Information Retrieval: Algorithms and Heuristics

✍ Scribed by David A. Grossman, Ophir Frieder (auth.)

Publisher: Springer US
Year: 1998
Tongue: English
Leaves: 261
Series: The Springer International Series in Engineering and Computer Science 461
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xvi
Introduction....Pages 1-9
Retrieval Strategies....Pages 11-81
Retrieval Utilities....Pages 83-132
Efficiency Issues Pertaining to Sequential IR Systems....Pages 133-151
Integrating Structured Data and Text....Pages 153-184
Parallel Information Retrieval Systems....Pages 185-199
Distributed Information Retrieval....Pages 201-219
The Text Retrieval Conference (TREC)....Pages 221-225
Future Directions....Pages 227-229
Back Matter....Pages 231-254

✦ Subjects

Information Storage and Retrieval; Data Structures, Cryptology and Information Theory; Data Structures

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