International Journal of
Information and Education Technology

Abstract—Currently, automated evaluation of Chinese composition still has limitations. Moreover, the human evaluation is possible subjective, time-consuming and laborious. Hence, to develop automatic evaluation of Chinese composition is very meaningful and potential. In this study, we adopted two methods: support vector machine (SVM) and feature vector space model (F-VSM) to evaluate 4193 Chinese compositions collected from 1st to 6th grade at an elementary school in Wuhan. This study integrated natural language processing techniques to extract features, and uses SVM and F-VSM to classify the composition level. We investigated 45 linguistic features and divided into four aspects: text structure, syntactic complexity, word complexity and lexical diversity. The result indicated that both SVM and F-VSM have good classification effect, and F-VSM effect is better than SVM.

Index Terms—F-VSM, linguistic features, natural language processing, SVM.

Weiping Liu, Calvin C. Y. Liao, Hercy N. H. Cheng, and Sannyuya Liu are with the National Engineering Research Center for e-Learning, Central China Normal University, Wuhan, China (e-mail: 1398518181@qq.com, CalvnCYLiao@gmail.com, HercyCheng.tw@gmail.com, lsy5918@gmail.com).
Wan-Chen Chang is with the Graduate Institute of Learning and Instruction, National Central University, Taoyuan, Taipei (e-mail: altheawcc@gmail.com).

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Cite: Weiping Liu, Calvin C. Y. Liao, Wan-Chen Chang, Hercy N. H. Cheng, and Sannyuya Liu, "Automatic Classification with SVM and F-VSM on Elementary Chinese Composition," International Journal of Information and Education Technology vol. 8, no. 5, pp. 327-331, 2018.