000 04255nam a22005175i 4500
001 978-3-031-02130-5
003 DE-He213
005 20240730163816.0
007 cr nn 008mamaa
008 220601s2009 sz | s |||| 0|eng d
020 _a9783031021305
_9978-3-031-02130-5
024 7 _a10.1007/978-3-031-02130-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aZhai, Chengxiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980623
245 1 0 _aStatistical Language Models for Information Retrieval
_h[electronic resource] /
_cby Chengxiang Zhai.
250 _a1st ed. 2009.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2009.
300 _aXII, 132 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aIntroduction -- 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 _aAs 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
710 2 _aSpringerLink (Online service)
_980624
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010026
776 0 8 _iPrinted edition:
_z9783031032585
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980625
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02130-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84997
_d84997