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003			ES-MaUEC
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007			cr nn 008mamaa
008			230411s2019 sz | s |||| 0|eng d
020			_a9783031023170
024	7		_a10.1007/978-3-031-02317-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aZA3075 _b2019 EB
100	1		_aLosee, Robert M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688046 _d1952- _q(Robert MacLean),
245	1	0	_aPredicting Information Retrieval Performance _cby Robert M. Losee
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIX, 59 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		_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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9147823 _aRecuperación de la información
776	0	8	_iPrinted edition: _z9783031002243
776	0	8	_iPrinted edition: _z9783031011894
776	0	8	_iPrinted edition: _z9783031034459
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02317-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI