 1015, SWITZERLAND
SN 1662-5188
J9 FRONT COMPUT NEUROSC
JI Front. Comput. Neurosci.
PD OCT 16
PY 2014
VL 8
AR 131
DI 10.3389/fncom.2014.00131
PG 23
WC Mathematical & Computational Biology; Neurosciences
SC Mathematical & Computational Biology; Neurosciences & Neurology
GA AX2QZ
UT WOS:000346790300001
PM 25360109
ER
PT J
AU Bielza, C
Larranaga, P
AF Bielza, Concha
Larranaga, Pedro
TI Discrete Bayesian Network Classifiers: A Survey
SO ACM COMPUTING SURVEYS
LA English
DT Article
DE Algorithms; Design; Performance; Supervised classification; Bayesian
network; naive Bayes; Markov blanket; Bayesian multinets; feature subset
selection; generative and discriminative classifiers
ID ONE-DEPENDENCE ESTIMATORS; FEATURE SUBSET-SELECTION; NAIVE-BAYES;
LOGISTIC-REGRESSION; TEXT CLASSIFICATION; TAN CLASSIFIER; TM ALGORITHM;
PROBABILITY; INFORMATION; LIKELIHOOD
AB We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, these classifiers have many strengths, like model interpretability, accommodation to complex data and classification problem settings, existence of efficient algorithms for learning and classification tasks, and successful applicability in real-world problems. In this article, we survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, k-dependence Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered.
C1 [Bielza, Concha; Larranaga, Pedro] Univ Politecn Madrid, Dept Inteligencia Artificial, E-28660 Madrid, Spain.
RP Bielza, C (reprint author), Univ Politecn Madrid, Dept Inteligencia Artificial, Campus Montegancedo, E-28660 Madrid, Spain.
EM mcbielza@fi.upm.es
RI Larranaga, Pedro/F-9293-2013; Bielza, Concha/F-9277-2013
OI Bielza, Concha/0000-0002-7813-1188
FU Spanish Ministry of Economy and Competitiveness [TIN2010-20900-C04-04];
Cajal Blue Brain
FX Research partially supported by the Spanish Ministry of Economy and
Competitiveness, projects TIN2010-20900-C04-04 and Cajal Blue Brain.
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NR 182
TC 1
Z9 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 0360-0300
EI 1557-7341
J9 ACM COMPUT SURV
JI ACM Comput. Surv.
PD JUL
PY 2014
VL 47
IS 1
AR 5
DI 10.1145/2576868
PG 43
WC Computer Science, Theory & Methods
SC Computer Science
GA AN7ZV
UT WOS:000340820600005
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.
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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
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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.
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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 Akbar, MA
Farooq, M
AF Akbar, Muhammad Ali
Farooq, Muddassar
TI Securing SIP-based VoIP infrastructure against flooding attacks and Spam
Over IP Telephony
SO KNOWLEDGE AND INFORMATION SYSTEMS
LA English
DT Article
DE SIP; Intrusion detection; VoIP security; SPAM Over IP Telephony; Denial
of service
ID PREVENTION
AB Security of session initiation protocol (SIP) servers is a serious concern of Voice over Internet (VoIP) vendors. The important contribution of our paper is an accurate and real-time attack classification system that detects: (1) application layer SIP flood attacks that result in denial of service (DoS) and distributed DoS attacks, and (2) Spam over Internet Telephony (SPIT). The major advantage of our framework over existing schemes is that it performs packet-based analysis using a set of spatial and temporal features. As a result, we do not need to transform network packet streams into traffic flows and thus save significant processing and memory overheads associated with the flow-based analysis. We evaluate our framework on a real-world SIP traffic-collected from the SIP server of a VoIP vendor-by injecting a number of application layer anomalies in it. The results of our experiments show that our proposed framework achieves significantly greater detection accuracy compared with existing state-of-the-art flooding and SPIT detection schemes.
C1 [Akbar, Muhammad Ali; Farooq, Muddassar] Natl Univ Comp & Emerging Sci, FAST NUCES, Next Generat Intelligent Networks Res Ctr NexGIN, Islamabad, Pakistan.
RP Akbar, MA (reprint author), Natl Univ Comp & Emerging Sci, FAST NUCES, Next Generat Intelligent Networks Res Ctr NexGIN, Islamabad, Pakistan.
EM ali.akbar@nexginrc.org; muddassar.farooq@nexginrc.org
FU National ICT R&D Fund, Ministry of Information Technology, Government of
Pakistan
FX This work is supported by the National ICT R&D Fund, Ministry of
Information Technology, Government of Pakistan. The information, data,
comments, and views detailed herein may not necessarily reflect the
endorsements of views of the National ICT R&D Fund.
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JI Knowl. Inf. Syst.
PD FEB
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BP 491
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DI 10.1007/s10115-012-0595-5
PG 20
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PT B
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Intelligence and Information Retrieval, a Comparative Study in Medicine
and Law
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LA English
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ID RELEVANCE
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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
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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 S
AU Jukss, M
Verbrugge, C
Varro, D
Vangheluwe, H
AF Jukss, Maris
Verbrugge, Clark
Varro, Daniel
Vangheluwe, Hans
BE Combemale, B
Pearce, DJ
Barais, O
Vinju, JJ
TI Dynamic Scope Discovery for Model Transformations
SO SOFTWARE LANGUAGE ENGINEERING, SLE 2014
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 7th International Conference on Software Language Engineering (SLE)
CY SEP 15-16, 2014
CL Vasteras, SWEDEN
SP Google, GEMOC Initiat, Itemis
HO Malardalen Univ
DE model transformations; learning from transformations; model
transformation optimization; supervised learning; scope
ID GRAPH TRANSFORMATION; LANGUAGE; GENERATION; TOOL
AB Optimizations to local-search based model transformations typically aim at effectively ordering the traversal of pattern edges to reduce the search space. In this paper we propose a dynamic approach to on-line discovery of rule application areas. Our approach incorporates tracking transformation progress in the input model using temperature-based coloring of model elements. The resulting heat map is used to discover possible rule application scopes ahead of rule execution. Further refinement of scopes is achieved by applying a Naive Bayes (NB) classifier to predict a set of possible match candidates. NB is well suited for the computationally intensive environment of model transformations due to its incremental training phase and low classification overhead. Our design is intended to take a runtime, black-box approach to observing and learning from the transformations as they are executed. Finally, we demonstrate a prototype evaluation of the approach in our transformation tool AToMPM [24] and address the benefits, limitations as well as future applications.
C1 [Jukss, Maris; Verbrugge, Clark; Vangheluwe, Hans] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada.
RP Jukss, M (reprint author), McGill Univ, Sch Comp Sci, Montreal, PQ, Canada.
EM mjukss@cs.mcgill.ca; clump@cs.mcgill.ca; hv@cs.mcgill.ca;
hans.vangheluwe@uantwerp.be
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NR 32
TC 0
Z9 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 978-3-319-11245-9; 978-3-319-11244-2
J9 LECT NOTES COMPUT SC
PY 2014
VL 8706
BP 302
EP 321
PG 20
WC Computer Science, Artificial Intelligence; Computer Science, Software
Engineering; Computer Science, Theory & Methods
SC Computer Science
GA BB9IS
UT WOS:000348352300018
ER
PT J
AU Bhoir, V
Potey, MA
AF Bhoir, Varsha
Potey, M. A.
BE Ganesan, A
Fong, S
MizeraPietraszko, J
TI Question Answering System : A Heuristic Approach
SO 2014 FIFTH INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL
INFORMATION AND WEB TECHNOLOGIES (ICADIWT)
LA English
DT Proceedings Paper
CT 5th International Conference on the Applications of Digital Information
and Web Technologies (ICADIWT)
CY FEB 17-19, 2014
CL Chennai, INDIA
SP IEEE Commun Soc, PRIST Univ
DE question answering; natural language; information retrieval; information
extraction; crawler; token; seed url
AB The exponential growth in digital information led to the need of increasingly sophisticated search tools like the web search engines. Search engines return ranked list of documents and are less effective when users need precise answers to natural language questions. Question Answering systems involve this critical capability required for the next generation web search engines, to reduce the painstaking search through the long list of documents. The proposed solution of Question Answering system works for a specific domain of tourism which is a global and routine activity for leisure. The users have to struggle to navigate through these overloaded sites for a short piece of information of their interest. The crawler developed in the system gathers web page information which is processed using Natural Language Processing and Procedure programming for a specific keyword. The system returns precise short string answers or list to natural language questions related to tourism domain like distance, person, date, list of hotels, list of forts, etc.
C1 [Bhoir, Varsha; Potey, M. A.] DY Patil Coll Engn, Dept Comp, Pune, Maharashtra, India.
RP Bhoir, V (reprint author), DY Patil Coll Engn, Dept Comp, Pune, Maharashtra, India.
EM varshabhoir279@gmail.com; mapotey@gmail.com
CR Bilotti M. W., 2004, THESIS MIT
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Ittycheriah A., 2001, P 9 TEXT RETRIEVAL C
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NR 17
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-2258-1
PY 2014
BP 165
EP 170
PG 6
WC Computer Science, Information Systems; Telecommunications
SC Computer Science; Telecommunications
GA BB0UC
UT WOS:000340720700028
ER
PT J
AU Zhang, P
Song, DW
Wang, J
Hou, YX
AF Zhang, Peng
Song, Dawei
Wang, Jun
Hou, Yuexian
TI Bias-variance analysis in estimating true query model for information
retrieval
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE Information retrieval; Query language model; Bias-variance
ID RELEVANCE
AB The estimation of query model is an important task in language modeling (LM) approaches to information retrieval (IR). The ideal estimation is expected to be not only effective in terms of high mean retrieval performance over all queries, but also stable in terms of low variance of retrieval performance across different queries. In practice, however, improving effectiveness can sacrifice stability, and vice versa. In this paper, we propose to study this tradeoff from a new perspective, i.e., the bias-variance tradeoff, which is a fundamental theory in statistics. We formulate the notion of bias-variance regarding retrieval performance and estimation quality of query models. We then investigate several estimated query models, by analyzing when and why the bias-variance tradeoff will occur, and how the bias and variance can be reduced simultaneously. A series of experiments on four TREC collections have been conducted to systematically evaluate our bias-variance analysis. Our approach and results will potentially form an analysis framework and a novel evaluation strategy for query language modeling. (C) 2013 Elsevier Ltd. All rights reserved.
C1 [Zhang, Peng; Song, Dawei; Hou, Yuexian] Tianjin Univ, Sch Comp Sci & Technol, Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300072, Peoples R China.
[Wang, Jun] UCL, Dept Comp Sci, London WC1E 6BT, England.
RP Song, DW (reprint author), Tianjin Univ, Sch Comp Sci & Technol, Tianjin Key Lab Cognit Comp & Applicat, Tianjin 300072, Peoples R China.
EM dawei.song2010@gmail.com
FU Chinese National Program on Key Basic Research Project (973 Program)
[2013CB329304, 2014CB744604]; Natural Science Foundation of China
[61272265, 61070044, 61105702]; EU's FP7 QONTEXT project [247590]
FX The authors thank anonymous reviewers for their constructive comments.
This work is supported in part by the Chinese National Program on Key
Basic Research Project (973 Program, Grant Nos. 2013CB329304,
2014CB744604), the Natural Science Foundation of China (Grant Nos.
61272265, 61070044, 61105702), and EU's FP7 QONTEXT project (Grant No.
247590).
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NR 45
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 JAN
PY 2014
VL 50
IS 1
BP 199
EP 217
DI 10.1016/j.ipm.2013.08.004
PG 19
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 256CO
UT WOS:000327287500012
ER
PT J
AU Hugill, A
Yang, HJ
Raczinski, F
Sawle, J
AF Hugill, Andrew
Yang, Hongji
Raczinski, Fania
Sawle, James
TI The pataphysics of creativity: developing a tool for creative search
SO DIGITAL CREATIVITY
LA English
DT Article
DE pataphysics; creativity; information retrieval; creative computing;
component-based software engineering
ID INFORMATION-RETRIEVAL; WEB
AB We introduce the idea of a new kind of web search tool that uses the literary and philosophical idea of pataphysics as a conceptual framework in order to return creative results. Pataphysics, the science of exceptions and imaginary solutions, can be directly linked to creativity and is therefore very suitable to guide the transformation from relevant into creative search results. To enable pataphysical algorithms within our system we propose the need for a new type of system architecture. We discuss a component-based software architecture that would allow the flexible integration of the new algorithms at any stage or location and the need for an index suitable to handle patadata, data which have been transformed pataphysically. This tool aims to generate surprising, novel and provocative search results rather than relevant ones, in order to inspire a more creative interaction that has applications in both creative work and learning contexts.
C1 [Hugill, Andrew; Yang, Hongji; Raczinski, Fania; Sawle, James] De Montfort Univ, Inst Creat Technol, Leicester LE1 9BH, Leics, England.
RP Hugill, A (reprint author), De Montfort Univ, Inst Creat Technol, Leicester LE1 9BH, Leics, England.
EM a.hugill@bathspa.ac.uk; hyang@dmu.ac.uk; fania@dmu.ac.uk;
jsawle@dmu.ac.uk
CR Baeza-Yates D. R., 2011, MODERN INFORM RETRIE
Baudrillard J., 2007, PATAPHYSICS
Benzecri J.P., 1973, ANAL DONNEES
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Bok Christian, 2002, PATAPHYSICS POETICS
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NR 31
TC 1
Z9 1
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND
SN 1462-6268
EI 1744-3806
J9 DIGIT CREAT
JI Digit. Creat.
PD SEP 1
PY 2013
VL 24
IS 3
SI SI
BP 237
EP 251
DI 10.1080/14626268.2013.813377
PG 15
WC Art
SC Art
GA 249MC
UT WOS:000326780300007
ER
PT J
AU Lloret, J
Tomas, J
Garcia, M
Lacuesta, R
AF Lloret, Jaime
Tomas, Jesus
Garcia, Miguel
Lacuesta, Raquel
TI A B2B ARCHITECTURE AND PROTOCOL FOR RESEARCHERS COOPERATION
SO INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS
LA English
DT Article
DE Partners search system; research cooperation; entities interconnection;
B2B; workflow
ID NETWORKS; COMMUNITY
AB Some works on the researchers cooperation's literature provide the key lines for building research networks and propose new protocols and standards for business to business (B2B) data exchange, but none of them explains how researchers should contact and the procedure to select the most appropriate partner of a research enterprise, institute or university. In this paper, we propose a B2B architecture and protocol between research entities, that uses ebXML protocol. The contacts for cooperation are established based on some defined parameters and an information retrieval system. We explain the information retrieval system, the researcher selection procedure, the XML-based protocol and the workflow of our proposal. We also show the information that has to be exchanged to contact other researchers. Several simulations demonstrate that our proposal is a feasible architecture and may be used to promote the research cooperation. The main purpose of this paper is to propose an efficient procedure for searching project partners.
C1 [Lloret, Jaime; Tomas, Jesus; Garcia, Miguel] Univ Politecn Valencia, Dept Comunicac, Valencia 46022, Spain.
[Lacuesta, Raquel] Univ Zaragoza, Escuela Univ Politect Teruel, Teruel, Spain.
RP Lloret, J (reprint author), Univ Politecn Valencia, Dept Comunicac, Camino Vera S-N, Valencia 46022, Spain.
EM jlloret@dcom.upv.es; jtomas@dcom.upv.es; migarpi@posgrado.upv.es;
lacuesta@unizar.es
RI Garcia, Miguel/C-3094-2014
OI Garcia, Miguel/0000-0003-2590-6370
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NR 34
TC 0
Z9 0
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-8430
EI 1793-6365
J9 INT J COOP INF SYST
JI Int. J. Coop. Inf. Syst.
PD JUN
PY 2013
VL 22
IS 2
AR UNSP 1350010
DI 10.1142/S021884301350010X
PG 27
WC Computer Science, Information Systems
SC Computer Science
GA 270LB
UT WOS:000328318800004
ER
PT J
AU Grimmer, J
Stewart, BM
AF Grimmer, Justin
Stewart, Brandon M.
TI Text as Data: The Promise and Pitfalls of Automatic Content Analysis
Methods for Political Texts
SO POLITICAL ANALYSIS
LA English
DT Article
ID POLICY POSITIONS; PRESS RELEASES; PREFERENCES; ALGORITHM; INFERENCE;
WORDS; MACHINE; SERIES; COSTS; MODEL
AB Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods-they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.
C1 [Grimmer, Justin] Stanford Univ, Dept Polit Sci, Stanford, CA 94305 USA.
[Stewart, Brandon M.] Harvard Univ, Dept Govt, Cambridge, MA 02138 USA.
[Stewart, Brandon M.] Harvard Univ, Inst Quantitat Social Sci, Cambridge, MA 02138 USA.
RP Grimmer, J (reprint author), Stanford Univ, Dept Polit Sci, Encina Hall West 616 Serra St, Stanford, CA 94305 USA.
EM jgrimmer@stanford.edu; bstewart@fas.harvard.edu
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NR 85
TC 35
Z9 35
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1047-1987
EI 1476-4989
J9 POLIT ANAL
JI Polit. Anal.
PD SUM
PY 2013
VL 21
IS 3
BP 267
EP 297
DI 10.1093/pan/mps028
PG 31
WC Political Science
SC Government & Law
GA 183OB
UT WOS:000321825000001
ER
PT J
AU Park, J
Lee, SG
AF Park, Jaehui
Lee, Sang-goo
TI Pragmatic correlation analysis for probabilistic ranking over relational
data
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Ranking for structured data; Correlation analysis; Probabilistic ranking
model
ID DATABASES
AB It is widely recognized that effective ranking methods for relational data (e.g., tuples) enable users to overcome the limitations of the traditional Boolean retrieval model and the hardness of structured query writing. To determine the rank of a tuple, term frequency-based methods, such as tf x idf (term frequency x inverse document frequency) schemes, have been commonly adopted in the literature by simply considering a tuple as a single document. However, in many cases, we have noted that tf x idf schemes may not produce effective rankings or specific orderings for relational data with categorical attributes, which is pervasive today. To support fundamental aspects of relational data, we apply the notions of correlation analysis to estimate the extent of relationships between queries and data. This paper proposes a probabilistic ranking model to exploit statistical relationships that exist in relational data of categorical attributes. Given a set of query terms, information on correlative attribute values to the query terms is used to estimate the relevance of the tuple to the query. To quantify the information, we compute the extent of the dependency between correlative attribute values on a Bayesian network. Moreover, we avoid the prohibitive cost of computing insignificant ranking features based on a limited assumption of node independence. Our probabilistic ranking model is domain-independent and leverages only data statistics without any prior knowledge such as user query logs. Experimental results show that our work improves the effectiveness of rankings for real-world datasets and has a reasonable query processing efficiency compared to related work. (C) 2013 Elsevier Ltd. All rights reserved.
C1 [Park, Jaehui] Elect & Telecommun Res Inst, Taejon 305606, South Korea.
[Lee, Sang-goo] Seoul Natl Univ, Sch Comp Sci & Engn, Seoul, South Korea.
RP Park, J (reprint author), ETRI, Knowledge Convergence Serv Res Team, 305-700 218 Gajeong Ro, Taejon 305700, South Korea.
EM jaehui@etri.re.kr
FU KCC (Korea Communications Commission), Korea [KCA-2012-12-911-04-005]
FX This research was supported by the KCC (Korea Communications
Commission), Korea, under the R&D program supervised by the KCA (Korea
Communications Agency) (KCA-2012-12-911-04-005).
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NR 23
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 JUN 1
PY 2013
VL 40
IS 7
BP 2649
EP 2658
DI 10.1016/j.eswa.2012.11.010
PG 10
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
Electronic; Operations Research & Management Science
SC Computer Science; Engineering; Operations Research & Management Science
GA 099JE
UT WOS:000315616500027
ER
PT J
AU Kose, G
Tonta, Y
Ahmadlouei, H
Polatkan, AC
AF Kose, Guven
Tonta, Yasar
Ahmadlouei, Hamid
Polatkan, Aydin Can
BE Raghavan, V
Hu, X
Liau, CJ
Treur, J
TI Story Link Detection in Turkish Corpus
SO 2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE
(WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1
LA English
DT Proceedings Paper
CT 12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence
and Intelligent Agent Technology (WI-IAT)
CY NOV 17-20, 2013
CL Atlanta, GA
SP IEEE, Web Intelligence Consortium, Assoc Comp Machinery, IEEE Comp Soc, ebay, Facebook, IEEE Comp Soc Tech Comm Intelligent Informat, ACM SIGART
DE story link detection; topic detection and tracking; vector space model;
relevance model
ID INFORMATION-RETRIEVAL; MODELS
AB Story Link Detection (SLD) is known as a sub-task of Topic Detection and Tracking (TDT). SLD aims to specify whether two randomly selected stories discuss the same topic or not. This sub-task drew special attention within the TDT research community as many tasks in TDT are thought to be solved automatically once SLD performs as expected. In this study, performance tests were carried out on the BilCol-2005 Turkish news corpus composed of approximately 209,000 news items using vector space model (VSM) and relevance model (RM) methods with respect to varied index term counts. Accordingly, best results obtained were as follows: the VSM method performed best with 30 terms (F-measure=0.2970) while RM method did with 4 terms (F-measure=0.1910). Furthermore, the combination of two methods using the AND and OR functions increased the precision ratio by 7.9% and recall ratio by 1.2%, respectively, indicating that retrieval performance of SLD algorithms can be increased to some extent by employing both VSM and RM models.
C1 [Kose, Guven; Tonta, Yasar; Ahmadlouei, Hamid] Hacettepe Univ, Dept Informat Management, Ankara, Turkey.
RP Kose, G (reprint author), Hacettepe Univ, Dept Informat Management, Ankara, Turkey.
EM guvenkose@gmail.com; tonta@hacettepe.edu.tr; hamid2026@gmail.com;
aydincan.polatkan@gmail.com
RI Tonta, Yasar/A-9629-2011
OI Tonta, Yasar/0000-0003-0285-1338
CR Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290954
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NR 33
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-2902-3
PY 2013
BP 154
EP 158
DI 10.1109/WI-IAT.2013.23
PG 5
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA BA0HL
UT WOS:000331265000023
ER
PT J
AU Huang, XL
Soergel, D
AF Huang, Xiaoli
Soergel, Dagobert
TI Relevance: An improved framework for explicating the notion
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Review
DE relevance; aboutness
ID OF-THE-LITERATURE; INFORMATION-RETRIEVAL SYSTEMS; DOCUMENT RELEVANCE;
SEARCH HISTORIES; CROSS-EVALUATION; RESEARCH-PROJECT; COGNITIVE MODEL;
PART II; SCIENCE; CRITERIA
AB Synthesizing and building on many ideas from the literature, this article presents an improved conceptual framework that clarifies the notion of relevance with its many elements, variables, criteria, and situational factors. Relevance is defined as a Relationship (R) between an Information Object (I) and an Information Need (N) (which consists of Topic, User, Problem/Task, and Situation/ Context) with focus on R. This defines Relevance-as-is (conceptual relevance, strong relevance). To determine relevance, an Agent A (a person or system) operates on a representation I of the information object and a representation N of the information need, resulting in relevance-as-determined (operational measure of relevance, weak relevance, an approximation). Retrieval tests compare relevance-as-determined by different agents. This article discusses and compares two major approaches to conceptualizing relevance: the entityfocused approach (focus on elaborating the entities involved in relevance) and the relationship-focused approach (focus on explicating the relational nature of relevance). The article argues that because relevance is fundamentally a relational construct the relationshipfocused approach deserves a higher priority and more attention than it has received. The article further elaborates on the elements of the framework with a focus on clarifying several critical issues on the discourse on relevance.
C1 [Huang, Xiaoli] Sun Yat Sen Univ, Sch Business, Guangzhou 510275, Guangdong, Peoples R China.
[Soergel, Dagobert] SUNY Buffalo, Grad Sch Educ, Buffalo, NY 14260 USA.
[Huang, Xiaoli; Soergel, Dagobert] Univ Maryland, Coll Informat Studies, College Pk, MD 20742 USA.
RP Huang, XL (reprint author), Sun Yat Sen Univ, Sch Business, 135 Xingang West St, Guangzhou 510275, Guangdong, Peoples R China.
EM hxiaoli3@sysu.edu.cn; dsoergel@buffalo.edu
FU Eugene Garfield Doctoral Dissertation Fellowship; National Natural
Science Foundation of China (NSFC) [71102100]; China Postdoctoral
Science Foundation [1204014]
FX This work is supported in part by the Eugene Garfield Doctoral
Dissertation Fellowship (2008), by the National Natural Science
Foundation of China (NSFC) research grant 71102100, and by China
Postdoctoral Science Foundation research grant 1204014. We thank the
anonymous reviewers who helped us improve the paper in both content and
clarity of presentation.
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NR 104
TC 3
Z9 3
PU WILEY-BLACKWELL
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1532-2882
J9 J AM SOC INF SCI TEC
JI J. Am. Soc. Inf. Sci. Technol.
PD JAN
PY 2013
VL 64
IS 1
BP 18
EP 35
DI 10.1002/asi.22811
PG 18
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 204QZ
UT WOS:000323384600003
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
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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 Lin, YX
Kao, HY
AF Lin, Yi-Xian
Kao, Hung-Yu
GP IEEE
TI An Adaptive Page Clustering Based weighting Method for Information
Retrieval
SO 2013 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL
INTELLIGENCE (TAAI)
SE Conference on Technologies and Applications of Artificial Intelligence
LA English
DT Proceedings Paper
CT Conference on Technologies and Applications of Artificial Intelligence
(TAAI)
CY DEC 06-08, 2013
CL Taipei, TAIWAN
SP Natl Sci Council, Inst Informat Sci, Academia Sinica, Res Ctr Informat Technol Innovati, Academia Sinica, Natl Chiao Tung Univ, Natl Chengchi Univ, AI Econ Res Ctr, Minist Educ, Intel NTU Connected Context Comp Ctr, Assoc Comp Machinery, Assoc Comp Machinery, SIGART, Japanese Soc Artificial Intelligence, Natl Taiwan Univ, Natl Univ Tainan, IEEE Comp Soc
DE Page Clustering; Information Retrieval; Topic Feature; Correlation
Coefficient
ID ALGORITHM
AB With the coming of the era of information explosion, using Internet to obtain information has become the most convenient pipeline information flow. However, the found information mostly based on keyword matching through the search engines, and the search engines do not generally conduct filtering and screening in order to enhance the returns. If the web pages pass a systematic arrangement and are divided into multiple categories or clusters, the users will be guided to obtain real help of information. In this paper, we propose an adaptive web pages clustering algorithm to perform this task. It extracts features to reduce feature dimensions, then filters automatically web pages into its appropriate cluster and enhances the features of the pages to site features for different coefficients to improve the effect. Finally, providing users a more accurate search data model. The experimental results show that compared to the traditional TF-IDF, the proposed approach can find the needed web pages and the topics of the web pages in the corresponding cluster that are highly similar.
C1 [Lin, Yi-Xian; Kao, Hung-Yu] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 70101, Taiwan.
RP Lin, YX (reprint author), Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 70101, Taiwan.
EM yixian0705@gmail.com; hykao@mail.ncku.edu.tw
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NR 27
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2376-6816
BN 978-1-4799-2528-5
J9 CONF TECHNOL APPL
PY 2013
BP 199
EP 204
DI 10.1109/TAAI.2013.48
PG 6
WC Computer Science, Artificial Intelligence
SC Computer Science
GA BC5JZ
UT WOS:000353341700034
ER
PT J
AU Jothilakshmi, R
Shanthi, N
Babisaraswathi, R
AF Jothilakshmi, R.
Shanthi, N.
Babisaraswathi, R.
GP IEEE
TI An approach for semantic query expansion based on Maximum Entropy-Hidden
Markov Model
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 Information Retrieval; Ontology; Query expansion; Hidden Markov Model;
Viterbi algorithm
ID INFORMATION-RETRIEVAL
AB The ineffectiveness of information retrieval systems is mostly caused by the inaccurate query formed by a few keywords that reflect actual user information need. One well known technique to overcome this limitation is Automatic Query Expansion (AQE), whereby the user's original query is improved by adding new features with a related meaning. It has long been accepted that capturing term associations is a vital part of information retrieval. It is therefore mainly to consider whether many sources of support may be combined to forecast term relations more precisely. This is mainly significant when frustrating to predict the probability of relevance of a set of terms given a query, which may involve both lexical and semantic relations between the terms. This paper presents a approach to expand the user query using three level domain model such as conceptual level(underlying Domain knowledge), linguistic level(term vocabulary based on Wordnet), stochastic model ME-HMM2 which combines (HMM (Hidden Markov Model and Maximum Entropy(ME) models) stores the mapping between such levels, taking into account the linguistic context of words.
C1 [Jothilakshmi, R.] RMD Engn Coll, Madras, Tamil Nadu, India.
RP Jothilakshmi, R (reprint author), RMD Engn Coll, Madras, Tamil Nadu, India.
EM rjothilakshmi@gmail.com
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NR 23
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:000346508600293
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).
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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 Tsinakos, A
Kazanidis, I
AF Tsinakos, Avgoustos
Kazanidis, Ioannis
TI Identification of Conflicting Questions in the PARES System
SO INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTANCE LEARNING
LA English
DT Article
DE Computer adaptive testing; CAT; conflict detection algorithm
AB Student testing and knowledge assessment is a significant aspect of the learning process. In a number of cases, it is expedient not to present the exact same test to all learners all the time (Pritchett, 1999). This may be desired so that cheating in the exam is made harder to carry out or so that the learners can take several practice tests on the same subject as part of the course.
This study presents an e-testing platform, namely PARES, which aims to provide assessment services to academic staff by facilitating the creation and management of question banks and powering the delivery of nondeterministically generated test suites. PARES uses a conflict detection algorithm based on the vector space model to compute the similarity between questions and exclude questions which are deemed to have an unacceptably large similarity from appearing in the same test suite. The conflict detection algorithm and a statistical evaluation of its accuracy are presented. Evaluation results show that PARES succeeds in detecting question types at about 90% and its efficiency can be further increased through continuing education and enrichment of the system's correlation vocabulary.
C1 [Tsinakos, Avgoustos; Kazanidis, Ioannis] Kavala Inst Technol, Thessaloniki, Greece.
RP Tsinakos, A (reprint author), Kavala Inst Technol, Thessaloniki, Greece.
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NR 13
TC 0
Z9 0
PU ATHABASCA UNIV PRESS
PI ATHABASCA
PA 1 UNIVERSITY DR, ATHABASCA, AB T9S 3A3, CANADA
SN 1492-3831
J9 INT REV RES OPEN DIS
JI Int. Rev. Res. Open Distance Learn.
PY 2012
VL 13
IS 3
BP 298
EP 314
PG 17
WC Education & Educational Research
SC Education & Educational Research
GA 984VU
UT WOS:000307221300017
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
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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 B
AU Sajja, PS
Akerkar, R
AF Sajja, Priti Srinivas
Akerkar, Rajendra
BA Sajja, PS
Akerkar, R
BF Sajja, PS
Akerkar, R
TI Web information retrieval
SO INTELLIGENT TECHNOLOGIES FOR WEB APPLICATIONS
SE Chapman & Hall-CRC Data Mining and Knowledge Discovery Series
LA English
DT Article; Book Chapter
C1 [Sajja, Priti Srinivas] Sardar Patel Univ, Dept Comp Sci, Vallabh Vidya Nagar, Gujarat, India.
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RP Sajja, PS (reprint author), Sardar Patel Univ, Dept Comp Sci, Vallabh Vidya Nagar, Gujarat, India.
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NR 32
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 978-1-4398-7164-5; 978-1-4398-7162-1
J9 CH CRC DATA MIN KNOW
PY 2012
BP 83
EP 109
D2 10.1201/b12118
PG 27
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA BC7NL
UT WOS:000355011900006
ER
PT S
AU Ahmed, F
Rafique, MZ
Abulaish, M
AF Ahmed, Faraz
Rafique, M. Zubair
Abulaish, Muhammad
BA Mazumdar, C
BF Mazumdar, C
BE Jajodia, S
TI A Data Mining Framework for Securing 3G Core Network from GTP Fuzzing
Attacks
SO INFORMATION SYSTEMS SECURITY
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 7th International Conference on Information Systems Security Conference
(ICISS)
CY DEC 15-19, 2011
CL Kolkata, INDIA
HO Jadavpur Univ
DE Intrusion Detection; Fuzzing attacks; GTP Security
AB Since the emergence of 30 cellular IP networks, internet usage via 30 data services has become ubiquitous. Therefore such network is an important target for imposters who can disrupt the internet services by attacking the network core, thereby causing significant revenue losses to mobile operators. GPRS Tunneling Protocol GTP is the primary protocol used between the 30 core network nodes. In this paper, we present; the design of a multi-layer framework to detect fuzzing attacks targeted to GTP control (GTP-C) packets. The framework analyzes each type of GTP-C packet separately for feature extraction, by implementing a Markov state space model at the G(n), interface of the 30 core network. The Multi-layered architecture utilizes standard data mining algorithms for classification. Our analysis is based on real world network traffic collected at the Gn interface. The analysis results show that for only 5% fuzzing introduced in a packet with average size of 85 bytes, the framework detects fuzzing in GTP-C packets with 99.9% detection accuracy and 0.01% false alarm rate.
C1 [Ahmed, Faraz; Rafique, M. Zubair; Abulaish, Muhammad] King Saud Univ, Ctr Excellence Informat Assurance CoEIA, Riyadh, Saudi Arabia.
RP Ahmed, F (reprint author), King Saud Univ, Ctr Excellence Informat Assurance CoEIA, Riyadh, Saudi Arabia.
EM fahmed.c@ksu.edu.sa; zrafique.c@ksu.edu.sa; mabulaish@ksu.edu.sa
CR [Anonymous], 21133 3GPP TS
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NR 17
TC 0
Z9 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 978-3-642-25559-5
J9 LECT NOTES COMPUT SC
PY 2011
VL 7093
BP 280
EP 293
PG 14
WC Computer Science, Information Systems; Computer Science, Theory &
Methods
SC Computer Science
GA BBO73
UT WOS:000307744800019
ER
PT S
AU Zhang, P
Song, DW
Wang, J
Zhao, XZ
Hou, YX
AF Zhang, Peng
Song, Dawei
Wang, Jun
Zhao, Xiaozhao
Hou, Yuexian
BE Salem, MVM
Shaalan, K
Oroumchian, F
Shakery, A
Khelalfa, H
TI On Modeling Rank-Independent Risk in Estimating Probability of Relevance
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 Probability of Relevance; Estimation; Risk Management;
Ranking-Independent Risk; Language Modeling
ID INFORMATION-RETRIEVAL
AB Estimating the probability of relevance for a document is fundamental in information retrieval. From a theoretical point of view, risk exists in the estimation process, in the sense that the estimated probabilities may not be the actual ones precisely. The estimation risk is often considered to be dependent on the rank. For example, the probability ranking principle assumes that ranking documents in the order of decreasing probability of relevance can optimize the rank effectiveness. This implies that a precise estimation can yield an optimal rank. However, an optimal (or even ideal) rank does not always guarantee that the estimated probabilities are precise. This means that part of the estimation risk is rank-independent. It imposes practical risks in the applications, such as pseudo relevance feedback, where different estimated probabilities of relevance in the first-round retrieval will make a difference even when two ranks are identical. In this paper, we will explore the effect and the modeling of such rank-independent risk. A risk management method is proposed to adaptively adjust the rank-independent risk. Experimental results on several TREC collections demonstrate the effectiveness of the proposed models for both pseudo-relevance feedback and relevance feedback.
C1 [Zhang, Peng; Song, Dawei] Robert Gordon Univ, Sch Comp, Aberdeen AB9 1FR, Scotland.
RP Zhang, P (reprint author), Robert Gordon Univ, Sch Comp, Aberdeen AB9 1FR, Scotland.
EM p.zhang1@rgu.ac.uk; d.song@rgu.ac.uk
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NR 17
TC 1
Z9 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 978-3-642-25630-1
J9 LECT NOTES COMPUT SC
PY 2011
VL 7097
BP 13
EP 24
PG 12
WC Computer Science, Information Systems; Computer Science, Software
Engineering; Computer Science, Theory & Methods
SC Computer Science
GA BZY36
UT WOS:000303361100002
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.
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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, TY
AF Liu, Tie-Yan
BA Liu, TY
BF Liu, TY
TI Learning to Rank for Information Retrieval Introduction
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LA English
DT Editorial Material; Book Chapter
ID ORDINAL REGRESSION; RELEVANCE
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C1 Microsoft Res Asia, Beijing 100080, Peoples R China.
RP Liu, TY (reprint author), Microsoft Res Asia, Bldg 2,5 Dan Ling St, Beijing 100080, Peoples R China.
EM Tie-Yan.Liu@microsoft.com; Tie-Yan.Liu@microsoft.com
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NR 96
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PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
BN 978-3-642-14266-6
PY 2011
BP 3
EP 30
DI 10.1007/978-3-642-14267-3_1
D2 10.1007/978-3-642-14267-3
PG 28
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Theory & Methods
SC Computer Science
GA BVJ02
UT WOS:000291631400001
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PT B
AU Kowalski, G
AF Kowalski, Gerald
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SO INFORMATION RETRIEVAL ARCHITECTURE AND ALGORITHMS
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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 Mendoza, M
Ortiz, I
Rojas, V
AF Mendoza, Marcelo
Ortiz, Ivette
Rojas, Victor
TI Text categorization in documentary databases using light computational
models
SO REVISTA SIGNOS
LA Spanish
DT Article
DE Text categorization; Bayesian models; information retrieval
ID CLASSIFICATION; RETRIEVAL
AB We introduce a new text categorization method for documentary databases. The proposed method is an extension of the Naive Bayes text categorization model which allows obtaining good performance results in documentary databases with unbalanced training data. Experimental results allow us to conclude that the categorization method overcomes Naive Bayes and compares favorably with more sophisticated categorization methods such as support vector machines and logistic regression without increasing the use of computational resources in the training phase.
EM marcelo.mendoza@usm.cl; iortiz@iale.cl; vrojas@iale.cl
RI Mendoza, Marcelo/D-2312-2014
OI Mendoza, Marcelo/0000-0002-7969-6041
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NR 25
TC 0
Z9 0
PU EDICIONES UNIV VALPARAISO
PI VALPARAISO
PA CASILLA 1415, VALPARAISO, 00000, CHILE
SN 0035-0451
J9 REV SIGNOS
JI Rev. Signos
PY 2011
VL 44
IS 77
BP 251
EP 274
DI 10.4067/S0718-09342011000300004
PG 24
WC Linguistics; Language & Linguistics
SC Linguistics
GA 887CR
UT WOS:000299903600004
ER
PT J
AU Jansen, BJ
Rieh, SY
AF Jansen, Bernard J.
Rieh, Soo Young
TI The Seventeen Theoretical Constructs of Information Searching and
Information Retrieval
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID OF-THE-LITERATURE; TASK COMPLEXITY; QUANTITATIVE ASPECTS; SCIENCE
RESEARCH; SEEKING; RELEVANCE; FOUNDATIONS; ENGINEERS; MODELS; BEHAVIOR
AB In this article, we identify, compare, and contrast theoretical constructs for the fields of information searching and information retrieval to emphasize the uniqueness of and synergy between the fields. Theoretical constructs are the foundational elements that underpin a field's core theories, models, assumptions, methodologies, and evaluation metrics. We provide a framework to compare and contrast the theoretical constructs in the fields of information searching and information retrieval using intellectual perspective and theoretical orientation. The intellectual perspectives are information searching, information retrieval, and cross-cutting; and the theoretical orientations are information, people, and technology. Using this framework, we identify 17 significant constructs in these fields contrasting the differences and comparing the similarities. We discuss the impact of the interplay among these constructs for moving research forward within both fields. Although there is tension between the fields due to contradictory constructs, an examination shows a trend toward convergence. We discuss the implications for future research within the information searching and information retrieval fields.
C1 [Jansen, Bernard J.] Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16801 USA.
[Rieh, Soo Young] Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA.
RP Jansen, BJ (reprint author), Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16801 USA.
EM jjansen@acm.org; rieh@umich.edu
RI Jansen, Bernard/
OI Jansen, Bernard/0000-0002-6468-6609
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NR 169
TC 13
Z9 13
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 AUG
PY 2010
VL 61
IS 8
BP 1517
EP 1534
DI 10.1002/asi.21358
PG 18
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 627DJ
UT WOS:000280019100001
ER
PT J
AU Boehm, K
Raak, C
Vollmar, HC
Ostermann, T
AF Boehm, Katja
Raak, Christa
Vollmar, Horst Christian
Ostermann, Thomas
TI An overview of 45 published database resources for complementary and
alternative medicine
SO HEALTH INFORMATION AND LIBRARIES JOURNAL
LA English
DT Article
ID TRADITIONAL CHINESE MEDICINE; INFORMATION-RETRIEVAL; ACCESS; CANCER
AB Background:
Complementary and alternative medicine (CAM) has succeeded to implement itself in the academic context of universities. In order to get information on CAM, clinicians, researchers and healthcare professionals as well as the lay public are increasingly turning to online portals and databases, which disseminate relevant resources. One specific type of online information retrieval systems, namely the database, is being reviewed in this article.
Question:
This overview aims at systematically retrieving and describing all databases covering the field of CAM. One of the requirements for inclusion was that the database would also have to be published in a medical journal.
Data sources:
The databases amed, CAMbase, embase, and medline/PubMed were searched between December 2008 and December 2009 for publications relevant to CAM databases. The authors' specialist library was also searched for grey literature to be included.
Study selection:
All included databases were then visited online and information on the context, structure and volume of the database was extracted.
Main results:
Forty-five databases were included in this overview. Databases covered herbal therapies (n = 11), traditional Chinese medicine (n = 9) and some dealt with a vast number of CAM modalities (n = 9), amongst others. The amount of time the databases had been in existence ranged from 4 to 53 years. Countries of origin included the USA (n = 14), UK (n = 7) and Germany (n = 6), amongst others. The main language in 42 of 45 databases was English.
Conclusions:
Although this overview is quite comprehensive with respect to the field of CAM, certain CAM practices such as chiropractic, massage, reflexology, meditation or yoga may not have been covered adequately. A more detailed assessment of the quality of the included databases might give additional insights into the listed resources. The creation of a personalised meta-search engine is suggested, towards which this overview could be seen as a first step.
C1 [Boehm, Katja; Raak, Christa; Ostermann, Thomas] Univ Witten Herdecke, Ctr Integrat Med, D-58313 Herdecke, Germany.
[Vollmar, Horst Christian] Helmholtz Assoc, Partner Site German Ctr Neurodegenerat Dis, Inst Res & Transfer Dementia Care, Witten, Germany.
RP Boehm, K (reprint author), Univ Witten Herdecke, Ctr Integrat Med, Gerhard Kienle Weg 4, D-58313 Herdecke, Germany.
EM drkatjaboehm@gmail.com
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NR 65
TC 5
Z9 5
PU WILEY-BLACKWELL
PI MALDEN
PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA
SN 1471-1834
J9 HEALTH INFO LIBR J
JI Heatlth Info. Libr. J.
PD JUN
PY 2010
VL 27
IS 2
BP 93
EP 105
DI 10.1111/j.1471-1842.2010.00888.x
PG 13
WC Information Science & Library Science
SC Information Science & Library Science
GA 595JS
UT WOS:000277607100002
PM 20565550
ER
PT S
AU Kaptein, R
Hiemstra, D
Kamps, J
AF Kaptein, Rianne
Hiemstra, Djoerd
Kamps, Jaap
BE Gurrin, C
He, Y
Kazai, G
Kruschwitz, U
Little, S
Roelleke, T
Ruger, S
VanRijsbergen, K
TI How Different Are Language Models and Word Clouds?
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 RETRIEVAL
AB Word clouds are a summarised representation of a document's text, similar to tag clouds which summarise the tags assigned to documents. Word clouds are similar to language models in the sense that they represent a document by its word distribution. In this paper we investigate the differences between word cloud and language modelling approaches, and specifically whether effective language modelling techniques also improve word clouds. We evaluate the quality of the language model using a system evaluation test bed, and evaluate the quality of the resulting word cloud with a user study. Our experiments show that different language modelling techniques can be applied to improve a standard word cloud that uses a TF weighting scheme in combination with stopword removal. Including bigrams in the word clouds and a parsimonious term weighting scheme are the most effective in both the system evaluation and the user study.
C1 [Kaptein, Rianne; Kamps, Jaap] Univ Amsterdam, NL-1012 WX Amsterdam, Netherlands.
RP Kaptein, R (reprint author), Univ Amsterdam, NL-1012 WX Amsterdam, Netherlands.
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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 556
EP 568
PG 13
WC Computer Science, Information Systems; Computer Science, Theory &
Methods
SC Computer Science
GA BPN36
UT WOS:000279393900048
ER
PT S
AU Ceglarek, D
Haniewicz, K
Rutkowski, W
AF Ceglarek, Dariusz
Haniewicz, Konstanty
Rutkowski, Wojciech
BE Nguyen, NT
Katarzyniak, R
Chen, SM
TI Semantic Compression for Specialised Information Retrieval Systems
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SE Studies in Computational Intelligence
LA English
DT Proceedings Paper
CT 2nd Asian Conference on Intelligent Information and Database Systems
(ACIIDS)
CY MAR 24-26, 2010
CL Hue City, VIETNAM
SP Hue Univ, Wroclaw Univ Technol
DE semantic compression; sense; intellectual property; semantic net;
thought matching; natural language processing
AB The aim of this work is to present methods some of the ongoing research done as a part of development of Semantically Enhanced Intellectual Property Protection System - SEIPro2S. Main focus is on description of methods that allow for creation of more concise documents preserving semantically the same meaning as their originals. Thus, compacting methods are denoted as a semantic compression.
C1 [Ceglarek, Dariusz] Wyzsza Szkola Bankowa, Poznaniu, Poland.
RP Ceglarek, D (reprint author), Wyzsza Szkola Bankowa, Poznaniu, Poland.
EM dariusz.ceglarek@wsb.poznan.pl; konstanty.haniewicz@ue.poznan.pl;
wojciech.rutkowski@bcc.com.pl
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TC 9
Z9 9
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1860-949X
BN 978-3-642-12089-3
J9 STUD COMP INTELL
PY 2010
VL 283
BP 111
EP 121
PG 11
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Theory & Methods
SC Computer Science
GA BTR47
UT WOS:000287889600010
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.
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RP Costa, A (reprint author), Ecole Polytech, LIX, F-91128 Palaiseau, France.
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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 J
AU Hou, YT
Chang, YM
Chen, TH
Laih, CS
Chen, CM
AF Hou, Yung-Tsung
Chang, Yimeng
Chen, Tsuhan
Laih, Chi-Sung
Chen, Chia-Mei
TI Malicious web content detection by machine learning
SO EXPERT SYSTEMS WITH APPLICATIONS
LA English
DT Article
DE Dynamic HTML; Malicious webpage; Machine learning
AB The recent development of the dynamic HTML gives attackers a new and powerful technique to compromise computer systems. A Malicious dynamic HTML code is usually embedded in a normal webpage. The malicious webpage infects the victim when a user browses it. Furthermore such DHTML code can disguise itself easily through obfuscation or transformation, which makes the detection even harder. Anti-virus software packages commonly use signature-based approaches which might not be able to efficiently identify camouflaged malicious HTML codes. Therefore, our paper proposes a malicious web page detection using the technique of machine learning. Our study analyzes the characteristic of a malicious webpage systematically and presents important features for machine learning. Experimental results demonstrate that our method is resilient to code obfuscations and can correctly determine whether a webpage is malicious or not. (C) 2009 Elsevier Ltd. All rights reserved.
C1 [Hou, Yung-Tsung; Chen, Chia-Mei] Natl Sun Yat Sen Univ, Kaohsiung 80424, Taiwan.
[Chang, Yimeng; Chen, Tsuhan] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA.
[Laih, Chi-Sung] Natl Cheng Kung Univ, Tainan 70101, Taiwan.
RP Hou, YT (reprint author), 8F-5,712,Huaxia Rd, Kaohsiung 813, Taiwan.
EM yungtsung.hou@gmail.com; yimengz@andrew.cmu.edu; tsuhan@cmu.edu;
laihcs@eembox.ncku.edu.tw; cmchen@mis.nsysu.edu.tw
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PD JAN
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BP 55
EP 60
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GA 516VI
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SE Texts in Computer Science
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NR 145
TC 15
Z9 15
PU SPRINGER-VERLAG LONDON LTD
PI GODALMING
PA SWEETAPPLE HOUSE CATTESHALL RD FARNCOMBE, GODALMING GU7 1NH, SURREY,
ENGLAND
SN 1868-0941
BN 978-1-84996-225-4
J9 TEXTS COMPUT SCI
PY 2010
BP 1
EP 226
DI 10.1007/978-1-84996-226-1
PG 226
WC Computer Science, Artificial Intelligence
SC Computer Science
GA BPK66
UT WOS:000279068000010
ER
PT B
AU Varadarajan, R
Hristidis, V
Farfan, F
Burke, R
AF Varadarajan, Ramakrishna
Hristidis, Vagelis
Farfan, Fernando
Burke, Redmond
BE Hristidis, V
TI Searching Electronic Health Records
SO INFORMATION DISCOVERY ON ELECTRONIC HEALTH RECORDS
SE Chapman & Hall-CRC Data Mining and Knowledge Discovery Series
LA English
DT Article; Book Chapter
ID XML QUERY; KEYWORD SEARCH; ENGINE; RETRIEVAL; RELEVANCE; TEXT;
DATABASES; RANKING; SYSTEMS
C1 [Varadarajan, Ramakrishna] Univ Wisconsin Madison, Dept Comp Sci, Madison, WI 53706 USA.
[Hristidis, Vagelis; Farfan, Fernando] Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA.
[Burke, Redmond] Miami Childrens Hosp, Miami, FL USA.
RP Varadarajan, R (reprint author), Univ Wisconsin Madison, Dept Comp Sci, Madison, WI 53706 USA.
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NR 105
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 978-1-4200-9041-3; 978-1-4200-9038-3
J9 CH CRC DATA MIN KNOW
PY 2010
BP 127
EP 163
PG 37
WC Computer Science, Artificial Intelligence; Computer Science,
Interdisciplinary Applications; Computer Science, Theory & Methods;
Health Care Sciences & Services
SC Computer Science; Health Care Sciences & Services
GA BC8CO
UT WOS:000355548500007
ER
PT B
AU Wang, KS
Li, XL
Gao, JF
AF Wang, Kuansan
Li, Xiaolong
Gao, Jianfeng
BE Chen, HH
Efthimiadis, EN
Savoy, J
Crestani, F
MarchandMaillet, S
TI Multi-Style Language Model for Web Scale 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 Information Retrieval; Mixture Language Models; Smoothing; Parameter
Estimation; Probabilistic Relevance Model
AB Web documents are typically associated with many text streams, including the body, the title and the URL that are determined by the authors, and the anchor text or search queries used by others to refer to the documents. Through a systematic large scale analysis on their cross entropy, we show that these text streams appear to be composed in different language styles, and hence warrant respective language models to properly describe their properties. We propose a language modeling approach to Web document retrieval in which each document is characterized by a mixture model with components corresponding to the various text streams associated with the document. Immediate issues for such a mixture model arise as all the text streams are not always present for the documents, and they do not share the same lexicon, making it challenging to properly combine the statistics from the mixture components. To address. these issues, we introduce an "open-vocabulary" smoothing technique so that all the component language models have the same cardinality and their scores can simply be linearly combined. To ensure that the approach can cope with Web scale applications, the model training algorithm is designed to require no labeled data and can be fully automated with few heuristics and no empirical parameter tunings. The evaluation on Web document ranking tasks shows that the component language models indeed have varying degrees of capabilities as predicted by the cross-entropy analysis, and the combined mixture model outperforms the state-of-the-art BM25F based system.
C1 [Wang, Kuansan; Gao, Jianfeng] Microsoft Res, Redmond, WA 98052 USA.
RP Wang, KS (reprint author), Microsoft Res, 1 Microsoft Way, Redmond, WA 98052 USA.
EM kuansan.wang@microsoft.com; xiaolong.li@microsoft.com;
jfgao@microsoft.com
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NR 32
TC 1
Z9 1
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 467
EP 474
PG 8
WC Computer Science, Information Systems
SC Computer Science
GA BTG52
UT WOS:000286904100061
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
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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 Imran, H
Sharan, A
AF Imran, Hazra
Sharan, Aditi
BE Wang, FL
Gong, ZG
Luo, XF
Lei, JS
TI A Framework for Automatic Query Expansion
SO WEB INFORMATION SYSTEMS AND MINING
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT International Conference on Web Information Systems and Mining
CY OCT 23-24, 2010
CL Sanya, PEOPLES R CHINA
SP Hainan Province Inst Comp, Qiongzhou Univ
DE Automatic Query Expansion (AQE); Pseudo Relevance Feedback (PRF);
Information Retrieval (IR)
ID DOCUMENT-RETRIEVAL; INFORMATION; RELEVANCE
AB The objective of this paper is to provide a framework and computational model for automatic query expansion using psuedo relevance feedback. We expect that our model can be helpful in dealing with many important aspects in automatic query expansion in an efficient way. We have performed experiments based on our model using TREC data set. Results are encouraging as they indicate improvement in retrieval efficiency after applying query 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; aditisharan@mail.jnu.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
CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683
Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121
Imran Hazra, 2009, International Journal of Computer Science & Information Technology, V1
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
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
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STAIRMAND MA, 1997, P 1997 ACM SIGIR C R
Vechtomova O, 2006, J INF SCI, V32, P324, DOI 10.1177/0165551506065787
VOORHEES EM, 1994, P 1994 ACM SIGIR C R
NR 15
TC 0
Z9 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 978-3-642-16514-6
J9 LECT NOTES COMPUT SC
PY 2010
VL 6318
BP 386
EP 393
PG 8
WC Computer Science, Information Systems; Computer Science, Theory &
Methods
SC Computer Science
GA BTB82
UT WOS:000286404600048
ER
PT J
AU Gayo-Avello, D
AF Gayo-Avello, Daniel
TI A survey on session detection methods in query logs and a proposal for
future evaluation
SO INFORMATION SCIENCES
LA English
DT Article
DE Web searching; Search engine; Query log; Topical session; Session
detection
ID NEURAL-NETWORK APPLICATIONS; TOPIC IDENTIFICATION;
INFORMATION-RETRIEVAL; SEARCH ENGINES; USER LOGS; WEB; MULTITASKING;
RELEVANCE
AB Search engine logs provide a highly detailed insight of users' interactions. Hence, they are both extremely useful and sensitive. The datasets publicly available to scholars are, unfortunately, too few, too dated and too small. There are few because search engine companies are reluctant to release such data; they are dated because they were collected in late 1990s or early 2000s: and they are small because they comprise data for at most one day and just a few hundreds of thousands of users. Even worse, the large query log disclosed by AOL in 2006 caused more harm than good because of a big privacy flaw. In this paper the author provides an overall view of the possible applications of query logs, the privacy concerns researchers must face when working on such datasets, and several ways in which query logs can be easily sanitized. One of such measures consists of segmenting the logs into short topical sessions. Therefore, the author offers a comprehensive survey of session detection methods, as well as a thorough description of a new evaluation framework with performance results for each of the different methods. Additionally, a new, simple, but outperforming session detection method is proposed. It is a heuristic-based technique which works on the basis of a geometric interpretation of both the time gap between queries and the similarity between them in order to flag a topic shift. (c) 2009 Elsevier Inc. All rights reserved.
C1 Univ Oviedo, Dept Comp Sci, Oviedo 33007, Spain.
RP Gayo-Avello, D (reprint author), Univ Oviedo, Dept Comp Sci, Edificio Ciencias,C Calvo Sotelo S-N, Oviedo 33007, Spain.
EM dani@uniovi.es
RI Gayo-Avello, Daniel/D-3791-2014
OI Gayo-Avello, Daniel/0000-0002-4705-6891
FU University of Oviedo [UNOV-08-MB-13]
FX The author would like to thank Dr.jim Jansen for providing access to
most of the query logs employed in this research. He is also grateful to
Professor Asuncion Lubiano-Gomez for many discussions on statistical
aspects and to the anonymous reviewers for their valuable comments and
advice. This work was partially financed by Grant UNOV-08-MB-13 from the
University of Oviedo and completed while the author was visiting the
University of Plymouth (UK).
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NR 83
TC 10
Z9 12
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 1822
EP 1843
DI 10.1016/j.ins.2009.01.026
PG 22
WC Computer Science, Information Systems
SC Computer Science
GA 444GG
UT WOS:000265968100004
ER
PT B
AU Shaw, D
Barnes, N
AF Shaw, David
Barnes, Nick
BE Shi, H
Zhang, YC
Bottema, MJ
Lovell, BC
Maeder, AJ
TI Perspective Invariant Angle Ordering
SO 2009 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2009)
LA English
DT Proceedings Paper
CT 11th Conference on Digital Image Computing: Techniques and Applications
CY DEC 01-03, 2009
CL Melbourne, AUSTRALIA
DE localisation; object recognition; bag-of-features; feature points;
geometric algorithms; real-time
AB In this paper we present the geometric property of perspective invariant angle ordering; the order of angles between point features. We describe how this can be used to exploit the structure of the appearance of features on planar or near planar surfaces to improve precision for localisation and object recognition. We show test results on real-world images that show marked improvement over straight bag-of-features approaches.
C1 [Shaw, David; Barnes, Nick] Australian Natl Univ, Natl ICT Australia, Dept Informat Engn, Canberra, ACT, Australia.
RP Shaw, D (reprint author), Australian Natl Univ, Natl ICT Australia, Dept Informat Engn, Canberra, ACT, Australia.
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NR 12
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-5297-2
PY 2009
BP 256
EP 263
DI 10.1109/DICTA.2009.50
PG 8
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
Electronic; Imaging Science & Photographic Technology
SC Computer Science; Engineering; Imaging Science & Photographic Technology
GA BUV70
UT WOS:000290469200038
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.
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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 Zuccon, G
Azzopardi, LA
van Rijsbergen, K
AF Zuccon, Guido
Azzopardi, Leif A.
van Rijsbergen, Keith
BE Azzopardi, L
Kazai, G
Robertspm, S
Ruger, S
Shokouhi, M
Song, D
Yilmaz, E
TI The Quantum Probability Ranking Principle 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 RELEVANCE
AB While the Probability Ranking Principle for Information Retrieval provides the basis for formal models, it makes a very strong assumption regarding the dependence between documents. However, it has been observed that in real situations this assumption does not always hold. In this paper we propose a reformulation of the Probability Ranking Principle based on quantum theory. Quantum probability theory naturally includes interference effects between events. We posit that this interference captures the dependency between the judgement of document relevance. The outcome is a more sophisticated principle, the Quantum Probability Ranking Principle, that provides a more sensitive ranking which caters for interference/dependence between documents' relevance.
C1 [Zuccon, Guido; Azzopardi, Leif A.; van Rijsbergen, Keith] Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland.
RP Zuccon, G (reprint author), Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland.
EM guido@dcs.gla.ac.uk; leif@dcs.gla.ac.uk; keith@dcs.gla.ac.uk
RI Zuccon, Guido/F-6360-2012
OI Zuccon, Guido/0000-0003-0271-5563
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NR 7
TC 6
Z9 6
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 232
EP 240
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Theory & Methods
SC Computer Science
GA BMB97
UT WOS:000271806000020
ER
PT B
AU Taghva, K
AF Taghva, Kazem
BE Argamon, S
Howard, N
TI Identification of Sensitive Unclassified Information
SO COMPUTATIONAL METHODS FOR COUNTERTERRORISM
LA English
DT Article; Book Chapter
ID RETRIEVAL
AB Sensitive Unclassified information is defined as any unclassified information that may cause adverse consequences against the government facilities. In this chapter, we explore the use of categorization techniques and information extraction to discover this kind of information in scanned documents.
We show here that the combined use of a K-Dependence Bayesian categorization engine and a semi-automated review application reduce by nearly 95% the number of man hours required to redact sensitive unclassified information. We also discuss and provide statistics on how OCR errors can affect the information extraction tasks.
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
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*US DOE, 2001, LIC SUPP NETW BAS DE
NR 10
TC 0
Z9 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
BN 978-3-642-01140-5
PY 2009
BP 89
EP 108
DI 10.1007/978-3-642-01141-2_6
D2 10.1007/978-3-642-01141-2
PG 20
WC Computer Science, Information Systems; Computer Science, Theory &
Methods; Criminology & Penology
SC Computer Science; Criminology & Penology
GA BKW86
UT WOS:000269491800006
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
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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 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
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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 J
AU Nolin, J
AF Nolin, Jan
TI "Relevance" as a boundary concept Reconsidering early information
retrieval
SO JOURNAL OF DOCUMENTATION
LA English
DT Article
DE Information retrieval; Media history; Information Science
ID OF-THE-LITERATURE; SCIENCE; FRAMEWORK; THINKING; NOTION; WORK;
ORGANIZATION; DEFINITION; BEHAVIOR; LIBRARY
AB Purpose - Throughout its history, information retrieval has struggled to handle contradictory needs of system oriented and user-oriented research. Information retrieval has gradually, starting in the 1960s, moved toward handling the needs of the user. This paper aims to consider the way boundaries toward the user and user-oriented perspectives are drawn, renegotiated and re-drawn.
Design/methodology/approach - The central concept of relevance is seen as a boundary concept, complex and flexible, that is continuously redefined in order to manage boundaries. Five influential research papers from the 1960s and early 1970s are analysed in order to understand usage of the concept during a period when psychological and cognitive research tools began to be discussed as a possibility.
Findings - Relevance does not only carry an explanatory function, but also serves a purpose relating to the identity of the field. Key contributions on research on relevance seems to, as a by-product, draw a boundary giving legitimacy to certain theoretical resources while demarcating against others. The strategies that are identified in the key texts are intent on finding, representing, justifying and strengthening a boundary that includes and excludes a reasonable amount of complexity associated with the user.
Originality/value - The paper explores a central concept within information retrieval and information science in a new way. It also supplies a fresh perspective on the development of information retrieval during the 1960s and 1970s.
C1 Univ Boras, Swedish Sch Lib & Informat Sci, Boras, Sweden.
RP Nolin, J (reprint author), Univ Boras, Swedish Sch Lib & Informat Sci, Boras, Sweden.
EM Jan.Nolin@hb.se
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NR 50
TC 0
Z9 0
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 2009
VL 65
IS 5
BP 745
EP 767
DI 10.1108/00220410910983092
PG 23
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 510UK
UT WOS:000271117900003
ER
PT J
AU Yang, Y
Webb, GI
AF Yang, Ying
Webb, Geoffrey I.
TI Discretization for naive-Bayes learning: managing discretization bias
and variance
SO MACHINE LEARNING
LA English
DT Article
DE Discretization; Naive-Bayes Learning; Bias; Variance
ID NETWORK CLASSIFIERS; ASSUMPTION
AB Quantitative attributes are usually discretized in Naive-Bayes learning. We establish simple conditions under which discretization is equivalent to use of the true probability density function during naive-Bayes learning. The use of different discretization techniques can be expected to affect the classification bias and variance of generated naive-Bayes classifiers, effects we name discretization bias and variance. We argue that by properly managing discretization bias and variance, we can effectively reduce naive-Bayes classification error. In particular, we supply insights into managing discretization bias and variance by adjusting the number of intervals and the number of training instances contained in each interval. We accordingly propose proportional discretization and fixed frequency discretization, two efficient unsupervised discretization methods that are able to effectively manage discretization bias and variance. We evaluate our new techniques against four key discretization methods for naive-Bayes classifiers. The experimental results support our theoretical analyses by showing that with statistically significant frequency, naive-Bayes classifiers trained on data discretized by our new methods are able to achieve lower classification error than those trained on data discretized by current established discretization methods.
C1 [Yang, Ying] Australian Taxat Off, Box Hill, Vic 3128, Australia.
[Webb, Geoffrey I.] Monash Univ, Fac Informat Technol, Clayton, Vic 3800, Australia.
RP Yang, Y (reprint author), Australian Taxat Off, 990 Whitehorse Rd, Box Hill, Vic 3128, Australia.
EM ying.yang@ato.gov.au; geoff.webb@infotech.monash.edu.au
RI Webb, Geoffrey/
OI Webb, Geoffrey/0000-0001-9963-5169
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NR 64
TC 38
Z9 40
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0885-6125
J9 MACH LEARN
JI Mach. Learn.
PD JAN
PY 2009
VL 74
IS 1
BP 39
EP 74
DI 10.1007/s10994-008-5083-5
PG 36
WC Computer Science, Artificial Intelligence
SC Computer Science
GA 389WO
UT WOS:000262125400003
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
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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 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.
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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
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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 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
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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 Maron, ME
AF Maron, M. E.
TI An historical note on the origins of Probabilistic Indexing
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE probabilistic information retrieval
AB The motivation behind "Probabilistic Indexing" was to replace two-valued thinking about information retrieval with probabilistic notions. This involved a new view of the information retrieval problem - viewing it as problem of inference and prediction, and introducing probabilistically weighted indexes and probabilistically ranked output. These ideas were first formulated and written up in August 1958. (c) 2007 Elsevier Ltd. All rights reserved.
C1 Univ Calif Berkeley, Berkeley, CA 94720 USA.
RP Maron, ME (reprint author), Univ Calif Berkeley, Berkeley, CA 94720 USA.
CR MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
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MARON ME, 1958, SOME EXPT PROBABILIS
NR 3
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 MAR
PY 2008
VL 44
IS 2
BP 971
EP 972
DI 10.1016/j.ipm.2007.02.012
PG 2
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 285YX
UT WOS:000254813200033
ER
PT B
AU Wong, WS
Aghvami, H
Wolak, SJ
AF Wong, Wing Shun
Aghvami, Hamid
Wolak, Stephen J.
GP IEEE
TI Context-Aware Personal Assistant Agent Multi-Agent System
SO 2008 IEEE 19TH INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE
RADIO COMMUNICATIONS
LA English
DT Proceedings Paper
CT 19th IEEE International Symposium on Personal, Indoor and Mobile Radio
Communications
CY SEP 15-18, 2008
CL Cannes, FRANCE
SP IEEE
ID MODEL
AB The main goal of this article is to show a Context-Aware PAA Multi-Agent System (CAPMAS) framework and how it conveys context-aware computing and agent technologies into ubiquitous system. We use context-awareness to provide computing environments with the ability to usefully adapt the services or information they provide. The 'agent technologies' take into account of different service solutions, which form the ultimate link to the context aware computing. In this article, we shall explore some of the basic agent technologies that lead us to develop our context awareness Personal Assistant Agent (PAA) based systems and concepts.
C1 [Wong, Wing Shun; Aghvami, Hamid] Kings Coll London, Ctr Telecommun Res, London WC2R 2LS, England.
RP Wong, WS (reprint author), Kings Coll London, Ctr Telecommun Res, London WC2R 2LS, England.
EM wing.s.wong@kcl.ac.uk; hamid.aghvami@kcl.ac.uk;
stephen.wolak@vodafone.com
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NR 13
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-2643-0
PY 2008
BP 2406
EP 2409
PG 4
WC Computer Science, Information Systems; Computer Science, Theory &
Methods; Engineering, Electrical & Electronic; Telecommunications
SC Computer Science; Engineering; Telecommunications
GA BRH60
UT WOS:000282721801165
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
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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 J
AU Wu, HC
Luk, RWP
Wong, KF
Kwok, KL
AF Wu, Ho Chung
Luk, Robert Wing Pong
Wong, Kam Fai
Kwok, Kui Lam
TI Interpreting TF-IDF term weights as making relevance decisions
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE design; experimentation; languages; performance; information retrieval;
term weight; relevance decision
ID INFORMATION-RETRIEVAL; PROBABILISTIC MODELS; ALGORITHM; RANKING; SYSTEM;
OKAPI
AB A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these "local" relevance decisions as the "document-wide" relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity -log p((r) over bar |t epsilon d) is interpreted as the probability of randomly picking a nonrelevant usage (denoted by (r) over bar) of term t. Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections.
C1 [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.
[Kwok, Kui Lam] CUNY, Queens Coll, Dept Comp Sci, Flushing, NY 11367 USA.
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
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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 13
DI 10.1145/1361684.1361686
PG 37
WC Computer Science, Information Systems
SC Computer Science
GA 317OC
UT WOS:000257028500002
ER
PT J
AU Frontiera, P
Larson, R
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TI A comparison of geometric approaches to assessing spatial similarity for
GIR
SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
LA English
DT Article
DE geographic information retrieval; GIR; spatial similarity; spatial
ranking; spatial search; geographic relevance; geometric approximations
ID GEOGRAPHIC INFORMATION-RETRIEVAL; HAUSDORFF DISTANCE; RANKING METHODS;
REGION
AB This research compares the geographic information retrieval (GIR) performance of a set of logistic regression models with those of five non-probabilistic methods that compute a spatial similarity score for a query-document pair. All methods are applied to a test collection of queries and documents indexed spatially by two convex conservative geometric approximations: the minimum bounding box (MBB) and the convex hull. In the comparison, the tested logistic regression models outperform, in terms of standard information retrieval recall and precision measures, all of the non-probabilistic methods. The retrieval performance achieved by the logistic regression models on MBB approximations is similar to that achieved by the use of the non-probabilistic methods on convex hulls. Although these results are valid only for the test collection used in this study, they suggest that a logistic regression approach to GIR provides an alternative to the use of higher-quality geometric representations that are more difficult to obtain, implement, and process. Additionally, this research demonstrates the ability of a probabilistic approach to effectively incorporate information about geographic context in the spatial ranking process.
C1 [Larson, Ray] Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA.
[Frontiera, Patricia; Radke, John] Univ Calif Berkeley, Geog Informat Sci Ctr, Berkeley, CA 94720 USA.
RP Larson, R (reprint author), Univ Calif Berkeley, Sch Informat, 102 S Hall, Berkeley, CA 94720 USA.
EM ray@sherlock.berkeley.edu
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NR 59
TC 8
Z9 11
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 3
BP 337
EP 360
DI 10.1080/13658810701626293
PG 24
WC Computer Science, Information Systems; Geography; Geography, Physical;
Information Science & Library Science
SC Computer Science; Geography; Physical Geography; Information Science &
Library Science
GA 275IO
UT WOS:000254065300008
ER
PT S
AU Fang, W
Wu, GS
Fu, XL
AF Fang Wu
Guoshi Wu
Xangling Fu
BE Xu, LD
Tjoa, AM
Chaudhry, SS
TI Design and implementation of ontology-based query expansion for
information retrieval
SO RESEARCH AND PRACTICAL ISSUES OF ENTERPRISE INFORMATION SYSTEMS II, VOL
1
SE International Federation for Information Processing
LA English
DT Proceedings Paper
CT 2nd International Conference on Research and Practical Issues of
Enterprise Information Systems
CY OCT 14-16, 2007
CL Beijing, PEOPLES R CHINA
DE search engine; ontology; web ontology language (OWL); knowledge
management; enterprise search
ID RELEVANCE
AB In Information Retrieval (IR), the user's input query conditions usually are not detailed enough, so the satisfactory query results can not be brought back. Query expansion of IR can help to solve this problem. However, the common query expansion in IR cannot get steady retrieval results. In this paper, we propose and implement query expansion method which combines domain ontology with the frequent of terms. Ontology is used to describe domain knowledge; logic reasoner and the frequency of terms arc used to choose fitting expansion words. By this way, higher recall and precise can be gotten as user' query results. Experimental results show that compared with the results of common query expansion, the method described in this paper can get statistically significant improvement in recall and precise combination.
C1 Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100879, Peoples R China.
EM w-fang@hotmail.com; xiangling.fu@263.net; wuguoshi@email.buptsse.cn
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
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NR 11
TC 0
Z9 0
PU SPRINGER
PI NEW YORK
PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES
SN 1571-5736
BN 978-0-387-75901-2
J9 INT FED INFO PROC
PY 2008
VL 254
BP 293
EP 298
PG 6
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications; Computer Science, Theory & Methods;
Information Science & Library Science; Management
SC Business & Economics; Computer Science; Information Science & Library
Science
GA BGU64
UT WOS:000250699100034
ER
PT S
AU Singh, S
Prasad, B
AF Singh, Shailendra
Prasad, Bhanu
BE Prasad, B
TI User Relevance Feedback Analysis in Text Information Retrieval: A Rough
Set Approach
SO SOFT COMPUTING APPLICATIONS IN BUSINESS
SE Studies in Fuzziness and Soft Computing
LA English
DT Article; Book Chapter
ID FUZZY-SETS; CORPUS; MODELS
AB User relevance feedback plays an important role in the development of efficient and successful business strategies for several online domains such as: modeling user preferences for information retrieval, personalized recommender systems, automatic categorization of emails, online advertising, online auctions, etc. To achieve success, the business models should have some kind of interactive interface to receive user feedback and also a mechanism for user relevance feedback analyis to extract relevant information from large information repositories such as WWW. We present a rough set based discernibility approach to expand the user preferences by including the relevant conceptual terms extracted from the collection of documents rated by the users. In addition, a rough membership based ranking methodology is proposed to filter out the irrelevant documents retrieved from the information repositories, using an extended set of conceptual terms. This paper provides a detailed implementation of the proposed approach as well as its advantages in the context of user relevance feedback analysis based text information retrieval.
C1 [Singh, Shailendra] Samsung India Software Ctr, Noida 201301, India.
[Prasad, Bhanu] Florida A&M Univ, Dept Comp & Informat Sci, Tallahassee, FL 32307 USA.
RP Singh, S (reprint author), Samsung India Software Ctr, D-5,Sector 59, Noida 201301, India.
EM bhanu.prasad@famu.edu; bhanu.prasad@famu.edu
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2007, WEB 2 0 BUSINESS MOD
NR 55
TC 0
Z9 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1434-9922
BN 978-3-540-79004-4
J9 STUD FUZZ SOFT COMP
PY 2008
VL 230
BP 147
EP 178
DI 10.1007/978-3-540-79005-1_9
D2 10.1007/978-3-540-79005-1
PG 32
WC Business; Computer Science, Artificial Intelligence; Computer Science,
Information Systems
SC Business & Economics; Computer Science
GA BJR77
UT WOS:000267050800009
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
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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 Abu El-Khair, I
AF Abu El-Khair, Ibrahim
TI Arabic information retrieval
SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID PROBABILISTIC MODEL; TEXT; SYSTEM; RELEVANCE; ENGLISH; SEARCH; WORDS;
TREC
C1 Menia Univ, Fac Arts, Dept Lib & Informat Sci, El Minia, Egypt.
RP Abu El-Khair, I (reprint author), Menia Univ, Fac Arts, Dept Lib & Informat Sci, El Minia, Egypt.
RI Abu El-Khair, Ibrahim/K-8387-2012
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NR 98
TC 1
Z9 1
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 2007
VL 41
BP 505
EP 533
PG 29
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 116PA
UT WOS:000242813600012
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.
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RP Jones, KS (reprint author), Univ Cambridge, Comp Lab, JJ Thomson Ave, Cambridge CB3 0FD, England.
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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
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PT B
AU Meadow, CT
Boyce, BR
Kraft, DH
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AF Meadow, CT
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NR 302
TC 15
Z9 15
PU ELSEVIER ACADEMIC PRESS INC
PI SAN DIEGO
PA 525 B STREET, SUITE 1900, SAN DIEGO, CA 92101-4495 USA
BN 978-0-12-369412-6
J9 LIBR INFORM SCI SER
PY 2007
BP 1
EP 371
PG 371
WC Information Science & Library Science
SC Information Science & Library Science
GA BLN40
UT WOS:000270583400017
ER
PT J
AU Kolter, JZ
Maloof, MA
AF Kolter, J. Zico
Maloof, Marcus A.
TI Learning to detect and classify malicious executables in the wild
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE data mining; concept learning; computer security; invasive software
ID CLASSIFICATION; RECOGNITION; ALGORITHMS
AB We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1; 971 benign and 1; 651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the most relevant n-grams for prediction, we evaluated a variety of inductive methods, including naive Bayes, decision trees, support vector machines, and boosting. Ultimately, boosted decision trees outperformed other methods with an area under the ROC curve of 0.996. Results suggest that our methodology will scale to larger collections of executables. We also evaluated how well the methods classified executables based on the function of their payload, such as opening a backdoor and mass-mailing. Areas under the ROC curve for detecting payload function were in the neighborhood of 0.9, which were smaller than those for the detection task. However, we attribute this drop in performance to fewer training examples and to the challenge of obtaining properly labeled examples, rather than to a failing of the methodology or to some inherent difficulty of the classification task. Finally, we applied detectors to 291 malicious executables discovered after we gathered our original collection, and boosted decision trees achieved a true-positive rate of 0.98 for a desired false-positive rate of 0.05. This result is particularly important, for it suggests that our methodology could be used as the basis for an operational system for detecting previously undiscovered malicious executables.
C1 Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA.
Georgetown Univ, Dept Comp Sci, Washington, DC 20057 USA.
Georgetown Univ, Dept Comp Sci, Washington, DC 20057 USA.
RP Kolter, JZ (reprint author), Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA.
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NR 44
TC 73
Z9 81
PU MICROTOME PUBLISHING
PI BROOKLINE
PA 31 GIBBS STREET, BROOKLINE, MA 02446 USA
SN 1532-4435
J9 J MACH LEARN RES
JI J. Mach. Learn. Res.
PD DEC
PY 2006
VL 7
BP 2721
EP 2744
PG 24
WC Automation & Control Systems; Computer Science, Artificial Intelligence
SC Automation & Control Systems; Computer Science
GA 152VT
UT WOS:000245390800009
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
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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 Stock, WG
AF Stock, Wolfgang G.
TI On relevance distributions
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID INFORMATION-RETRIEVAL; SCIENCE
AB There are at least three possible ways that documents are distributed by relevance: informetric (power law), inverse logistic, and dichotomous. The nature of the type of distribution has implications for the construction of relevance ranking algorithms for search engines, for automated (blind) relevance feedback, for user behavior when using Web search engines, for combining of outputs of search engines for metasearch, for topic detection and tracking, and for the methodology of evaluation of information retrieval systems.
C1 Univ Dusseldorf, Dept Informat Sci, D-40225 Dusseldorf, Germany.
RP Stock, WG (reprint author), Univ Dusseldorf, Dept Informat Sci, Univ Str 1, D-40225 Dusseldorf, Germany.
EM stocknmw@aol.com
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NR 28
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 JUN
PY 2006
VL 57
IS 8
BP 1126
EP 1129
DI 10.1002/asi.20359
PG 4
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 049GW
UT WOS:000238005200013
ER
PT B
AU Bajwa, IS
Siddique, MI
Choudhary, MA
AF Bajwa, Imran Sarwar
Siddique, M. Imran
Choudhary, M. Abbas
GP IEEE
TI Rule based production systems for automatic code generation in Java
SO 2006 1st International Conference on Digital Information Management
LA English
DT Proceedings Paper
CT 1st International Conference on Digital Information Management
CY DEC 06-08, 2006
CL Bangalore, INDIA
ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL
AB Unified modeling language is being used as a premier tool for modeling the user requirements. These CASE tools provide an easy way to get efficient solutions. This paper presents a natural language processing based automated system for generating code in multi-languages after modeling the user requirements based on UML. UML diagrams are first generated by analyzing the given business scenario provided by the user. A new model is presented for analyzing the natural languages and extracting the relative and required information from the given requirement notes by the user. User writes the requirements in simple English in a few paragraphs and the designed system has conspicuous ability to analyze the given script. After compound analysis and extraction of associated information, the designed system draws various UML diagrams as activity diagrams, sequence diagrams, class diagrams and Uses cases diagrams. The designed system has robust ability to create code automatically without external environment. The designed system provides a quick and reliable way to generate UML diagrams and generate respective code to save the time and budget of both the user and system analyst.
C1 Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur, Pakistan.
RP Bajwa, IS (reprint author), Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur, Pakistan.
RI Bajwa, Imran/A-1454-2010
OI Bajwa, Imran/0000-0002-5161-6441
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NR 19
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4244-0682-1
PY 2006
BP 300
EP 305
PG 6
WC Computer Science, Information Systems
SC Computer Science
GA BGN10
UT WOS:000248555800049
ER
PT B
AU Hage, H
Aimeur, E
AF Hage, Hicham
Aimeur, Esma
GP IEEE
TI ICE: A system for Identification of Conflicts in Exams
SO 2006 IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS,
VOLS 1-3
SE International Conference on Computer Systems and Applications
LA English
DT Proceedings Paper
CT 4th IEEE/ACS International Conference on Computer Systems and
Applications (AICCSA-06)
CY MAR 08-11, 2006
CL Sharjah, U ARAB EMIRATES
SP IEEE, ACS
AB Although E-learning has advanced considerably in the last decade, some of its aspects, such as E-testing, are still in the development phase. Authoring tools and test banks for E-tests are becoming an integral and indispensable part of E-learning platforms and with the implementation of E-learning standards, such as IMS QTI, E-testing material can be easily shared and reused across various platforms. With the knowledge available for re-use and exam automation comes a new challenge: making sure that created exams are free of conflicts. A Conflict exists in an exam if at least two questions within that exam are redundant in content, and/or if at least one question reveals the answer to another question within the same exam. In this paper we propose using Information Retrieval techniques to detect conflicts within an exam. Our solution, ICE (Identification of Conflicts in Exams), is based on the vector space model relying on tfi-df weighing and the cosine function to calculate similarity. ICE also combines the hybrid recommendation techniques of the EQRS (Exam Question Recommender System) in order to propose replacements for conflicting questions.
C1 Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3C 3J7, Canada.
RP Hage, H (reprint author), Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3C 3J7, Canada.
EM hagehich@iro.umontreal.ca; aimeur@iro.umontreal.ca
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NR 11
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 1-4244-0211-5
J9 I C COMP SYST APPLIC
PY 2006
BP 979
EP 986
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
Methods; Telecommunications
SC Computer Science; Telecommunications
GA BFN66
UT WOS:000243337100149
ER
PT J
AU Zobel, J
Moffat, A
AF Zobel, Justin
Moffat, Alistair
TI Inverted files for text search engines
SO ACM COMPUTING SURVEYS
LA English
DT Review
DE algorithms; experimentation; performance; inverted file indexing;
document database; text retrieval; information retrieval; Web search
engine
ID INFORMATION-RETRIEVAL SYSTEMS; PARTITIONED SIGNATURE FILES;
DOCUMENT-RETRIEVAL; STATISTICAL RANKING; CACHING STRATEGIES; INDEX
COMPRESSION; QUERY EVALUATION; ACCESS METHODS; BIT-VECTORS; PERFORMANCE
AB The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.
C1 RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia.
Univ Melbourne, Dept Comp Sci & Software Engn, Parkville, Vic 3052, Australia.
RP Zobel, J (reprint author), RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia.
EM jz@cs.rmit.edu.au
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NR 205
TC 0
Z9 0
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.
PY 2006
VL 38
IS 2
DI 10.1145/1132956.1132959
PG 56
WC Computer Science, Theory & Methods
SC Computer Science
GA 076TJ
UT WOS:000239980300003
ER
PT S
AU Shin, SE
Seo, YH
AF Shin, Seung-Eun
Seo, Young-Hoon
BE Sugimoto, S
Hunter, J
Rauber, A
Morishima, A
TI Query generation using semantic features
SO Digital Libraries: Achievements, Challenges and Opportunities,
Proceedings
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Proceedings Paper
CT 9th International Conference on Asian Digital Libraries
CY NOV 27-30, 2006
CL Kyoto, JAPAN
SP Kyoto Univ, Grad Sch Informat, 21st Cent Ctr Excellence Program, Univ Tsukuba, Grad Sch Informat, Res Ctr Knowledge Comm
DE question analysis; query generation; semantic feature; question
answering; information retrieval
ID INFORMATION-RETRIEVAL
AB This paper describes a query generation using semantic features to represent the information demand of users for question answering and information retrieval. One of fundamental reasons why unwanted results are included in responses of all information retrieval systems is because queries do not exactly represent the information demand of users. To solve this problem, a query generaton using the semantic feature is intended to extract semantic features which appear commonly in natural language questions of similar type and utilize them for question answering and information retrieval. We extract semantic features from natural language questions using a grammar and generate queries which represent enough information demands of users using semantic features and syntactic structures. For performance improvement of question answering and information retrieval, we introduce a query-document similarity used to rank documents which include generated queries in the high position. We evaluated our mechanism using 100 queries about a person in the web. There was a notable improvement in the precision at N documents when our approach is applied. Especially, we found that an efficient document retrieval is possible by a question analysis based on semantic features on natural language questions which are comparatively short but fully expressing the information demand of users.
C1 Chungbuk Natl Univ, BK21 Chungbuk Informat Technol Ctr, Cheongju 361763, Chungbuk, South Korea.
Chungbuk Natl Univ, Sch Elect & Comp Engn, Cheongju 361763, Chungbuk, South Korea.
RP Shin, SE (reprint author), Chungbuk Natl Univ, BK21 Chungbuk Informat Technol Ctr, Cheongju 361763, Chungbuk, South Korea.
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PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-49375-1
J9 LECT NOTES COMPUT SC
PY 2006
VL 4312
BP 234
EP 243
PG 10
WC Computer Science, Information Systems; Computer Science, Theory &
Methods
SC Computer Science
GA BFN08
UT WOS:000243132700025
ER
PT B
AU Bajwa, IS
Siddique, I
Choudhary, MA
AF Bajwa, Imran Sarwar
Siddique, Imran
Choudhary, M. Abbas
BE Salem, MH
ElHadidi, MT
TI Automatic domain specific terminology extraction using a decision
support system
SO Information Processing in the Service of Mankind and Health
LA English
DT Proceedings Paper
CT ITI 4th International Conference on Information and Communications
Technology
CY DEC 10-12, 2006
CL Cairo, EGYPT
SP ITI
DE terminology extraction; automatic text understanding; speech language
processing; information extraction
ID INFORMATION-RETRIEVAL
AB Speech languages or Natural languages contents are major tools of communication. This research paper presents a natural language processing based automated system for understanding speech language text. A new rule based model is presented for analyzing the natural languages and extracting the relative meanings from the given text. User writes the natural language scenario in simple English in a few paragraphs and the designed system has an obvious capability of analyzing the given script by the user. After composite analysis and extraction of associated information, the designed system gives particular meanings to an assortment of speech language text on the basis of its context. The designed system uses standard speech language rules that are clearly defined for all speech languages as English, Urdu, Chinese, Arabic, French,...etc. The designed system provides a quick and reliable way to comprehend speech language context and generates respective meanings. The application with such abilities can be more intelligent and pertinent specifically for the user to save the time.
C1 Islamia Univ Bahawalpur, Dept Comp Sci & Technol, Bahawalpur, Pakistan.
RP Bajwa, IS (reprint author), Islamia Univ Bahawalpur, Dept Comp Sci & Technol, Bahawalpur, Pakistan.
RI Bajwa, Imran/A-1454-2010
OI Bajwa, Imran/0000-0002-5161-6441
CR ANDRAKE MA, 2000, FEBS LETT, P12
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NR 17
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-0-7803-9770-5
PY 2006
BP 651
EP 659
PG 9
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Interdisciplinary Applications; Engineering,
Biomedical; Medical Informatics; Imaging Science & Photographic
Technology
SC Computer Science; Engineering; Medical Informatics; Imaging Science &
Photographic Technology
GA BGD95
UT WOS:000246217900051
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
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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 S
AU Bajwa, IS
Hyder, SI
Choudhary, MA
AF Bajwa, Imran S.
Hyder, Syed Irfan
Choudhary, M. Abbas
BE Nguyen, HT
Zhao, X
Peng, J
TI Suitable reusable components mining to assist developers using a rule
based system
SO Proceedings of the Fifth International Conference on Information and
Management Sciences
SE Series of Information and Management Sciences
LA English
DT Proceedings Paper
CT 5th International Conference on Information and Management Sciences
CY JUL 01-08, 2006
CL Chengdu, PEOPLES R CHINA
SP Int Assoc Informat & Management Sci, Tsinghua Univ, Chinese Univ Hong Kong, KAIST, Waseda Univ, Natl Nat Sci Fdn China
DE reusable components; rule based technique; language processing;
information extraction
ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL
AB Modem software engineering technologies and paradigms have been vastly affected by using reusable components. Reusability of software components stands for using the existing design and developed components in new software applications to save the cost and time. Against these persuading facts, some vexing problems also subsist there as to reuse which components and how to search those particular components from the given designed scenario. The newly designed rule based framework is fairly efficient in searching desired information from a given scenario. This research has been conducted to incorporate this obvious capability of the designed framework of analyzing the given scenario and extract the required reusable components to assist the developers according to the given guidelines. The designed system provides a quick and reliable way to extract required components that are used for reuse purposed on later stages. The application with such abilities can be more intelligent and pertinent specifically for the user to save the time.
RI Bajwa, Imran/A-1454-2010
OI Bajwa, Imran/0000-0002-5161-6441
CR Blaschke C, 1999, AUTOMATIC EXTRACTION, P60
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Partee Barbara Hall, 1990, MATH METHODS LINGUIS
REHMAN U, FACTOR REUSABILITY O
Salton G., 1983, INTRO MODERN INFORM
STRZALKOWSKI T, 1995, INFORM PROCESS MANAG, V31, P397, DOI 10.1016/0306-4573(94)00055-8
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NR 19
TC 0
Z9 0
PU CALIFORNIA POLYTECHNIC STATE UNIV
PI SAN LUIS OBISPO
PA CAL POLY, SAN LUIS OBISPO, CA 93407 USA
SN 1539-2023
J9 SER INF MANAGE SCI
PY 2006
VL 5
BP 266
EP 270
PG 5
WC Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications; Engineering, Industrial; Operations
Research & Management Science; Mathematics, Applied
SC Computer Science; Engineering; Operations Research & Management Science;
Mathematics
GA BER45
UT WOS:000239044100049
ER
PT S
AU Bajwa, IS
Choudhary, MA
AF Bajwa, Imran Sarwar
Choudhary, M. Abbas
BE Nguyen, HT
Zhao, X
Peng, J
TI Knowledge engineering from a web smart space using speech language
processing techniques
SO PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INFORMATION AND
MANAGEMENT SCIENCES
SE Series of Information and Management Sciences
LA English
DT Proceedings Paper
CT 5th International Conference on Information and Management Sciences
CY JUL 01-08, 2006
CL Chengdu, PEOPLES R CHINA
SP Int Assoc Informat & Management Sci, Tsinghua Univ, Chinese Univ Hong Kong, KAIST, Waseda Univ, Natl Nat Sci Fdn China
DE web smart space; information retrieval; natural language processing;
ontology engineering
ID INFORMATION-RETRIEVAL
AB A smart space is commonplace, where the solution of the specific domain problem comes with intelligence. Web smart space is a model where, the data is stored and retrieved through intelligent queries. In the smart space scenario, space is permeated with high intelligence instead of being empty and inactive construct. Functionally, smart spaces can be more intelligent than human brains by involving human wit into AI based systems. Ontology defines that what actually exists in a domain and how the domain elements relate with each other. Information can intelligently and smartly be retrieved from a web smart space using ontology engineering. This paper presents an automated system for ontology based information retrieval from a web smart space using natural language processing techniques. Natural language scripts from web documents are read, understood and analyzed using a rule base method presented in this research. This type of intelligent searching can be useful in various business and commercial areas. The designed system provides a quick and reliable way to extract information from various web ontologies to save the time for effective and efficient solution.
C1 Balochistan Univ Informat Technol & Management Sc, Fac Comp & Emerging Sci, Quetta, Pakistan.
RP Bajwa, IS (reprint author), Balochistan Univ Informat Technol & Management Sc, Fac Comp & Emerging Sci, Quetta, Pakistan.
EM Imransbajwa@yahoo.com; abbas@buitms.edu.pk
RI Bajwa, Imran/A-1454-2010
OI Bajwa, Imran/0000-0002-5161-6441
CR BITTNER T., 2003, FDN GEOGRAPHIC INFOR, P117, DOI 10.1201/9780203009543.ch7
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Guarino N, 2002, COMMUN ACM, V45, P61
JARRAR M, 2002, LNCS, V2519
Kagal L., 2003, 2 INT SEM WEB C ISWC
KALFOGLOU Y, 2003, ONTOLOGY MAPPING STA, V18
Kavalec M, 2004, LECT NOTES COMPUT SC, V2932, P249
KAVALEC M, 2002, ECAI WORKSH NLP ML O
KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810
Losee RM, 1996, INFORM PROCESS MANAG, V32, P185, DOI 10.1016/S0306-4573(96)85005-9
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Manning C. D., 1999, FDN STAT NATURAL LAN
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Partee Barbara Hall, 1990, MATH METHODS LINGUIS
NR 20
TC 0
Z9 0
PU CALIFORNIA POLYTECHNIC STATE UNIV
PI SAN LUIS OBISPO
PA CAL POLY, SAN LUIS OBISPO, CA 93407 USA
SN 1539-2023
J9 SER INF MANAGE SCI
PY 2006
VL 5
BP 271
EP 275
PG 5
WC Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications; Engineering, Industrial; Operations
Research & Management Science; Mathematics, Applied
SC Computer Science; Engineering; Operations Research & Management Science;
Mathematics
GA BER45
UT WOS:000239044100050
ER
PT S
AU Motiee, S
Nematzadeh, A
Shamsfard, M
AF Motiee, Sarah
Nematzadeh, Azadeh
Shamsfard, Mehrnoush
BE Ardil, C
TI A Hybrid Ontology Based Approach for Ranking Documents
SO PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL
11
SE Proceedings of World Academy of Science Engineering and Technology
LA English
DT Proceedings Paper
CT Conference of the World-Academy-of-Science-Engineering-and-Technology
CY FEB 24-26, 2006
CL Prague, CZECH REPUBLIC
DE Document ranking; Ontology; Spread activation algorithm; Annotation
ID INFORMATION-RETRIEVAL
AB Increasing growth of information volume in the internet causes an increasing need to develop new (semi)automatic methods for retrieval of documents and ranking them according to their relevance to the user query. In this paper, after a brief review on ranking models, a new ontology based approach for ranking HTML documents is proposed and evaluated in various circumstances. Our approach is a combination of conceptual, statistical and linguistic methods. This combination reserves the precision of ranking without loosing the speed. Our approach exploits natural language processing techniques to extract phrases from documents and the query and doing stemming on words. Then an ontology based conceptual method will be used to annotate documents and expand the query. To expand a query the spread activation algorithm is improved so that the expansion can be done flexible and in various aspects. The annotated documents and the expanded query will be processed to compute the relevance degree exploiting statistical methods. The outstanding features of our approach are (1) combining conceptual, statistical and linguistic features of documents, (2) expanding the query with its related concepts before comparing to documents, (3) extracting and using both words and phrases to compute relevance degree, (4) improving the spread activation algorithm to do the expansion based on weighted combination of different conceptual relationships and (5) allowing variable document vector dimensions. A ranking system called ORank is developed to implement and test the proposed model. The test results will be included at the end of the paper.
C1 [Motiee, Sarah; Nematzadeh, Azadeh] Amir Kabir Univ Technol, Tehran, Iran.
RP Motiee, S (reprint author), Amir Kabir Univ Technol, Tehran, Iran.
CR Baeza-Yates Ricardo, 2004, P 13 INT WORLD WID W, P328, DOI 10.1145/1013367.1013459
Brin S., 1998, P 7 INT WORLD WID WE, P107
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LCNETTOOLS
AEROSWARM
NR 16
TC 0
Z9 0
PU WORLD ACAD SCI, ENG & TECH-WASET
PI CANAKKALE
PA PO BOX 125, CANAKKALE, 17100, TURKEY
SN 1307-6884
J9 PROC WRLD ACAD SCI E
PY 2006
VL 11
BP 55
EP 60
PG 6
WC Computer Science, Interdisciplinary Applications
SC Computer Science
GA BIK63
UT WOS:000260419100011
ER
PT S
AU Weber, A
Reuther, P
Walter, B
Ley, M
Klink, S
AF Weber, Alexander
Reuther, Patrick
Walter, Bernd
Ley, Michael
Klink, Stefan
BE Gonzalo, J
Thanos, C
Verdejo, MF
Carrasco, RC
TI Multi-layered browsing and visualisation for digital libraries
SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article; Proceedings Paper
CT 10th European Conference on Research and Advanced Technology for Digital
Libraries
CY SEP 17-22, 2006
CL Alicante, SPAIN
SP DELOS Network Excellence Digital Libraries, Grp Santander, Minist Educ & Ciencia, Patronato Municipal Turismo Alicante, Red Investigac Bibliotecas Digitales, Fdn Biblioteca Miguel Cervantes, Univ Alciante, Dept Lenguajes & Sistemas Informat, UNED
ID INFORMATION-RETRIEVAL
AB For a scientific researcher it is more and more vital to find relevant publications with their correct bibliographical data, not only for accurate citations but particularly for getting further information about their current research topic.
This paper describes a new approach to develop user-friendly interfaces: Multi-Layered-Browsing. Two example applications are introduced that play a central role in searching, browsing and visualising bibliographical data.
C1 Univ Trier, Dept Databases & Informat Syst, Trier, Germany.
Univ Karlsruhe, Inst Appl Informat & Formal Descript Methods, Karlsruhe, Germany.
RP Weber, A (reprint author), Univ Trier, Dept Databases & Informat Syst, Trier, Germany.
EM aweber@uni-trier.de; reuther@uni-trier.de; walter@uni-trier.de;
ley@uni-trier.de; Stefan.Klink@aifb.uni-karlsruhe.de
CR Cleverdon C., 1984, Information Services & Use, V4
Ding Y, 2000, J AM SOC INFORM SCI, V51, P1190, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1031>3.0.CO;2-B
KLINK S, 2004, VISSYM, P237
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
NR 4
TC 0
Z9 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-44636-2
J9 LECT NOTES COMPUT SC
PY 2006
VL 4172
BP 520
EP 523
PG 4
WC Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications; Computer Science, Theory & Methods;
Information Science & Library Science
SC Computer Science; Information Science & Library Science
GA BFD07
UT WOS:000241101500058
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
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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 Boldareva, L
Hiemstra, D
AF Boldareva, L
Hiemstra, D
TI Interactive retrieval of video using pre-computed shot-shot similarities
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 IMAGE RETRIEVAL; RELEVANCE FEEDBACK; INFORMATION; DATABASES; SYSTEMS
AB A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision.
C1 Univ Twente, EEMCS Fac, NL-7500 AE Enschede, Netherlands.
RP Boldareva, L (reprint author), Univ Twente, EEMCS Fac, POB 217, NL-7500 AE Enschede, Netherlands.
EM boldarli@ewi.utwente.nl
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NR 37
TC 0
Z9 2
PU INST ENGINEERING TECHNOLOGY-IET
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 919
EP 926
DI 10.1049/ip-vis:20045201
PG 8
WC Engineering, Electrical & Electronic
SC Engineering
GA 998HA
UT WOS:000234308000033
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;
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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
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Chiang, IJ
AF Lin, TY
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TI A simplicial complex, a hypergraph, structure in the latent semantic
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LA English
DT Article
DE document clustering; association rules; topology; hierarchical
clustering; simplicial complex
ID RETRIEVAL
AB This paper presents a novel approach to document clustering based on some geometric structure in Combinatorial Topology. Given a set of documents, the set of associations among frequently co-occurring terms in documents forms naturally a simplicial complex. Our general thesis is each connected component of this simplicial complex represents a concept in the collection. Based on these concepts, documents can be clustered into meaningful classes. However, in this paper, we attack a softer notion, instead of connected components, we use maximal simplexes of highest dimension as representative of connected components, the concept so defined is called maximal primitive concepts.
Experiments with three different data sets from Web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and Hierarchical Clustering (HAG). This abstract geometric model seems have captured the latent semantic structure of documents. (c) 2005 Published by Elsevier Inc.
C1 San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA.
Taipei Med Univ, Grad Inst Med Informat, Taipei 110, Taiwan.
RP Lin, TY (reprint author), San Jose State Univ, Dept Comp Sci, 1 Washington Sq, San Jose, CA 95192 USA.
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SN 0888-613X
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PD JUL
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VL 40
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BP 55
EP 80
DI 10.1016/j.ijar.2004.11.005
PG 26
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SC Computer Science
GA 938DY
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PT J
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Connell, ME
AF Larkey, LS
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TI Structured queries, language modeling, and relevance modeling in
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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.
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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.
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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 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
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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
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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 B
AU Lin, TY
Chiang, IJ
AF Lin, TY
Chiang, IJ
BE Hu, XH
Liu, Q
Skowron, A
Lin, TY
Yager, RR
Zhang, B
TI Semantic based clustering of web documents
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
DE clustering; document; polyhedron; semantics; web
ID RETRIEVAL
AB A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: A primitive concept is represented by a top dimension simplex. and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes., Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and Hierarchical Clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.
C1 San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA.
RP Lin, TY (reprint author), San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA.
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NR 23
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 189
EP 192
PG 4
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
Methods
SC Computer Science
GA BCZ83
UT WOS:000232157200042
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
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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 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
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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
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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
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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 J
AU Bates, MJ
AF Bates, MJ
TI Information science at the University of California at Berkeley in the
1960s: A memoir of student days
SO LIBRARY TRENDS
LA English
DT Article
ID SYSTEM; NEEDS
AB The author's experiences as a master's and doctoral student at the University of California at Berkeley School of Library and Information Studies during a formative period in the history of information science, 1966-71, are described. The relationship between documentation and information science as experienced in that program is discussed, as well as the various influences, both social and intellectual, that shaped the author's understanding of information science at that time.
C1 Univ Calif Los Angeles, Dept Informat Studies, Los Angeles, CA 90095 USA.
RP Bates, MJ (reprint author), Univ Calif Los Angeles, Dept Informat Studies, Los Angeles, CA 90095 USA.
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NR 57
TC 9
Z9 9
PU GSLIS PUBLICATIONS
PI CHAMPAIGN
PA 501 E DANIEL ST, CHAMPAIGN, IL 61820-6211 USA
SN 0024-2594
J9 LIBR TRENDS
JI Libr. Trends
PD SPR
PY 2004
VL 52
IS 4
BP 683
EP 701
PG 19
WC Information Science & Library Science
SC Information Science & Library Science
GA 870UQ
UT WOS:000225084500002
ER
PT S
AU Cybenko, G
Giani, A
Thompson, P
AF Cybenko, G
Giani, A
Thompson, P
BE Zelkowitz, MV
TI Cognitive hacking
SO ADVANCES IN COMPUTERS, VOL. 60: INFORMATION SECURITY
SE Advances In Computers
LA English
DT Article
ID INFORMATION-RETRIEVAL
AB In this chapter, we define and propose countermeasures for a category of computer security exploits which we call "cognitive hacking." Cognitive hacking refers to a computer or information system attack that relies on changing human users' perceptions and corresponding behaviors in order to be successful. This is in contrast to denial of service (DOS) and other kinds of well-known attacks that operate solely within the computer and network infrastructure. Examples are given of several cognitive hacking techniques, and a taxonomy for these types of attacks is developed. Legal, economic, and digital government implications are discussed.
C1 Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA.
Dartmouth Coll, Thayer Sch Engn, Inst Secur Technol Studies, Hanover, NH 03755 USA.
RP Cybenko, G (reprint author), Dartmouth Coll, Thayer Sch Engn, 8000 Cummings Hall, Hanover, NH 03755 USA.
EM george.cybenko@dartmouth.edu; annarita.giani@dartmouth.edu;
paul.thompson@dartmouth.edu
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TC 0
Z9 0
PU ELSEVIER ACADEMIC PRESS INC
PI SAN DIEGO
PA 525 B STREET, SUITE 1900, SAN DIEGO, CA 92101-4495 USA
SN 0065-2458
BN 0-12-012160-3
J9 ADV COMPUT
JI Adv. Comput.
PY 2004
VL 60
BP 35
EP 73
DI 10.1016/S0065-2458(03)60002-1
PG 39
WC Computer Science, Hardware & Architecture; Computer Science, Software
Engineering
SC Computer Science
GA BAQ36
UT WOS:000223200800002
ER
PT J
AU Dumais, ST
AF Dumais, ST
TI Latent semantic analysis
SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Review
ID LITERATURE-BASED DISCOVERY; INFORMATION-RETRIEVAL; VECTOR-SPACES; MODEL;
CLASSIFICATION; LITERATURES; VOCABULARY; QUESTIONS; RELEVANCE; KNOWLEDGE
C1 Microsoft Res, Adapt Syst & Interact Grp, Redmond, WA USA.
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NR 136
TC 30
Z9 30
PU WILEY-BLACKWELL
PI MALDEN
PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA
SN 0066-4200
J9 ANNU REV INFORM SCI
JI Annu. Rev. Inform. Sci. Technol.
PY 2004
VL 38
BP 189
EP 230
PG 42
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 879UD
UT WOS:000225743500005
ER
PT S
AU Lin, TY
Chiang, IJ
AF Lin, TY
Chiang, IJ
BE Dasarathy, BV
TI Automatic document clustering of concept hypergraph decompositions
SO DATA MINING AND KNOWLEDGE DISCOVERY: THEORY, TOOLS, AND TECHNOLOGY VI
SE PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
(SPIE)
LA English
DT Proceedings Paper
CT 6th Conference on Data Mining and Knowledge Discovery
CY APR 12-13, 2004
CL Orlando, FL
SP SPIE
DE document clustering; hypergraph partition; association rules
ID RETRIEVAL
AB This paper presents an approach to classify/cluster the web documents by decompositions of hypergraphs. The various levels of co-occurring frequent terms, called association rules (undirected rules), of documents form a hypergraph. Clustering methods is then applied to analyze such hypergraphs; a simple and fast clustering algorithm is used to decomposing hypergraph into connected components. Each connected component represents a primitive concept within the given documents. The documents will then be classified/clustered by such primitive concepts.
C1 San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA.
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NR 43
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 0-8194-5356-0
J9 P SOC PHOTO-OPT INS
PY 2004
VL 5433
BP 168
EP 177
DI 10.1117/12.543817
PG 10
WC Computer Science, Artificial Intelligence
SC Computer Science
GA BAI03
UT WOS:000222348700019
ER
PT S
AU Thompson, P
AF Thompson, P
BE Trevisani, DA
Sisti, AF
TI Cognitive hacking and intelligence and security informatics
SO ENABLING TECHNOLOGIES FOR SIMULATION SCIENCE VIII
SE PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
(SPIE)
LA English
DT Proceedings Paper
CT Conference on Enabling Technologies for Simulation Science VIII
CY APR 13-15, 2004
CL Orlando, FL
SP SPIE
DE cognitive hacking; semantic hacking; intelligence informatics; security
informatics; deception detection
ID RETRIEVAL
AB This paper describes research on cognitive and semantic attacks on computer systems and their users. Several countermeasures against such attacks are described, including a description of a prototype News Verifier system. It is argued that because misinformation and deception play a much more significant role in intelligence and security informatics than in other informatics disciplines such as science, medicine, and the law, a new science of intelligence and security informatics must concern itself with semantic attacks and countermeasures.
C1 Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA.
RP Thompson, P (reprint author), Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA.
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NR 44
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 0-8194-5346-3
J9 P SOC PHOTO-OPT INS
PY 2004
VL 5423
BP 142
EP 151
DI 10.1117/12.554454
PG 10
WC Computer Science, Interdisciplinary Applications; Computer Science,
Theory & Methods; Operations Research & Management Science
SC Computer Science; Operations Research & Management Science
GA BAV93
UT WOS:000223848700014
ER
PT B
AU Fowler, RH
Navarro, D
Lawrence-Fowler, WA
Wang, XS
AF Fowler, RH
Navarro, D
Lawrence-Fowler, WA
Wang, XS
BE Arabnia, HR
Droegehorn, O
TI Dynamic visualization of hubs and authorities during web search
SO IC'04: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERNET
COMPUTING, VOLS 1 AND 2
LA English
DT Proceedings Paper
CT International Conference on Internet Computing/International Symposium
on Web Services and Applications
CY JUN 21-24, 2004
CL Las Vegas, NV
SP Comp Sci Res, Educ & Applicat Press, Int Technol Inst, Korean Soc Internet Informat, World Acad Sci Informat Technol
AB The Information Retrieval Visualizer (IRV) is a web-based retrieval system that provides the user with dynamic visualizations of search results. It combines well-known techniques of information retrieval, including stemming, keyword matching, and cosine similarity, with the new and relatively successful hubs and authority approach for describing Web document relevance based on a document's relationship to other documents. The system develops a new and unique approach to document visualization that encodes these metrics in a single visual representation. This new, easily scannable representation, allows the user to interact with search results as the scope of search is expanded dynamically across the Web.
C1 Univ Texas, Dept Comp Sci, Edinburg, TX 78539 USA.
RP Fowler, RH (reprint author), Univ Texas, Dept Comp Sci, Edinburg, TX 78539 USA.
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NR 16
TC 0
Z9 0
PU C S R E A PRESS
PI ATHENS
PA 115 AVALON DR, ATHENS, GA 30606 USA
BN 1-932415-46-7
PY 2004
BP 517
EP 521
PG 5
WC Computer Science, Information Systems; Computer Science, Software
Engineering; Computer Science, Theory & Methods
SC Computer Science
GA BBK73
UT WOS:000225901700079
ER
PT J
AU Klink, S
AF Klink, S
TI Improving document transformation techniques with collaborative learned
term-based concepts
SO READING AND LEARNING
SE LECTURE NOTES IN COMPUTER SCIENCE
LA English
DT Article
ID QUERY EXPANSION; RETRIEVAL; RELEVANCE
AB Document Transformation techniques have been studied for decades. In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.
C1 Univ Trier, Dept Database & Informat Syst, DBIS, D-54286 Trier, Germany.
RP Klink, S (reprint author), Univ Trier, Dept Database & Informat Syst, DBIS, D-54286 Trier, Germany.
EM klink@uni-trier.de
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NR 36
TC 10
Z9 10
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
J9 LECT NOTES COMPUT SC
PY 2004
VL 2956
BP 281
EP 305
PG 25
WC Computer Science, Artificial Intelligence
SC Computer Science
GA BAC11
UT WOS:000221517400016
ER
PT S
AU Larson, RR
Frontiera, P
AF Larson, RR
Frontiera, P
BE Heery, R
Lyon, L
TI Spatial ranking methods for Geographic Information Retrieval (GIR) in
digital libraries
SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES
SE Lecture Notes in Computer Science
LA English
DT Article; Proceedings Paper
CT 8th 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
AB This paper presents results from an evaluation of algorithms for ranking results by probability of relevance for Geographic Information Retrieval (GIR) applications. We review the work done on GIR and especially on ranking algorithms for GIR. We evaluate these algorithms using a test collection of 2500 metadata records from a geographic digital library. We present an algorithm for GIR ranking based on logistic regression from samples of the test collection. We also examine the effects of different representations of the geographic regions being searched, including minimum bounding rectangles, and convex hulls.
C1 Univ Calif Berkeley, Sch Informat & Syst, Berkeley, CA 94720 USA.
Univ Calif Berkeley, Coll Environm Design, Berkeley, CA 94720 USA.
RP Larson, RR (reprint author), Univ Calif Berkeley, Sch Informat & Syst, Berkeley, CA 94720 USA.
EM ray@sherlock.berkeley.edu; pattyf@regis.berkeley.edu
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NR 19
TC 11
Z9 11
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 45
EP 56
PG 12
WC Computer Science, Information Systems; Computer Science, Theory &
Methods
SC Computer Science
GA BAX48
UT WOS:000224092600005
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.
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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 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;
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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.
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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 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
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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 J
AU Zhou, L
Zhang, DS
AF Zhou, L
Zhang, DS
TI NLPIR: A theoretical framework for applying natural language processing
to information retrieval
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
AB The role of information retrieval (IR) in support of decision making and knowledge management has become increasingly significant. Confronted by various problems in traditional keyword-based IR, many researchers have been investigating the potential of natural language processing (NLP) technologies. Despite widespread application of NLP in IR and high expectations that NLP can address the problems of traditional IR, research and development of an NLP component for an IR system still lacks support and guidance from a cohesive framework. In this paper, we propose a theoretical framework called NLPIR that aims at integrating NLP into IR and at generalizing broad application of NLP in IR. Some existing NLP techniques are described to validate the framework, which not only can be applied to current research, but is also envisioned to support future research and development in IR that involve NLP.
C1 Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21250 USA.
RP Zhou, L (reprint author), Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21250 USA.
EM zhoul@umbc.edu; zhangd@umbc.edu
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NR 40
TC 5
Z9 5
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 JAN 15
PY 2003
VL 54
IS 2
BP 115
EP 123
DI 10.1002/asi.10193
PG 9
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 631PD
UT WOS:000180175400003
ER
PT S
AU Lin, C
Ma, SP
Zhang, M
AF Lin, C
Ma, SP
Zhang, M
GP IEEE
IEEE
TI Parameter optimization for information retrieval with genetic algorithm
SO 2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS
1-5, CONFERENCE PROCEEDINGS
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 (SMC 03)
CY OCT 05-08, 2003
CL WASHINGTON, D.C.
SP IEEE, Syst, Man & Cybernet Soc
DE information retrieval; criterion; IR system; probabilistic model;
genetic algorithm; parameter setting; fitness function
AB In most of the modern information retrieval (IR) systems, such as Okapi system, there are a variety of parameters to be tuned which are data-dependent and sensitive. Manual parameter setting with fixed experimental values is not always feasible and reliable in practical cases. Furthermore, the supervised learning approaches are not applicable for lacking of relevant information while retrieving. Therefore, an automatic unsupervised parameter learning mechanism is necessary and important. In this paper, a genetic algorithm (GA) based parameter optimization approach is proposed and experimented on Okapi system using large scale data sets of TREC11, TREC10 and TREC9 web track collections. It indicates that our algorithm is effective to adjust system parameters and improve the retrieval performance significantly.
C1 Tsing Hua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Hsinchu, Taiwan.
RP Lin, C (reprint author), Tsing Hua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Hsinchu, Taiwan.
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NR 9
TC 0
Z9 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1062-922X
BN 0-7803-7952-7
J9 IEEE SYS MAN CYBERN
PY 2003
BP 3822
EP 3827
PG 6
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
Computer Science, Cybernetics; Computer Science, Interdisciplinary
Applications; Computer Science, Software Engineering; Robotics
SC Automation & Control Systems; Computer Science; Robotics
GA BX83D
UT WOS:000186578600624
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.
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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 B
AU Thompson, P
AF Thompson, P
BE Chen, SH
Cheng, HD
Chiu, DKY
Das, S
Duro, R
Kerre, EE
Leong, HV
Li, Q
Lu, M
Romay, MG
Ventura, D
Wu, J
TI Utility-theoretic information retrieval, cognitive hacking, and
intelligence and security informatics
SO PROCEEDINGS OF THE 7TH JOINT CONFERENCE ON INFORMATION SCIENCES
LA English
DT Proceedings Paper
CT 7th Joint Conference on Information Sciences (JCIS)
CY SEP 26-30, 2003
CL RES TRIANGLE PK, NC
SP Assoc Intelligent Machinery, Duke Univ, Elsevier Publ Inc, Informat Sci Journal, Harbin Inst Technol, NIEHS
C1 Dartmouth Coll, Hanover, NH 03755 USA.
RP Thompson, P (reprint author), Dartmouth Coll, Hanover, NH 03755 USA.
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NR 14
TC 0
Z9 0
PU ASSOC INTELLIGENT MACHINERY
PI DURHAM
PA PO BOX 90291, DURHAM, NC 27708-0291 USA
BN 0-9707890-2-5
PY 2003
BP 452
EP 454
PG 3
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Interdisciplinary Applications; Computer
Science, Theory & Methods
SC Computer Science
GA BX96T
UT WOS:000187061500114
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
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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 Furner, J
AF Furner, J
TI On recommending
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
ID INFORMATION-RETRIEVAL; SYSTEMS; SEARCH
AB The core of any document retrieval system is a mechanism that ranks the documents in a large collection in order of the likelihood with which they match the preferences of any person who interacts with the system. Given a broader interpretation of "recommending" than is commonly accepted, such a preference ordering may be viewed as a recommendation, made by the system to the information seeker, that is itself typically derived through synthesis of multiple preference orderings expressed as recommendations by indexers, information seekers, and document authors. The ERIn (Evaluation-Recommendation-Information) model, a decision-theoretic framework for understanding information-related activity, highlights the centrality of recommending in the document retrieval process, and may be used to clarify the respects in which indexing, rating, and citation may be considered analogous, as well as to make explicit the points at which content-based, collaboration-based, and context-based flavors of document retrieval systems vary.
C1 Univ Calif Los Angeles, Los Angeles, CA 90036 USA.
RP Furner, J (reprint author), Univ Calif Los Angeles, 300 Young Dr N,Mailbox 951520, Los Angeles, CA 90036 USA.
RI Furner, Jonathan/; Furner, Jonathan/
OI Furner, Jonathan/0000-0001-8839-9930; Furner,
Jonathan/0000-0001-8839-9930
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NR 50
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 JUL
PY 2002
VL 53
IS 9
BP 747
EP 763
DI 10.1002/asi.10080
PG 17
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 564ME
UT WOS:000176317500006
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
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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 J
AU Losee, RM
AF Losee, RM
TI Natural language processing in support of decision-making: phrases and
part-of-speech tagging
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; TERM DEPENDENCE;
SPECIAL-ISSUE; DISAMBIGUATION; DISTRIBUTIONS; PROBABILITY; EXPANSION;
NETWORKS; MODEL
AB The use of natural language information can improve decision-making. Darwinian considerations suggest that language may have developed because it leads to improved decision-making and survival, justifying the study of language's contribution to decision-making. The study of information-based decision-making within the context of evolution provides a view of information use that allows us to both describe the phenomenon of information use as well as to explain why an information use occurs as it does. Increasing information retrieval performance using phrases and part-of-speech (POS) information is one example of a type of decision-making performance that is improved when using this linguistic information. By studying a set of phrases used in a text retrieval system, we are able to show the relative effectiveness of using multi-term phrases as opposed to individual terms, as well as the relative worth of POS tagged terms or phrases, as opposed to untagged terms or phrases. An explanation is suggested for why POS tags contribute less to higher order grammatical constructs. We propose a measure of those needs for POS disambiguation that can be addressed by tagging; some example terms are analyzed using this measure, and specific degrees of ambiguity are proposed. (C) 2001 Elsevier Science Ltd. All rights reserved.
C1 Univ N Carolina, Sch Lib & Informat Sci, Chapel Hill, NC 27599 USA.
RP Losee, RM (reprint author), Univ N Carolina, Sch Lib & Informat Sci, Manning Hall,CB 3360, Chapel Hill, NC 27599 USA.
EM losee@ils.unc.edu
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NR 56
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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 NOV
PY 2001
VL 37
IS 6
BP 769
EP 787
DI 10.1016/S0306-4573(00)00061-3
PG 19
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 466JP
UT WOS:000170642700001
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
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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 S
AU Taghva, K
Nartker, T
Borsack, J
Lumos, S
Condit, A
Young, R
AF Taghva, K
Nartker, T
Borsack, J
Lumos, S
Condit, A
Young, R
BE Kantor, PB
Lopresti, DP
Zhou, J
TI Evaluating text categorization in the presence of OCR errors
SO DOCUMENT RECOGNITION AND RETRIEVAL VIII
SE PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
(SPIE)
LA English
DT Proceedings Paper
CT 8th Annual Document Recognition and Retrieval Conference
CY JAN 24-25, 2001
CL SAN JOSE, CA
SP Soc Imaging Sci & Technol, SPIE
DE OCR; errors; text categorization; naive Bayes model; categorization;
classification
ID MODEL
AB In this paper we describe experiments that investigate the effects of OCR errors on text categorization. In particular, we show that in our environment. OCR errors have no effect on categorization when we use a classifier based on the naive Bayes model. We also observe that dimensionality reduction techniques eliminate a large number of OCR errors and improve categorization results.
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.
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NR 16
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 0-8194-3985-1
J9 P SOC PHOTO-OPT INS
PY 2001
VL 4307
BP 68
EP 74
PG 7
WC Computer Science, Information Systems; Computer Science, Software
Engineering
SC Computer Science
GA BR90Y
UT WOS:000168019700007
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
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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 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 2
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE information retrieval; retrieval theory; probabilistic model; term
weighting; experiments
ID CO-OCCURRENCE DATA; DOCUMENT-RETRIEVAL; TREC
AB The paper combines a comprehensive account of the 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 are 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
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NR 62
TC 10
Z9 10
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 809
EP 840
DI 10.1016/S0306-4573(00)00016-9
PG 32
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 354HZ
UT WOS:000089325100002
ER
PT J
AU Li, Y
Zhang, C
Swan, JR
AF Li, Y
Zhang, C
Swan, JR
TI An information filtering model on the Web and its application in
JobAgent
SO KNOWLEDGE-BASED SYSTEMS
LA English
DT Article
DE information filtering; rough set; intelligent information agent
ID RETRIEVAL; INFERENCE; RELEVANCE
AB Machine-learning techniques play the important roles for information filtering. The main objective of machine-learning is to obtain users' pro files. To decrease the burden of on-line learning, it is important to seek suitable structures to represent user information needs. This paper proposes a model for information filtering on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. An experimental system JobAgent is also presented to verify this model, and it shows that the rough set based model can provide an efficient approach to solve the information overload problem. (C) 2000 Elsevier Science B.V. All rights reserved.
C1 Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4001, Australia.
Deakin Univ, Sch Comp & Math, Geelong, Vic 3217, Australia.
RP Li, Y (reprint author), Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4001, Australia.
EM yuefeng@deakin.edu.au; chengqi@deakin.edu.au; jrswan@deakin.edu.au
RI Li, Yuefeng/I-9883-2012
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NR 27
TC 51
Z9 54
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 OCT
PY 2000
VL 13
IS 5
SI SI
BP 285
EP 296
DI 10.1016/S0950-7051(00)00088-5
PG 12
WC Computer Science, Artificial Intelligence
SC Computer Science
GA 380DN
UT WOS:000165683500006
ER
PT J
AU Makhoul, J
Kubala, F
Leek, T
Liu, DB
Nguyen, L
Schwartz, R
Srivastava, A
AF Makhoul, J
Kubala, F
Leek, T
Liu, DB
Nguyen, L
Schwartz, R
Srivastava, A
TI Speech and language technologies for audio indexing and retrieval
SO PROCEEDINGS OF THE IEEE
LA English
DT Article
DE audio browsing; audio indexing; information extraction; information
retrieval; named-entity extraction; name spotting; speaker change
detection; speaker clustering; speaker identification; speech
recognition; story segmentation; topic classification
ID HIDDEN MARKOV-MODELS; MAXIMUM-LIKELIHOOD; ALGORITHM; RECOGNITION
AB With the advent of essentially unlimited data storage capabilities and with the proliferation of the use of the Internet. it becomes reasonable to imagine a world in which it would be possible to access any of the stored information at will with a few keystrokes or Voice commands. Since much of this data will be in the form of speech from various sources, it becomes important to develop the technologies necessary for indexing and browsing arch audio data. This paper describes some of the requisite speech and language technologies that would be required and introduces an effort aimed at integrating these technologies into a system, called Rough 'n' Ready, which indexes speech darn, creates a structural summarization, and provides tools for browsing the stored data. The technologies highlighted in this paper include speaker-independent continuous speech recognition, speaker segmentation and identification. name spotting, topic classification, story segmentation, and information retrieval. The system automatically segments the continuous audio input stream by speaker clusters audio segments from the same speaker identifies speakers known to the system, and transcribes the spoken words. It also segments the input stream into stories Based on their topic content, and locates the names of persons, places, and organizations. These structural features are stored in a database and are used to construct highly selective search queries for retrieving specific content from large audio archives.
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RP Makhoul, J (reprint author), BBN Technol, Cambridge, MA 02138 USA.
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NR 38
TC 50
Z9 52
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA
SN 0018-9219
J9 P IEEE
JI Proc. IEEE
PD AUG
PY 2000
VL 88
IS 8
BP 1338
EP 1353
DI 10.1109/5.880087
PG 16
WC Engineering, Electrical & Electronic
SC Engineering
GA 369GR
UT WOS:000165058000012
ER
PT S
AU Zhang, CQ
Li, YF
AF Zhang, CQ
Li, YF
BE Mohammadian, M
TI Rough-set based agent model for intelligent information gathering on
Internet
SO NEW FRONTIERS IN COMPUTATIONAL INTELLIGENCE AND ITS APPLICATIONS
SE FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
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 RETRIEVAL
AB In this paper, an agent model for intelligent information gathering applications on Internet is proposed. In this model, a method to generate agent beliefs about users' queries is presented, in which users' queries are described as an approximate concepts. Based on this understanding, the agent belief about a user's query can be described into two levels: the interesting categories, and the relevant terms about the interesting categories. The rough set decision model is also used to generate agent protocols. By using this kind of protocol, the dynamic document stream is divided into three parts: positive region (relevant documents), boundary region (possible relevant documents), and negative region (irrelevant documents), instead of the two parts in the traditional research which are relevant documents and irrelevant documents.
C1 Deakin Univ, Sch Comp & Math, Geelong, Vic 3217, Australia.
RP Zhang, CQ (reprint author), Deakin Univ, Sch Comp & Math, Geelong, Vic 3217, Australia.
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NR 29
TC 0
Z9 0
PU I O S PRESS
PI AMSTERDAM
PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS
SN 0922-6389
BN 90-5199-476-1
J9 FR ART INT
PY 2000
VL 57
BP 354
EP 363
PG 10
WC Automation & Control Systems; Computer Science, Artificial Intelligence
SC Automation & Control Systems; Computer Science
GA BP98N
UT WOS:000086824400039
ER
PT B
AU Xu, JX
Weischedel, R
AF Xu, JX
Weischedel, R
BE Schutze, H
Su, KY
TI Cross-lingual information retrieval using hidden Markov models
SO PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN
NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA
LA English
DT Proceedings Paper
CT Joint SIGDAT Conference on Empirical Methods in Natural Language
Processing and Very Large Corpora held in Conjunction with the 38th
Annual Meeting of the Association-for-Computational-Linguistics
CY OCT 07-08, 2000
CL Hong Kong, PEOPLES R CHINA
SP Behavior Design Corp, GroupFire Inc, Intel China Res Ctr, LEXIS-NEXIS
HO Hong Kong Univ Sci & Technol
AB This paper presents empirical results in cross-lingual information retrieval using English queries to access Chinese documents (TREC-5 and TREC-6) and Spanish documents (TREC-4). Since our interest is in languages where resources may be minimal, we use an integrated probabilistic model that requires only a bilingual dictionary as a resource. We explore how a combined probability model of term translation and retrieval can reduce the effect of translation ambiguity. In addition, we estimate an upper bound on performance, if translation ambiguity were a solved problem. We also measure performance as a function of bilingual dictionary size.
C1 BBN Technol, Cambridge, MA 02138 USA.
RP Xu, JX (reprint author), BBN Technol, 70 Fawcett St, Cambridge, MA 02138 USA.
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
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Oard D. W., 1998, P 3 C ASS MACH TRANS
Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957
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XU J, 1998, ACM T INFORMATION SY, V16
NR 23
TC 2
Z9 2
PU ASSOCIATION COMPUTATIONAL LINGUISTICS
PI SOMERSET
PA PO BOX 6090, SOMERSET, NJ 08875 USA
PY 2000
BP 95
EP 103
PG 9
WC Computer Science, Artificial Intelligence; Computer Science,
Interdisciplinary Applications; Linguistics
SC Computer Science; Linguistics
GA BAP00
UT WOS:000223092900012
ER
PT J
AU Sparck-Jones, K
AF Sparck-Jones, K
TI Information retrieval and artificial intelligence
SO ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE information retrieval; probabilistic model; Artificial Intelligence
ID SELECTION
AB This paper addresses the relations between information retrieval (IR) and AI. It examines document retrieval, summarising its essential features and illustrating the state of its art by presenting one probabilistic model in detail, with some test results showing its value. The paper then analyses this model and related successful approaches, concentrating on and justifying their use of weak, redundant representation and reasoning. It goes on to other information management tasks and considers how the concepts and methods developed for retrieval may be applied to these, concluding by arguing that such ways of dealing with information may also have wider relevance to AI. (C) 1999 Published by Elsevier Science B.V. All rights reserved.
C1 Univ Cambridge, Comp Lab, Cambridge CB2 3QG, England.
RP Sparck-Jones, K (reprint author), Univ Cambridge, Comp Lab, New Museums Site,Pembroke St, Cambridge CB2 3QG, England.
EM sparckjones@cl.cam.ac.uk
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NR 58
TC 4
Z9 4
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0004-3702
J9 ARTIF INTELL
JI Artif. Intell.
PD OCT
PY 1999
VL 114
IS 1-2
BP 257
EP 281
DI 10.1016/S0004-3702(99)00075-2
PG 25
WC Computer Science, Artificial Intelligence
SC Computer Science
GA 263XZ
UT WOS:000084151700009
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.
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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 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.
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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 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
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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
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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 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.
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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 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.
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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
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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 Plaunt, C
Norgard, BA
AF Plaunt, C
Norgard, BA
TI An association-based method for automatic indexing with a controlled
vocabulary
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
ID CONSISTENCY
AB in this article, we describe and test a two-stage algorithm based on a lexical collocation technique which maps from the lexical clues contained in a document representation into a controlled vocabulary list of subject headings. Using a collection of 4,626 INSPEC documents, we create a "dictionary" of associations between the lexical items contained in the titles, authors, and abstracts, and controlled vocabulary subject headings assigned to those records by human indexers using a likelihood ratio statistic as the measure of association. In the deployment stage, we use the dictionary to predict which of the controlled vocabulary subject headings best describe new documents when they are presented to the system. Our evaluation of this algorithm, in which we compare the automatically assigned subject headings to the subject headings assigned to the test documents by human catalogers, shows that we can obtain results comparable to, and consistent with, human cataloging. In effect, we have cast this as a classic partial match information retrieval problem. We consider the problem to be one of "retrieving" (or assigning) the most probably "relevant" (or correct) controlled vocabulary subject headings to a document based on the clues contained in that document.
C1 Univ Calif Berkeley, Sch Lib & Informat Studies, Berkeley, CA 94720 USA.
RP Plaunt, C (reprint author), Univ Calif Berkeley, Sch Lib & Informat Studies, Berkeley, CA 94720 USA.
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NR 25
TC 16
Z9 15
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 1998
VL 49
IS 10
BP 888
EP 902
PG 15
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 100HQ
UT WOS:000074811100004
ER
PT J
AU Chen, HC
Shankaranarayanan, G
She, LL
Iyer, A
AF Chen, HC
Shankaranarayanan, G
She, LL
Iyer, A
TI A machine learning approach to inductive query by examples: An
experiment using relevance feedback, ID3, genetic algorithms, and
simulated annealing
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
ID DOCUMENT-RETRIEVAL; INFORMATION-RETRIEVAL; SYSTEMS; NETWORKS
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 inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems, In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval, An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze users' preferred documents (or records), identify users' underlying information needs, and also suggest alternatives for search for database management systems and Internet applications.
C1 Univ Arizona, Coll Business & Publ Adm, Dept Management Informat Syst, Tucson, AZ 85721 USA.
I2 Technol Inc, Irving, TX 75039 USA.
RP Chen, HC (reprint author), Univ Arizona, Coll Business & Publ Adm, Dept Management Informat Syst, Tucson, AZ 85721 USA.
EM hchen@bpa.arizona.edu; gshankar@bpa.arizona.edu; lshe@bpa.arizona.edu;
lyer@l2.com
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NR 45
TC 45
Z9 46
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 JUN
PY 1998
VL 49
IS 8
BP 693
EP 705
PG 13
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA ZM670
UT WOS:000073564100004
ER
PT J
AU Chen, HC
Chung, YM
Ramsey, M
Yang, CC
AF Chen, HC
Chung, YM
Ramsey, M
Yang, CC
TI A smart itsy bitsy spider for the Web
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
ID INFORMATION-RETRIEVAL; GENETIC ALGORITHMS; DOCUMENT-RETRIEVAL;
VOCABULARY PROBLEM; DISCOVERY; DATABASES; INTERNET; NETWORKS; AGENTS
AB As part of the ongoing Illinois Digtal Library initiative project, this research proposes an intelligent agent approach to Web searching. In this experiment, we developed two Web personal spiders based on best first search and genetic algorithm techniques, respectively, These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the a Web, based on the links and keyword indexing. A graphical, dynamic, Java-based interface was developed and is available for Web access, a system architecture for implementing such an agent-based spider is presented, followed by detailed discussions of benchmark testing and user evaluation results. In benchmark testing, although the genetic algorithm spider did not outperform the best first search spider, rare bound both results to be comparable and complementary, an user evaluation, the genetic algorithm spider obtained significantly higher recall value than that of the best first: search spider, However their precision values were not statistically different, The mutation process introduced in genetic algorithm allows users Po find other potential relevant homepages that cannot be explored via a conventional local search process, In addition, we Found the Java-based interface to be a necessary component for design of a truly interactive and dynamic Web agent.
C1 Univ Arizona, Dept Management Informat Syst, Artificial Intelligence Lab, Tucson, AZ 85721 USA.
Univ Arizona, Dept Elect & Comp Engn, Tucson, AZ 85721 USA.
RP Chen, HC (reprint author), Univ Arizona, Dept Management Informat Syst, Artificial Intelligence Lab, Tucson, AZ 85721 USA.
EM hchen@bpa.arizona.edu; ychung@bpa.arizona.edu; mramsey@bpa.arizona.edu;
chrisy@ece.arizona.edu
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NR 56
TC 43
Z9 45
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 15
PY 1998
VL 49
IS 7
BP 604
EP 618
PG 15
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA ZG834
UT WOS:000073044700003
ER
PT J
AU Wilbur, WJ
AF Wilbur, WJ
TI The knowledge in multiple human relevance judgments
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE algorithms; experimentation; measurement; performance; document
retrieval test set construction; human relevance judgments; inverse
document frequency weights; log odds of relevance; minimal-length
least-squares fit; vector model; word weights
ID DOCUMENT-RETRIEVAL SYSTEM; PERFORMANCE
AB We show first that the pooling of multiple human judgments of relevance provides a predictor of relevance that is Superior to that obtained from a single human's relevance judgments. A learning algorithm applied to a set of relevance judgments obtained from a single human would be expected to perform on new material at a level somewhat below that human. However, we examine two learning methods which when trained on the superior source of pooled human relevance judgments are able to perform at the level of a single human on new material. All performance comparisons are based on an independent human judge. Both algorithms function by producing term weights - one by a log odds calculation and the other by producing a least-squares fit to human relevance ratings. Some characteristics of the algorithms are examined.
C1 Natl Ctr Biotechnol Informat, NIH, Bethesda, MD USA.
RP Wilbur, WJ (reprint author), Natl Lib Med, Bldg 38A,Rm 8S806,8600 Rockville Pike, Bethesda, MD 20894 USA.
CR ALLEN JF, 1987, NATURAL LANGUAGE UND
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NR 33
TC 6
Z9 6
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 1998
VL 16
IS 2
BP 101
EP 126
DI 10.1145/279339.279340
PG 26
WC Computer Science, Information Systems
SC Computer Science
GA 106GN
UT WOS:000075121700001
ER
PT J
AU Ishida, E
AF Ishida, E
TI An experiment of automatic classification of books using Nippon Decimal
Classification
SO LIBRARY AND INFORMATION SCIENCE
LA Japanese
DT Article
AB In information retrieval, texts are usually retrieved by them with queries. In this study, an approach was suggested that texts are automatically classified into categories and retrieved by matching them with queries classified in the same way. For an efficient information retrieval using automatic classification, extracting methods of words from texts and matching methods are essential. Some extracting methods from Japanese texts have been suggested in natural languages processing. However, it is difficult to extract significant words from Japanese texts because Japanese texts are written without blank space separating words. As for matching methods, many weighting methods have been suggested as well as vector space models and probabilistic models.
This article reports the results of an experiment of classifying Japanese texts into Nippon Decimal Classification (NDC) categories based on the title information in Japanese MARC records. In this experiment, three extracting methods: -juman, MHSA, n-gram--are tested on a set of 1,000 books. Four weighting methods: --relative term frequency between categories, tf idf and tf (max).idf--are tested. The results indicate that the extracting method using juman achieved best and the best weighting method was the relative term frequency between categories, being able to select correct classification categories (upper three digits of NDC) for about 55.9% of 1,000 books.
C1 Keio Univ, Grad Sch Lib & Informat Sci, Minato Ku, Tokyo 1088345, Japan.
RP Ishida, E (reprint author), Keio Univ, Grad Sch Lib & Informat Sci, Minato Ku, 2-15-45 Mita, Tokyo 1088345, Japan.
CR HAMILL K, 1994, J AM SOC INFORM SCI, V31, P252
HASEBE K, 1995, P INT S DIG LIB, P181
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MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
NR 4
TC 0
Z9 0
PU MITA SOC LIBRARY INFORMATION SCIENCE
PI TOKYO
PA C/O SCH LIB & INFO SCIENCE, KEIO UNIV 2-15-45 MITA, MINATO-KU, TOKYO,
108-8345, JAPAN
SN 0373-4447
J9 LIBR INFORM SC
JI Libr. Inf. Sci.
PY 1998
IS 39
BP 31
EP 45
PG 15
WC Information Science & Library Science
SC Information Science & Library Science
GA 288PQ
UT WOS:000085572600002
ER
PT J
AU Mizzaro, S
AF Mizzaro, S
TI Relevance: The whole history
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Review
ID INFORMATION-RETRIEVAL; DOCUMENT RELEVANCE; SEARCH INTERFACE; JUDGMENTS;
USERS; PERFORMANCE; SCIENCE; SEEKING; UTILITY; DEFINITION
AB Relevance is a fundamental, though not completely understood, concept for documentation, information science, and information retrieval, This article presents the history of relevance through an exhaustive review of the literature, Such history being very complex (about ISO papers are discussed), it is not simple to describe it in a comprehensible way, Thus, first of all a framework for establishing a common ground is defined, and then the history itself is illustrated via the presentation in chronological order of the papers on relevance, The history is divided irate three periods (''Before 1958,'' ''1959-1976,'' and ''1977l-present'') and, inside each period, the papers on relevance are analyzed under seven different aspects (methodological foundations, different kinds of relevance, beyond-topical criteria adopted by users, modes for expression of the relevance judgment, dynamic nature of relevance, types of document representation, and agreement among different judges).
RP Mizzaro, S (reprint author), UNIV UDINE,DIPARTIMENTO MATEMAT & INFORMAT,VIA SCI 206 LOC RIZZI,I-33100 UDINE,ITALY.
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NR 184
TC 189
Z9 191
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 1997
VL 48
IS 9
BP 810
EP 832
DI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U
PG 23
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA XR116
UT WOS:A1997XR11600006
ER
PT J
AU Berger, FC
VanBommel, P
AF Berger, FC
VanBommel, P
TI Augmenting a characterization network with semantic information
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
ID RETRIEVAL
AB A searcher in a list of phrases which serves as an index to a set of documents often has problems finding the right words when the information sought for has to be described. Offering alternative phrasings and pointing to related concepts in the index could be a great help in this difficult process of query formulation. Usually the index is obtained by characterizing the set of documents. This paper examines the effect of adding semantic relations to the index. Various ways in which nodes in an index can be related are discussed, and criteria for adding new index entries are introduced. The effects of adding relations on the process of offering support during the formulation process are treated as well. (C) 1997 Elsevier Science Ltd.
RP Berger, FC (reprint author), CATHOLIC UNIV NIJMEGEN,INST COMP SCI,CSI-R9606,NL-6525 ED NIJMEGEN,NETHERLANDS.
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NR 24
TC 2
Z9 2
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 1997
VL 33
IS 4
BP 453
EP 479
DI 10.1016/S0306-4573(97)00001-0
PG 27
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA XV148
UT WOS:A1997XV14800003
ER
PT J
AU Fowler, RH
Fowler, WAL
Williams, JL
AF Fowler, RH
Fowler, WAL
Williams, JL
TI 3D visualization of WWW semantic content for browsing and query
formulation
SO JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
LA English
DT Article
ID GRAPHS; ALGORITHM; DIAGRAMS
AB Visualization is a promising technique for both enhancing users' perception of structure in the Internet and providing navigation facilities for its large information spaces. This paper describes an application of the Document Explorer to the visualization of WWW content structure. The system provides visualization, browsing, and query formulation mechanisms based on documents' semantic content. These mechanisms complement text and link-based search by supplying a visual search and query formulation environment using semantic associations among documents. The user can view and interact with visual representations of WWW document relations to traverse this derived document space. The relationships among individual keywords in the documents are also represented visually to support query formulation by direct manipulation of content words in the document set. A suite of navigation and orientation tools is provided which focuses on orientation and navigation using the visual representations of document set and term collection. (C) 1997 Academic Press Limited.
C1 UNIV TEXAS,DEPT COMP SCI,EDINBURG,TX 78539.
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NR 36
TC 0
Z9 0
PU ACADEMIC PRESS LTD
PI LONDON
PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX
SN 1084-8045
J9 J NETW COMPUT APPL
JI J. Netw. Comput. Appl.
PD APR
PY 1997
VL 20
IS 2
BP 135
EP 149
DI 10.1006/jnca.1997.0042
PG 15
WC Computer Science, Hardware & Architecture; Computer Science,
Interdisciplinary Applications; Computer Science, Software Engineering
SC Computer Science
GA XX654
UT WOS:A1997XX65400003
ER
PT J
AU Nie, JY
Brisebois, M
AF Nie, JY
Brisebois, M
TI An inferential approach to information retrieval and its implementation
using a manual thesaurus
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE information retrieval; logical inference; fuzzy modal logic; thesaurus;
learning; relevance feedback
ID DOCUMENT-RETRIEVAL; QUERY EXPANSION; MODEL; SYSTEMS; LOGIC
AB Most inferential approaches to Information Retrieval (IR) have been investigated within the probabilistic framework. Although these approaches allow one to cope with the underlying uncertainty of inference in IR, the strict formalism of probability theory often confines our use of knowledge to statistical knowledge alone (e.g. connections between terms based on their co-occurrences). Human-defined knowledge (e.g. manual thesauri) can only be incorporated with difficulty. In this paper, based on a general idea proposed by van Rijsbergen, we first develop an inferential approach within a fuzzy modal logic framework. Differing from previous approaches, the logical component is emphasized and considered as the pillar in our approach. In addition, the flexibility of a fuzzy modal logic framework offers the possibility of incorporating human-defined knowledge in the inference process. After defining the model, we describe a method to incorporate a human-defined thesaurus into inference by taking user relevance feedback into consideration, Experiments on the CACM corpus using a general thesaurus of English, Wordnet, indicate a significant improvement in the system's performance.
RP Nie, JY (reprint author), UNIV MONTREAL,DEPT INFORMAT & RECH OPERAT,CP 6128,SUCCURSALE CTR VILLE,MONTREAL,PQ H3C 3J7,CANADA.
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NR 53
TC 5
Z9 5
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 OCT
PY 1996
VL 10
IS 5-6
BP 409
EP 439
DI 10.1007/BF00130693
PG 31
WC Computer Science, Artificial Intelligence
SC Computer Science
GA VQ256
UT WOS:A1996VQ25600003
ER
PT J
AU Chen, HC
Schatz, B
Ng, T
Martinez, J
Kirchhoff, A
Lin, CT
AF Chen, HC
Schatz, B
Ng, T
Martinez, J
Kirchhoff, A
Lin, CT
TI A parallel computing approach to creating engineering concept spaces for
semantic retrieval: The Illinois Digital Library Initiative project
SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
LA English
DT Article
DE semantic retrieval; concept space; concept association; parallel
computing; digital library
ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; CONNECTION MACHINE; NEURAL
NETWORKS; SYSTEMS; SEARCH; PERFORMANCE; DATABASES; DESIGN; MODEL
AB This research presents preliminary results generated from the semantic retrieval research component of the illinois Digital Library Initiative (DLI) project. Using a variation of the automatic thesaurus generation techniques, to which we refer as the concept space approach, we aimed to create graphs of domain-specific concepts (terms) and their weighted co-occurrence relationships for all major engineering domains. Merging these concept spaces and providing traversal paths across:different concept spaces could potentially help alleviate the vocabulary (difference) problem evident in large-scale information retrieval. We have experimented previously with such a technique for a smaller molecular biology domain (Worm Community System, with 10+ MBs of document collection) with encouraging results.
In order to address the scalability issue related to large-scale information retrieval and analysis for the current Illinois DLI project, we recently conducted experiments using the concept space approach on parallel supercomputers. Our test collection included 2+ GBs of computer science and electrical engineering abstracts extracted from the INSPEC database. The concept space approach called for extensive textual and statistical analysis (a form of knowledge discovery) based on automatic indexing and cooccurrence analysis algorithms, both previously tested in the biology domain. Initial testing results using a 512-node CM-5 and a 16-processor SGI Power Challenge were promising. Power Challenge was later selected to create a comprehensive computer engineering concept space of about 270,000 terms and 4,000,000+ links using 24.5 hours of CPU time. Our system evaluation involving 12 knowledgeable subjects revealed that the automatically-created computer engineering concept space generated significantly higher concept recall than the human-generated INSPEC computer engineering thesaurus. However, the INSPEC was more precise than the automatic concept space. Our current work mainly involves creating concept spaces for other major engineering domains and developing robust graph matching and traversal algorithms for cross-domain, concept-based retrieval. Future work also will include generating individualized concept spaces for assisting user-specific concept-based information retrieval.
C1 UNIV ILLINOIS,NATL CTR SUPERCOMP APPLICAT,BECKMAN INST,URBANA,IL 61801.
UNIV ARIZONA,SCI & ENGN LIB,TUCSON,AZ 85712.
UNIV ARIZONA,DEPT LIB & INFORMAT STUDIES,TUCSON,AZ 85712.
RP Chen, HC (reprint author), UNIV ARIZONA,KARL ELLER GRAD SCH MANAGEMENT,MIS DEPT,MCCLELLAND HALL,TUCSON,AZ 85721, USA.
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NR 41
TC 43
Z9 46
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264
SN 0162-8828
J9 IEEE T PATTERN ANAL
JI IEEE Trans. Pattern Anal. Mach. Intell.
PD AUG
PY 1996
VL 18
IS 8
BP 771
EP 782
PG 12
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
Electronic
SC Computer Science; Engineering
GA VE318
UT WOS:A1996VE31800002
ER
PT J
AU Chen, HC
Schuffels, C
Orwig, R
AF Chen, HC
Schuffels, C
Orwig, R
TI Internet categorization and search: A self-organizing approach
SO JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
LA English
DT Article
ID DOCUMENT-RETRIEVAL; INFORMATION-RETRIEVAL; ALGORITHMS; HYPERTEXT; SYSTEM
AB The problems of information overload and vocabulary differences have become more pressing with the emergence of increasingly popular Internet services. The main information retrieval mechanisms provided by the prevailing Internet WWW software are based on either keyword search (e.g., the Lycos server at CNU, the Yahoo server at Stanford) or hypertext browsing (e.g., Mosaic and Netscape). This research aims to provide an alternative concept-based categorization and search capability for WWW servers based on selected machine learning algorithms. Our proposed approach, which is grounded on automatic textual analysis of Internet documents (homepages), attempts to address the Internet search problem by first categorizing the content of Internet documents. We report results of our recent testing of a multilayered neural network clustering algorithm employing the Kohonen self-organizing feature map to categorize (classify) Internet homepages according to their content. The category hierarchies created could serve to partition the vast Internet services into subject-specific categories and databases and improve Internet keyword searching and/or browsing. (C) 1996 Academic Press, Inc.
RP Chen, HC (reprint author), UNIV ARIZONA,DEPT MANAGEMENT INFORMAT SYST,TUCSON,AZ 85721, USA.
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NR 62
TC 130
Z9 136
PU ACADEMIC PRESS INC JNL-COMP SUBSCRIPTIONS
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495
SN 1047-3203
J9 J VIS COMMUN IMAGE R
JI J. Vis. Commun. Image Represent.
PD MAR
PY 1996
VL 7
IS 1
BP 88
EP 102
DI 10.1006/jvci.1996.0008
PG 15
WC Computer Science, Information Systems; Computer Science, Software
Engineering
SC Computer Science
GA UA750
UT WOS:A1996UA75000008
ER
PT J
AU Gordon, MD
Lindsay, RK
AF Gordon, MD
Lindsay, RK
TI Toward discovery support systems: A replication, re-examination, and
extension of Swanson's work on literature-based discovery of a
connection between Raynaud's and fish oil
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
ID UNDISCOVERED PUBLIC KNOWLEDGE; MEDICAL LITERATURES; ONLINE SEARCH;
RELEVANCE; IMPLICIT
AB Don R. Swanson has undertaken a program of research to use the published medical literature as a source of discoveries. We have attempted to replicate his discovery of a connection between Raynaud's disease and dietary fish oil, as well as develop computer-based searching methods that could usefully support literature-based discoveries. We have been successful in replicating Swanson's discovery and have developed a method of discovery support based on the complete text of MEDLINE records. From these, we compute statistics based both on the frequency of tokens within a literature and on the number of records containing various tokens. We discuss the use of these statistics, suggesting that token and record frequencies are good indicators of literatures profitably related to some source literature, and that relative record frequencies are useful in isolating literatures with the potential of containing a discovery.
C1 UNIV MICHIGAN,SCH MED,MENTAL HLTH RES INST,ANN ARBOR,MI 48109.
RP Gordon, MD (reprint author), UNIV MICHIGAN,SCH BUSINESS,ANN ARBOR,MI 48109, USA.
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NR 24
TC 80
Z9 85
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 1996
VL 47
IS 2
BP 116
EP 128
DI 10.1002/(SICI)1097-4571(199602)47:2<116::AID-ASI3>3.0.CO;2-1
PG 13
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA TR396
UT WOS:A1996TR39600008
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.
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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 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.
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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 LOSEE, RM
AF LOSEE, RM
TI DETERMINING INFORMATION-RETRIEVAL AND FILTERING PERFORMANCE WITHOUT
EXPERIMENTATION
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
ID DOCUMENT-RETRIEVAL; SYSTEM PERFORMANCE; RELEVANCE; RANKING; PROBABILITY
AB The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of two similarly performing systems is superior. For both a single query term and for a multiple query term retrieval model, a method for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering are described. Simulations illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems.
RP LOSEE, RM (reprint author), UNIV N CAROLINA,MANNING HALL,CHAPEL HILL,NC 27599, USA.
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NR 44
TC 12
Z9 12
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 555
EP 572
DI 10.1016/0306-4573(95)00072-O
PG 18
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA RY937
UT WOS:A1995RY93700008
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.
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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.
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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 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
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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.
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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 S
AU vanRijsbergen, CJ
AF vanRijsbergen, CJ
BE vanLeeuwen, J
TI Information retrieval and informative reasoning
SO COMPUTER SCIENCE TODAY: RECENT TRENDS AND DEVELOPMENTS
SE Lecture Notes in Computer Science
LA English
DT Article
AB This is a brief account of some of the formal models underlying recent research in information retrieval. In particular I discuss the central role that uncertain implication plays in viewing retrieval as a form of inference. There is some hope that a paradigm borrowed from quantum mechanics may lead to a unified model, that is, a framework within which the different formal models can be expressed.
RP vanRijsbergen, CJ (reprint author), UNIV GLASGOW, DEPT COMP SCI, GLASGOW G12 8RZ, LANARK, SCOTLAND.
CR BELTRAMETETTI EG, 1981, LOGIC QUATUM MECH
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WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713
NR 22
TC 0
Z9 0
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 3-540-60105-8
J9 LECT NOTES COMPUT SC
PY 1995
VL 1000
BP 549
EP 559
PG 11
WC Computer Science, Theory & Methods
SC Computer Science
GA BG18S
UT WOS:A1995BG18S00034
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
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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.
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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 CHEN, H
DANOWITZ, AK
LYNCH, KJ
GOODMAN, SE
MCHENRY, WK
AF CHEN, H
DANOWITZ, AK
LYNCH, KJ
GOODMAN, SE
MCHENRY, WK
TI EXPLAINING AND ALLEVIATING INFORMATION MANAGEMENT INDETERMINISM - A
KNOWLEDGE-BASED FRAMEWORK
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
ID RETRIEVAL-SYSTEMS; EXPERT SYSTEM; DESIGN; MODEL
AB Our research attempted to identify the nature and causes of information management indeterminism in an online research environment and to propose solutions for alleviating this indeterminism. We conducted two empirical studies of information management activities. The first study identified the types and nature of information management indeterminism by evaluating archived texts. The second study focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. A knowledge-based design for alleviating indeterminism, which contains a system-generated thesaurus and an inferencing engine, is also proposed in this article.
C1 GEORGETOWN UNIV,SCH BUSINESS ADM,WASHINGTON,DC 20057.
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NR 63
TC 2
Z9 2
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 557
EP 577
DI 10.1016/0306-4573(94)90039-6
PG 21
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA NK607
UT WOS:A1994NK60700006
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
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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 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.
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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.
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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 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
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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 SHALINI, R
AF SHALINI, R
TI CITATION PROFILES TO IMPROVE RELEVANCE IN A 2-STAGE RETRIEVAL-SYSTEM - A
PROPOSAL
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
ID INDIVIDUAL-DIFFERENCES; INFORMATION-RETRIEVAL; SCIENCE; USERS
AB Individual differences in relevance judgements are not entirely erratic, and it is productive to consider citation profiles as a simple, convenient, and automatic method of reflecting the knowledge structure/past experience/viewpoints of the authors of papers. A citation profile is nothing but the list of citations appended to the paper. The list of references normally reflects a good sample of the complete or partial survey of that literature considered relevant by the author. Therefore, the source paper, together with its list of references, are to be kept together to provide a more complete picture of the 'content' and 'structure' of the document. The citation profile is believed to reflect the 'state of knowing' of the author, and higher commonality between the citation profile and the mental profile of a user would help improve a relevance decision. A mental profile is the state of knowing of the user, in terms of a set of documents known to the user as relevant. A step in the direction of designing and developing interactive, user-oriented, user-driven systems would be a two-stage information retrieval system where, in the first stage, a topical set of document representations would be produced by a Boolean search, and in the second stage, the citation profile of each document would be provided to the user, either in addition to or in lieu of abstracts.
RP SHALINI, R (reprint author), UNIV MYSORE, DEPT STUDIES LIB & INFORMAT SCI, MYSORE 570006, KARNATAKA, INDIA.
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NR 33
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.
PD JUL-AUG
PY 1993
VL 29
IS 4
BP 463
EP 470
DI 10.1016/0306-4573(93)90042-C
PG 8
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA LN321
UT WOS:A1993LN32100006
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.
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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 EVERETT, DM
CATER, SC
AF EVERETT, DM
CATER, SC
TI TOPOLOGY OF DOCUMENT-RETRIEVAL SYSTEMS
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
AB Document-retrieval systems can be classified on the basis of the topology each system induces on its set of possible documents. The induced topology measures closeness among documents, with two documents considered close if a retrieval which includes one document is likely to also include the other. The induced topology may be distinct from the usual topology which the set of documents inherits from its indexing system. Under a reasonably general definition of document-retrieval systems, the existence of a query which returns one of two preselected documents, but not the other, is a necessary and sufficient condition for the document-retrieval system to induce not only a topology, but also a metric on its set of documents. The topological structure and induced metric of the following document-retrieval systems are analyzed: the vector-space model, the fuzzy-set model, the extended-Boolean model, a probabilistic model, and the TIRS model.
RP EVERETT, DM (reprint author), UNIV GEORGIA,DEPT COMP SCI,ATHENS,GA 30602, USA.
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YU CT, 1985, 8TH P ANN INT ACMSIG
NR 17
TC 7
Z9 7
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 1992
VL 43
IS 10
BP 658
EP 673
DI 10.1002/(SICI)1097-4571(199212)43:10<658::AID-ASI3>3.0.CO;2-H
PG 16
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA JX779
UT WOS:A1992JX77900003
ER
PT J
AU WESTLAND, JC
AF WESTLAND, JC
TI ECONOMIC INCENTIVES FOR DATABASE NORMALIZATION
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
DE ECONOMICS OF INFORMATION TECHNOLOGY; DATABASE NORMALIZATION; RELATIONAL
DATABASE MODEL; COST-BENEFIT ANALYSIS
ID RETRIEVAL; INFORMATION; RELEVANCE; SYSTEM; MARKET; LAW
AB Database systems are central to business information processing. The conceptual basis for most commercial database managers is the relational model. Little research exists concerning the cost effectiveness of relational database normalization, but there is anecdotal evidence that normalization-induced fragmentation may create inefficiencies. Supply and demand for normalization is investigated given management policies for response time, database capacity, and deletion policies. On the supply side, normalization reduces costs associated with insertion, deletion, and change anomalies. The expected cost of removing change anomalies is linearly proportional to both minimum database size and to database capacity. The occurrence rates of either insertion or deletion anomalies are shown to be moderate for all but microcomputer sized databases. But because insertion or deletion anomalies tend to result in significant cost, even small probabilities of occurrence can result in significant costs. On the demand side, normalization can create retrieval inefficiencies where a comparatively small amount of information is being sought and retrieved from the database. Both an increase in clustering, and an increase in database size will exacerbate these inefficiencies. This can result in fragmentation inefficiencies and information overload. It is suggested that normalization reduces the opportunity cost associated with information retrieval from a database by improving recall and is most pronounced when recall is low. Where retrieval rates are high with respect to update events, the database fragmentation caused by normalization costs end users through slower retrieval response.
RP WESTLAND, JC (reprint author), UNIV SO CALIF,SCH BUSINESS ADM,LOS ANGELES,CA 90089, USA.
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NR 56
TC 8
Z9 8
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-OCT
PY 1992
VL 28
IS 5
BP 647
EP 662
DI 10.1016/0306-4573(92)90034-W
PG 16
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA JQ789
UT WOS:A1992JQ78900009
ER
PT J
AU FUHR, N
AF FUHR, N
TI PROBABILISTIC MODELS IN INFORMATION-RETRIEVAL
SO COMPUTER JOURNAL
LA English
DT Article
ID DOCUMENT-RETRIEVAL; RANKING PRINCIPLE; SYSTEMS
AB In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability-ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters involved. For the estimation of these parameters, three different learning strategies are distinguished, namely query-related, document-related and description-related learning. As a representative for each of these strategies, a specific model is described. A new approach regards IR as uncertain inference; here, imaging is used as a new technique for estimating the probabilistic parameters, and probabilistic inference networks support more complex forms of inference. Finally, the more general problems of parameter estimations, query expansion and the development of models for advanced document representations are discussed.
RP FUHR, N (reprint author), UNIV DORTMUND, POB 500500, W-4600 DORTMUND, GERMANY.
CR Biebricher P., 1988, 11th International Conference on Research and Development in Information Retrieval
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1989, IEEE DATA ENG, V12
NR 53
TC 86
Z9 89
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 243
EP 255
DI 10.1093/comjnl/35.3.243
PG 13
WC Computer Science, Hardware & Architecture; Computer Science, Information
Systems; Computer Science, Software Engineering; Computer Science,
Theory & Methods
SC Computer Science
GA JA889
UT WOS:A1992JA88900007
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.
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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 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.
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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.
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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
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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 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.
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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 TURTLE, H
CROFT, WB
AF TURTLE, H
CROFT, WB
TI EVALUATION OF AN INFERENCE NETWORK-BASED RETRIEVAL MODEL
SO ACM TRANSACTIONS ON INFORMATION SYSTEMS
LA English
DT Article
DE EXPERIMENTATION; PERFORMANCE; THEORY; DOCUMENT RETRIEVAL; INFERENCE
NETWORKS; NETWORK RETRIEVAL MODELS
ID INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; TERM DEPENDENCIES; BOOLEAN
QUERIES; EXPERT SYSTEMS; TEXT RETRIEVAL; RELEVANCE; LOGIC; UNCERTAINTY
AB The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models. The performance of a retrieval system based on the inference network model is evaluated and compared to performance with conventional retrieval models.
RP TURTLE, H (reprint author), UNIV MASSACHUSETTS,DEPT COMP & INFORMAT SCI,AMHERST,MA 01003, USA.
CR Cheeseman P., 1988, Computational Intelligence, V4, DOI 10.1111/j.1467-8640.1988.tb00091.x
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NR 45
TC 181
Z9 188
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 187
EP 222
DI 10.1145/125187.125188
PG 36
WC Computer Science, Information Systems
SC Computer Science
GA GU977
UT WOS:A1991GU97700002
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.
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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
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Salton G., 1975, THEORY INDEXING
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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 WESTLAND, JC
AF WESTLAND, JC
TI ECONOMIC-CONSTRAINTS IN HYPERTEXT
SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
LA English
DT Article
AB Distribution of very large commercial databases via CD-ROM is an emerging and cost advantageous alternative to distribution of information vai online telecommunications access. Hypertext organization is being touted as the appropriate data organization for effective CD-ROM information retrieval. But the producer of a hypertext database incurs significant costs in providing these benefits because of the expense of constructing indices and linkages. Indexing and linkage information in hypertext may very well occupy more space, and a larger chunk of the update and maintenance budget than the information actually being referenced. This research investigates the cost of constructing a hypertext database and provides a straightforward approach to forecasting costs of creating and maintaining a hypertext database. Cost forecasting can determine whether organizations can internally fund a hypertext database; it can also determine whether producers can afford to bring hypertext databases or technology to market. This research demonstrates that careful analysis of the costs and benefits of hypertext organization prior to implementation is much more important than in the construction of traditional databases, and that there may be many large databases for which hypertext access cannot be justified.
RP WESTLAND, JC (reprint author), UNIV SO CALIF,SCH BUSINESS ADM,LOS ANGELES,CA 90089, USA.
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NR 27
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 APR
PY 1991
VL 42
IS 3
BP 178
EP 184
DI 10.1002/(SICI)1097-4571(199104)42:3<178::AID-ASI4>3.0.CO;2-Y
PG 7
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA FG046
UT WOS:A1991FG04600004
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
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HJERRPE R, 1986, INTELLIGENT INFORMAT
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PRABHA C, 1989, ANN REV OCLC RES JUL
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Robertson S. E., 1982, Information Technology: Research and Development, V1
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STEINBERG D, 1984, COLL RES LIBR, V45, P66
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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
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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 DRISCOLL, JR
RAJALA, DA
SHAFFER, WH
THOMAS, DW
AF DRISCOLL, JR
RAJALA, DA
SHAFFER, WH
THOMAS, DW
TI THE OPERATION AND PERFORMANCE OF AN ARTIFICIALLY INTELLIGENT KEYWORDING
SYSTEM
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
ID RETRIEVAL; TEXT
AB A new approach to text analysis for automating the key phrase indexing process, using artifical intelligence techniques, is presented. This new approach mimics the behavior of human experts by using a rule base consisting of "insertion" and "deletion" rules generated by subject-matter experts. The insertion rules are based on the idea that some phrases found in a text imply or trigger other phrases. The deletion rules apply to semantically ambiguous phrases were text presence alone does not determine appropriateness as a key phrase. The insertion and deletion rules are used to transform a list of found phrases to a list of key phrases for indexing a document. Statistical data are provided to demonstrate the performance of this expert rule based system.
C1 JOINT DOCTRINE CTR,JCS,TAMPA,FL 33608.
UNIV CENT FLORIDA,INST SIMULAT & TRAINING,ORLANDO,FL 32816.
RP DRISCOLL, JR (reprint author), UNIV CENT FLORIDA,DEPT COMP SCI,ORLANDO,FL 32816, USA.
CR BAXENDALE PB, 1958, IBM J RES DEV, V2, P354
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HARTER PS, 1975, J ASIS 1, V26, P197
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JONES SK, 1971, AUTOMATIC KEYWORD CL
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Sager Naomi, 1981, NATURAL LANGUAGE INF
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
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SMITH LC, 1976, INFORM PROCESS MANAG, V12, P189, DOI 10.1016/0306-4573(76)90005-4
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STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
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NR 32
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 1991
VL 27
IS 1
BP 43
EP 54
DI 10.1016/0306-4573(91)90030-P
PG 12
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA EX755
UT WOS:A1991EX75500004
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
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BOOKSTEIN A, 1977, J ACM, V24, P418, DOI 10.1145/322017.322022
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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.O., 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
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HARTER PS, 1975, J ASIS 2, V26, P280
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PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036
SN 1046-8188
J9 ACM T INFORM SYST
JI ACM Trans. Inf. Syst.
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PY 1990
VL 8
IS 4
BP 363
EP 386
DI 10.1145/102675.102677
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GA FN624
UT WOS:A1990FN62400002
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PA 1515 BROADWAY, NEW YORK, NY 10036
SN 0362-5915
J9 ACM T DATABASE SYST
JI ACM Trans. Database Syst.
PD SEP
PY 1990
VL 15
IS 3
BP 341
EP 358
DI 10.1145/88636.87943
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BP 77
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PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
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BP 371
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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 FUHR, N
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AF FUHR, N
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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 WESTLAND, JC
AF WESTLAND, JC
TI A NET BENEFITS APPROACH TO MEASURING RETRIEVAL PERFORMANCE
SO INFORMATION PROCESSING & MANAGEMENT
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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 579
EP 581
DI 10.1016/0306-4573(89)90027-7
PG 3
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA CA147
UT WOS:A1989CA14700010
ER
PT J
AU GOYAL, P
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TC 0
Z9 0
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 4
BP 351
EP 358
DI 10.1016/0306-4379(89)90054-9
PG 8
WC Computer Science, Information Systems
SC Computer Science
GA CA156
UT WOS:A1989CA15600007
ER
PT J
AU GORDON, M
AF GORDON, M
TI PROBABILISTIC AND GENETIC ALGORITHMS FOR DOCUMENT-RETRIEVAL
SO COMMUNICATIONS OF THE ACM
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RP GORDON, M (reprint author), UNIV MICHIGAN,GRAD SCH BUSINESS ADM,ANN ARBOR,MI 48109, USA.
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TC 84
Z9 84
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 THOMPSON, P
AF THOMPSON, P
TI SUBJECTIVE-PROBABILITY AND INFORMATION-RETRIEVAL - A REVIEW OF THE
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TC 8
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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 1988
VL 44
IS 2
BP 119
EP 143
DI 10.1108/eb026821
PG 25
WC Computer Science, Information Systems; Information Science & Library
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SC Computer Science; Information Science & Library Science
GA N9721
UT WOS:A1988N972100002
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PT J
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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 1988
VL 39
IS 3
BP 135
EP 143
PG 9
WC Information Science & Library Science
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GA P3521
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PT J
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TC 80
Z9 80
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 1988
VL 39
IS 2
BP 92
EP 98
DI 10.1002/(SICI)1097-4571(198803)39:2<92::AID-ASI4>3.0.CO;2-P
PG 7
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA M1993
UT WOS:A1988M199300004
ER
PT J
AU RORVIG, ME
AF RORVIG, ME
TI PSYCHOMETRIC MEASUREMENT AND INFORMATION-RETRIEVAL
SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Review
RP RORVIG, ME (reprint author), UNIV TEXAS,GRAD SCH LIB & INFORMAT SCI,AUSTIN,TX 78712, USA.
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NR 115
TC 28
Z9 27
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 1988
VL 23
BP 157
EP 189
PG 33
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA R7155
UT WOS:A1988R715500008
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
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RP RADECKI, T (reprint author), UNIV NEBRASKA,DEPT COMP SCI,LINCOLN,NE 68588, USA.
CR ARNOLD BH, 1962, LOGIC BOOLEAN ALGEBR
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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 MARON, ME
AF MARON, ME
TI PROBABILISTIC DESIGN PRINCIPLES FOR CONVENTIONAL AND FULL-TEXT
RETRIEVAL-SYSTEMS
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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 RADECKI, T
AF RADECKI, T
TI PROBABILISTIC METHODS FOR RANKING OUTPUT DOCUMENTS IN CONVENTIONAL
BOOLEAN RETRIEVAL-SYSTEMS
SO INFORMATION PROCESSING & MANAGEMENT
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RP RADECKI, T (reprint author), UNIV NEBRASKA,DEPT COMP SCI,LINCOLN,NE 68588, USA.
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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 WALLACE, DP
BOYCE, BR
KRAFT, DH
AF WALLACE, DP
BOYCE, BR
KRAFT, DH
TI ESTIMATING EFFECTIVE DISPLAY SIZE IN ONLINE RETRIEVAL-SYSTEMS
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
C1 LOUISIANA STATE UNIV,SCH LIB & INFORMAT SCI,BATON ROUGE,LA 70808.
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RP WALLACE, DP (reprint author), INDIANA UNIV,SCH LIB & INFORMAT SCI,BLOOMINGTON,IN 47405, USA.
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NR 40
TC 2
Z9 2
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 331
EP 337
DI 10.1016/0306-4573(88)90099-4
PG 7
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA N8302
UT WOS:A1988N830200012
ER
PT J
AU GORDON, MD
AF GORDON, MD
TI THE NECESSITY FOR ADAPTATION IN MODIFIED BOOLEAN DOCUMENT-RETRIEVAL
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NR 19
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 1988
VL 24
IS 3
BP 339
EP 347
DI 10.1016/0306-4573(88)90100-8
PG 9
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA N8302
UT WOS:A1988N830200013
ER
PT J
AU PANYR, J
AF PANYR, J
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NR 77
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
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JI Nachr. Dok.
PD FEB
PY 1987
VL 38
IS 1
BP 13
EP 20
PG 8
WC Information Science & Library Science
SC Information Science & Library Science
GA G9017
UT WOS:A1987G901700003
ER
PT J
AU MEUNIER, JG
BERTRANDGASTALDY, S
LEBEL, H
AF MEUNIER, JG
BERTRANDGASTALDY, S
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TC 1
Z9 1
PU INT SOC KNOWLEDGE ORGANIZATION
PI FRANKFURT 50
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PY 1987
VL 14
IS 1
BP 2
EP 10
PG 9
WC Information Science & Library Science
SC Information Science & Library Science
GA H3149
UT WOS:A1987H314900002
ER
PT J
AU PANYR, J
AF PANYR, J
TI FUZZY SET-THEORY AND INFORMATION-RETRIEVAL SYSTEMS
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PU VERLAG HOPPENSTEDT & CO
PI DARMSTADT 1
PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY
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PI DARMSTADT 1
PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY
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J9 NACHR DOK
JI Nachr. Dok.
PD APR
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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.
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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 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.
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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 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
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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
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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 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
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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 KRAFT, DH
BUELL, DA
AF KRAFT, DH
BUELL, DA
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TC 62
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PU ACADEMIC PRESS LTD
PI LONDON
PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX
SN 0020-7373
J9 INT J MAN MACH STUD
PY 1983
VL 19
IS 1
BP 45
EP 56
DI 10.1016/S0020-7373(83)80041-8
PG 12
WC Computer Science, Cybernetics; Ergonomics; Psychology
SC Computer Science; Engineering; Psychology
GA RB942
UT WOS:A1983RB94200007
ER
PT J
AU BOOKSTEIN, A
AF BOOKSTEIN, A
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PA 605 THIRD AVE, NEW YORK, NY 10158-0012
SN 0002-8231
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JI J. Am. Soc. Inf. Sci.
PY 1983
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IS 5
BP 331
EP 342
DI 10.1002/asi.4630340504
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WC Computer Science, Information Systems; Information Science & Library
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GA RF880
UT WOS:A1983RF88000003
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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 SALTON, G
BUCKLEY, C
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AF SALTON, G
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PI BUCHAREST
PA CALEA VICTORIEI 125, BUCHAREST 79717, ROMANIA
SN 0302-9743
J9 LECT NOTES COMPUT SC
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BP 151
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PA CALEA VICTORIEI 125, BUCHAREST 79717, ROMANIA
SN 0302-9743
J9 LECT NOTES COMPUT SC
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BP 174
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PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU
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BP 26
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NR 76
TC 6
Z9 6
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 1982
VL 17
BP 99
EP 120
PG 22
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA PJ789
UT WOS:A1982PJ78900005
ER
PT J
AU TRAVIS, IL
FIDEL, R
AF TRAVIS, IL
FIDEL, R
TI SUBJECT ANALYSIS
SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY
LA English
DT Article
C1 UNIV MARYLAND,COLLEGE PK,MD 20742.
RP TRAVIS, IL (reprint author), PLANNING RES CORP,7600 OLD SPRINGHOUSE RD,MCLEAN,VA 22102, USA.
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NR 203
TC 8
Z9 8
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 1982
VL 17
BP 123
EP 157
PG 35
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA PJ789
UT WOS:A1982PJ78900006
ER
PT J
AU CHOROS, K
DANILOWICZ, C
AF CHOROS, K
DANILOWICZ, C
TI RELATIVE INDEXING - WEIGHTED DESCRIPTORS AND RELATIVE INDEXING IN A
DOCUMENT-RETRIEVAL SYSTEM MODEL
SO INFORMATION PROCESSING & MANAGEMENT
LA English
DT Article
C1 POLYTECH INST WROCLAW,CTR,SCI INFORMAT,PL-50370 WROCLAW,POLAND.
RP CHOROS, K (reprint author), POLYTECH INST WROCLAW,MAIN LIB,PL-50370 WROCLAW,POLAND.
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TC 8
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PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
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J9 INFORM PROCESS MANAG
JI Inf. Process. Manage.
PY 1982
VL 18
IS 4
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DI 10.1016/0306-4573(82)90060-7
PG 14
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA PH662
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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
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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
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STEVENS, 1965, NBS MISCELLANEOUS PU, V269
NR 4
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PU JOHN WILEY & SONS INC
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J9 J AM SOC INFORM SCI
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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
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PT J
AU BUELL, DA
AF BUELL, DA
TI A GENERAL-MODEL OF QUERY-PROCESSING IN INFORMATION-RETRIEVAL SYSTEMS
SO INFORMATION PROCESSING & MANAGEMENT
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DT Article
RP BUELL, DA (reprint author), LOUISIANA STATE UNIV,DEPT COMP SCI,BATON ROUGE,LA 70803, USA.
RI Buell, Duncan/
OI Buell, Duncan/0000-0002-4668-5848
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NR 41
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PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB
SN 0306-4573
J9 INFORM PROCESS MANAG
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IS 5
BP 249
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DI 10.1016/0306-4573(81)90019-4
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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
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IS 4
BP 275
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GA LV485
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ER
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AF CROFT, WB
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PA 605 THIRD AVE, NEW YORK, NY 10158-0012
SN 0002-8231
J9 J AM SOC INFORM SCI
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PU JOHN WILEY & SONS INC
PI NEW YORK
PA 605 THIRD AVE, NEW YORK, NY 10158-0012
SN 0002-8231
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PY 1980
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PU ACADEMIC PRESS INC JNL-COMP SUBSCRIPTIONS
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495
SN 0049-089X
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PI PHILADELPHIA
PA 3624 MARKET ST UNIV CITY SCI CTR, PHILADELPHIA, PA 19104
SN 0012-6160
J9 DREXEL LIBR QUART
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GA GY548
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PT J
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RP ROBERTSON, SE (reprint author), UCL, SCH LIB ARCHIVE & INFORMAT STUDIES, LONDON WC1E 6BT, ENGLAND.
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PU EMERALD GROUP PUBLISHING LIMITED
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
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J9 J DOC
JI J. Doc.
PY 1978
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PT J
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PA 1515 BROADWAY, NEW YORK, NY 10036
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JI J. ACM
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BP 67
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UT WOS:A1978EJ11000006
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PT J
AU KRAFT, DH
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JI J. Am. Soc. Inf. Sci.
PY 1978
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PT J
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PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
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PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
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TC 17
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PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
J9 INFORM STORAGE RET
PY 1970
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IS 2
BP 137
EP &
DI 10.1016/0020-0271(70)90057-4
PG 0
WC Computer Science, Information Systems; Information Science & Library
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SC Computer Science; Information Science & Library Science
GA G6077
UT WOS:A1970G607700003
ER
PT J
AU JACKSON, DM
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PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
J9 INFORM STORAGE RET
PY 1970
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IS 2
BP 187
EP &
DI 10.1016/0020-0271(70)90061-6
PG 0
WC Computer Science, Information Systems; Information Science & Library
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UT WOS:A1970G607700007
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PU GORDON BREACH SCI PUBL LTD
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PA C/O STBS LTD, PO BOX 90, READING RG1 8JL, BERKS, ENGLAND
SN 0020-7233
J9 INT J ENVIRON STUD
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IS 1
BP 67
EP 79
DI 10.1080/00207237008709398
PG 13
WC Environmental Sciences
SC Environmental Sciences & Ecology
GA Y3046
UT WOS:A1970Y304600009
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PT J
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MOOERS CN, 1966, COMMUN ACM, V9, P215, DOI 10.1145/365230.365270
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VASWANI PKT, 1965, REV INT DOC, V32, P19
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1964, B MED LIB ASSOC, V52, P148
1966, NATURE, V212, P867
1966, ANNUAL REPORT NATION
NR 35
TC 4
Z9 4
PU MACMILLAN MAGAZINES LTD
PI LONDON
PA PORTERS SOUTH, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 0028-0836
J9 NATURE
JI Nature
PY 1966
VL 212
IS 5069
BP 1402
EP &
DI 10.1038/2121402a0
PG 0
WC Multidisciplinary Sciences
SC Science & Technology - Other Topics
GA 86627
UT WOS:A19668662700008
PM 21090404
ER
PT J
AU TAUBE, M
AF TAUBE, M
TI A NOTE ON THE PSEUDO-MATHEMATICS OF RELEVANCE
SO AMERICAN DOCUMENTATION
LA English
DT Article
CR HAYES RM, 1963, NATURAL LANGUAGE COM, P268
HENSLEY CB, 1962, JUN IRE T PROF GR EM, V9
LANCASTER F, 1964, AM DOC, V15, P4, DOI 10.1002/asi.5090150104
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NR 7
TC 27
Z9 27
PU JOHN WILEY & SONS INC
PI NEW YORK
PA 605 THIRD AVE, NEW YORK, NY 10158-0012
SN 0096-946X
J9 AM DOC
PY 1965
VL 16
IS 2
BP 69
EP 72
DI 10.1002/asi.5090160204
PG 4
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 64158
UT WOS:A19656415800001
ER
PT J
AU DOYLE, LB
AF DOYLE, LB
TI IS AUTOMATIC CLASSIFICATION A REASONABLE APPLICATION OF STATISTICAL
ANALYSIS OF TEXT
SO JOURNAL OF THE ACM
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DT Article
CR BAKER FB, 1962, J ACM, V9, P512, DOI 10.1145/321138.321148
BORKO H, 1964, J ACM, V11, P138, DOI 10.1145/321217.321219
BORKO H, IN PRESS
BRACKEN RH, 1957, J ACM, V4, P131, DOI 10.1145/320868.320870
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DOYLE LB, 1959, 15 P WEST JOINT COMP, P60
DOYLE LB, IN PRESS
DOYLE LB, 1962, AM DOC, V13, P378, DOI 10.1002/asi.5090130404
DOYLE LB, 1965, COMMUN ACM, V8, P238, DOI 10.1145/363831.364903
DOYLE LB, 1964, SP1481 SYST DEV CORP
FOSKETT DJ, 1958, P INTERNATIONAL C SC, V2, P868
GIULIANO VE, 1963, VISTAS INFORMATION H, V1, P30
HOWERTON PW, 1963, VISTAS INFORMATIO ED, V1, P30
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
MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084
MEYER WE, 1964, TM1840 SYST DEV CORP
NEEDHAM RM, 1960, ML126 CAMBR LANG RES
PARKERRHODES AF, 1961, ML138 CAMBR LANG RES
RICHMOND PA, 1965, INFORMAL CORRESPONDE
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
TANIMOTO TT, 1958, NOV IBM INT REP
WARD JH, 1963, EDUC PSYCHOL MEAS, V23, P69, DOI 10.1177/001316446302300107
NR 25
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 1965
VL 12
IS 4
BP 473
EP &
DI 10.1145/321296.321298
PG 0
WC Computer Science, Hardware & Architecture; Computer Science, Information
Systems; Computer Science, Software Engineering; Computer Science,
Theory & Methods
SC Computer Science
GA 69633
UT WOS:A19656963300002
ER
PT J
AU KUHNS, JI
AF KUHNS, JI
TI CONTINUUM OF COEFFICIENTS OF ASSOCIATION
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR Carnap Rudolf, 1950, LOGICAL FOUNDATIONS
HOEL PG, 1954, INTRODUCTION MATHEMA
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
Yule GU, 1912, J R STAT SOC, V75, P579, DOI 10.2307/2340126
1961, AF306022223C1071U12
1963, NASW538 NASA INF SYS
NR 7
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 33
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500005
ER
PT J
AU TAGUE, J
AF TAGUE, J
TI MATCHING OF QUESTION AND ANSWER TERMINOLOGY IN AN EDUCATION RESEARCH
FILE
SO AMERICAN DOCUMENTATION
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DT Article
CR Cleverdon C. W., 1962, REPORT TESTING ANAL
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
OSGOOD CE, 1957, MEASUREMENT MEANING, P90
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NR 4
TC 4
Z9 4
PU JOHN WILEY & SONS INC
PI NEW YORK
PA 605 THIRD AVE, NEW YORK, NY 10158-0012
SN 0096-946X
J9 AM DOC
PY 1965
VL 16
IS 1
BP 26
EP 32
DI 10.1002/asi.5090160107
PG 7
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 61268
UT WOS:A19656126800004
ER
PT J
AU JONES, PE
AF JONES, PE
TI HISTORICAL FOUNDATIONS OF RESEARCH ON STATISTICAL ASSOCIATION TECHNIQUES
FOR MECHANIZED DOCUMENTATION
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR Baldwin AL, 1942, J ABNORM SOC PSYCH, V37, P163, DOI 10.1037/h0061697
BERELSON B, 1952, CONTENT ANALYSIS COM
BRIDGMAN PW, 1946, LOGIC MODERN PHYSICS
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Dirac PAM., 1947, PRINCIPLES QUANTUM M
GEORGE AL, 1959, PROPAGANDA ANALYSIS, P29
GOODMAN L, J AM STAT ASSN, V49, P732
GOODMAN L, 1959, J AM STAT ASSN, P124
HARMAN H, 1960, MODERN FACTOR ANALYS
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Harris ZS, 1954, WORD, V10, P146
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LASSWELL N, 1949, LANGUAGE POLITICS ST
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Morris Charles W., 1946, SIGNS LANGUAGE BEHAV
Mosier CI, 1941, J SOC PSYCHOL, V13, P123
Osgood C. E., 1957, MEASUREMENT MEANING
OSGOOD CE, 1952, PSYCHOL BULL, V49, P197, DOI 10.1037/h0055737
POOL IS, 1959, TRENDS CONTENT AN ED
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STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
Thurstone L. L., 1947, MULTIPLE FACTOR ANAL
WARREN HC, 1921, HISTORY ASSOCIATION
NR 27
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 3
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500001
ER
PT J
AU MARON, ME
AF MARON, ME
TI MECHANIZED DOCUMENTATION - LOGIC BEHIND A PROBABILISTIC INTERPRETATION
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR Bush V., 1945, ATLANTIC MONTHLY, P101
CHERRY EC, INFORMATION THEOR ED
CHERRY EC, 1956, INFORMATION THEOR ED, P215
KOCHEN M, 1964, RM4065PR RAND CORP
LIVINGSTON H, 1964, CROSSCULTURAL UND ED
Mackay D. M., 1954, SYNTHESE, VIX, P182
Mackay D. M., 1961, INFORMATION THEORY, P469
MACKAY DM, 1964, CROSSCULTURAL UNDERS
MACKAY DM, 1956, INFORMATION THEORY, P215
MARON ME, 1964, P2879 RAND CORP
MARON ME, 1963, BEHAV SCI, V8, P250
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084
NR 13
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 9
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500002
ER
PT J
AU DOYLE, LB
AF DOYLE, LB
TI SOME COMPROMISES BETWEEN WORD GROUPING AND DOCUMENT GROUPING
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR BARHILLEL Y, 1960, 3 US OFF NAV RES TEC
BORKO H, 1964, J ASSOC COMP MACH 2, V11
DOYLE LB, 1961, J ACM, V8, P553, DOI 10.1145/321088.321095
DOYLE LB, 1962, AM DOC, V13, P378, DOI 10.1002/asi.5090130404
DOYLE LB, TO BE PUBLISHED
DOYLE LB, 1963, INFORM STORAGE RET, V1, P189, DOI 10.1016/0020-0271(63)90017-2
DOYLE LB, 1963, STATISTICAL SEMANTIC, P335
LUHN HP, 1957, IBM J RES DEV, V1, P309
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
STILES HE, UNPUBLISHED MEMORAND
WARD JH, 1963, EDUC PSYCHOL MEAS, V23, P69, DOI 10.1177/001316446302300107
WARD JH, 1961, WADDTN6129 WRIGHT AI
NR 13
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 15
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500003
ER
PT J
AU GIULIANO, VE
AF GIULIANO, VE
TI INTERPRETATION OF WORD ASSOCIATIONS
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR DOYLE LB, 1963, SP1083 SYST DEV CORP
DOYLE LB, 1962, AM DOC, V13, P378, DOI 10.1002/asi.5090130404
GIULIANO VE, 1963, IEEE T MIL ELEC, VMIL7, P221
GIULIANO VE, 1963, VISTAS INFORMATION H, pCH2
GIULIANO VE, 1962, 1 P C INF SYST SCIEN
GOODMAN LA, 1959, AM STATISTICAL ASS J, V54, P123
HOWERTON P, 1963, VISTAS INFORMATIO ED, pCH2
JONES PE, 1965, STAT ASSOC METHOD M, P3
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MARON ME, 1965, STATISTICAL ASS METH, P9
SALTON G, 1963, J ACM, V10, P440, DOI 10.1145/321186.321188
SAPORTA S, 1961, PSYCHOLINGUISTICS
SPIEGEL J, 1965, STAT ASSOC METHOD M, P47
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
1963, CALC3 ARTH D LITTL I
NR 15
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 25
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500004
ER
PT J
AU SPIEGEL, J
BENNETT, E
AF SPIEGEL, J
BENNETT, E
TI A MODIFIED STATISTICAL ASSOCIATION PROCEDURE FOR AUTOMATIC DOCUMENT
CONTENT ANALYSIS AND RETRIEVAL
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR BOAZ M, 1959, MODERN TRENDS DOC ED, P45
DOYLE LB, 1961, J ACM, V8, P553, DOI 10.1145/321088.321095
DOYLE LB, 1962, AM DOC, V13, P378, DOI 10.1002/asi.5090130404
Luhn H. P., 1959, MODERN TRENDS DOCUME, P45
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
NR 6
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 47
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500007
ER
PT J
AU BAKER, FB
AF BAKER, FB
TI LATENT CLASS ANALYSIS AS AN ASSOCIATION MODEL FOR INFORMATION RETRIEVAL
SO STATISTICAL ASSOCIATION METHODS FOR MECHANIZED DOCUMENTATION SYMPOSIUM
PROCEEDINGS
LA English
DT Meeting Abstract
CR ANDERSON TW, 1954, PSYCHOMETRIKA, V19, P1
ANDERSON TW, 1958, INTRODUCTION MULTIVA, pCH1
BAKER FB, 1962, J ACM, V9, P512, DOI 10.1145/321138.321148
BORKO H, 1963, TM771001000 SYST DEV
BORKO H, 1962, 1962 P SPRING JOINT, P279
BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165
DOYLE LB, 1961, J ACM, V8, P553, DOI 10.1145/321088.321095
DOYLE LB, 1963, INFORM STORAGE RET, V1, P189, DOI 10.1016/0020-0271(63)90017-2
FEDERER WT, 1951, ANN MATH STAT, V22, P102, DOI 10.1214/aoms/1177729697
FREUND RJ, 1963, AM STAT, V17, P13, DOI 10.2307/2685670
GIBSON WA, 1955, PSYCHOMETRIKA, V20, P60
GIBSON WA, 1962, PSYCHOMETRIKA, V27, P73, DOI 10.1007/BF02289666
GREEN BF, 1951, PSYCHOMETRIKA, V16, P151
GUTTMAN L, 1953, PSYCHOMETRIKA, V18, P277
Harary F, 1959, J MATH PHYS, V38, P104
HARRIS CW, 1963, IAMGE ANALYSIS ED
HARRIS CW, 1962, PSYCHOMETRIKA, V27, P247, DOI 10.1007/BF02289622
KAISER HF, 1963, IMAGE ANALYSIS
LAZARSFELD PF, 1950, AMERICAN SOLDIER, V4, pCH11
LAZARSFELD PF, 1950, AMERICAN SOLDIER, V4, pCH10
MADANSKY A, 1960, PSYCHOMETRIKA, V25, P183, DOI 10.1007/BF02288581
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084
MCHUGH RB, 1956, PSYCHOMETRIKA, V21, P331, DOI 10.1007/BF02296300
PARKERRHODES AF, 1961, ML138 CAMBR LANG RES
SALTON G, 1963, J ACM, V10, P440, DOI 10.1145/321186.321188
STEWARD DV, 1962, SIAM REV, V4, P321, DOI 10.1137/1004088
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
STOUFFER SA, 1950, AMERICAN SOLDIER ED, V4, pCH11
STOUFFER SA, 1950, AMERICAN SOLDIER ED, V4, pCH10
Torgerson W. S., 1958, THEORY METHODS SCALI
NR 31
TC 0
Z9 0
J9 STAT ASSOC METHOD M
PY 1965
VL 1964
IS NBS26
BP 149
EP &
PG 0
WC Computer Science, Information Systems; Statistics & Probability
SC Computer Science; Mathematics
GA 79065
UT WOS:A19657906500009
ER
PT J
AU CUADRA, CA
AF CUADRA, CA
TI IDENTIFYING KEY CONTRIBUTIONS TO INFORMATION SCIENCE
SO AMERICAN DOCUMENTATION
LA English
DT Article
CR BALZ CF, 1962, LETERATURE INFORMATI
BECKER J, 1963, INFORMATION STORAGE
BOURNE CP, 1963, METHODS INFORMATION
CASEY RW, 1958, PUNCHED CARDS THEIR
CITRON J, 1958, PERMUTATION INDEX PR
FAIRTHORNE RA, 1961, INFORMATION RETRIE 2, V3, pCH44
FAIRTHORNE RA, 1961, TOWARDS INFORMATION
HAYES RM, 1960, INFORMATION RETRIE 1, V3, pCH19
HOWERTON PW, 1963, INFORMATION HANDLING
JANASKE PC, 1962, INFORMATION HANDLING
KENT A, 1961, INFORMATION RETRIE 2, V3, pCH44
KENT A, 1892, TEXTBOOK MECHANIZED
KENT A, 1960, INFORMATION RETRIE 1, V3, pCH19
LUHN HP, 1963, AUTOMATION SCIENTIFI, P127
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MOOERS CN, 1963, AUTOMATION SCIENTIFI, P127
PERRY JW, 1958, TOOLS MACHINE LITERA
PERRY JW, 1956, MACHINE LITERATURE S
PERRY JW, 1957, DOCUMENTATION INOFRM
SHAW RS, 1949, J DOC, V5, P164, DOI 10.1108/eb026148
SNODEY SR, 1951, INFORMATION RETRIEVA
SPANGLER M, 1962, GENERAL BIBLIOGRAPHY
SWANSON DR, 1960, SCIENCE, V132, P1099, DOI 10.1126/science.132.3434.1099
Vickery B. C., 1959, CLASSIFICATION INDEX
VICKERY BC, 1961, RETRIEVAL SYSTEM THE
VICKERY BC, 1960, GUIDE CONSTRUCTION S
NR 26
TC 14
Z9 14
PU JOHN WILEY & SONS INC
PI NEW YORK
PA 605 THIRD AVE, NEW YORK, NY 10158-0012
SN 0096-946X
J9 AM DOC
PY 1964
VL 15
IS 4
BP 289
EP &
DI 10.1002/asi.5090150407
PG 0
WC Computer Science, Information Systems; Information Science & Library
Science
SC Computer Science; Information Science & Library Science
GA 2561A
UT WOS:A19642561A00006
ER
PT J
AU OCONNOR, J
AF OCONNOR, J
TI MECHANIZED INDEXING METHODS + THEIR TESTING
SO JOURNAL OF THE ACM
LA English
DT Article
CR BARHILLEL Y, 1959, 10 NAT PHYS LAB S
BARHILLEL Y, 1960, AD236772 AST SOM THE
BAXENDALE PB, 1958, IBM J RES DEV, V2, P354
BERNIER CL, 1960, AM DOC, V11, P277, DOI 10.1002/asi.5090110402
BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165
CLEVERDON C, 1962, REPORT TESTING ANALY
EDMUNDSON HP, 1961, COMM ACM, V4
FELS EM, 1963, AM DOC, V14, P28, DOI 10.1002/asi.5090140105
GARFIELD E, PRIVATE COMMUNICATIO
GIULIANO VE, 1963, VISTAS INFORMATION H, V1, pCH2
HARRIS Z, 1959, P INT C SCI INFORM N, P937
HOWERTON, 1963, VISTAS INFORMATIO ED, V1, pCH2
KEHL WB, 1961, COMMUN ACM, V4, P380, DOI 10.1145/366696.366744
KESSLER MM, 1961, R1 REPS MIT LIBR BIB
KUNO S, 1962, P C INTERNAT FED INF
LUHN HP, 1957, IBM J RES DEVELOP, V1
LUHN HP, 1959, POTENTIALITIES AUTOE
LUHN HP, 1958, IBM J RES DEV, V2, P159
MARON ME, 1961, J ACM, V8
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MONTGOMERY C, 1962, AM DOC, V13, P359, DOI 10.1002/asi.5090130401
NEEDHAM R, 1962, P C INT FED INF P SO
OCONNOR J, AD25029 AST MECH IND
OCONNOR J, 1962, MACHINE INDEXING, P262
SALTON G, 1962, P AFIPS FALL JOINT C, P234, DOI 10.1145/1461518.1461544
SCHULTZ C, PRIVATE COMMUNICATIO
SHER I, PRIVATE COMMUNICATIO
STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074
STORM E, 1961, SR1 COMP LAB HARV U, pI1
SWANSON DR, 1962, P C INTERNAT FED INF
SWANSON DR, 1962, MACHINE INDEXING, P281
SWANSON DR, 1960, SCIENCE, V132, P1099, DOI 10.1126/science.132.3434.1099
NR 32
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 1964
VL 11
IS 4
BP 437
EP &
DI 10.1145/321239.321245
PG 0
WC Computer Science, Hardware & Architecture; Computer Science, Information
Systems; Computer Science, Software Engineering; Computer Science,
Theory & Methods
SC Computer Science
GA 2929B
UT WOS:A19642929B00008
ER
PT J
AU KATTER, RV
AF KATTER, RV
TI LANGUAGE STRUCTURE + INTERPERSONAL COMMONALITY
SO JOURNAL OF VERBAL LEARNING AND VERBAL BEHAVIOR
LA English
DT Article
CR ATTNEAVE F, 1959, APPLICATIONS INFORMA
BAXENDALE PB, 1961, MACHINE INDEXING PRO, P207
BORKO H, 1962, P SPRING JOINT COMPU, V21, P279
DOYLE LB, 1962, AM DOC, V13, P378, DOI 10.1002/asi.5090130404
EDMUNDSON HP, 1961, P NATIONAL S MACHINE, P286
GARVIN PL, 1961, P NAT S MACH TRANSL, P286
GLANZER M, 1962, J VERB LEARN VERB BE, V1, P31, DOI 10.1016/S0022-5371(62)80016-4
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MILLER GA, 1962, AM PSYCHOL, V17, P748, DOI 10.1037/h0044708
MONTGOMERY C, 1962, AM DOC, V13, P359, DOI 10.1002/asi.5090130401
Osgood C. E., 1957, MEASUREMENT MEANING
RATH GJ, 1961, AM DOC, V12, P139, DOI 10.1002/asi.5090120210
SKINNER BF, 1945, PSYCHOL REV, V52, P270, DOI 10.1037/h0062535
TAYLOR WL, 1956, JOURNALISM QUART, V33, P42
Underwood B. J., 1960, MEANINGFULNESS VERBA
YNGVE VH, 1962, SCI AM, V206, P68
NR 16
TC 3
Z9 3
PU ACADEMIC PRESS INC
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495
SN 0022-5371
J9 J VERB LEARN VERB BE
PY 1964
VL 3
IS 2
BP 132
EP &
DI 10.1016/S0022-5371(64)80030-X
PG 0
WC Psychology, Educational; Language & Linguistics; Psychology; Psychology,
Experimental
SC Psychology; Linguistics
GA CQ881
UT WOS:A1964CQ88100005
ER
PT J
AU MARON, ME
AF MARON, ME
TI PROBABILITY AND THE LIBRARY PROBLEM
SO BEHAVIORAL SCIENCE
LA English
DT Article
CR BELLO F, 1960, FORTUNE, V62, P162
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
NR 2
TC 6
Z9 6
PU INT SOC SYSTEMS SCIENCE
PI BALTIMORE
PA 428 EAST PRESTON ST, BALTIMORE, MD 21202
SN 0005-7940
J9 BEHAV SCI
JI Behav. Sci.
PY 1963
VL 8
IS 3
BP 250
EP 257
PG 8
WC Psychology; Social Sciences, Interdisciplinary
SC Psychology; Social Sciences - Other Topics
GA CBE22
UT WOS:A1963CBE2200012
ER
PT J
AU BAKER, FB
AF BAKER, FB
TI USE OF COMPUTERS IN EDUCATIONAL-RESEARCH
SO REVIEW OF EDUCATIONAL RESEARCH
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DT Article
CR ALLEN M, 1962, BEHAV SCI, V7, P247
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NR 53
TC 1
Z9 1
PU AMER EDUCATIONAL RESEARCH ASSOC
PI WASHINGTON
PA 1230 17TH ST NW, WASHINGTON, DC 20036-3078
SN 0034-6543
J9 REV EDUC RES
JI Rev. Educ. Res.
PY 1963
VL 33
IS 5
BP 566
EP 578
PG 13
WC Education & Educational Research
SC Education & Educational Research
GA CCU20
UT WOS:A1963CCU2000010
ER
PT J
AU SWETS, JA
AF SWETS, JA
TI INFORMATION RETRIEVAL SYSTEMS
SO SCIENCE
LA English
DT Article
CR BORKO H, 1961, 564900001 SYST DEV C
BORNSTEIN H, 1961, AM DOC, V12, P254, DOI 10.1002/asi.5090120403
BOURNE CP, 1961, 3741 STANF RES I PRO
CLEVERDON CW, 1962, ASSOCIATION SPECIAL
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PERRY JW, 1958, TOOLS MACHINE LITERA, P3
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SWANSON DR, 1962, C INT FEDERATION INF
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SWETS JA, 1961, PSYCHOMETRIKA, V26, P49, DOI 10.1007/BF02289684
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TANNER WP, 1954, PSYCHOL REV, V61, P401, DOI 10.1037/h0058700
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WORDSWORTH HM, 1959, AFOSRTN59418 WEST RE
WYLLYS RE, 1962, 1 T C INF SYST SCI H
NR 18
TC 106
Z9 109
PU AMER ASSOC ADVANCEMENT SCIENCE
PI WASHINGTON
PA 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005
SN 0036-8075
J9 SCIENCE
JI Science
PY 1963
VL 141
IS 357
BP 245
EP &
DI 10.1126/science.141.3577.245
PG 0
WC Multidisciplinary Sciences
SC Science & Technology - Other Topics
GA 3050C
UT WOS:A19633050C00120
PM 17841558
ER
PT J
AU BRANNEN, GB
AF BRANNEN, GB
TI A LITERATURE SURVEY OF TECHNICAL-INFORMATION SERVICES
SO SPECIAL LIBRARIES
LA English
DT Review
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ADKINSON BW, 1956, DOCUMENTATION ACTION, P163
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NR 181
TC 1
Z9 1
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 1963
VL 54
IS 2
BP 94
EP 101
PG 8
WC Information Science & Library Science
SC Information Science & Library Science
GA CEH16
UT WOS:A1963CEH1600004
ER
PT J
AU COSTELLO, JC
AF COSTELLO, JC
TI AN INTRODUCTION TO DEEP (COORDINATE) INDEXES
SO JOURNAL OF CHEMICAL DOCUMENTATION
LA English
DT Article
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1961, GUIDELINES USING AST
1961, CHEM ENG NEWS, V39, P102
1961, REFERENCE MANUAL IND
INFORMATION RETRIEVA
1961, CHEM ENG NEWS, V39, P90
1955, UNITERM SYSTEM INDEX
NR 79
TC 1
Z9 1
PU J CHEMICAL DOCUMENTATION
PI WASHINGTON
PA WASHINGTON, DC
SN 0021-9576
J9 J CHEM DOC
PY 1963
VL 3
IS 3
BP 164
EP 168
DI 10.1021/c160010a012
PG 5
WC Computer Science, Interdisciplinary Applications
SC Computer Science
GA XG089
UT WOS:A1963XG08900012
ER
PT J
AU QUIMBY, G
AF QUIMBY, G
TI WRITINGS ON ARCHIVES, CURRENT RECORDS, AND HISTORICAL MANUSCRIPTS - JULY
1960-JUNE 1961
SO AMERICAN ARCHIVIST
LA English
DT Review
CR AAKJAER S, 1960, MEDDELELSER LANDSARK
AHLUWALIA ML, 1961, AM ARCHIVIST, V24, P85
ALEGRIA RE, 1960, I CULTURA PUERTORRIQ, P11
*AM HIST ASS, 1961, GUID HIST LIT
*AM HIST ASS COMM, 1960, GUID GERM REC MICR, V18
*AM LIBR ASS, 1960, 1ST ANN REP
*AM LIBR ASS, 1959, GUID LIN LIBR PLANN
ANDREWS DD, 1960, J PATENT TRADEMARK, V270, P34, DOI 10.1016/0016-0032(60)90897-8
ANTON AE, 1960, AM J COMP L, V9, P441, DOI 10.2307/838039
*ARCH STAAT WIRTS, 1960, ORG TECKN
*ASS INT OFF MAN, 1960, OFF MAN, V21, P165
ATHERTON P, 1961, AM DOC, V12, P38, DOI 10.1002/asi.5090120109
*AUSTR BUS ARCH CO, 1960, REC MAN
AVRAMESCU A, 1960, INTRO DOCUMENTAREA S
BABELON JP, 1958, MUSEE HIST FRANCE, V3
BABELON JP, 1958, MUSEE HIST FRANCE, V2
BABELON JP, 1958, MUSEE HIST FRANCE, V1
BAHMER RH, 1960, 4E C INT ARCH STOCKH
BAHMER RH, 1955, AM ARCHIVIS, V18, P207
Bahmer RH, 1955, AM ARCHIVIST, V18, P195
BANKOWSKI P, 1960, ARCHIVMITTEILUNGEN, V10, P22
BAPTIE AL, 1960, LIBRARY J, V85, P3926
BARDEN WA, 1959, AUTOMATION ASTIA PRE
BARHILLEL Y, 1960, SOME THEORETICAL ASP
BARROW WJ, 1960, B I PATOLOGIA LIBRO, V19, P40
BASU P, 1960, ARCH RECORDS WHAT AR
BAUDOT M, 1960, GAZETTE ARCH, P5
BAUDOT M, 1960, GAZETTE ARCH MAR, P158
BAUTIER RH, 1959, ARCHIVUM, V9, P125
BAWA VK, 1959, INDIAN J PUBLIC ADM, V6, P405
BECKWITH HH, 1959, APPLICATIONS AUTOMAT
BEDNAR K, 1958, ARCH CASOPIS, V8, P125
BEDNAR K, 1958, ARCH CASOPIS, V8, P182
BELDA LS, 1958, GUIA ARCH HIST NACIO
BELDA LS, 1958, REV ARCH BIBLIOTECAS, V65, P283
BELLO F, 1960, FORTUNE, V62, P162
BELLO F, 1960, FORTUNE, V62, P180
BELLO F, 1960, FORTUNE, V62, P182
BELOV GA, 1959, ISTORICHESKII ARKHIV, P169
BELOV GA, 1958, TEORIYA PRAKTIKA ARK, P325
BENES F, 1958, ARCH CASOPIS, V8, P189
BENNETT AF, 1959, ARCHIVUM, V9, P121
BERENDS WN, 1960, NEDERLANDS ARCHIEVEN, V64, P74
BERNER RC, 1960, AM ARCHIVIST, V23, P395
BERRY PL, 1961, LIBR RESOUR TECH SER, V5, P60
BEUC I, 1959, ARCHIVIST, V8, P21
BISKUP P, 1961, AUSTR LIBRARY J, V10, P40
BLEGEN TC, 1960, AM ARCHIVIST, V23, P259
BOBB FW, 1960, OFFICE EXECUTIVE, V35, P9
BORN LK, 1960, AM ARCHIVIST, V23, P311
BOURNE CP, 1960, BIBLIO MECHANIZATION
BOURNE CP, 1960, BIBLIO MECHANIZATI S, V2
BRACHMANN B, 1960, ARCHIVMITTEILUNGEN, V10, P1
BRAIBANT C, 1959, WIESBADEN 1958
BRAIBANT C, 1959, HIST FILLE ARCH
BRILLING B, 1960, ARCHIVAR, V13
*BRIT STAND I, 1958, UN DEC CLASS
BROOKS PC, 1961, PUBLIC RECORDS MANAG
BRYAN MG, 1961, AM ARCHIVIST, V24, P3
BULPITT WR, 1961, ILLINOIS LIBRARIES, V43, P462
BURCHARD C, 1959, BIBLIO HANDSCHRIFTEN
BUTTERFIELD LH, 1961, Q J CURRENT ACQUISIT, V18, P53
BUTTERFIELD LH, 1961, AM ARCHIVIS, V24, P140
*C ARCH HIST OC IN, 1960, EXTR B MAD, V170
*CA FIN DEP ORG CO, 1961, PAP MAN HDB
*CA FIN DEP ORG CO, 1961, PROGR PAP MAN
*CAN CIV SERV COMM, 1960, MAN FIL SERV
*CAN PUBL ARCH, 1960, PREL INV
CARMAN HJ, 1960, GUIDE PRINCIPAL SOUR
CASE FH, 1960, AM ARCHIVIST, V23, P419
CASSADY TJ, 1961, ILLINOIS LIBRARIES, V43, P421
CATON JT, 1961, ILLINOIS LIBRARIES, V43, P425
CEZARD P, 1961, GAZETTE ARCH, P13
CHATFIELD HL, 1961, AM ARCHIVIST, V24, P171
CHAVES A, 1959, ARCH NACIONAL HIST B, V5, P3
Church RW, 1943, AM ARCHIVIST, V6, P145
CLAUSSEN MP, 1961, AM ARCHIVIST, V24, P83
CLEMENT AV, 1960, NEDERLANDS ARCH, V64, P176
COBB JE, 1961, ALABAMA HIST Q, V23, P73
CODDINGTON L, 1960, OFFICE MANAGEMENT AM, V21, P84
COLOM FS, 1960, ARCH GENERAL NACION
COLOM S, 1958, B COMITE ARCHIVOS, V1, P7
CORDONNIER G, 1960, REVUE DOCUMENTATION, V27, P12
CORTESALONSO V, 1959, REV ARCH BIBLIOTECAS, V67, P21
*COUNC LIBR RES, 1959, ANN REP
*COUNC LIBR RES, 1960, ANN REP
COX CT, 1960, LIBR J, V85, P4523
COX JC, 1960, 1960 CATH LIBR ASS P, P186
*CT VALL HIST MUS, 1960, SPRINGF MASS
DAHM H, 1960, ARCHIVAR, V12
DALY LJ, 1960, LIBR TRENDS, V9, P246
DAVIES JH, 1960, S A ARGIEFBLAD, P7
DAVISON GH, 1961, LIBRARY ASSOCIATION, V63, P69
*DEP U, 1960, ANN REP
DESAINTBLANQUAT O, 1960, PROBLEMA TRANSFERENC
DETHMAN JPH, 1960, AM ARCHIVIST, V23, P439
DEWING CE, 1960, AM ARCHIVIST, V23, P315
DIAZ AJ, 1961, IN PRESS GUIDE MICRO
DIEZEL R, 1959, ARCHIVMITTEILUNGEN, V9, P68
DOUSSET F, 1960, GAZETTE ARCH, P169
DUNIWAY DC, 1961, AM ARCHIVIST, V24, P55
DUNIWAY DC, 1961, ROLE STATE ARCH OREG
DUYVIS FD, 1960, UNESCO BULL LIBR, V14, P241
ECKHARDT WA, 1960, ARCHIVAR, V13
EDDY HH, 1961, AM ARCHIVIST, V24, P75
ELLIS JT, 1959, GUIDE AM CATHOLIC HI
ELLIS R, 1960, 1960 P PAP SUMM DISC, P9
ELLIS R, 1960, ARCHIVES, V4, P218
ENDERS G, 1959, ARCHIVMITTEILUNGEN, V9, P143
ENDERS L, 1960, ARCHIVMITTEILUNGEN, V10, P96
ENGEL F, 1960, ARCHIVAR, V13
ESCHERDESRIVIER.J, 1961, GAZETTE ARCH, P18
EVANS D, 1960, NEDERLANDS ARCHIEVEN, V64, P164
*FAC CORP ARCH LIB, 1958, REV ARCH BIBL MUS, V65
FACIUS F, 1961, BUNDESARCHIV SEINE B
FANT HB, 1960, AM ARCHIVIST, V23, P285
FERRANDO JEM, 1958, ARCHIVO GENERAL CORO
*FONDS NAT RECH SC, 1959, COMM INT MICR FNR
FRANK O, 1960, GRUNDBEGRIFFE MIKROF
FRANK O, 1959, HDB REPROGRAPHIE, V1
FRANK O, 1960, REVUE DOCUMENTATION, V27, P41
FRANKLIN WN, 1959, RECORDS NATIONAL ARC
FREEDMAN SB, 1960, LIBR J, V85, P3926
FREEMAN JF, 1961, ETHNOHISTORY, V8, P156, DOI 10.2307/480765
FROME J, 1960, J PATENT OFFICE SOC, V270, P3, DOI 10.1016/0016-0032(60)90895-4
GALAZ JDP, 1952, ELEMENTOS ARCH MANUA
GARRISON RW, 1960, AM ARCHIVIST, V23, P415
GICOVATE A, 1960, THESIS U N CAROLINA
GINDER CE, 1961, OFFICE EXECUTIVE, V34, P12
GOLDENBERG LA, 1959, ARCHIVMITTEILUNGEN, V9, P189
GOLDIE LFE, NOTES ARCH LEGISLATI
GRIFFIN M, 1961, REV DOCUMENTATION, V28, P18
GROVER WC, 1957, INDIAN ARCH, V11, P1
GUANDOLO J, 1961, FEDERAL PROCEDURE FO
HAHN A, 1960, ARCHIVMITTEILUNGEN, V10, P26
Hamer Philip M., 1961, GUIDE ARCH MANUSCRIP
HARDENBERG H, 1960, NEDERLANDS ARCH, V64, P29
HARDENBERG H, 1958, NEDERLANDS ARCH, V63, P57
HART S, 1959, ARCH AMSTERDAM
HAWKEN WR, STATE LIBRARY ART 3, V5
HAWKINS R, STATE LIBRARY ART 1, V5
HEBARRE JL, 1959, ETUDES PRESSE, V11, P65
HEMPHILL WE, 1961, AM ARCHIVIST, V24, P160
HERLITZ G, 1961, AM ARCHIVIS, V24, P92
HESS U, 1960, ARCHIVMITTEILUNGEN, V10, P83
HINE JD, 1959, ARCH MANUSCRIPTS, V1, P1
HOFF BV, 1961, NEDERLANDS ARCHIEVEN, V65, P49
HOHNEL K, 1959, ARCHIVMITTEILUNGEN, V9, P178
HOLMBERG I, 1960, NORDISK ARK, V5, P1
HOLMES DC, 1960, LIBRARIES, V31, P61
HOPPENER R, 1958, NEDERLANDS ARCHIEVEN, V63, P73
HORTON A, 1960, ARCH MANUSCRIPTS, V1, P8
HVIDTFELDT J, 1960, NORDISK ARKIVNYT, V5, P42
*IA HIST ARCH DEP, 1960, 35TH BIENN REP
*INDIA NAT COMM AR, 1959, PROC, V2
*INT C SCI INF, 1958, PROC
*INT C STAND COMM, 1959, W RES U
*INT FED DOC, 1960, BIBL DOC BIBL ANN 19, V332
JAEGER F, 1960, ARCHIVAR, V13
JAHN R, 1960, ARCHIVMITTEILUNGEN, V10, P43
*JAMAICA ISL REC O, 1960, 1958 ANN REP
JENKINS WS, 1961, 1960 U N CAR BUR PUB
JOHNSON HC, 1960, ARCHIVES, V4, P214
JOLLEY L, 1960, PRINCIPLES CATALOGUI
JONES HG, 1960, COUNTY RECORDS MANUA
JONES HG, 1961, AM ARCHIVIST, V24, P25
JONES HG, 1960, COUNTY OFFICER, V26, P181
JOVANOVIC L, 1959, ARCHIVIST, V8, P44
KAHN G, 1961, PROGR FILING
Kahn H, 1944, AM ARCHIVIST, V7, P243
KAISER L, 1960, ARCHIVAR, V13
KANAI M, 1960, BEIKOKU NI OKERU KOM
KANE LM, 1960, B AM ASS STATE LOCAL, V2
KARAMAN I, 1959, ARCHIVIST, V8, P33
KENT GO, 1961, AM ARCHIVIST, V24, P43
KIERSKY LJ, 1961, SPEC LIBR, V52, P132
KOCMAN A, 1958, ARCHIVNI CASOPIS, V8, P192
KRAISKAIA ZV, 1957, ISTORICHESKII ARCH, P245
KUEBLER D, 1960, RES STUDY, V1
KYLE B, 1961, UNESCO BULL LIBR, V15, P53
*LA STAT ARCH REC, 1960, GUID REC LA CONS DEP
LACOUR R, 1960, GAZETTE ARCH, P13
LAGERQUIST PD, 1960, JOURNAL, V1, P1
LAKE V, 1961, ILLINOIS LIBRARIES, V43, P428
Land RH, 1954, AM ARCHIVIST, V17, P195
LAWSON DF, 1960, TECHNIQUE PHOTOMICRO
LEAVITT AH, 1961, AM ARCHIVIST, V24, P175
LEFEVRE J, 1960, ARCH BIBLIOTHEQUES M, V31, P133
LEWINSON P, 1960, AM ARCHIVIST, V23, P297
*LIBR ASS AUSTR AR, 1959, ARCH MAN, P27
LIPETZ BA, 1960, AM DOC, V11, P241, DOI 10.1002/asi.5090110308
LISAC AL, 1956, ARHIVIST, V6, P175
LISAC AL, 1957, ARHIVIST, V7, P159
LISAC AL, 1958, ARHIVIST, V7, P217
LISAC AL, 1959, ARHIVIST, V8, P138
LISAC AL, 1959, ARHIVIST, V8, P105
LISAC AL, 1957, ARHIVIST, V7, P122
LISAC AL, 1958, ARHIVIST, V7, P209
LLORENTE JMA, 1958, POPAYAN, V26, P27
LODOLINI E, 1958, ARCHIVI, V25, P335
*LONGW LIBR, 1960, KENN SQUAR
LORCH WT, 1961, UNESCO BULL LIBR, V15, P25
LOTZKE H, 1960, ARCHIVMITTEILUNGEN, V10, P12
LOTZKE H, 1960, ARCHIVMITTEILUNGEN, V10, P117
LOVETT RW, 1960, BUS HIST REV, V34, P345, DOI 10.2307/3111879
LUKIS M, 1960, ARCH MANUSCRIPTS, V1, P1
LUTHER F, 1961, LIBR J, V86, P929
LUTHER F, 1961, LIBR J, V86, P2425
LYDENBERG HM, 1960, CARE REPAIR BOOKS
MACDERMOT A, 1960, AM ARCHIVIST, V23, P407
MADY J, 1960, OS ARQUIVOS OS DOCUM
MAGDALENO R, 1958, REV ARCH BIBLIO MUSE, V65, P213
MAHIEU B, 1960, GAZETTE ARCH, P53
MAJUMDAR PC, 1957, INDIAN ARCH, V11, P77
MARIA J, 1958, ARCH GENERAL INDIAS
MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035
MARQUANT R, 1960, 4E C INT ARCH STOCKH
MARQUANT R, 1961, GAZETTE ARCH, P5
*MARYL HALL REC CO, PUBL
MAS MB, 1961, DIRECCION GENERAL AR, V57, P77
*MAUR ARCH DEP, 1961, 1960 ANN REP
MEARNS DC, 1961, AM ARCHIVIST, V24, P81
MEISNER HO, 1960, ARCHIVMITTEILUNGEN, V10, P134
MESTAYER M, 1960, GAZETTE ARCH, P174
*MI HIST COMM, 1960, GUID PHOT RES MI HIS
*MI U MI HIST COLL, 1960, 1959 60 REP
MIATEV P, 1957, INDIAN ARCH, V11, P7
MILHOLLEN HD, 1961, CIVIL WAR PHOTOGRAPH
MORTON L, 1961, WORLD POLIT, V13, P435, DOI 10.2307/2009484
MULLER W, 1960, ARCHIVAR, V13
MUNDEN K, 1956, AM ARCHIVIST, V19, P291
MURAWSKI E, 1960, ARCHIVAR, V13
*NC ARCH HIST DEP, 1960, 28TH BIENN REP
*NC ARCH HIST DEP, 1960, COUNT REC MAN
*NOTT U DEP MAN, 1958, 1ST REP
*NZ NAT ARCH, 1961, PREL INV, V9
*OH ATT GEN OFF, 1960, ATT GEN OP, V1348
ORNE J, 1959, ASS CUBANA BIBLIO B, V11, P2
*PAN AM I GEOGR HI, 1960, 1ST P M
*PAN AM UN, 1960, DOC OFF ORG EST AM, V1
PAPRITZ J, 1960, 4E C INT ARCH STOCKH
PARASKIV I, 1959, ISTORICHESKII ARKHIV, P184
PARDO JJ, 1958, CATALOGO MANUSCRITOS
PEASE MJ, 1961, ILLINOIS LIBRARIES, V43, P433
Peckham HH, 1954, AM ARCHIVIST, V17, P319
PETERSEN EN, 1960, LIBR J, V85, P4089
PETROVIC D, 1959, ARHIVIST, V8, P31
PHILIPPI H, 1960, ARCHIVAR, V13
PHILIPPI H, 1958, ARCHIVAR, V11
PIERCE L, 1960, B RECORDS P UNITED C, V13, P1
PIERSON RM, 1960, LIBR TRENDS, V9, P213
PIUS XII, 1958, ARCHIVI, V25, P258
PLATON J, 1960, TEXAS LIBRARIES, V22, P96
POSNER E, 1961, AM ARCHIVIST, V24, P65
POSNER E, 1960, AM ARCHIVIST, V23, P263
QUEGUINER J, 1960, GAZETTE ARCH, P63
QUIMBY G, 1961, AM ARCHIVIST, V24, P234
RADTKE I, 1960, ARCHEION, V33, P3
*REC CONTR INC, 1961, RET PRES REC
*REC CONTR INC, 1958, GUID RET PRES REC
REINGOLD N, 1961, AM ARCHIVIST, V24, P163
RENZE DC, 1960, AM ARCH, V23, P259
RICAURTE EO, 1958, B COMITE ARCH, V1, P7
RICHARDSON GD, 1959, ARCH MANUSCRIPTS, V1, P12
RICHE RW, 1960, MON LABOR REV, V83, P933
RODRIGUES JH, 1959, SITUACAO ARQUIVO NAC
ROPER PP, 1960, CLEVELAND MARSHALL L, V9, P523
ROURKE FE, 1960, AM POLIT SCI REV, V54, P684, DOI 10.2307/1953946
*S A ARCH, 1959, ANN REP CHIEF ARCH
*S A SOC ARCH, 1960, SA ARG, P59
SABBE E, 1960, 4E C INT ARCH STOCKH
SAMBERGER Z, 1958, ARCH CASOPIS, V8, P112
SANTE GW, 1960, ARCHIVAR, V13
*SASKATCHEWAN ARCH, 1960, 9TH REP
SCHATZ R, 1961, BEHORDENSCHRIFTGUT A, P362
SCHEFFLER EM, 1961, ILLINOIS LIBRARIES, V43, P449
SCHELL E, 1958, CARE CUSTODY METHODI
SCHELLENBERG TR, 1961, AM ARCHIVIST, V24, P11
SCHELLENBERG TR, 1960, PROBLEMAS ARQUIVISTI
SCHERFT P, 1958, NEDERLANDS ARCHIEVEN, V63, P141
SCHETELICH E, 1959, ARCHIVMITTEILUNGEN, V9, P134
SCHMECKEBIER LF, 1961, GOVT PUBLICATIONS TH
SCHNEIDER P, 1960, OFFICE, V51, P12
SCHWIEBERT EG, 1960, LIBR TRENDS, V9, P253
SCUFFLAIRE A, 1960, ARCH BIBLIOTHEQUES M, V31, P121
Sharman R. C., 1960, AUSTR LIB J, V9, P125
SHARMAN RC, 1960, ARCH MANUSCRIPTS, V1, P15
SHIFF R, 1960, OFFICE MANAGEMENT, V21, P40
SIGBAND NB, 1960, EFFECTIVE REPORT WRI
SIMMONS J, 1960, LIBRARY ASS RECORD, V62, P328
SKIPPER JE, 1960, LIBRARY TRENDS, V8
SMITH RM, 1959, Q J CURRENT ACQUISIT, V17, P13
*SOC AM ARCH, 1960, DIR
*SOC AM ARCH STAT, 1960, STAT ARCH HEADS APPR
SOGNERADSARKIVER, 1960, NORDISK ARK, V5, P13
SOWERBY EM, 1959, CATALOGUE LIBRARY T, V5
*SPAIN ARCH GEN SI, 1958, DIR GEN ARCH BIBL
STEWART JF, 1960, STATE LIBRARY ART 5, V5
STULLI B, 1959, ARHIVIST, V8, P15
TAUBE M, 1961, LIBR QUART, V31, P90
TAUBER MF, 1960, STATE LIBRARY ART 1, V1
THOM JM, 1960, OFFICE, V51, P14
THOMPSON A, 1958, VOCABULARIUM BIBLI S
TIRMIZI AI, 1957, INDIAN ARCH, V11, P64
TOYNBEE A, 1961, NY TIMES 0430
ULIBARRI GS, 1960, HDB LATIN AM STUDIES, V22, P334
*UN STAT OFF, 1959, HDB DAT PROC METH
*US CIV DEF MOB OF, 1959, MEM PRES ESS REC ACT
*US CIV DEF MOB OF, 1960, MEM PRES ESS REC ACT
*US FOR SERV, 1960, HDB FIL SYST
*US GEN SERV ADM, 1960, 11TH ANN REP
*US INT REC ADM C, 1960, ORG SUCC REC ADM
*US INT REC ADM C, 1961, REC SPAC AG PAST PRE
*US INT REC ADM C, 1961, NEW STAND MAN POS
*US INT REC ADM C, 1960, MAN INV REC
*US INT REC ADM C, 1961, INF MOT REV PAP MAN
*US INT REC ADM C, 1961, DOC SYST AUT NOT AUT
*US INT REC ADM C, 1961, FORMS FOSDIC
*US LIBR C, 1961, ANN REP
*US LIBR C, 1960, ANN REP
*US NAT ARCH, 1960, STAFF INF PAP, V17
*US NAT ARCH, 1961, PREL INV, V133
*US NAT ARCH, 1961, AG CIT REC NAT ARCH
*US NAT ARCH, 1960, PREL INV, V123
*US NAT ARCH, 1961, LIST NAT ARCH MICR
*US NAT ARCH, 1960, PREL INV, V131
*US NAT ARCH, 1960, PREL INV, V132
*US NAT ARCH, 1960, NAT ARCH ACC, V55
*US NAT ARCH, 1960, PREL INV, V130
*US NAT ARCH, 1960, PREL INV, V129
*US PHS, 1961, MAN CURR REC
*US SMITHS I BUR A, 1959, MAN COLL BUR AM ETHN
*VA STAT LIBR, 1959, REP
*VA STAT LIBR, 1958, REP
*VA STAT LIBR, 1960, REP
*VA STAT LIBR, 1960, PUBL, V16
VANDERGOUW JL, 1960, NEDERLANDS ARCHIEVEN, V64, P103
VANEEGHEN IH, 1960, NEDERLANDS ARCHIEVEN, V64, P160
VANKERSEN LW, 1960, AM ARCHIVIST, V23, P319
VANSCHILFGAARDE AP, 1960, NEDERLANDS ARCHIEVEN, V64, P152
VANTHOFF B, 1960, NEDERLANDS ARCH, V64, P129
VEENENDAAL AJ, 1958, NEDERLANDS ARCHIEVEN, V63, P25
VERRY HR, 1961, REV DOCUMENTATION, V28, P70
Vickery B. C., 1960, FACETED CLASSIFICATI
*W WILS F, 1961, 2 YEAR REP
WACHTER F, 1959, ARCHIVMITTEILUNGEN, V9, P75
WAGNER FS, 1960, AM DOC, V11, P102, DOI 10.1002/asi.5090110204
WELCH I, 1960, OFFICE, V51, P255
WELCH I, 1960, OFFICE, V51, P179
WELCH I, 1960, OFFICE, V51, P181
WHITEHILL WM, 1961, AM ARCHIVIST, V24, P133
WILLEMSEN J, 1958, NEDERLANDS ARCH, V63, P65
WINFREY DH, 1960, TEXAS LIBRARIES, V22, P112
WINFREY DH, 1960, AM ARCHIVIST, V23, P431
WINFREY DH, 1960, SW HIST Q, V64, P171
WOLTRING J, 1958, NEDERLANDS ARCHIEVEN, V63, P143
WRAY EH, 1960, AM ARCHIVIST, V23, P277
WRIGHT M, 1961, Q J CURRENT ACQUISIT, V18, P121
WYLIE HL, 1960, OFFICE, V51, P80
WYLIE HL, 1960, OFFICE, V51, P243
YEAGER CG, 1960, I N REPORTER, V19, P19
*ZANZ ARCH MUS DEP, 1960, 1959 ANN REP
*ZURICH STAATS, 1959, JAHR
*ZURICH STAATS, 1960, JAHR
1959, 25TH ANN REP ARCH
1961, NORDISK ARK, V6, P3
1958, NEDERLANDS ARCHIEVEN, V63, P13
1961, N CAROLINA LIBRARIES, V19
1960, ARCHIVES, V4, P189
1960, OFFICE MANAGEMENT, V21, P13
1961, ILLINOIS LIBRARIES, V43, P68
1960, NORDISK ARKIVNYT, V5, P10
1959, NORDISK ARK, V4, P25
1960, FINANCIAL WORLD, V113, P13
1961, AM ARCHIVIS, V28, P203
1961, 1960 MIDDL COUNT ANN
1960, 1959 MIDDL COUNT ANN
1961, JOURNAL, V271, P349
1960, GAZETTE ARCH, P143
1960, ARCHIVMITTEILUNGEN, V10, P37
1960, JOURNAL, V270, P1
15TH ANN REP 1958 59
1960, NEDERLANDS ARCHIEVEN, V65, P13
1959, MINNESOTA HIST, V36, P215
1960, OFFICE MANAGEMENT AM, V21, P80
1961, ASLIB P, V13, P89
1958, B COMITE ARCHIVOS, V1, P23
1961, AM DOCUMENTATION, V12, P108
1960, FEDERAL REGISTER 2, V25, P2281
1960, PRELIMINARY LIST CIV
1960, NORDISK ARK, V5, P29
1961, Q J CURRENT ACQUISIT, V18, P129
1960, NEDERLANDS ARCHIEVEN, V64, P59
1961, TOWN CLERKS TOPICS, V29, P1
1960, SWEDEN WORLD DOCUMEN
NR 395
TC 0
Z9 0
PU SOC AMER ARCHIVISTS
PI CHICAGO
PA SUITE 504, 600 S FEDERAL, CHICAGO, IL 60605
SN 0360-9081
J9 AM ARCHIVIST
JI Am. Arch.
PY 1962
VL 25
IS 1
BP 83
EP 107
PG 25
WC History; Information Science & Library Science
SC History; Information Science & Library Science
GA CDW95
UT WOS:A1962CDW9500013
ER
PT J
AU DOYLE, LB
AF DOYLE, LB
TI INDEXING AND ABSTRACTING BY ASSOCIATION
SO AMERICAN DOCUMENTATION
LA English
DT Article
CR BARHILLEL Y, 1960, 3 APR US OFF NAV RES
BORKO H, 1962, P WESTERN JOINT COMP
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