The Beta-Binomial Mixture Model For Word Frequencies In Documents With Applications To Information Retrieval


This paper describes a continuous-mixture statistical model for word occurrence frequencies in documents, and the application of that model to the DARPA-sponsored TDT topic identification tasks [1]. This model was originally proposed in 1990 by L. Gillick [2] as a means to account for variation in word frequencies across documents more accurately than the binomial model. The present paper presents further mathematical development of the model, leading to the implementation of a topic-tracking system. Performance results for this system on the Tracking Task in the December 1998 DARPA TDT Evaluation will be shown and compared with Dragon's existing, more complex multinomial-model-based system. (Results from other systems applied to this task are available in [3].) We will conclude with plans for further development. 1

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