Can Human Verb Associations Help Identify Salient Features for Semantic Verb Classification?

This paper investigates whether human associations to verbs as collected in a web experiment can help us to identify salient verb features for semantic verb classes. Assuming that the associations model aspects of verb meaning, we apply a clustering to the verbs, as based on the associations, and validate the resulting verb classes against standard approaches to semantic verb classes, i.e. GermaNet and FrameNet. Then, various clusterings of the same verbs are performed on the basis of standard corpus-based types, and evaluated against the association-based clustering as well as GermaNet and FrameNet classes. We hypothesise that the corpusbased clusterings are better if the instantiations of the feature types show more overlap with the verb associations, and that the associations therefore help to identify salient feature types.