  Statistical models of word-sense disambiguation are often based on a small
number of contextual features or on a model that is assumed to characterize the
interactions 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.
