Bayesian Analysis in Natural Language Processing, Second Edition

Shay Cohen

ISBN 10: 3031010426 / ISBN 13: 9783031010422

Published by Springer, 2019

About this Item

Bayesian Analysis in Natural Language Processing, Second Edition | Shay Cohen | Taschenbuch | Synthesis Lectures on Human Language Technologies | xxxi | Englisch | 2019 | Springer | EAN 9783031010422 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Bibliographic Details

• Title: Bayesian Analysis in Natural Language Processing, Second Edition
• Publisher: Springer
• Publication Date: 2019
• Language: English
• ISBN 10: 3031010426
• ISBN 13: 9783031010422
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 235W x 191H x 19D millimeters 235 millimeters width by 191 millimeters height by 19 millimeters depth
• Weight: 643 grams
• Seller catalogs:
  • Bücher

About this title

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 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.

About the Author

Shay Cohen is a Lecturer at the Institute for Language, Cognition and Computation at the School of Informatics at the University of Edinburgh. He received his Ph.D. in Language Technologies from Carnegie Mellon University (2011), his M.Sc. in Computer Science fromTel-Aviv University (2004) and his B.Sc. in Mathematics and Computer Science from Tel-Aviv University (2000). He was awarded a Computing Innovation Fellowship for his postdoctoral studies at Columbia University (2011–2013) and a Chancellors Fellowship in Edinburgh (2013–2018). His research interests are in natural language processing and machine learning, with a focus on problems in structured prediction, such as syntactic and semantic parsing.