Towards Large-scale High-Performance English Verb Sense Disambiguation by Using Linguistically Motivated Features

In this paper we describe the results of training high performance word sense disambiguation (WSD) systems on a new data set based on groupings of WordNet senses. This data set is designed to provide clear sense distinctions with sufficient examples in order to provide high quality training data. The sense distinctions are based on explicit syntactic and semantic criteria. Our WSD features utilize similar syntactic and semantic linguistic information. We demonstrate that this approach, using both maximum entropy and SVM models, produces systems whose performance is comparable to that of humans.