GW QA at SemEval-2017 Task 3: Question Answer Re-ranking on Arabic Fora

Abstract

This paper describes our submission to SemEval-2017 Task 3 Subtask D,”Question Answer Ranking in Arabic Community Question Answering”. In this work, we applied a supervised machine learning approach to automatically re-rank a set of QA pairs according to their relevance to a given question. We employ features based on latent semantic models, namely WTMF, as well as a set of lexical features based on string length and surface level matching. The proposed system ranked first out of 3 submissions, with a MAP score of 61.16%.