Automatic Classification with SVM and F-VSM on Elementary Chinese Composition

Weiping Liu, Calvin C. Y. Liao, Wan-Chen Chang, Hercy N. H. Cheng, Sannyuya Liu

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)327-331
Number of pages5
JournalInternational Journal of Information and Education Technology
Issue number5
Publication statusPublished - 2018
Externally publishedYes


  • F-VSM
  • linguistic features
  • natural language processing
  • SVM


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