A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Finding the right facts in the crowd: factoid question answering over social media
Proceedings of the 17th international conference on World Wide Web
A community question-answering refinement system
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
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Due to the lexical gap between questions and answers, automatically detecting right answers becomes very challenging for community question-answering sites. In this paper, we propose an analogical reasoning-based method. It treats questions and answers as relational data and ranks an answer by measuring the analogy of its link to a query with the links embedded in previous relevant knowledge; the answer that links in the most analogous way to the new question is assumed to be the best answer. We based our experiments on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of the approach.