This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which targeted real-life Web forums, and was offered in both Arabic and English. We apply a supervised machine learning approach considering a manifold of features including among others word n-grams, text similarity, sentiment analysis, the presence of specific words, and the context of a comment. Our approach was the best performing one in the Arabic subtask and the third best in the two English subtasks.
QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English
Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da, Alessandro Moschitti, Kareem Darwish, Llu\is Màrquez, Shafiq R., and Walid Magdy. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval'15) , pages 203-209, 2015.
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