Poisson Mixtures (1995) [53 citations — 3 self]
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author = {Kenneth W. Church and William A. Gale},
title = {Poisson Mixtures},
journal = {Natural Language Engineering},
year = {1995},
volume = {1},
pages = {163--190}
}
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Abstract:
Shannon (1948) showed that a wide range of practical problems can be reduced to the problem of estimating probability distributions of words and ngrams in text. It has become standard practice in text compression, speech recognition, information retrieval and many other applications of Shannon's theory to introduce a "bag-of-words" assumption. But obviously, word rates vary from genre to genre, author to author, topic to topic, document to document, section to section, and paragraph to paragraph. The proposed Poisson mixture captures much of this heterogeneous structure by allowing the Poisson parameter theta to vary over documents subject to a density function phi. phi is intended to capture dependencies on hidden variables such [as] genre, author, topic, etc. (The Negative Binomial is a well-known special case where phi is a Gamma distribution.) Poisson mixtures fit the data better than standard Poissons, producing more accurate estimates of the variance over documents (sigma^2), entropy (H), inverse document frequency (IDF), and adaptation (Pr(x>=2|x>=1)).
