A model for word clustering

Abstract

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.