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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010160779 | QA76.9.D343 N37 2007 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Table of Contents
1 Overview | p. 1 |
2 Extracting Product Features and Opinions from Reviews | p. 9 |
3 Extracting Relations from Text: From Word Sequences to Dependency Paths | p. 29 |
4 Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles | p. 45 |
5 A Case Study in Natural Language Based Web Search | p. 69 |
6 Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models | p. 91 |
7 Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures | p. 107 |
8 Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling | p. 123 |
9 Evolving Explanatory Novel Patterns for Semantically-Based Text Mining | p. 145 |
10 Handling of Imbalanced Data in Text Classification: Category-Based Term Weights | p. 171 |
11 Automatic Evaluation of Ontologies | p. 193 |
12 Linguistic Computing with UNIX Tools | p. 221 |
Index | p. 259 |