Information Retrieval: Algorithms and Heuristics

Pirmais vāks
Springer Science & Business Media, 2004. gada 21. okt. - 332 lappuses

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 retrieving documents 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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Populāri fragmenti

330. lappuse - A study of smoothing methods for language models applied to ad hoc information retrieval.
309. lappuse - Extending the Boolean and Vector Space Models of Information Retrieval with P-Norm Queries and Multiple Concept Types, Ph.D. Dissertation, Computer Science, Cornell University, 1983.
322. lappuse - In Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 302-310, Dublin, Ireland, 1994.
329. lappuse - A generalized term dependence model in information retrieval. Information Technology: Research and Development, 2: 129-154; 1983.
322. lappuse - Ratnasamy, S., Francis, P., Handley, M., Karp, R., and Shenker, S. (2001). A Scalable Content- Addressable Network.

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