Learning to grade short answer questions using semantic similarity measures and dependency graph alignments

Authors:
Michael Mohler;Razvan Bunescu;Rada Mihalcea
Affiliations:
University of North Texas, Denton, TX;Ohio University, Athens, Ohio;University of North Texas, Denton, TX
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Year:
2011

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Abstract

In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately 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 automatic grading of student answers.