| 000 | 05507nam a2200613 i 4500 | ||
|---|---|---|---|
| 001 | 6813364 | ||
| 003 | IEEE | ||
| 005 | 20200413152902.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 100504s2010 caua foab 000 0 eng d | ||
| 020 | _z9781608453573 (pbk.) | ||
| 020 | _a9781608453580 (electronic bk.) | ||
| 024 | 7 |
_a10.2200/S00235ED1V01Y201004ICR015 _2doi |
|
| 035 | _a(CaBNVSL)swl00006009 | ||
| 035 | _a(OCoLC)631830853 | ||
| 040 |
_aCaBNVSL _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aTK5105.884 _b.C274 2010 |
|
| 082 | 0 | 4 |
_a025.04 _222 |
| 100 | 1 | _aCarmel, David. | |
| 245 | 1 | 0 |
_aEstimating the query difficulty for information retrieval _h[electronic resource] / _cDavid Carmel and Elad Yom-Tov. |
| 260 |
_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _cc2010. |
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| 300 |
_a1 electronic text (ix, 77 p. : ill.) : _bdigital file. |
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| 490 | 1 |
_aSynthesis lectures on information concepts, retrieval, and services, _x1947-9468 ; _v# 15 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (p. 69-71). | ||
| 505 | 8 | _a9. Summary and conclusions -- Summary -- What next -- Concluding remarks -- Bibliography -- Authors' biographies. | |
| 505 | 8 | _a8. Applications of query difficulty estimation -- Feedback: to the user and to the system -- Federation and metasearch -- Content enhancement using missing content analysis -- Selective query expansion -- Selective expansion based on query drift estimation -- Adaptive use of pseudo relevance feedback -- Other uses of query difficulty prediction -- Summary -- | |
| 505 | 8 | _a7. A general model for query difficulty -- Geometrical illustration -- General model -- Validating the general model -- The relationship between aspect coverage and query difficulty -- Validating the relationship between aspect coverage and query difficulty -- Summary -- | |
| 505 | 8 | _a6. Combining predictors -- Linear regression -- Combining pre-retrieval predictors -- Combining post-retrieval predictors -- Combining predictors based on statistical decision theory -- Evaluating the UEF framework -- Results -- Combining predictors in the UEF model -- Summary -- | |
| 505 | 8 | _a5. Post-retrieval prediction methods -- Clarity -- Definition -- Examples -- Other clarity measures -- Robustness -- Query perturbation -- Document perturbation -- Retrieval perturbation -- Cohesion -- Score distribution analysis -- Evaluating post-retrieval methods -- Prediction sensitivity -- Summary -- | |
| 505 | 8 | _a3. Query performance prediction methods -- | |
| 505 | 8 | _a4. Pre-retrieval prediction methods -- Linguistic approaches -- Statistical approaches -- Definitions -- Specificity -- Similarity -- Coherency -- Term relatedness -- Evaluating pre-retrieval methods -- Summary -- | |
| 505 | 8 | _a2. Basic concepts -- The retrieval task -- The prediction task -- Linear correlation -- Rank correlation -- Prediction robustness -- Summary -- | |
| 505 | 0 | _a1. Introduction: the robustness problem of information retrieval -- Reasons for retrieval failures, the RIA workshop -- Instability in retrieval, the TREC's robust tracks -- Estimating the query difficulty -- Summary -- | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aGoogle book search | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aCompendex | |
| 520 | 3 | _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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF t.p. (viewed on May 4, 2010). | ||
| 650 | 0 | _aInformation retrieval. | |
| 650 | 0 | _aQuerying (Computer science) | |
| 650 | 0 |
_aSearch engines _xProgramming. |
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| 700 | 1 | _aYom-Tov, Elad. | |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on information concepts, retrieval, and services, _x1947-9468 ; _v# 15. |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813364 |
| 999 |
_c561845 _d561845 |
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