Learning the relative usefulness of questions in community QA

  • Authors:
  • Razvan Bunescu;Yunfeng Huang

  • Affiliations:
  • Ohio University, Athens, OH;Ohio University, Athens, OH

  • Venue:
  • EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2010

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Abstract

We present a machine learning approach for the task of ranking previously answered questions in a question repository with respect to their relevance to a new, unanswered reference question. The ranking model is trained on a collection of question groups manually annotated with a partial order relation reflecting the relative utility of questions inside each group. Based on a set of meaning and structure aware features, the new ranking model is able to substantially outperform more straightforward, unsupervised similarity measures.