Information Retrieval: Algorithms and Heuristics

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Springer Science & Business Media, Sep 30, 1998 - Computers - 254 pages
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Information Retrieval: Algorithms and Heuristics is a comprehensive introduction to the study of information retrieval covering both effectiveness and run-time performance. The focus of the presentation is on algorithms and heuristics used to find documents relevant to the user request and to find them fast. Through multiple examples, the most commonly used algorithms and heuristics needed are tackled. To facilitate understanding and applications, introductions to and discussions of computational linguistics, natural language processing, probability theory and library and computer science are provided. While this text focuses on algorithms and not on commercial product per se, the basic strategies used by many commercial products are described. Techniques that can be used to find information on the Web, as well as in other large information collections, are included.
This volume is an invaluable resource for researchers, practitioners, and students working in information retrieval and databases. For instructors, a set of Powerpoint slides, including speaker notes, are available online from the authors.
  

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Contents

INTRODUCTION
1
RETRIEVAL STRATEGIES
11
21 Vector Space Model
13
22 Probabilistic Retrieval Strategies
22
23 Inference Networks
48
24 Extended Boolean Retrieval
58
25 Latent Semantic Indexing
60
26 Neural Networks
64
INTEGRATING STRUCTURED DATA AND TEXT
153
51 Review of the Relational Model
157
52 A Historical Progression
163
53 Information Retrieval Functionality Using the Relational Model
168
54 Boolean Retrieval
176
55 Proximity Searches
179
56 Computing Relevance Using Unchanged SQL
181
57 Relevance Feedback in the Relational Model
183

27 Genetic Algorithms
70
28 Fuzzy Set Retrieval
74
29 Summary
80
210 Exercises
81
RETRIEVAL UTILITIES
83
31 Relevance Feedback
84
32 Clustering
94
33 Passagebased Retrieval
100
34 Ngrams
102
35 Regression Analysis
106
36 Thesauri
108
37 Semantic Networks
118
38 Parsing
125
39 Summary
131
EFFICIENCY ISSUES PERTAINING TO SEQUENTIAL IR SYSTEMS
133
41 Inverted Index
134
42 Query Processing
142
43 Signature Files
146
44 Summary
149
45 Exercises
150
58 Summary
184
PARALLEL INFORMATION RETRIEVAL SYSTEMS
185
61 Parallel Text Scanning
186
62 Parallel Indexing
191
63 Parallel Implementation of Clustering and Classification
198
65 Exercises
199
DISTRIBUTED INFORMATION RETRIEVAL
201
71 A Theoretical Model of Distributed IR
202
72 Replication in Distributed IR Systems
206
73 Implementation Issues of a Distributed IR System
209
74 Improving Performance of Webbased IR Systems
212
75 Web Search Engines
214
76 Summary
217
77 Exercises
219
THE TEXT RETRIEVAL CONFERENCE TREC
221
FUTURE DIRECTIONS
227
References
231
Index
253
Copyright

Common terms and phrases

Popular passages

Page 234 - In Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 302-310, Dublin, Ireland, 1994.
Page 252 - Salton, G. (1983). A Generalized Term Dependence Model in Information Retrieval.
Page 252 - G. (1981). The Estimation of Term Relevance Weights using Relevance Feedback. Journal of Documentation, 37(4), 194-214.

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About the author (1998)

Ophir Frieder holds the Robert L. McDevitt, K.S.G., K.C.H.S. and Catherine H. McDevitt L.C.H.S. Chair in Computer Science and Information Processing and is Chair of the Department of Computer Science at Georgetown University. He is also Professor of Biostatistics, Bioinformatics and Biomathematics in the Georgetown University Medical Center. He is a Fellow of the AAAS, ACM, and IEEE.

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