Paper: Word-Sense Disambiguation Using Decomposable Models

ACL ID P94-1020
Title Word-Sense Disambiguation Using Decomposable Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1994

Most probabilistic classifiers used for word-sense disam- biguation 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 pro- duced in this manner for the disambiguation of the noun interest. We describe a method for formulating proba- bilistic 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 inter- actions, thereby limiting the n...