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Unsupervised learning of VerbNet argument structure

Abstract

The relationship between a verb and the syntactic frames in which it can appear has been closely studied by psychol-ogists and linguists. Research suggests that the semantics of a verb and its arguments determine the verb’s syntactic frames,but various theories (Levin & Hovav, 2005) disagree on the nature and complexity of these relationships, in part because mostinvestigations have focused on a small subset of verbs that may not generalize. Investigating the semantic and syntactic rela-tionships present in larger sets of verbs would provide more substantial evidence for evaluating and selecting theories of verbargument structure. We report on initial analyses of the 6000+ verbs and 280+ syntactic frames of VerbNet (Kipper et al., 2008),the largest English verb syntax resource available, using nonparametric Bayesian methods (e.g. Shafto et al., 2006) for clusteranalysis and dimensionality reduction.

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