Paper: Text-to-Text Semantic Similarity for Automatic Short Answer Grading

ACL ID E09-1065
Title Text-to-Text Semantic Similarity for Automatic Short Answer Grading
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

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 mea- sures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel tech- nique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently out- performs other unsupervised methods for short answer grading that have been pro- posed in the past.