• Dec 26, 2017 News! IJIET Vol. 6, No. 1-No. 10 have been indexed by EI (Inspec).   [Click]
  • Apr 26, 2018 News!Vol. 8, No. 6 has been indexed by Crossref.
  • Apr 12, 2018 News!Vol. 8, No. 6 issue has been published online!   [Click]
General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
QUniversity, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good-quality papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the related fields.

IJIET 2018 Vol.8(5): 327-331 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.5.1057

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

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

[PDF]

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.

Copyright © 2008-2018. International Journal of Information and Education Technology. All rights reserved.
E-mail: ijiet@ejournal.net