000			02916nam a2200409 i 4500
001			000696882
003			OCoLC
005			20240105153043.0
008			170707t20162016caua rb 001 0 eng d
020			_a1627058737
020			_a9781627058735
035			_a419529
040			_aYDXCP _bspa _erda _cYDXCP _dUIASF
050		4	_aQA 76.9.N38 _bC64.2016
100	1		_aCohen, Shay _eautor
245	1	0	_aBayesian analysis in natural language processing / _cShay Cohen.
264		1	_aSan Rafael, California : _bMorgan & Claypool Publishers, _c2016
264		4	_c©2016
300			_axxvii, 246 páginas : _bilustraciones, diagramas ; _c24 cm
336			_atexto _btxt _2rdacontent
337			_asin mediación _bn _2rdamedia
338			_avolumen _bnc _2rdacarrier
490	1		_aSynthesis lectures on human language technologies _v35
504			_aIncluye bibliografía (páginas 221-240) e índice.
505	0		_aPreliminaries -- Introduction -- Priors -- Bayesian estimation -- Sampling methods -- Variational inference -- Nonparametric priors -- Bayesian grammar models -- Closing Remarks -- Basic concepts -- Distribution catalog.
520			_a"Natural 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."--Publisher's website.
650		0	_aNatural language processing (Computer science)
650		4	_aProcesamiento de lenguaje natural (Computación)
650		4	_aTeoría bayesiana de decisiones estadísticas _9167830
830		0	_aSynthesis lectures on human language technologies _v# 35.
905			_a01
942		1	_cNEWBFXC1
999			_c652551 _d652551
980			_851 _gRonald RUIZ