An algorithm of Identifying Semantic Arguments of a Verb From Structured Data

We discuss a method for identifying semantic arguments of a verb from a sentence. It differs from existing methods by an unique feature that represents all semantic arguments of a verb in a syntactic parse tree. The feature is a path in which at least one of the children of a node is a root of a subtree that associates with a semantic argument. Experiments on WSJ data from Penn TreeBank and PropBank show that our method achieves an average of precision 92.3% and an average of recall 94.2% on identifying semantic arguments of over six hundred verbs.