000			03232nam a22004215i 4500
999			_c387102 _d387102
001			387102
003			ES-MaUEC
005			20230218150712.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230218s2016 sz | s |||| 0|eng d
020			_a9783031021619
024	7		_a10.1007/978-3-031-02161-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2016 EB
100	1		_aCohen, Shay _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686961
245	1	0	_aBayesian Analysis in Natural Language Processing _cby Shay Cohen
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (IV, 274 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aPreface -- Acknowledgments -- Preliminaries -- Introduction -- Priors -- Bayesian Estimation -- Sampling Methods -- Variational Inference -- Nonparametric Priors -- Bayesian Grammar Models -- Closing Remarks -- Bibliography -- Author's Biography -- Index .
520			_aNatural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
988			_aSynthesis Collection of Technology_2016
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9160470 _aEstadística bayesiana
776	0	8	_iPrinted edition: _z9783031001758
776	0	8	_iPrinted edition: _z9783031010330
776	0	8	_iPrinted edition: _z9783031032899
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02161-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI