Automatic Identification of Best Answers in Online Enquiry Communities
نویسندگان
چکیده
Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.
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Mitch Parsell is an Associate Lecturer in philosophy with research interests in online teaching, computer ethics and the philosophy of mind. Jennifer Duke-Yonge is an Associate Lecturer and Coordinator of The Department of Philosophy's Open University Australia Program. She is interested in philosophical and pedagogical issues concerning the development and generalisability of reasoning skills....
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تاریخ انتشار 2012