Semantic role labeling systems for Arabic using Kernel Methods

Mona Diab, Alessandro Moschitti, Daniele Pighin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

There is a widely held belief in the natural language and computational linguistics communities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and information extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich morphological features of the language. The experiments 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.

Original languageEnglish
Title of host publicationACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
Pages798-806
Number of pages9
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT - Columbus, OH, United States
Duration: 15 Jun 200820 Jun 2008

Other

Other46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT
CountryUnited States
CityColumbus, OH
Period15/6/0820/6/08

    Fingerprint

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Networks and Communications
  • Linguistics and Language

Cite this

Diab, M., Moschitti, A., & Pighin, D. (2008). Semantic role labeling systems for Arabic using Kernel Methods. In ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 798-806)