Paper: Semantic Role Labeling Systems for Arabic using Kernel Methods

ACL ID P08-1091
Title Semantic Role Labeling Systems for Arabic using Kernel Methods
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

There is a widely held belief in the natural lan- guage and computational linguistics commu- nities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and in- formation extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich mor- phological features of the language. The ex- periments on the pilot Arabic Propbank data show that our system based on Support Vector Machines and Kernel Methods yields a global SRL F1 score of 82.17%, which improves the current state-of-the-art in Arabic SRL.