Paper: Parametric Models Of Linguistic Count Data

ACL ID P03-1037
Title Parametric Models Of Linguistic Count Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003

It is well known that occurrence counts of words in documents are often mod- eled poorly by standard distributions like the binomial or Poisson. Observed counts vary more than simple models predict, prompting the use of overdispersed mod- els like Gamma-Poisson or Beta-binomial mixtures as robust alternatives. Another deficiency of standard models is due to the fact that most words never occur in a given document, resulting in large amounts of zero counts. We propose using zero- inflated models for dealing with this, and evaluate competing models on a Naive Bayes text classification task. Simple zero-inflated models can account for prac- tically relevant variation, and can be easier to work with than overdispersed models.