A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics
Abstract
We present in this paper a novel, optimal semantic similarity approach based on word-to-word similarity metrics to solve the important task of assessing natural language student input in dialogue-based intelligent tutoring systems. The optimal matching is guaranteed using the sailor assignment problem, also known as the job assignment problem, a well-known combinatorial optimization problem. We compare the optimal matching method with a greedy method as well as with a baseline method on data sets from two intelligent tutoring systems, AutoTutor and iSTART.
Publication Title
Proceedings of the 7th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2012
Recommended Citation
Rus, V., & Lintean, M. (2012). A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics. Proceedings of the 7th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2012, 157-162. Retrieved from https://digitalcommons.memphis.edu/facpubs/2350
