Paper: Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments

ACL ID P11-1076
Title Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

In this work we address the task of computer- assisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures us- ing machine learning techniques and show that the student answers can be more accu- rately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the au- tomatic grading of student answers.