Automatic Classification of English Verbs Using Rich Syntactic Features

Previous research has shown that syntactic features are the most informative features in automatic verb classification. We experiment with a new, rich feature set, extracted from a large automatically acquired subcategorisation lexicon for English, which incorporates information about arguments as well as adjuncts. We evaluate this feature set using a set of supervised classifiers, most of which are new to the task. The best classifier (based on Maximum Entropy) yields the promising accuracy of 60.1% in classifying 204 verbs to 17 Levin (1993) classes. We discuss the impact of this result on the stateof-art, and propose avenues for future work.