北京航空航天大学学报2026，Vol.52Issue(6)：1890-1902,13.DOI:10.13700/j.bh.1001-5965.2024.0223
基于增强逐点图卷积网络的民航短文本组合分类方法
Civil aviation short text combined classification method based on enhanced point-wise graph convolutional networks
摘要
Abstract
The improvement of classification accuracy is currently hampered by the fact that most short text classification approaches suffer from inadequate information mining and insufficient attention to local information.In light of this,an enhanced semantic-syntactic point-wise graph convolutional network(ESS-PWGCN)short-text combination classification model with few samples and semi-supervised civil aviation was proposed.Firstly,the model selects training set high-confidence keyword information to enrich and enhance the expression of key information within civil aviation short texts,thereby broadening the applicability of the model.Secondly,it balances the influence weights of global-local information within the textual graph structure while learning the semantic-syntactic information of civil aviation short texts by combining point-wise convolution with graph convolutional networks(GCN)and multi-head attention mechanisms.Then,a fully connected layer is employed to amalgamate the acquired information for outputting classification results.Finally,experiments conducted on aviation datasets and other public domain datasets demonstrate that the ESS-PWGCN model not only surpasses the current state-of-the-art self-training text graph convolution networks(ST-TextGCN)model in terms of accuracy and F1 score by 4.59%and 6.53%,respectively,but also exhibits superior robustness and generalizability.关键词
短文本分类/深度学习/图卷积网络/逐点卷积/注意力机制/长短期记忆网络Key words
short text classification/deep learning/graph convolutional networks/pointwise convolution/attention mechanisms/long short-term memory networks分类
航空航天引用本文复制引用
刘晓琳,宋营营,李卓..基于增强逐点图卷积网络的民航短文本组合分类方法[J].北京航空航天大学学报,2026,52(6):1890-1902,13.基金项目
天津市自然科学基金(17JCYBJC18200) Natural Science Foundation of Tianjin,China(17JCYBJC18200) （17JCYBJC18200）
