000 03786nam a22004695i 4500
001 978-3-031-02272-2
003 DE-He213
005 20240730163852.0
007 cr nn 008mamaa
008 220601s2010 sz | s |||| 0|eng d
020 _a9783031022722
_9978-3-031-02272-2
024 7 _a10.1007/978-3-031-02272-2
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aCarmel, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980922
245 1 0 _aEstimating the Query Difficulty for Information Retrieval
_h[electronic resource] /
_cby David Carmel, Elad Yom-Tov.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aX, 77 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 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.
650 0 _aComputer networks .
_931572
650 1 4 _aComputer Communication Networks.
_980923
700 1 _aYom-Tov, Elad.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980924
710 2 _aSpringerLink (Online service)
_980925
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011443
776 0 8 _iPrinted edition:
_z9783031034008
830 0 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
_980926
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02272-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c85068
_d85068