An intelligent grading system for descriptive examination papers based on probabilistic latent semantic analysis

Authors:
Yu-Seop Kim;Jung-Seok Oh;Jae-Young Lee;Jeong-Ho Chang
Affiliations:
Division of Information Engineering and Telecommunications, Hallym University, Gangwon, Korea;Division of Information Engineering and Telecommunications, Hallym University, Gangwon, Korea;Division of Information Engineering and Telecommunications, Hallym University, Gangwon, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

In this paper, we developed an intelligent grading system, which scores descriptive examination papers automatically, based on Probabilistic Latent Semantic Analysis (PLSA) For grading, we estimated semantic similarity between a student paper and a model paper PLSA is able to represent complex semantic structures of given contexts, like text passages, and are used for building linguistic semantic knowledge which could be used in estimating contextual semantic similarity In this paper, we marked the real examination papers and we can acquire about 74% accuracy of a manual grading, 7% higher than that from the Simple Vector Space Model.