网络安全与数据治理2025，Vol.44Issue(6)：42-48,7.DOI:10.19358/j.issn.2097-1788.2025.06.006
基于混合粒度全局图的多标签文本分类方法
A multi-label text classification method based on a mixed-granularity global graph
摘要
Abstract
Multi-label text classification is designed to assign multiple labels to each instance of text.Traditional multi-label text classification methods usually rely on coarse-grained feature representations,ignoring the multi-level and multi-scale semantic in-formation in the text.In order to solve this problem,this paper proposes a multi-label text classification method based on mixed granularity global graph,which extracts fine-grained text features through MHA to capture the interaction information between words and labels,and uses Bi-LSTM to extract coarse-grained text features.Subsequently,the two features are fused through the gated fusion mechanism to obtain mixed granular features with multi-level semantics.The fused mixed granular word representa-tions,texts,and labels are used together to construct a global graph,and the global graph is processed through a graph convolu-tional network for classification.Experiments are carried out on two datasets,AAPD and RCV1-V2,and the experimental results show that the proposed method can effectively improve the performance of the model.关键词
多标签文本分类/多头注意力机制/双向长短期记忆网络/门控融合机制/图卷积网络Key words
multi-label text classification/multi-head attention mechanism/bidirectional long short-term memory network/gated fusion mechanism/graph convolutional networks分类
信息技术与安全科学引用本文复制引用
王哲,温秀梅..基于混合粒度全局图的多标签文本分类方法[J].网络安全与数据治理,2025,44(6):42-48,7.基金项目
河北建筑工程学院研究生创新基金(XY2025029) （XY2025029）
