Abstract

Statistical models of word-sense disam- biguation are often based on a small num- ber of contextual features or on a model that is assumed to characterize the inter- actions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.

Citations