David A. Grossman, Ophir Frieder

Information Retrieval

Algorithms and Heuristics

• Gebundenes Buch

Produktbeschreibung

Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions.

This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms.

The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrievingdocuments in multiple languages, and using parallel or distributed processing to expedite the search are likewise described.

This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.

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Produktdetails

• The Kluwer International Series on Information Retrieval 15
• Verlag: Springer / Springer Netherlands
• Artikelnr. des Verlages: 978-1-4020-3003-1
• 2nd ed.
• Seitenzahl: 352
• Erscheinungstermin: 21. Oktober 2004
• Englisch
• Abmessung: 241mm x 160mm x 24mm
• Gewicht: 750g
• ISBN-13: 9781402030031
• ISBN-10: 1402030037
• Artikelnr.: 13933116

Inhaltsangabe

1. Introduction.- 2. Retrieval Strategies.- 2.1 Vector Space Model.- 2.2 Probabilistic Retrieval Strategies.- 2.3 Language Models.- 2.4 Inference Networks.- 2.5 Extended Boolean Retrieval.- 2.6 Latent Semantic Indexing.- 2.7 Neural Networks.- 2.8 Genetic Algorithms.- 2.9 Fuzzy Set Retrieval.- 2.10 Summary.- 2.11 Exercises.- 3. Retrieval Utilities.- 3.1 Relevance Feedback.- 3.2 Clustering.- 3.3 Passage-based Retrieval.- 3.4 N-grams.- 3.5 Regression Analysis.- 3.6 Thesauri.- 3.7 Semantic Networks.- 3.8 Parsing.- 3.9 Summary.- 3.10 Exercises.- 4. Cross-Language Information Retrieval.- 4.1 Introduction.- 4.2 Crossing the Language Barrier.- 4.3 Cross-Language Retrieval Strategies.- 4.4 Cross Language Utilities.- 4.5 Summary.- 4.6 Exercises.- 5. Efficiency.- 5.1 Inverted Index.- 5.2 Query Processing.- 5.3 Signature Files.- 5.4 Duplicate Document Detection.- 5.5 Summary.- 5.6 Exercises.- 6. Integrating Structured Data and Text.- 6.1 Review of the Relational Model.- 6.2 A Historical Progression.- 6.3 Information Retrieval as a Relational Application.- 6.4 Semi-Structured Search using a Relational Schema.- 6.5 Multi-dimensional Data Model.- 6.6 Mediators.- 6.7 Summary.- 6.8 Exercises.- 7. Parallel Information Retrieval.- 7.1 Parallel Text Scanning.- 7.2 Parallel Indexing.- 7.3 Clustering and Classification.- 7.4 Large Parallel Systems.- 7.5 Summary.- 7.6 Exercises.- 8. Distributed Information Retrieval.- 8.1 A Theoretical Model of Distributed Retrieval.- 8.2 Web Search.- 8.3 Result Fusion.- 8.4 Peer-to-Peer Information Systems.- 8.5 Other Architectures.- 8.6 Summary.- 8.7 Exercises.- 9. Summary and Future Directions.- References.