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Using Bins to Empirically Estimate Term Weights for Text Categorization

Sable, Carl; Church, Kenneth W.

This paper introduces a term weighting method for text categorization based on smoothing ideas borrowed from speech recognition. Empirical estimates of weights (likelihood ratios) become unstable when counts are small. Instead of estimating weights for individual words, as Naive Bayes does, words with similar features are grouped into bins, and a single weight is estimated for each bin. This weight is then assigned to all of the words in the bin. The bin-based method is intended for tasks where there is insufficient training data to estimate a separate weight for each word. Experiments show the bin-based method is highly competitive with other current methods. In particular, this method is most similar to Naive Bayes; it generally performs at least as well as Naive Bayes, and sometimes better.

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More About This Work

Academic Units
Computer Science
Publisher
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing
Published Here
May 10, 2013
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