Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity
Résumé
Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, which causes the loss of important information for word expectations, such as word order and interrelations. In this paper, we present a DSM which addresses the issue by defining verb contexts as joint dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts are more efficient than single dependencies, even with a relatively small amount of training data.