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2009
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Learning to recognize reliable users and content in social media with coupled mutual reinforcement

9 years 7 months ago
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data ? which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framewor...
Jiang Bian, Yandong Liu, Ding Zhou, Eugene Agichte
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2009
Where WWW
Authors Jiang Bian, Yandong Liu, Ding Zhou, Eugene Agichtein, Hongyuan Zha
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