000			01259nab a22002417 4500
001			H004.414 INF
003			AR-sfUTN
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040			_cAR-sfUTN
080			_aH004.414 INF
100			_aWong, Shik Kam Michael
700			_aYao, Yiyu Y.
245			_aOn modeling information retrieval with probabilistic inference
336			_2rdacontent _atexto _btxt
337			_2rdamedia _asin mediación _bn
338			_2rdacarrier _avolumen _bnc
505			_aThis article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic inference. This new approach provides a common conceptual and mathematical basis for many retrieval models, such as the Boolean, fuzzy set, vector space, and conventional probabilistic models.
650			_aINFORMATION STORAGE AND RETRIEVAL
650			_aARTIFICIAL INTELLIGENCE
773			_tACM Transactions on Information Systems _wH004.414 INF _nS.T.:H004.414 INF PP3498 _g(vol. 13, nro. 1, Jan. 1995), p. 38-68
942			_cAN
999			_c37649 _d37649