Educational Question Answering based on Social Media Content

  • Authors:

  • Iryna Gurevych;Delphine Bernhard;Kateryna Ignatova;Cigdem Toprak

  • Affiliations:

  • Ubiquitous Knowledge Processing (UKP) Lab, Computer Science Department, Technical University of Darmstadt, Germany;Ubiquitous Knowledge Processing (UKP) Lab, Computer Science Department, Technical University of Darmstadt, Germany;Ubiquitous Knowledge Processing (UKP) Lab, Computer Science Department, Technical University of Darmstadt, Germany;Ubiquitous Knowledge Processing (UKP) Lab, Computer Science Department, Technical University of Darmstadt, Germany

  • Venue:
  • Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
  • Year:
  • 2009

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Abstract

We analyze the requirements for an educational Question Answering (QA) system operating on social media content. As a result, we identify a set of advanced natural language processing (NLP) technologies to address the challenges in educational QA. We conducted an inter-annotator agreement study on subjective question classification in the Yahoo!Answers social Q&A site and propose a simple, but effective approach to automatically identify subjective questions. We also developed a two-stage QA architecture for answering learners' questions. In the first step, we aim at re-using human answers to already answered questions by employing question paraphrase identification [1]. In the second step, we apply information retrieval techniques to perform answer retrieval from social media content. We show that elaborate techniques for question preprocessing are crucial.