This article addresses the issue of incomplete tagging in social-oriented question answering forums, where around 40% of questions have at most one manually labeled tag. This lack of comprehensive tagging impedes various tag-based functionalities like topic feeds, knowledge organization, and statistical analysis. The article proposes a novel unsupervised scheme to comprehensively learn and assign descriptive tags to each question. Extensive evaluations on a real-world dataset show significant improvements in question annotation, demonstrating the scheme's effectiveness and scalability for large-scale data.
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Nie, Liqiang, et al. "Learning to recommend descriptive tags for questions in social forums." ACM Transactions on Information Systems (TOIS) 32.1 (2014): 1-23.
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