  Most probabilistic classifiers used for word-sense disambiguation have either
been based on only one contextual feature or have used a model that is simply
assumed to characterize the interdependencies among multiple contextual
features. In this paper, a different approach to formulating a probabilistic
model is presented along with a case study of the performance of models
produced in this manner for the disambiguation of the noun "interest". We
describe a method for formulating probabilistic models that use multiple
contextual features for word-sense disambiguation, without requiring untested
assumptions regarding the form of the model. Using this approach, the joint
distribution of all variables is described by only the most systematic variable
interactions, thereby limiting the number of parameters to be estimated,
supporting computational efficiency, and providing an understanding of the
data.
