Statistical Language Models for Information Retrieval (Record no. 84997)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04255nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02130-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163816.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2009 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031021305 |
| -- | 978-3-031-02130-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Zhai, Chengxiang. |
| 245 10 - TITLE STATEMENT | |
| Title | Statistical Language Models for Information Retrieval |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2009. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XII, 132 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Human Language Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Overview of Information Retrieval Models -- Simple Query Likelihood Retrieval Model -- Complex Query Likelihood Model -- Probabilistic Distance Retrieval Model -- Language Models for Special Retrieval Tasks -- Language Models for Latent Topic Analysis -- Conclusions. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details. Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02130-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2009. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural language processing (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational linguistics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural Language Processing (NLP). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Linguistics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1947-4059 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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