摘要
Abstract
Teaching evaluation text in colleges and universities has the characteristics of multiple evalua-tion dimensions and long text content,which makes it difficult to mine evaluation information.Based on this,this paper designed an unsupervised teaching evaluation text matching algorithm that integrated dimension construction and data enhancement.Firstly,TextRank method was used to extract keywords from the evaluation text,and the evaluation index system was constructed by dimensional induction and recursion based on the keywords.Secondly,the short text was disassembled,and the pre-training model based on the attention mechanism was used to mine the matching features between the short text and the dimensions.Finally,based on each pre-trained model,the SimCSE strategy was adopted for data enhancement,and by compared the experimental data,the best dimension matching result of the short text was obtained.The experimental results show that the models after using this strategy are better than the original training model on the accuracy RAcc and F1 indicators.Among them,the Simcse-Wobert model has the best matching effect,RAcc is 72.50%,and F1 reaches 84.06%,which indicates that the SimCSE model is introduced into evaluation text matching fields can achieve good application effects.This algorithm can realize automatic matching of teaching evaluation content and teaching evaluation dimen-sions,thereby can more accurately mine the fine-grained information of university evaluation personnel on each evaluation dimension,which is convenient for analyzing the focus points on the teaching links evalua-tion of evaluation personnel,and can provide theoretical basis for fine-grained emotion mining of teaching evaluation texts.关键词
高校评教/评教体系/数据增强/文本匹配/数据挖掘Key words
college evaluation/evaluation system/data augmentation/text matching/data mining分类
计算机与自动化
