Automatic Ambiguity Resolution in Natural Language Processing

An Empirical Approach

By: Alexander Franz

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Paperback | 13 November 1996

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184 Pages

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23.5 x 15.88 x 1.27

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This is an exciting time for Artificial Intelligence, and for Natural Language Processing in particular. Over the last five years or so, a newly revived spirit has gained prominence that promises to revitalize the whole field: the spirit of empiricism.
This book introduces a new approach to the important NLP issue of automatic ambiguity resolution, based on statistical models of text. This approach is compared with previous work and proved to yield higher accuracy for natural language analysis. An effective implementation strategy is also described, which is directly useful for natural language analysis. The book is noteworthy for demonstrating a new empirical approach to NLP; it is essential reading for researchers in natural language processing or computational linguistics.

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You Can Find This Book In

Non-Fiction Language & Linguistics Linguistics Computing & I.T.Computer Science Artificial Intelligence Natural Language & Machine Translation Mathematics Mathematical Foundation Mathematical Logic Graphical & Digital Media Applications 3D Graphics & Modelling

Probability & Statistics Sociology & Anthropology Sociology Social Research & Statistics

Introduction	p. 1
Previous Work on Syntactic Ambiguity Resolution	p. 11
Loglinear Models for Ambiguity Resolution	p. 35
Modeling New Words	p. 51
Part-of-Speech Ambiguity	p. 71
Prepositional Phrase Attachment Disambiguation	p. 97
Conclusions	p. 109
Entropy	p. 117
Penn Treebank Tags	p. 119
Obtaining Random Samples	p. 121
Confusion Matrices for POS Tagging	p. 123
Converting Treebank Files	p. 125
Input to and Output from the Estimation Routines	p. 129
References	p. 133
Index	p. 147
Table of Contents provided by Blackwell. All Rights Reserved.

Automatic Ambiguity Resolution in Natural Language Processing

An Empirical Approach

At a Glance

Paperback

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