Learning to Recommend Descriptive Tags for Questions in Social Forums

  • Authors:

  • Liqiang Nie;Yi-Liang Zhao;Xiangyu Wang;Jialie Shen;Tat-Seng Chua

  • Affiliations:

  • National University of Singapore;National University of Singapore;National University of Singapore;Singapore Management University;National University of Singapore

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2014

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Abstract

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.