Semantic role labeling using different syntactic views

Authors:
Sameer Pradhan;Wayne Ward;Kadri Hacioglu;James H. Martin;Daniel Jurafsky
Affiliations:
University of Colorado, Boulder, CO;University of Colorado, Boulder, CO;University of Colorado, Boulder, CO;University of Colorado, Boulder, CO;Stanford University, Stanford, CA
Venue:
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Year:
2005

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Abstract

Semantic role labeling is the process of annotating the predicate-argument structure in text with semantic labels. In this paper we present a state-of-the-art baseline semantic role labeling system based on Support Vector Machine classifiers. We show improvements on this system by: i) adding new features including features extracted from dependency parses, ii) performing feature selection and calibration and iii) combining parses obtained from semantic parsers trained using different syntactic views. Error analysis of the baseline system showed that approximately half of the argument identification errors resulted from parse errors in which there was no syntactic constituent that aligned with the correct argument. In order to address this problem, we combined semantic parses from a Minipar syntactic parse and from a chunked syntactic representation with our original baseline system which was based on Charniak parses. All of the reported techniques resulted in performance improvements.