A Sequential Algorithm for Training Text Classifiers.
David D. Lewis, William A. Gale:
A Sequential Algorithm for Training Text Classifiers.
SIGIR 1994: 3-12@inproceedings{DBLP:conf/sigir/LewisG94,
author = {David D. Lewis and
William A. Gale},
editor = {W. Bruce Croft and
C. J. van Rijsbergen},
title = {A Sequential Algorithm for Training Text Classifiers},
booktitle = {Proceedings of the 17th Annual International ACM-SIGIR Conference
on Research and Development in Information Retrieval. Dublin,
Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum)},
publisher = {ACM/Springer},
year = {1994},
isbn = {3-540-19889-X},
pages = {3-12},
ee = {db/conf/sigir/LewisG94.html},
crossref = {DBLP:conf/sigir/94},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
The ability to cheaply train text classifiers is critical to their use in information retrieval,
content analysis, natural language processing, and other tasks involving data which is partly or
fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers
was developed and tested on a newswire text categorization task. This method, which we call uncertainty
sampling, reduced by as much as 500-fold the amount of training data that would have to be manually
classified to achieve a given level of effectiveness.
Copyright © 1994 by the ACM,
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BibTeX
Printed Edition
W. Bruce Croft, C. J. van Rijsbergen (Eds.):
Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum).
ACM/Springer 1994, ISBN 3-540-19889-X
Contents BibTeX
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Referenced by
- Oren Etzioni:
The World-Wide Web: Quagmire or Gold Mine?
Commun. ACM 39(11): 65-68(1996)
- Markus Tresch, Neal Palmer, Allen Luniewski:
Type Classification of Semi-Structured Documents.
VLDB 1995: 263-274
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