Bayesian analysis in natural language processing / Shay Cohen.
Tipo de material: TextoSeries Synthesis lectures on human language technologies ; # 35.Editor: San Rafael, California : Morgan & Claypool Publishers, 2016Fecha de copyright: ©2016Descripción: xxvii, 246 páginas : ilustraciones, diagramas ; 24 cmTipo de contenido:- texto
- sin mediación
- volumen
- 1627058737
- 9781627058735
- QA 76.9.N38 C64.2016
Tipo de ítem | Biblioteca actual | Colección | Signatura topográfica | Copia número | Estado | Fecha de vencimiento | Código de barras | |
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Libros | Biblioteca Francisco Xavier Clavigero Acervo | Acervo General | QA 76.9.N38 C64.2016 (Navegar estantería(Abre debajo)) | ej. 1 | Disponible | UIA167406 |
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QA 76.9.M65 E843.2001 Ethics and computing : living responsibly in a computerized world / | QA 76.9.M65 F67.1990 Computer ethics : cautionary tales and ethical dilemmas in computing / | QA 76.9.M65 H5618.2001 La Etica del hacker y el espíritu de la era de la información / | QA 76.9.N38 C64.2016 Bayesian analysis in natural language processing / | QA 76.9.N38 F56.1997 Finite-state language processing / | QA 76.9.N38 S77.2016 Domain-sensitive temporal tagging / | QA 76.9.N4 L3 Using novell NetWare / |
Incluye bibliografía (páginas 221-240) e índice.
Preliminaries -- Introduction -- Priors -- Bayesian estimation -- Sampling methods -- Variational inference -- Nonparametric priors -- Bayesian grammar models -- Closing Remarks -- Basic concepts -- Distribution catalog.
"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.