Disambiguating noun and verb senses using automatically acquired selectional preferences

Our system for the Senseval-2 all words task uses automatically acquired selectional preferences to sense tag subject and object head nouns, along with the associated verbal predicates. The selectional preferences comprise probability distributions over WordNet nouns, and these distributions are conditioned on WordNet verb classes. The conditional distributions are used directly to disambiguate the head nouns. We use prior distributions and Bayes rule to compute the highest probability verb class, given a noun class. We also use anaphora resolution and the `one sense per discourse ' heuristic to cover nouns and verbs not occurring in these relationships in the target text. The selectional preferences are acquired without recourse to sense tagged data so our system is unsupervised