Answering learners' questions by retrieving question paraphrases from social Q&A sites

  • Authors:
  • Delphine Bernhard;Iryna Gurevych

  • Affiliations:
  • Technische Universität Darmstadt, Darmstadt, Germany;Technische Universität Darmstadt, Darmstadt, Germany

  • Venue:
  • EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
  • Year:
  • 2008

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Abstract

Information overload is a well-known problem which can be particularly detrimental to learners. In this paper, we propose a method to support learners in the information seeking process which consists in answering their questions by retrieving question paraphrases and their corresponding answers from social Q&A sites. Given the novelty of this kind of data, it is crucial to get a better understanding of how questions in social Q&A sites can be automatically analysed and retrieved. We discuss and evaluate several pre-processing strategies and question similarity metrics, using a new question paraphrase corpus collected from the WikiAnswers Q&A site. The results show that viable performance levels of more than 80% accuracy can be obtained for the task of question paraphrase retrieval.