Natural Language Processing and Text Mining

By Anne Kao , Steve R. Poteet

Natural Language Processing and Text Mining Cover Image

  • ISBN13: 978-1-8462-8175-4
  • 280 Pages
  • User Level: Science
  • Publication Date: March 6, 2007
  • Available eBook Formats: PDF
  • eBook Price: $109.00
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Full Description
This book discusses applications of certain NLP techniques to certain Text Mining tasks, & the use of Text Mining to facilitate NLP. It explores real-world applications of NLP & text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, & detailing the associated difficulties that must be resolved before the algorithm can be applied & its full benefits realized. Topics & features: • Describes novel & high-impact text mining and/or natural language applications • Points out typical traps in trying to apply NLP to text mining • Surveys related supporting techniques, problem types, & potential technique enhancements • Examines the interaction of text mining & NLP This state-of-the-art, practical volume will be an essential resource for professionals & researchers who wish to learn how to apply text mining & language processing techniques to real world problems. It can also be used as a supplementary text by advanced students in text mining & NLP.
Table of Contents

Table of Contents

  1. Overview.
  2. Extracting Product Features and Opinions from Reviews.
  3. Extracting Relations from Text.
  4. Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles.
  5. A Case Study in Natural Language Based Web Search.
  6. Evaluating Self
  7. explanations in iSTART:Word Matching, Latent Semantic Analysis, and Topic Models.
  8. Textual Signatures: Identifying Text
  9. Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures.
  10. Automatic Document Separation
  11. A Combination of Probabilistic Classification and Finite
  12. State Sequence Modeling.
  13. Evolving Explanatory Novel Patterns for Semantically
  14. based Text Mining.
  15. Handling of Imbalanced Data in Text Classification: Category Based Term Weights.
  16. Automatic Evaluation of Ontologies.
  17. Linguistic Computing with UNIX Tools.
  18. Index
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