Abstract

This paper describes the participation of the HITSZ-ICRC team on the Answer Selection Challenge in SemEval-2015. Our team parti-cipated in English subtask A, English subtask B and Arabic task. Two approaches, ensemble learning and hierarchical classification were proposed for answer selection in each task. Bag-of-words features, lexical features and non-textual features were employed. For the Arabic task, features were extracted from both Arabic data and English data that translated from the Arabic data. Evaluation demonstrat-ed that the proposed methods were effective, achieving a macro-averaged F1 of 56.41% (rank 2nd) in English subtask A, 53.60 % (rank 3rd) in English subtask B and 67.70 % (rank 3rd) in Arabic task, respectively. 1