Collaborative question answering (CQA) services such as Yahoo! Answers and Sina Iask have become more and more popular during recent years in providing platforms for people to share knowledge and search information online. However, there are relatively fewer work done on CQA system compared to other information retrieval system. In this thesis, we investigate several key problems in CQA system. The main contributions include following issues: (1) similar question retrieval A word relevance model is trained based on the whole question archive which is made up of millions of natural language questions proposed by users on the web; then a novel method to calculate similarities between questions is proposed with the help of word relevance model by question expansion. (2) answer ranking within a question-answering thread Relations between a question and its candidate answers are built based on the statistical translation model. Besides, inter-answer similarities are calculated. The manifold ranking is taken to propagate ranks among the question and answers. After ranking propagation, each answer gets its ranking score, and candidates answers are sorted by their ranking scores. (3) quality determination of user-generated answers Four types of features are extracted to describe answers, including surface linguistic patterns, question-answer relationships, answer provider's features and structural context features. These types of features are incorporated into the linear regression model to determine the quality the answers. (4) user searching for new questions Interests of the answerers are modeled by tracking users’ answering history. Relationship between the answerer and a new question is measured by language model and the LDA topic model. User authority and user activity are also taken into consideration. A probabilistic framework is utilized to combine all information about users to predict best answerers for new questions. (5) a new CQA prototype system A CQA prototype system is designed, and we have implemented several key modules of the system, including q&a retrieval and user searching for new questions. Meanwhile, we also study the problem of question categorization by comparing two classification models. Key Words: collaborative question answering, question retrieval, answer ranking, answer quality, user modeling, user search, question categorization, prototype system
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