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.
Reference REF compared number of knowledge-based and corpus-based measures of semantic similarity.
Text-to-Text Semantic Similarity for Automatic Short Answer Grading
EACL
Computer Science