Handbook of Natural Language Processing, Second Edition

Overview

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment...

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Overview

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.

New to the Second Edition

  • Greater prominence of statistical approaches
  • New applications section
  • Broader multilingual scope to include Asian and European languages, along with
    English
  • An actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developments

Divided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.

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Editorial Reviews

From the Publisher
… The need for a revised second edition of this book arose because of the growth of the field and the introduction of new methods. … The chapters have been exhaustively reviewed to maintain quality and homogeneity. The handbook has numerous diagrams and tables. The chapters are arranged so that they may be read independently. The style of presentation is good and the index is useful. Adequate references to current literature are provided. When compared to the previous edition, this edition focuses on statistical approaches, new and emerging applications, and multilingual scope, and has an actively maintained Wiki. Outdated chapters present in the first edition have been removed, and the remaining chapters have been rewritten and updated to reflect current trends and applications. When compared to other handbooks on NLP, this one is cheaper and certainly worth every penny. It provides a lot of useful information to those who are interested in NLP and its applications. … I highly recommend this handbook to practitioners of NLP as a very useful resource.
Computing Reviews, January 2011

… the handbook covers the wide area of NLP and its applications. This will essentially help researchers and graduate students to access starting-point material for a particular area of interest. The handbook also covers the associated algorithms with examples which will help to develop prototype systems … a high quality compilation of up-to-date theories and applications of NLP.
— Sandipan Dandapat

… If you need a readable introduction to this important subject — this is it. … This is a good way to get into NLP. … this does provide a basic course on the subject suitable both for academic and practical development. Highly recommended.
—Mike James, iProgrammer, 2010

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Product Details

  • ISBN-13: 9781420085921
  • Publisher: Taylor & Francis
  • Publication date: 2/22/2010
  • Edition number: 2
  • Pages: 704
  • Product dimensions: 7.00 (w) x 10.40 (h) x 1.50 (d)

Meet the Author

Nitin Indurkhya is an associate professor in the School of Computer Science and Engineering at the University of New South Wales in Sydney, Australia. He is also the founder and president of Data-Miner Pty Ltd, which offers education, training, and consulting services in data/text analytics and human language technologies.

Before his death, Fred J. Damerau was a researcher at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, where he worked on machine learning approaches to natural language processing.

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Table of Contents

CLASSICAL APPROACHES
Classical Approaches to Natural Language Processing, Robert Dale

Text Preprocessing, David D. Palmer

Lexical Analysis, Andrew Hippisley

Syntactic Parsing, Peter Ljunglöf and Mats Wirén

Semantic Analysis, Cliff Goddard and Andrea C. Schalley

Natural Language Generation, David D. McDonald

EMPIRICAL AND STATISTICAL APPROACHES
Corpus Creation, Richard Xiao

Treebank Annotation, Eva Hajičová, Anne Abeillé, Jan Hajič, Jiři Mirovský, and Zdeňka Urešová

Fundamental Statistical Techniques, Tong Zhang

Part-of-Speech Tagging, Tunga Güngör

Statistical Parsing, Joakim Nivre

Multiword Expressions, Timothy Baldwin and Su Nam Kim

Normalized Web Distance and Word Similarity, Paul M.B. Vitányi and Rudi L. Cilibrasi

Word-Sense Disambiguation, David Yarowsky

An Overview of Modern Speech Recognition, Xuedong Huang and Li Deng

Alignment, Dekai Wu

Statistical Machine Translation, Abraham Ittycheriah

APPLICATIONS
Chinese Machine Translation, Pascale Fung

Information Retrieval, Jacques Savoy and Eric Gaussier

Question Answering, Diego Mollá-Aliod and José-Luis Vicedo

Information Extraction, Jerry R. Hobbs and Ellen Riloff

Report Generation, Leo Wanner

Emerging Applications of Natural Language Generation in Information Visualization, Education, and Healthcare, Barbara Di Eugenio and Nancy L. Green

Ontology Construction, Philipp Cimiano, Johanna Völker, and Paul Buitelaar

BioNLP: Biomedical Text Mining, K. Bretonnel Cohen

Sentiment Analysis and Subjectivity, Bing Liu

Index

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