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Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)have proved useful, but have limited coverage. Verb classes automaticallyinduced from corpus data such as those from VerbKB (Wijaya, 2016), on the otherhand, can give clusters with much larger coverage, and can be adapted tospecific corpora such as Twitter. We present a method for clustering theoutputs of VerbKB: verbs with their multiple argument types, e.g."marry(person, person)", "feel(person, emotion)." We make use of a novellow-dimensional embedding of verbs and their arguments to produce high qualityclusters in which the same verb can be in different clusters depending on itsargument type. The resulting verb clusters do a better job than hand-builtclusters of predicting sarcasm, sentiment, and locus of control in tweets.

Deriving Verb Predicates By Clustering Verbs with Arguments