Alignment Based Approach for Arabic Textual Entailment

Abstract

Recognizing Textual Entailment (RTE) is a task of Natural language processing (NLP) in which two text fragments denoted Text (T) and Hypothesis (H) are processed by a system to determine whether the meaning of H is entailed from T or not. This task is necessary for numerous natural language processing applications. Thus, several approaches have been proposed to deal with the RTE task, running from shallow approaches based on measuring lexical similarity to advanced approaches based on semantic interpretation and deep application of machine learning algorithms. In this paper, we present an approach to deal with Textual Entailment for Arabic language based on machine learning and text alignment modeled as an optimization problem. The aim of this work is to experiment how well state of the art Arabic NLP tools, resources and alignment technique work when applied to Arabic RTE. We report the performance of our system on an existing Arabic RTE dataset and we achieve encouraging results.