Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can be extended to handle large-scale data.
Knowledge organization, Question annotation, Social QA
Databases and Information Systems
Data Management and Analytics
ACM Transactions on Information Systems
NIE, Liqiang; ZHAO, Yiliang; WANG, Xiangyu; SHEN, Jialie; and CHUA, Tat-Seng.
Learning to recommend descriptive tags for questions in social forums. (2014). ACM Transactions on Information Systems. 32, (1), 1-23. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1963
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