Text-to-text semantic similarity for automatic short answer grading

  • Authors:
  • Michael Mohler;Rada Mihalcea

  • Affiliations:
  • University of North Texas;University of North Texas

  • Venue:
  • EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
  • Year:
  • 2009

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Abstract

In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.