A Maximum Entropy Approach To Disambiguating VerbNet Classes

This paper focuses on verb sense disambiguation cast as inferring the VerbNet class to which a verb belongs. To train three different supervised learning models –Maximum Entropy (MaxEnt), Naive Bayes and Decision Tree– we used lexical, co-occurrence and typed-dependency features. For each model, we built three classifiers: one single classifier for all verbs, one single classifier for polysemous verbs only, and an ensemble of classifiers, one per each polysemous verb. Among those algorithms, Naive Bayes performs surprisingly badly. In general, MaxEnt models perform better, but Decision Trees models are competitive. Our best results are obtained with classifier ensembles.