基于文本语义离散度的自动作文评分关键技术研究

该文尝试从文本语义离散度的角度去提升自动作文评分的效果,提出了两种文本语义离散度的表示方法,并给出了数学化的计算公式。基于现有的LDA模型、段落向量、词向量等具体方法,提取出四种表征文本语义离散度的实例,应用于自动作文评分。该文从统计学角度将文本语义离散度向量化,从去中心化的角度将文本语义离散度矩阵化,并使用多元线性回归、卷积神经网络和循环神经网络三种方法进行对比实验。实验结果表明,在50篇作文的验证集上,在加入文本语义离散度特征后,预测分数与真实分数之间均方根误差最大降低10.99%,皮尔逊相关系数最高提升2.7倍。该表示方法通用性强,没有语种限制,可以扩展到任何语言。

Research on Key Technology of Automatic Essay Scoring #br# Based on Text Semantic Dispersion

Based on the existing methods, including LDA model, paragraph vector, word vector text, we extract four kinds of text semantic dispersion representations, and apply them on the automatic essay scoring. This paper gives a vector form of the text semantic dispersion from the statistical point of view and gives a matrix form from the perspective of decentralized text semantic dispersion, experimented on the multiple linear regression, convolution neural network and recurrent neural network. The results showed that, on the test data of 50 essays, after the addition of text semantic dispersion feature, the Root Mean Square Error is reduced by 10.99% and the Pearson correlation coefficient increases 2.7 times.