基于篇章结构的英文作文自动评分方法

English Automated Essay Scoring Methods Based on Discourse Structure

作文自动评分(Automated Essay Scoring AES)是指使用统计学、自然语言处理及语言学等领域的技术对作文进行评价和评分的系统。篇章结构分析是自然语言处理领域的一个重要研究方向,也是作文自动评分系统的重要组成部分之一。目前国外的作文自动评分系统虽有广泛应用,但对篇章结构评分的研究还存在不足,且对中国学生英语作文的针对性不强;国内对英语作文自动评分的研究处于起步阶段,忽视了篇章结构对英语作文评分的重要性。针对这些问题,提出一种基于篇章结构的英文作文自动评分方法,在词、句、段落3个层面上提取作文的词汇、句法以及结构等特征,并使用支持向量机、随机森林以及极端梯度上升等算法对篇章成分进行分类,最后构建线性回归模型对作文的篇章结构进行评分。实验结果表明,基于随机森林的篇章成分识别模型(Discourse Element Identification based Random Forest,DEI-RF)的准确率为94.13%;基于线性回归的篇章结构自动评分模型(Discourse Structures Scoring based Linear Regression,DSS-LR)在背景介绍段(Introduction)、论证段(Argumentation)以及让步段(Concession)的均方差可达到0.02,0.11和0.08。

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.