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Natural Language Processing and Text Mining

  • Book
  • © 2007

Overview

Editors:
  1. Anne Kao

    1. Bellevue, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Stephen R. Poteet

    1. Bellevue, USA

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  • While there are a large number of books on Natural Language Processing (NLP) and several on Text Mining, there are almost none that discuss them together in any depth. This book not only discusses applications of certain NLP techniques to certain Text Mining tasks, but also the converse, i.e. use of Text Mining to help NLP
  • Brings together a variety of views from various internationally recognized researchers and emphasizes caveats in the attempt to apply NLP to Text Mining.

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Table of contents (12 chapters)

  1. Front Matter

    Pages I-XII
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  2. Overview

    • Anne Kao, Stephen R. Poteet
    Pages 1-7

  3. Extracting Product Features and Opinions from Reviews

    • Ana-Maria Popescu, Orena Etzioni
    Pages 9-28

  4. Extracting Relations from Text: From Word Sequences to Dependency Paths

    • Razvan C. Bunescu, Raymond J. Mooney
    Pages 29-44

  5. Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles

    • Eni Mustafaraj, Martin Hoof, Bernd Freisleben
    Pages 46-67

  6. A Case Study in Natural Language Based Web Search

    • Giovanni Marchisio, Navdeep Dhillon, Jisheng Liang, Carsten Tusk, Krzysztof Koperski, Thien Nguyen et al.
    Pages 69-90

  7. Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models

    • Chutima Boonthum, Irwin B. Levinstein, Danielle S. McNamara
    Pages 91-106

  8. Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures

    • Philip M. McCarthy, Stephen W. Briner, Vasile Rus, Danielle S. McNamara
    Pages 107-122

  9. Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling

    • Mauritius A. R. Schmidtler, Jan W. Amtrup
    Pages 123-144

  10. Evolving Explanatory Novel Patterns for Semantically-Based Text Mining

    • John Atkinson
    Pages 145-169

  11. Handling of Imbalanced Data in Text Classification: Category-Based Term Weights

    • Ying Liu, Han Tong Loh, Kamal Youcef-Toumi, Shu Beng Tor
    Pages 171-192

  12. Automatic Evaluation of Ontologies

    • Janez Brank, Marko Grobelnik, Dunja Mladenić
    Pages 193-219

  13. Linguistic Computing with UNIX Tools

    • Lothar M. Schmitt, Kiel Christianson, Renu Gupta
    Pages 221-258

  14. Back Matter

    Pages 259-265
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Keywords

About this book

The topic this book addresses originated from a panel discussion at the 2004 ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Conference held in Seattle, Washington, USA. We the editors or- nized the panel to promote discussion on how text mining and natural l- guageprocessing,tworelatedtopicsoriginatingfromverydi?erentdisciplines, can best interact with each other, and bene?t from each other’s strengths. It attracted a great deal of interest and was attended by 200 people from all over the world. We then guest-edited a special issue of ACM SIGKDD Exp- rations on the same topic, with a number of very interesting papers. At the same time, Springer believed this to be a topic of wide interest and expressed an interest in seeing a book published. After a year of work, we have put - gether 11 papers from international researchers on a range of techniques and applications. We hope this book includes papers readers do not normally ?nd in c- ference proceedings, which tend to focus more on theoretical or algorithmic breakthroughs but are often only tried on standard test data. We would like to provide readers with a wider range of applications, give some examples of the practical application of algorithms on real-world problems, as well as share a number of useful techniques.

Reviews

From the reviews:

"The papers in this book describe a range of natural language processing (NLP) techniques and applications, all originating from an ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) panel discussion. … Overall, the contributions are well balanced with respect to the different approaches presented … . The volume appears to serve its intended purpose, which is to provide an electric overview of the international research efforts in text mining, featuring relevant tools and techniques from NLP and machine learning." (Peter Jackson, Computing Reviews, March, 2008)

Editors and Affiliations

  • Bellevue, USA

    Anne Kao, Stephen R. Poteet

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