Semantic Tree Kernels to classify Predicate Argument Structures

Authors:
Alessandro Moschitti;Bonaventura Coppola;Daniele Pighin;Roberto Basili
Affiliations:
University of Rome “Tor Vergata”, moschitti@info.uniroma2.it;ITC-Irst and University of Trento, coppolab@itc.it;University of Rome “Tor Vergata”, daniele.pighin@gmail.com;University of Rome “Tor Vergata”, basili@info.uniroma2.it
Venue:
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Year:
2006

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Abstract

Recent work on Semantic Role Labeling (SRL) has shown that syntactic information is critical to detect and extract predicate argument structures. As syntax is expressed by means of structured data, i.e. parse trees, its encoding in learning algorithms is rather complex. In this paper, we apply tree kernels to encode the whole predicate argument structure in Support Vector Machines (SVMs). We extract from the sentence syntactic parse the subtrees that span potential argument structures of the target predicate and classify them in incorrect or correct structures by means of tree kernel based SVMs. Experiments on the PropBank collection show that the classification accuracy of correct/incorrect structures is remarkably high and helps to improve the accuracy of the SRL task. This is a piece of evidence that tree kernels provide a powerful mechanism to learn the complex relation between syntax and semantics.