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Table of contents (9 chapters)
About this book
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 various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.
In this book, 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. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
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Bibliographic Information
Book Title: Bayesian Analysis in Natural Language Processing, Second Edition
Authors: Shay Cohen
Series Title: Synthesis Lectures on Human Language Technologies
DOI: https://doi.org/10.1007/978-3-031-02170-1
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 7, eBColl Synthesis Collection 8
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-031-01042-2Published: 09 April 2019
eBook ISBN: 978-3-031-02170-1Published: 31 May 2022
Series ISSN: 1947-4040
Series E-ISSN: 1947-4059
Edition Number: 2
Number of Pages: XXXI, 311
Topics: Artificial Intelligence, Natural Language Processing (NLP), Computational Linguistics