PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Modeling Word Burstiness Using The Dirichlet Distribution
Rasmus Elsborg Madsen, Charles Elkan and David Kauchak
In: ICML 2005, Bohn, Germany(2005).

Abstract

Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word appears once, it is more likely to appear again. In this paper, we propose the Dirichlet compound multinomial model (DCM) as an alternative to the multinomial. The DCM model has one additional degree of freedom, which allows it to capture burstiness. We show experimentally that the DCM is substantially better than the multinomial at modeling text data, measured by perplexity. We also show using three standard document collections that the DCM leads to better classification than the multinomial model. DCM performance is comparable to that obtained with multiple heuristic changes to the multinomial model.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:1454
Deposited By:Rasmus Elsborg Madsen
Deposited On:28 November 2005