Journal of Intelligent Information Systems
Document Clustering Using Incremental and Pairwise Approaches
Focused Access to XML Documents
An autonomous assessment system based on combined latent semantic kernels
Expert Systems with Applications: An International Journal
Combining structure and content similarities for XML document clustering
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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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.