Natural Language Processing and Text Mining

  • Anne Kao
  • Stephen R. Poteet

Table of contents

  1. Front Matter
    Pages I-XII
  2. Anne Kao, Stephen R. Poteet
    Pages 1-7
  3. Ana-Maria Popescu, Orena Etzioni
    Pages 9-28
  4. Razvan C. Bunescu, Raymond J. Mooney
    Pages 29-44
  5. Eni Mustafaraj, Martin Hoof, Bernd Freisleben
    Pages 46-67
  6. Giovanni Marchisio, Navdeep Dhillon, Jisheng Liang, Carsten Tusk, Krzysztof Koperski, Thien Nguyen et al.
    Pages 69-90
  7. Chutima Boonthum, Irwin B. Levinstein, Danielle S. McNamara
    Pages 91-106
  8. Philip M. McCarthy, Stephen W. Briner, Vasile Rus, Danielle S. McNamara
    Pages 107-122
  9. Ying Liu, Han Tong Loh, Kamal Youcef-Toumi, Shu Beng Tor
    Pages 171-192
  10. Janez Brank, Marko Grobelnik, Dunja Mladenić
    Pages 193-219
  11. Lothar M. Schmitt, Kiel Christianson, Renu Gupta
    Pages 221-258
  12. Back Matter
    Pages 259-265

About this book

Introduction

With the increasing importance of the Web and other text-heavy application areas, the demands for and interest in both text mining and natural language processing (NLP) have been rising. Researchers in text mining have hoped that NLP—the attempt to extract a fuller meaning representation from free text—can provide useful improvements to text mining applications of all kinds.

Bringing together a variety of perspectives from internationally renowned researchers, Natural Language Processing and Text Mining not only discusses applications of certain NLP techniques to certain Text Mining tasks, but also the converse, i.e., use of Text Mining to facilitate NLP. It explores a variety of real-world applications of NLP and text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, and detailing the associated difficulties that must be resolved before the algorithm can be applied and its full benefits realized. In addition, it explores a number of cutting-edge techniques and approaches, as well as novel ways of integrating various technologies. Nevertheless, even readers with only a basic knowledge of data mining or text mining will benefit from the many illustrative examples and solutions.

Topics and features:

• Describes novel and high-impact text mining and/or natural language applications

• Points out typical traps in trying to apply NLP to text mining

• Illustrates preparation and preprocessing of text data – offering practical issues and examples

• Surveys related supporting techniques, problem types, and potential technique enhancements

• Examines the interaction of text mining and NLP

This state-of-the-art, practical volume will be an essential resource for professionals and researchers who wish to learn how to apply text mining and language processing techniques to real world problems. In addition, it can be used as a supplementary text for advanced students studying text mining and NLP.

Keywords

Matching Processing Signatur Text-Mining UNIX algorithms classification data mining latent semantic analysis learning linear optimization modeling natural language processing semantic analysis service-oriented computing

Editors and affiliations

  • Anne Kao
    • 1
  • Stephen R. Poteet
    • 1
  1. 1.BellevueUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84628-754-1
  • Copyright Information Springer-Verlag London Limited 2007
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-84628-175-4
  • Online ISBN 978-1-84628-754-1
  • About this book
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