Learning for Text Categorization
Papers from the AAAI Workshop
Mehran Sahami, Program Chair
Technical Report WS-98-05 published by The AAAI Press, Menlo Park, California
This technical report is also available in book and CD format.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members. PDF file sizes may be large!
Contents
Preface / 1
Mehran Sahami
Style-based Text Categorization: What Newspaper Am I Reading? / 1
Shlomo Argamon-Engelson, Moshe Koppel and Galit Avneri (Bar-Ilan University)
A Case Study in Using Linguistic Phrases for Text Categorization on the WWW / 5
Johannes Fuernkranz, Tom Mitchell (Carnegie Mellon University) and Ellen Riloff (University of Utah)
Automated Concept Extraction from Plain Text / 13
Boris Gelfand, Marilyn Wulfekuler, and William F. Punch III (Michigan State University)
A Redundant Covering Algorithm Applied to Text Classification / 18
David Hsu, Oren Etzioni and Stephen Soderland (University of Washington)
Learning Complex Patterns for Document Categorization / 26
Markus Junker and Andreas Abecker (German Research Center for AI)
Adaptive Information Filtering: Learning in the Presence of Concept Drifts / 33
Ralf Klinkenberg (Universitaet Dortmund) and Ingrid Renz (Daimler-Benz)
A Comparison of Event Models for Naive Bayes Text Classification / 41
Andrew McCallum (Just Research) and Kamal Nigam (Carnegie Mellon University)
Book Recommending Using Text Categorization with Extracted Information / 49
Raymond J. Mooney, Paul N. Bennett and Loriene Roy (University of Texas, Austin)
A Bayesian Approach to Filtering Junk E-Mail / 55
Mehran Sahami (Stanford University), Susan Dumais, David Heckerman and Eric Horvitz (Microsoft Research)
Intelligent Agents for Web-based Tasks: An Advice-Taking Approach / 63
Jude Shavlik and Tina Eliassi-Rad (University of Wisconsin, Madison)
Poster Spotlights
How Machine Learning Can Be Beneficial for Textual Case-Based Reasoning / 71
Stefanie Brueninghaus and Kevin D. Ashley (University of Pittsburgh)
Learning for Question Answering and Text Classification: Integrating
Knowledge-Based and Statistical Techniques / 75
Jay Budzik and Kristian J. Hammond (University of Chicago)
Classifying Text Documents using Modular Categories and
Linguistically Motivated Indicators / 79
Eleazar Eskin and Matt Bogosian (Columbia University)
Learning Preference Relations for Information Retrieval / 83
Ralf Herbirch, Thore Graepel, Peter Bollmann-Sdorra, and Klaus Obermayer
(Technical University of Berlin)
Some Issues in the Automatic Classification of U.S. Patents / 87
Leah S. Larkey (University of Massachusetts, Amherst)
Active Learning with Committees in Text Categorization:
Preliminary Results in Comparing Winnow and Perceptron / 91
Ray Liere and Prasad Tadepalli (Oregon State University)
SpamCop: A Spam Classification & Organization Program / 95
Patrick Pantel and Dekang Lin (University of Manitoba)
A Multi-Agent System for Generating a Personalized Newspaper Digest / 99
Georg Veltmann (Daimler-Benz)
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