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.
Original language | English |
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Pages (from-to) | 327-331 |
Number of pages | 5 |
Journal | International Journal of Information and Education Technology |
Volume | 8 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2018 |
Externally published | Yes |
Keywords
- F-VSM
- linguistic features
- natural language processing
- SVM