Speaker
Affilliation
Columbia University, USA
Abstract
There is a widely held belief in the natural language and computational linguistics communities that identifying and defining roles of predicate arguments in a sentence has a lot of potential for and Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and information extraction. Despite SRL Systems have been largely studied for English, a long path has still to be done to design an satisfying system for Arabic. In this talk, I will 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 shows 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. In the process I will introduce features of the Arabic language that are relevant for automatic processing in general and to the task of SRL In particular. I will also describe the Arabic propbank highlighting how different it is than the English Propbank.
[Please note unusual day and time]