Semantic Argument Classification Exploiting Argument Interdependence

Abstract

This paper describes our research on automatic semantic argument classification, using the PropBank data [Kingsbury et al., 2002]. Previous research employed features that were based either on a full parse or shallow parse of a sentence. These features were mostly based on an individual semantic argument and the relation between the predicate and a semantic argument, but they did not capture the interdependence among all arguments of a predicate. In this paper, we propose the use of the neighboring semantic arguments of a predicate as additional features in determining the class of the current semantic argument. Our experimental results show significant improvement in the accuracy of semantic argument classification after exploiting argument interdependence. Argument classification accuracy on the standard Section 23 test set improves to 90.50%, representing a relative error reduction of 18%.