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020 _a9783031023170
_9978-3-031-02317-0
024 7 _a10.1007/978-3-031-02317-0
_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 _aLosee, Robert M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981003
245 1 0 _aPredicting Information Retrieval Performance
_h[electronic resource] /
_cby Robert M. Losee.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIX, 59 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 _aPreface -- Acknowledgments -- Information Retrieval: A Predictive Science -- Probabilities and Probabilistic Information Retrieval -- Information Retrieval Performance Measures -- Single-Term Performance -- Performance with Multiple Binary Features -- Applications: Metadata and Linguistic Labels -- Conclusion -- Bibliography -- Author's Biography .
520 _aInformation Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively. This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.
650 0 _aComputer networks .
_931572
650 1 4 _aComputer Communication Networks.
_981004
710 2 _aSpringerLink (Online service)
_981005
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031002243
776 0 8 _iPrinted edition:
_z9783031011894
776 0 8 _iPrinted edition:
_z9783031034459
830 0 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
_981006
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02317-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c85085
_d85085