Human Verb Associations as the Basis for Gold Standard Verb Classes: Validation against GermaNet and FrameNet

We describe a gold standard for semantic verb classes which is based on human associations to verbs. The associations were collected in a web experiment and then applied as verb features in a hierarchical cluster analysis. We claim that the resulting classes represent a theory-independent gold standard classification which covers a variety of semantic verb relations, and whose features can be used to guide the feature selection in automatic processes. To evaluate our claims, the association-based classification is validated against two standard approaches to semantic verb classes, GermaNet and FrameNet.