We observe that the word-specific classifier model obtains comparable performance when trained with either in-class or outof-the-class verb instances.
the single-classifier models are able to effectively transfer common syntactic (and semantic) alternation patterns across verbs in the same VerbNet class and, thus, significantly improve the performance when trained on in-class verb instance.