Al-Bayan: A Knowledge-based System for Arabic Answer Selection

Abstract

This paper describes Al-Bayan team participation in SemEval-2015 Task 3, Subtask A. Task 3 targets semantic solutions for answer selection in community question answering systems. We propose a knowledge-based solution for answer selection of Arabic questions, specialized for Islamic sciences. We build a Semantic Interpreter to evaluate the semantic similarity between Arabic question and answers using our Quranic ontology of concepts. Using supervised learning, we classify the candidate answers according to their relevance to the users questions. Results show that our system achieves 74.53% accuracy which is comparable to the other participating systems.