doi: 10.18178/ijiet.2018.8.5.1057
Automatic Classification with SVM and F-VSM on Elementary Chinese Composition
Weiping Liu1
,
Calvin C. Y. Liao1 ,
Wan-Chen Chang2 ,
Hercy N. H. Cheng1 ,
Sannyuya Liu1
- 1National Engineering Research Center for e-Learning, Central China Normal University, Wuhan, China
- 2Graduate Institute of Learning and Instruction, National Central University, Taoyuan, Taipei
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.
Keywords
- F-VSM
- linguistic features
- natural language processing
- SVM
How to 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. https://doi.org/10.18178/ijiet.2018.8.5.1057
Copyright & License
Copyright © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).