Abstract
This paper proposes an automated grading model (AGM) for English text. The AGM combines the Latent Semantic Analysis (LSA) with Text Clustering (TC) to measure the content quality of English text. In the AGM, LSA represents the meaning of words as vectors in semantic space using statistical analysis technique applied to large corpus, and TC is applied to classify the texts in the corpus into different categories. By comparing the similarity of a text with reference texts which are belong to the same cluster on basis of semantic content, our model can grade the text in the same cluster. In addition, the AGM can judge the to-be-graded text whether related to the given subject. Our experiments show that the AGM is competent in grading the content of texts.
This work is supported by the Educational Science & Research Foundation of Guangxi (No.200911MS83).
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Guimin, H., Guoyuan, C., Ya, Z., Kuangyu, Q., Yan, Z. (2011). An Automated Grading Model Integrated LSA and Text Clustering Together for English Text. In: Zhiguo, G., Luo, X., Chen, J., Wang, F.L., Lei, J. (eds) Emerging Research in Web Information Systems and Mining. WISM 2011. Communications in Computer and Information Science, vol 238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24273-1_16
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DOI: https://doi.org/10.1007/978-3-642-24273-1_16
Publisher Name: Springer, Berlin, Heidelberg
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