In this paper, we propose innovative repre- sentations for automatic classification of verbs according to mainstream linguistic theories, namely VerbNet and FrameNet. First, syntac- tic and semantic structures capturing essential lexical and syntactic properties of verbs are defined. Then, we design advanced similarity functions between such structures, i.e., seman- tic tree kernel functions, for exploiting distri- butional and grammatical information in Sup- port Vector Machines. The extensive empir- ical analysis on VerbNet class and frame de- tection shows that our models capture mean- ingful syntactic/semantic structures, which al- lows for improving the state-of-the-art.

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