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Abstract: Text categorization is the task of classifying text into one of several predefined categories. In this paper we will evaluate the effectiveness of several ILP methods for text categorization, and also compare them to their propositional analogs. The methods considered are FOIL, the propositional rule-learning system RIPPER, and a first-order version of RIPPER called FLIPPER. We show that the benefit of using a first-order representation in this domain is relatively modest; in particular, the... (Update)
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BibTeX entry: (Update)
William W. Cohen. Learning to classify English text with ILP methods. In Luc De Raedt, editor, Advances in ILP. IOS Press, 1995. http://citeseer.ist.psu.edu/cohen95bag.html More
@misc{ cohen95learning,
author = "W. Cohen",
title = "Learning to classify English text with ILP methods",
text = "William W. Cohen. Learning to classify English text with ILP methods. In
Luc De Raedt, editor, Advances in ILP. IOS Press, 1995.",
year = "1995",
url = "citeseer.ist.psu.edu/cohen95bag.html" }
Citations (may not include all citations):
2177
programs for machine learning (context) - Quinlan - 1994
492
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Information Retrieval
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Fast effective rule induction
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The CN2 induction algorithm
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FOIL: A midterm report
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Drug design by machine learning: the use of inductive logic .. (context) - Ross, King et al. - 1992
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Induction of first-order decision lists: Results on learning..
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Grammatically biased learning: learning logic programs using.. (context) - William, Cohen - 1994
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Text categorization and relational learning
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Rapid prototyping of ILP systems using explicit bias
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First-order learning (context) - Quinlan, Cameron-Jones et al. - 1993
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David Lewis and William Gale (context) - Gale - 1994
3
Jorg-Uwe Kietz and Stephan Wrobel (context) - Wrobel - 1992
3
David Lewis and Jason Catlett (context) - Catlett - 1994
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