Noise Reduction in a Statistical Approach to Text Categorization.
Yiming Yang:
Noise Reduction in a Statistical Approach to Text Categorization.
SIGIR 1995: 256-263@inproceedings{DBLP:conf/sigir/Yang95,
author = {Yiming Yang},
editor = {Edward A. Fox and
Peter Ingwersen and
Raya Fidel},
title = {Noise Reduction in a Statistical Approach to Text Categorization},
booktitle = {SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR
Conference on Research and Development in Information Retrieval.
Seattle, Washington, USA, July 9-13, 1995 (Special Issue of
the SIGIR Forum)},
publisher = {ACM Press},
year = {1995},
isbn = {0-89791-714-6},
pages = {256-263},
ee = {db/conf/sigir/Yang95.html},
crossref = {DBLP:conf/sigir/95},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
This paper studies noise reduction for computational efficiency improvements in a statistical learning method for text categorization, the Linear Least Squares Fh (LLSF) mapping.
Multiple noise reduction strategies are proposed and evaluated, including: an aggressive removal of "non-informative words" from texts before training; the use of a truncated singular value decomposition to cut off noisy "latent semantic structures" during training; the elimination of non-influential components in the LLSF solution (a word-concept association matrix) after training.
Text collections in different domains were used for evaluation.
Significant improvements in computational efficiency without losing categorization accuracy were evident in the testing results.
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BibTeX
Printed Edition
Edward A. Fox, Peter Ingwersen, Raya Fidel (Eds.):
SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, Washington, USA, July 9-13, 1995 (Special Issue of the SIGIR Forum).
ACM Press 1995, ISBN 0-89791-714-6
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