Context-sensitive Learning Methods for Text Categorization.
William W. Cohen, Yoram Singer:
Context-sensitive Learning Methods for Text Categorization.
SIGIR 1996: 307-315@inproceedings{DBLP:conf/sigir/CohenS96,
author = {William W. Cohen and
Yoram Singer},
title = {Context-sensitive Learning Methods for Text Categorization},
booktitle = {SIGIR},
year = {1996},
pages = {307-315},
ee = {db/conf/sigir/CohenS96.html},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
Abstract
Two recently implemented machine learning algorithms, RIPPER and sleeping
experts for phrases, are evaluated on a number of large text categorization
problems. These algorithms both construct classifiers that allow the "context"
of a word w to affect how (or even whether) the presence or absence of w will
contribute to a classification. However, RIPPER and sleeping experts differ
radically in many other respects: differences include different notions as to
what constitutes a context, different ways of combining contexts to construct
a classifier, different methods to search for a combination of contexts;
and different criteria as to what contexts should be included in such a
combination. In spite of these differences, both RIPPER and sleeping experts
perform extremely well across a wide variety of categorization problems,
generally outperforming previously applied learning methods. We view this
result as a confirmation of the usefulness of classifiers that represent
contextual information.
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Printed Edition
Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.):
Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum).
ACM 1996, ISBN 0-89791-792-8
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Copyright © Mon Nov 16 22:43:56 2009
by Michael Ley (ley@uni-trier.de)
