30.1843, Books: Bayesian Analysis in Natural Language Processing: Cohen

[The LINGUIST List]

LINGUIST List: Vol-30-1843. Tue Apr 30 2019. ISSN: 1069 - 4875.

Subject: 30.1843, Books: Bayesian Analysis in Natural Language Processing: Cohen

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Date: Tue, 30 Apr 2019 22:36:53
From: Bebe Barrow [barrow at morganclaypool.com]
Subject: Bayesian Analysis in Natural Language Processing: Cohen

 


Title: Bayesian Analysis in Natural Language Processing 
Subtitle: Second Edition 
Series Title: Synthesis Lectures on Human Language Technologies edited by Graeme Hirst  

Publication Year: 2019 
Publisher: Morgan & Claypool Publishers
	   http://www.morganclaypool.com
	

Book URL: http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1385 


Author: Shay Cohen

Electronic: ISBN:  9781681735276 Pages: 343 Price: U.S. $ 63.96
Hardback: ISBN:  9781681735283 Pages: 343 Price: U.S. $ 99.95
Paperback: ISBN:  9781681735269 Pages: 343 Price: U.S. $ 79.95


Abstract:

Editor's Note: This is a new edition of a previously announced 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.
 



Linguistic Field(s): Computational Linguistics
                     Text/Corpus Linguistics


Written In: English  (eng)

See this book announcement on our website: 
http://linguistlist.org/pubs/books/get-book.cfm?BookID=135573




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