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Exploring Answer Information for Question Classification in Community Question Answering
Jian Wang, Hongfei Lin, Hualei Dong, Daping Xiong and Zhihao Yang

Community question answer (CQA) services, such as Yahoo! Answers and Baidu Knows, have been becoming more and more flourishing. When users submit questions to such CQA sites, they need to choose the nearest category. Choosing category is difficult for users. The user can post the questions without choosing the suitable category. We can classify the questions using the answers, since the questions have been settled. Therefore, question classification is very important for CQA sites. In this paper, we propose two methods to solve these problems. Firstly, we present a general classification model, which combines the question classifier and answer classifier using the surface text. Secondly, we enrich questions by leveraging answer semantic knowledge to tackle the data sparseness. We conducted the experiments using 5-fold cross validation on the corpus of Yahoo! Answers with ten categories and showed the effectiveness of our approaches.

Keywords: Community question answer, question classification, semantic knowledge, answer set, data sparseness, cross validation

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