Supervised Learning of a Probabilistic Lexicon of Verb Semantic Classes

The work presented in this paper explores a supervised method for learning a probabilistic model of a lexicon of VerbNet classes. We intend for the probabilistic model to provide a probability distribution of verb-class associations, over known and unknown verbs, including polysemous words. In our approach, training instances are obtained from an existing lexicon and/or from an annotated corpus, while the features, which represent syntactic frames, semantic similarity, and selectional preferences, are extracted from unannotated corpora. Our model is evaluated in type-level verb classification tasks: we measure the prediction accuracy of VerbNet classes for unknown verbs, and also measure the dissimilarity between the learned and observed probability distributions. We empirically compare several settings for model learning, while we vary the use of features, source corpora for feature extraction, and disambiguated corpora. In the task of verb classification into all VerbNet classes, our best model achieved a 10.69% error reduction in the classification accuracy, over the previously proposed model.