This paper proposes a model for term reoccurrence in a text collection based on the gaps between successive occurrences of a term. These gaps are modeled using a mixture of exponential distributions. Parameter estimation is based on a Bayesian framework that allows us to fit a flexible model. The model provides measures of a term's re-occurrence rate and withindocument burstiness. The model works for all kinds of terms, be it rare content word, medium frequency term or frequent function word. A measure is proposed to account for the term's importance based on its distribution pattern in the corpus.
Sarkar et al. used a double Poisson Bayesian mixture model for term "burstiness", to determine such "content" words REF .
A Bayesian Mixture Model For Term Re-Occurrence And Burstiness
Conference On Computational Natural Language Learning CoNLL
Computer Science