Finding similar questions in large question and answer archives
Proceedings of the 14th ACM international conference on Information and knowledge management
Modeling information-seeker satisfaction in community question answering
ACM Transactions on Knowledge Discovery from Data (TKDD)
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
"TV answers" - using the wisdom of crowds to facilitate searches with rich media context
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Identifying new categories in community question answering archives: a topic modeling approach
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Understanding user intent in community question answering
Proceedings of the 21st international conference companion on World Wide Web
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As online Collaborative Question Answering (CQA) servicessuch as Yahoo! Answers and Baidu Knows are attracting users, questions, and answers at an explosive rate, the truly urgent and important questions are increasingly getting lost in the crowd. That is, questions that require immediate responses are pushed out of the way by the trivial but more recently arriving questions. Unlike other questions in collaborative question answering (CQA) for which users might be willing to wait until good answers appear, urgent questions are likely to be of interest to the asker only if answered in the next few minutes or hours. For such questions, late responses are either not useful or are simply not applicable. Unfortunately, current collaborative question-answering systems do not distinguish urgent questions from the rest, and could thus be ineffective for urgent information needs. We explore text- and data- mining methods for automatically identifying urgent questions in the CQA setting. Our results indicate that modeling the question context (i.e., the particular forum/category where the question was posted) can increase classification accuracy compared to the text of the question alone.