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Poteet / Kao

Natural Language Processing and Text Mining

Medium: Buch
ISBN: 978-1-84628-175-4
Verlag: Springer
Erscheinungstermin: 12.12.2006
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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.

Produkteigenschaften

  • Artikelnummer: 9781846281754
  • Medium: Buch
  • ISBN: 978-1-84628-175-4
  • Verlag: Springer
  • Erscheinungstermin: 12.12.2006
  • Sprache(n): Englisch
  • Auflage: 1. Auflage. 2006
  • Produktform: Gebunden
  • Gewicht: 1260 g
  • Seiten: 265
  • Format (B x H x T): 160 x 241 x 20 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

Overview.- Extracting Product Features and Opinions from Reviews.- Extracting Relations from Text: From Word Sequences to Dependency Paths.- Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles.- A Case Study in Natural Language Based Web Search.- Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models.- Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures.- Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling.- Evolving Explanatory Novel Patterns for Semantically-Based Text Mining.- Handling of Imbalanced Data in Text Classification: Category-Based Term Weights.- Automatic Evaluation of Ontologies.- Linguistic Computing with UNIX Tools.