Woo Jin Cho , Jung Seok Oh , Jae Young Lee , Yu Seop Kim

The KIPS Transactions:PartA, Vol. 12, No. 6, pp. 539-546, Dec. 2005
10.3745/KIPSTA.2005.12.6.539,   PDF Download:

Abstract

Recently, as the number of Internet users are growing explosively, e-learning has been wide spread, as well as remote evaluation of intellectual capacity. However, only the multiple choice and/or the objective tests have been applied to the e-learning, because of difficulty of natural language processing. For the intelligent marking of short-essay typed answer papers with rapidness and fairness, this work utilize heterogenous linguistic knowledges. Firstly, we construct the semantic kernel from untagged corpus. Then the answer papers of students and instructors are transformed into the vector form. Finally, we evaluate the similarity between the papers by using the semantic kernel and decide whether the answer paper is correct or not, based on the similarity values. For the construction of the semantic kernel, we used latent semantic analysis based on the vector space model. Further we try to reduce the problem of information shortage, by integrating Korean WordNet. For the construction of the semantic kernel, we collected 38,727 newspaper articles and extracted 75,175 indexed terms. In the experiment, about 0.894 correlation coefficient value, between the marking results from this system and the human instructors, was acquired.

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