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  Using bins to empirically estimate term weights for text categorization (2001) [6 citations — 3 self]

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by Carl Sable, Kenneth W. Church
In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP-01
http://www.cs.columbia.edu/~sable/research/emnlp01.ps
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Abstract:

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 insu-cient 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

Citations

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3 Empirical term weighting and expansion frequency – Umemura, Church - 2000
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1 Categorlzlng multimedia documents using associated text (thesis proposal). http://www.cs.columbia.edu/~sable/proposal – Sable - 2000
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