Bayesian analysis in natural language processing

Bayesian analysis in natural language processing

by Cohen, Shay

Type Book
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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 a…

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Title Statements

  • Full title :Bayesian analysis in natural language processing / Shay Cohen.

Summary

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.

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Edition Statements

  • Publication Date:2016.

Publication Statements

  • Publisher(s):Morgan & Claypool
  • Place of Publication:San Rafael, California
  • Language:eng
  • Publication Date:2016.

Notes

Titre de l'écran-titre (visionné le 27 septembre 2016).

In Morgan and Claypool publishers.

Fait partie de : Synthesis digital library of engineering and computer science.

The Physical Object

  • Description:1 ressource en ligne (xxvii, 246 pages) : : illustrations, fichier PDF.

Edition Identifiers

Table of Contents

1. Preliminaries -- 1.1 Probability measures -- 1.2 Random variables -- 1.2.1 Continuous and discrete random variables -- 1.2.2 Joint distribution over multiple random variables -- 1.3 Conditional distributions -- 1.3.1 Bayes' rule -- 1.3.2 Independent and conditionally independent random variables -- 1.3.3 Exchangeable random variables -- 1.4 Expectations of random variables -- 1.5 Models -- 1.5.1 Parametric vs. nonparametric models -- 1.5.2 Inference with models -- 1.5.3 Generative models -- 1.5.4 Independence assumptions in models -- 1.5.5 Directed graphical models -- 1.6 Learning from data scenarios -- 1.7 Bayesian and frequentist philosophy (tip of the iceberg) -- 1.8 Summary -- 1.9 Exercises

2. Introduction -- 2.1 Overview: where Bayesian statistics and NLP meet -- 2.2 First example: The latent Dirichlet allocation model -- 2.2.1 The Dirichlet distribution -- 2.2.2 Inference -- 2.2.3 Summary -- 2.3 Second example: Bayesian text regression -- 2.4 Conclusion and summary -- 2.5 Exercises

3. Priors -- 3.1 Conjugate priors -- 3.1.1 Conjugate priors and normalization constants -- 3.1.2 The use of conjugate priors with latent variable models -- 3.1.3 Mixture of conjugate priors -…

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Subjects

Bayesian statistical decision theory e-books Livres numériques Natural Language Processing Natural language processing (Computer s… Théorie de la décision bayésienne Traitement automatique des langues natu…

Writers

Cohen, Shay

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