Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (3): 234-241.doi: 10.11896/j.issn.1002-137X.2019.03.035

• Artificial Intelligence • Previous Articles     Next Articles

English Automated Essay Scoring Methods Based on Discourse Structure

ZHOU Ming1,3,JIA Yan-ming2,ZHOU Cai-lan1,XU Ning1,3   

  1. (School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China)1
    (Research Center for Artificial Intelligence and Big Data,Global Wisdom Inc,Beijing 100085,China)2
    (Hubei Key Laboratory of Transportation Internet of Things,Wuhan University of Technology,Wuhan 430070,China)3
  • Received:2018-01-24 Revised:2018-05-13 Online:2019-03-15 Published:2019-03-22

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Abstract: Automated essay scoring is defined as the computer technology that evaluates and scores the composition,based on the technologies of statistics,natural language processing,linguistics and some other fields.Discourse structure analysis is not only an important research field of natural language processing,but also an important component of the AES system.Nowadays,AES system has widely application.However,there is not enough research on the structure of the essay,and the AES system does not focus on the Chinese students.The domestic researches on the AES are in infancy,ignoring the importance of discourse structure in essay scoring.In view of these problems,this paper proposed a method of automated essay scoring based on discourse structure.Firstly,the method extracts essay’s features,such as vocabulary,lexical and discourse structure from levels of words,sentences and paragraphs.Then,the composition of essays is classified by support vector machines,random forests and extreme gradient boosting,and then the linear regression model with the discourse element is constructed to score the compositions.The experimental results show that the accuracy of discourse element identification based random forest (DEI-RF) can reach 94.13%,and the mean squared error of automated discourse structure scoring based on linear regression (DSS-LR) model can reach 0.02,0.11 and 0.08 on introduction,argumentation and concession respectively.

Key words: Automated essay scoring, Discourse element, Discourse structure analysis, Linear regression

第3期周 明, Natural language processing, Random forest, 等:基于篇章结构的英文作文自动评分方法

CLC Number: 

  • TP391.1
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