Paper: Improving Verb Clustering with Automatically Acquired Selectional Preferences

ACL ID D09-1067
Title Improving Verb Clustering with Automatically Acquired Selectional Preferences
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

In previous research in automatic verb classification, syntactic features have proved the most useful features, although manual classifications rely heavily on se- mantic features. We show, in contrast with previous work, that considerable ad- ditional improvement can be obtained by using semantic features in automatic clas- sification: verb selectional preferences ac- quired from corpus data using a fully unsu- pervised method. We report these promis- ing results using a new framework for verb clustering which incorporates a re- cent subcategorization acquisition system, rich syntactic-semantic feature sets, and a variation of spectral clustering which performs particularly well in high dimen- sional feature space.