面向英语教学的作文主题偏离自动检测算法  

Automatic Detection Algorithm of Composition Subject Deviation Oriented to College English Teaching

作　　者：叶佩[1] YE Pei(College of Tourism and Media,Xi'an Siyuan University,Xi'an 710038,China)

机构地区：[1]西安思源学院旅游与融媒学院,西安710038

出　　处：《吉林大学学报（信息科学版）》2022年第6期1033-1038,共6页Journal of Jilin University（Information Science Edition）

基　　金：西安思源学院校级科研基金资助项目(SYHX-2017005)。

摘　　要：针对当前已有算法未能计算语义相似度,导致检测结果不理想的问题,提出一种面向大学英语教学的作文主题偏离自动检测算法。在大学英语教学环境下,以分布式和结构化两种语义空间为基础,组建语义表示模型,在英语单词以及短语中,得到语义相似度。通过LDA(Latent Dirichlet Allocation)模型对全部文档进训练,同时对文档中各个主体和特征词进行概率加权求和,根据设定的合理阈值检测出偏离主体的作文。仿真实验结果表明,所提算法能获取高精度的作文主题偏离自动检测结果。Because the existing algorithms fail to calculate the semantic similarity, the detection results are not ideal, and an automatic detection algorithm for the deviation of the composition subject for college English teaching is proposed. In the college English teaching environment, combining distributed semantic space and structured semantic space, a semantic representation model is constructed to obtain the semantic similarity between English words and phrases. Through the LDA(Latent Dirichlet Allocation) model, all documents are trained, and the probabilistic weighted summation of each subject and feature words in the document is carried out, and the composition of the subject deviation is detected according to the set reasonable threshold. The results of simulation experiments show that the proposed algorithm can obtain high-precision automatic detection results of composition subject deviation.

关 键 词：大学英语教学 作文主题 偏离 自动检测 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]