河北科技大学学报2024，Vol.45Issue(1)：52-58,7.DOI:10.7535/hbkd.2024yx01006
基于文本图神经网络的小样本文本分类技术研究
Research on few-shot text classification techniques based on text-level-graph neural networks
摘要
Abstract
In order to solve the problem of poor accuracy of text classification in text graph neural network with small samples,a text level graph neural network-prototypical(LGNN-Proto)was designed.An advanced pre-training language model was adopted,and the text graph neural network was used to construct the graph for each input text,then the global parameters were shared.The result of the text graph neural network was used as the input of the prototype network to classify the unlabeled text,and the validity of the new model on multiple text classification data sets was verified.The results show that the accuracy of unlabeled text classification is improved by 1％～3％compared with that of supervised learning,which requires a large number of labeled documents,and the new model is validated on multiple text classification data sets with advanced performance and lower memory consumption.The research results can provide reference for solving the problem of text classification with small sample size.关键词
自然语言处理/小样本文本分类/预训练模型/图神经网络/原型网络Key words
natural language processing/few-shot text classification/pre-trained model/graph neural network/prototype network分类
信息技术与安全科学引用本文复制引用
安相成,刘保柱,甘精伟..基于文本图神经网络的小样本文本分类技术研究[J].河北科技大学学报,2024,45(1):52-58,7.基金项目
河北省智能化信息感知与处理重点实验室发展基金(SXX22138X002) （SXX22138X002）
LZH联合QB数据融合与共享服务项目 （）
