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007			cr nn 008mamaa
008			220601s2009 sz | s |||| 0|eng d
020			_a9783031021305
024	7		_a10.1007/978-3-031-02130-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP98.5.S83 _b2009 EB
100	1		_aZhai, ChengXiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687370
245	1	0	_aStatistical Language Models for Information Retrieval _cby Chengxiang Zhai
250			_a1st edition 2009
264		1	_aCham _bSpringer International Publishing _c2009
300			_a1 recurso en línea (XII, 132 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2009
650		7	_2embne _9666075 _aLingüística computacional _xMétodos estadísticos
650		7	_2embne _9147823 _aRecuperación de la información
776	0	8	_iPrinted edition: _z9783031010026
776	0	8	_iPrinted edition: _z9783031032585
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02130-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI