HITSZ-ICRC: Exploiting Classification Approach for Answer Selection in Community Question Answering
نویسندگان
چکیده
This paper describes the participation of the HITSZ-ICRC team on the Answer Selection Challenge in SemEval-2015. Our team participated 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 demonstrated that the proposed methods were effective, achieving a macro-averaged F1 of 56.41% (rank 2) in English subtask A, 53.60 % (rank 3) in English subtask B and 67.70% (rank 3) in Arabic task, respectively.
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