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Semantic Role Labeling Systems for Arabic using Kernel Methods

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by Mona Diab , Alessandro Moschitti , Daniele Pighin
Citations:6 - 2 self
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BibTeX

@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   

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