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Semantic Role Labeling Systems for Arabic using Kernel Methods
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@MISC{Diab_semanticrole,
author = {Mona Diab and Alessandro Moschitti and Daniele Pighin},
title = {Semantic Role Labeling Systems for Arabic using Kernel Methods},
year = {}
}
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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. 1
Keyphrases
kernel method semantic role labeling system natural language srl system information extraction support vector machine significant step rich morphological feature global srl f1 score pilot arabic propbank data show important application computational linguistics community current state-of-the-art many aspect modern standard arabic arabic srl semantic role labeling
