Abstract
In this paper, we propose an intelligent grading system using heterogeneous linguistic resources. We used latent semantic kernel as one resource in former research and found that a deficit of indexed terms gave rise to performance bottleneck. To solve this, we expand answer papers, written by students and instructors, by utilizing one of widely used linguistic resources, WordNet. We supplement the papers with words semantically related to indexed terms of papers. The added words are selected from the synonyms and hyponyms on WordNet. And to get rid of the criterion decision problem, we use partial score of each question and evaluate the correlation coefficient between grading results of the proposed approach and human instructors. The proposed approach in this research achieves maximally 0.94 correlation coefficient to instructors, which is 0.06 higher than that of the former research.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, YS., Cho, WJ., Lee, JY., Oh, YJ. (2005). An Intelligent Grading System Using Heterogeneous Linguistic Resources. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_14
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DOI: https://doi.org/10.1007/11508069_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
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