A Supervised Learning Approach using the Combination of Semantic and Lexical Features for Arabic Community Question Answering

In this paper we address the problem of Community Question Answering (CQA) for Arabic language. We mainly explore the direction of combining both lexical and semantic features for enhancing the retrieval task of possible answers to a posted question. We show a comprehensive evaluation on the SEMEval2017 CQA dataset for Arabic language. Our Mean Average Precision (MAP) achieves 62.85% when using a supervised machine learning approach (linear SVM). We outperformed best reported results on such dataset. This is achieved by defining a mix of word embedding, latent semantic similarity features and other lexical similarity features.