CoCQA: Co-Training over Questions and Answers with an Application to Predicting Question Subjectivity Orientation

Baoli Li, Yandong Liu, Eugene Agichtein

Abstract: An increasingly popular method for finding information online is via the Community Question Answering (CQA) portals such as Yahoo! Answers, Naver, and Baidu Knows. Searching the CQA archives, and ranking, filtering, and evaluating the submitted answers requires intelligent processing of the questions and answers posed by the users. One important task is automatically detecting the question's subjectivity orientation: namely, whether a user is searching for subjective or objective information. Unfortunately, real user questions are often vague, ill-posed, poorly stated. Furthermore, there has been little labeled training data available for real user questions. To address these problems, we present CoCQA, a co-training system that exploits the association between the questions and contributed answers for question analysis tasks. The co-training approach allows CoCQA to use the effectively unlimited amounts of unlabeled data readily available in CQA archives. In this paper we study the effectiveness of CoCQA for the question subjectivity classification task by experimenting over thousands of real users' questions.