28.4069, Books: Bayesian Analysis in Natural Language Processing: Cohen

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LINGUIST List: Vol-28-4069. Wed Oct 04 2017. ISSN: 1069 - 4875.

Subject: 28.4069, Books: Bayesian Analysis in Natural Language Processing: Cohen

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Date: Wed, 04 Oct 2017 12:23:41
From: David Schlangen [schlangen at morganclaypool.com]
Subject: Bayesian Analysis in Natural Language Processing: Cohen

 


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

Publication Year: 2017 
Publisher: Morgan & Claypool Publishers
	   http://www.morganclaypool.com
	

Book URL: http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=953 


Author: Shay Cohen

Electronic: ISBN:  9781627054218 Pages: 274 Price: U.S. $ 68
Paperback: ISBN:  9781627058735 Pages: 274 Price: U.S. $ 85


Abstract:

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.
 



Linguistic Field(s): Computational Linguistics


Written In: English  (eng)

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http://linguistlist.org/pubs/books/get-book.cfm?BookID=119953

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