Semantic Role Labeling Systems for Arabic using Kernel Methods

Proceedings of ACL-08: HLT, pages 798–806, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Semantic Role Labeling Systems for Arabic using Kernel Methods Mona Diab CCLS, Columbia University New York, NY 10115, USA mdiab@ccls.columbia.edu Alessandro Moschitti DISI, University of Trento Trento, I-38100, Italy moschitti@disi.unitn.it Daniele Pighin FBK-irst; DISI, University of Trento Trento, I-38100, Italy pighin@fbk.eu Abstract 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. 1 Introduction Shallow approaches to semantic processing are mak- ing large strides in the direction of efficiently and effectively deriving tacit semantic information from text. Semantic Role Labeling (SRL) is one such ap- proach. With the advent of faster and more power- ful computers, more effective machine learning al- gorithms, and importantly, large data resources an- notated with relevant levels of semantic information, such as the FrameNet (Baker et al., 1998) and Prob- Bank (Kingsbury and Palmer, 2003), we are seeing a surge in efficient approaches to SRL (Carreras and M`arquez, 2005). SRL is the process by which predicates and their arguments are identified and their roles are defined in a sentence. For example, in the English sen- tence, ‘John likes apples.’, the predicate is ‘likes’ whereas ‘John’ and ‘apples’, bear the semantic role labels agent (ARG0) and theme (ARG1). The cru- cial fact about semantic roles is that regardless of the overt syntactic structure variation, the underly- ing predicates remain the same. Hence, for the sen- tence ‘John opened the door’ and ‘the door opened’, though ‘the door’ is the object of the first sentence and the subject of the second, it is the ‘theme’ in both sentences. Same idea applies to passive con- structions, for example. There is a widely held belief in the NLP and com- putational linguistics communities that identifying and defining roles of predicate arguments in a sen- tence has a lot of potential for and is a significant step toward improving important applications such as document retrieval, machine translation, question answering and information extraction (Moschitti et al., 2007). To date, most of the reported SRL systems are for English, and most of the data resources exist for En- glish. We do see some headway for other languages such as German and Chinese (Erk and Pado, 2006; Sun and Jurafsky, 2004). The systems for the other languages follow the successful models devised for English, e.g. (Gildea and Jurafsky, 2002; Gildea and Palmer, 2002; Chen and Rambow, 2003; Thompson et al., 2003; Pradhan et al., 2003; Moschitti, 2004; Xue and …

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