Bridging the lexical chasm: statistical approaches to answer-finding
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
The Journal of Machine Learning Research
Finding semantically similar questions based on their answers
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Identifying authoritative actors in question-answering forums: the case of Yahoo! answers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tapping on the potential of q&a community by recommending answer providers
Proceedings of the 17th ACM conference on Information and knowledge management
Predicting best answerers for new questions in community question answering
WAIM'10 Proceedings of the 11th international conference on Web-age information management
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Community question answering (CQA) services provide an open platform for people to share their knowledge and have attracted great attention for its rapidly increasing popularity. As the more knowledge people provided are shared in CQA, how to use the historical knowledge for solving new questions has become a crucial problem. In this paper, we investigate the problem as predicting best responders for new questions and tackle the problem from two perspectives, one is from the asker of the new question, and the other is from the question itself. We propose two supervised topic models, Asker-Responder Topic Model (ARTM) and Question-Responder Topic Model (QRTM) for both two perspectives by tracking people's answering history as background knowledge. Our experiments show that the two supervised topic models can effectively predict best responders for new questions in CQA without any additional works and have significant improvement over the baseline method.