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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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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