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: email@example.com, CalvnCYLiao@gmail.com, HercyCheng.firstname.lastname@example.org, email@example.com).
Wan-Chen Chang is with the Graduate Institute of Learning and Instruction, National Central University, Taoyuan, Taipei (e-mail: firstname.lastname@example.org).
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.