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Bayesian Analysis in Natural Language Processing

Synthesis Lectures on Human Language Technologies

By (author) Shay Cohen
Format: Paperback / softback
Publisher: Morgan & Claypool Publishers, San Rafael, CA, United States
Imprint: Morgan and Claypool Life Sciences
Published: 30th Jun 2016
Dimensions: w 191mm h 235mm d 15mm
Weight: 481g
ISBN-10: 1627058737
ISBN-13: 9781627058735
Barcode No: 9781627058735
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Synopsis
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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