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论文类型：期刊论文

发表时间：2018-01-01

发表刊物：JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING

收录刊物：SCIE

卷号：31

期号：1-2

页面范围：67-84

ISSN号：1542-3980

关键字：Community question answer; question classification; semantic knowledge; answer set; data sparseness; cross validation

摘要：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.