融合维度构建和数据增强的评教文本匹配算法  

Text Matching Algorithm Based on Dimension Construction and Data Augmentation for Teaching Evaluation

作　　者：殷秀秀 檀健 朱金秋 张诗韵 YIN Xiuxiu;TAN Jian;ZHU Jinqiu;ZHANG Shiyun(School of Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学理学院,江苏南京210023

出　　处：《中北大学学报(自然科学版)》2025年第1期10-18,共9页Journal of North University of China(Natural Science Edition)

摘　　要：高校评教文本存在着评教维度多、文本内容长的特点,造成了评教信息难以挖掘的问题,鉴于此,本文设计了一种融合维度构建与数据增强的无监督评教文本匹配算法。首先,采用TextRank方法提取评教文本中的关键词,并根据关键词进行维度归纳与递进,从而构建评教指标体系。接着,对评教文本进行短文本拆解,利用基于注意力机制的预训练模型挖掘短文本与维度间的匹配特征。最后,在各个预训练模型的基础上,采取SimCSE策略进行数据增强,通过对比实验数据,得到短文本的最佳维度匹配结果。实验结果表明,使用该策略后的模型在准确率RAcc和F1指标上均优于原预训练模型,其中SimCSE-WoBERT模型匹配效果最好,RAcc达72.50%,F1达84.06%,这表明将SimCSE模型引入评教文本匹配领域能取得较好的应用效果。本文算法能够实现评教内容与评教维度的自动化匹配,从而更精准地挖掘高校评教人员关于各个评教维度的细粒度信息,便于分析评教人员在听课中重点关注的教学环节,进而为评教文本细粒度情感挖掘提供理论依据。Teaching evaluation text in colleges and universities has the characteristics of multiple evaluation 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 dimensions,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 evaluation 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]