Natural Language Processing for Online Applications

Text retrieval, extraction and categorization

Second revised edition

Peter Jackson | Thomson Corporation

Isabelle Moulinier | Thomson Corporation

Hardbound – Available

ISBN 9789027249920 | EUR 105.00 | USD 158.00

Paperback – Available

ISBN 9789027249937 | EUR 33.00 | USD 49.95

e-Book –

ISBN 9789027292445 | EUR 105.00/33.00*
| USD 158.00/49.95*

This text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation.

This title replaces:
Natural Language Processing for Online Applications: Text retrieval, extraction and categorization, Peter Jackson and Isabelle Moulinier (2002)

[Natural Language Processing, 5] 2007. x, 232 pp.

Publishing status: Available
Published online on 1 July 2008

https://doi.org/10.1075/nlp.5

Table of Contents

Preface to the 2nd edition | p. ix

Chapter 1. Natural language processing | p. 1

1.1 What is NLP?
1.2 NLP and linguistics
1.3 Linguistic tools
1.4 Plan of the book

Chapter 2. Document retrieval | p. 23

2.1 Information retrieval
2.2 Indexing technology
2.3 Query processing
2.4 Evaluating search engines
2.5 Attempts to enhance search performance
2.6 The future ofWeb searching

Chapter 3. Information extraction | p. 69

3.1 The message understanding conferences
3.2 Regular expressions
3.3 Finite automata in FASTUS
3.4 Context-free grammars
3.5 Limitations of current technology and future research
3.6 Summary of information extraction

Chapter 4. Text categorization | p. 113

4.1 Overview of categorization tasks
4.2 Handcrafted rule based methods
4.3 Inductive learning for text classification
4.4 Nearest neighbor algorithms
4.5 Combining classifiers
4.6 Evaluation of text categorization systems

Chapter 5. Text mining | p. 163

5.1 What is text mining?
5.2 Resolving reference and coreference
5.3 Automatic summarization
5.4 Testing of automatic summarization programs
5.5 Prospects for text mining and NLP

Index | p. 227

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Erratum

For errata please go to http://www.jacksonpeter.com/nlp

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