Dirichlet Process Mixture Models for Verb Clustering

Abstract

In this work we apply Dirichlet Process Mix- ture Models to a learning task in natural language processing (NLP): lexical-semantic verb clustering. We assess the performance on a dataset based on Levin’s (1993) verb classes using the recently introduced V- measure metric. In, we present a method to add human supervision to the model in or- der to to influence the solution with respect to some prior knowledge. The quantitative evaluation performed highlights the benefits of the chosen method compared to previously used clustering approaches.