Automatic Classification with SVM and F-VSM on Elementary Chinese Composition Article Swipe
YOU?
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· 2017
· Open Access
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· DOI: https://doi.org/10.18178/ijiet.2018.8.5.1057
· OA: W2780941379
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.
