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A Model for Word Clustering

It is common to model the distribution of words in text by measures such as the Poisson approximation. However, these measures ignore effects such as clustering: our analysis of document collections demonstrates that the Poisson approximation can significantly overestimate the probability that a document contains a word. Based on our analysis, we propose a new model for distribution of words in text, and show how this model can be used to estimate the probability that a document contains a word and the number of distinct words in a document. © 1992 John Wiley & Sons, Inc.