Cover image for Natural language processing and text mining
Title:
Natural language processing and text mining
Publication Information:
London : Springer, 2007
Physical Description:
xii, 265 p. : digital ; 25 cm.
ISBN:
9781846281754
General Note:
Available online version
Electronic Access:
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30000010160779 QA76.9.D343 N37 2007 Open Access Book Book
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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

Anne Kao and Stephen R. PoteetAna-Maria Popescu and Oren EtzioniRazvan C. Bunescu and Raymond J. MooneyEni Mustafaraj and Martin Hoof and Bernd FreislebenGiovanni Marchisio and Navdeep Dhillon and Jisheng Liang and Carsten Tusk and Krzysztof Koperski and Thien Nguyen and Dan White and Lubos PochmanChutima Boonthum and Irwin B. Levinstein and Danielle S. McNamaraPhilip M. McCarthy and Stephen W. Briner and Vasile Rus and Danielle S. McNamaraMauritius A. R. Schmidtler and Jan W. AmtrupJohn AtkinsonYing Liu and Han Tong Loh and Kamal Youcef-Toumi and Shu Beng TorJanez Brank and Marko Grobelnik and Dunja MladenicLothar M. Schmitt and Kiel Christianson and Renu Gupta
1 Overviewp. 1
2 Extracting Product Features and Opinions from Reviewsp. 9
3 Extracting Relations from Text: From Word Sequences to Dependency Pathsp. 29
4 Mining Diagnostic Text Reports by Learning to Annotate Knowledge Rolesp. 45
5 A Case Study in Natural Language Based Web Searchp. 69
6 Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Modelsp. 91
7 Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structuresp. 107
8 Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modelingp. 123
9 Evolving Explanatory Novel Patterns for Semantically-Based Text Miningp. 145
10 Handling of Imbalanced Data in Text Classification: Category-Based Term Weightsp. 171
11 Automatic Evaluation of Ontologiesp. 193
12 Linguistic Computing with UNIX Toolsp. 221
Indexp. 259