Natural Language Processing and Text Mining / Edition 1

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Overview

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.

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Editorial Reviews

From the Publisher
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)

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Product Details

  • ISBN-13: 9781846281754
  • Publisher: Springer London
  • Publication date: 9/26/2007
  • Edition description: 2007
  • Edition number: 1
  • Pages: 265
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.69 (d)

Table of Contents


Overview   Anne Kao   Stephen R. Poteet     1
Extracting Product Features and Opinions from Reviews   Ana-Maria Popescu   Oren Etzioni     9
Extracting Relations from Text: From Word Sequences to Dependency Paths   Razvan C. Bunescu   Raymond J. Mooney     29
Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles   Eni Mustafaraj   Martin Hoof   Bernd Freisleben     45
A Case Study in Natural Language Based Web Search   Giovanni Marchisio   Navdeep Dhillon   Jisheng Liang   Carsten Tusk   Krzysztof Koperski   Thien Nguyen   Dan White   Lubos Pochman     69
Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models   Chutima Boonthum   Irwin B. Levinstein   Danielle S. McNamara     91
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     107
Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling   Mauritius A. R. Schmidtler   Jan W. Amtrup     123
Evolving Explanatory Novel Patterns for Semantically-Based Text Mining   John Atkinson     145
Handling of Imbalanced Data in Text Classification: Category-Based Term Weights   Ying Liu   Han Tong Loh   Kamal Youcef-Toumi   Shu Beng Tor     171
Automatic Evaluation of Ontologies   Janez Brank   Marko Grobelnik   Dunja Mladenic     193
Linguistic Computing with UNIX Tools   Lothar M. Schmitt   Kiel Christianson   Renu Gupta     221
Index     259
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