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999			_c387446 _d387446
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003			ES-MaUEC
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007			cr nn 008mamaa
008			230401s2010 sz | s |||| 0|eng d
020			_a9783031022722
024	7		_a10.1007/978-3-031-02272-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.884 _b2010 EB
100	1		_aCarmel, David _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687943
245	1	0	_aEstimating the Query Difficulty for Information Retrieval _cby David Carmel, Elad Yom-Tov
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (X, 77 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Information Concepts Retrieval and Services _x1947-9468
505	0		_aIntroduction - The Robustness Problem of Information Retrieval -- Basic Concepts -- Query Performance Prediction Methods -- Pre-Retrieval Prediction Methods -- Post-Retrieval Prediction Methods -- Combining Predictors -- A General Model for Query Difficulty -- Applications of Query Difficulty Estimation -- Summary and Conclusions.
520			_aMany information retrieval (IR) systems suffer from a radical variance in performance when responding to users' queries. Even for systems that succeed very well on average, the quality of results returned for some of the queries is poor. Thus, it is desirable that IR systems will be able to identify "difficult" queries so they can be handled properly. Understanding why some queries are inherently more difficult than others is essential for IR, and a good answer to this important question will help search engines to reduce the variance in performance, hence better servicing their customer needs. Estimating the query difficulty is an attempt to quantify the quality of search results retrieved for a query from a given collection of documents. This book discusses the reasons that cause search engines to fail for some of the queries, and then reviews recent approaches for estimating query difficulty in the IR field. It then describes a common methodology for evaluating the prediction quality of those estimators, and experiments with some of the predictors applied by various IR methods over several TREC benchmarks. Finally, it discusses potential applications that can utilize query difficulty estimators by handling each query individually and selectively, based upon its estimated difficulty. Table of Contents: Introduction - The Robustness Problem of Information Retrieval / Basic Concepts / Query Performance Prediction Methods / Pre-Retrieval Prediction Methods / Post-Retrieval Prediction Methods / Combining Predictors / A General Model for Query Difficulty / Applications of Query Difficulty Estimation / Summary and Conclusions.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9156735 _aBuscadores de Internet
650		7	_2embne _9147823 _aRecuperación de la información
700	1		_aYom-Tov, Elad _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686430
776	0	8	_iPrinted edition: _z9783031011443
776	0	8	_iPrinted edition: _z9783031034008
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02272-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI