计算机与数字工程2024，Vol.52Issue(1)：18-22,42,6.DOI:10.3969/j.issn.1672-9722.2024.01.003
基于关系挖掘和对抗训练的多标签文本分类
Multi-label Text Classification Based on Relationship Mining and Adversarial Training
摘要
Abstract
Traditional multi-label text classification methods ignore the label semantics and do not fully exploit the relation-ship between text and label as well as between label and label.In this paper,a multi-label text classification model is proposed based on relationship mining and adversarial training to solve the above problems.The BERT model and Graph Attention Network(GAT)are used to extract the semantic information of the text and mine the relationship between labels,respectively.First,the text is encoded using the BERT model to obtain semantic information of the text.Then,GAT is used to mine the relationships between la-bels to better understand the dependencies between labels.To further mine the relationship between text and learnable label embed-dings,the model employs a multi-head self-attention mechanism.Moreover,to improve the robustness of the model,the R-drop strategy is used for model training in this paper.Experimental results on AAPD and RCV1 datasets show that the proposed model not only focuses on textual information,but also effectively captures the dependencies between text and labels and the relationships be-tween labels to achieve better performance compared to some of the current mainstream multi-label text classification models.关键词
BERT/注意力机制/R-drop/图注意网络/归一化Key words
BERT/attention mechanism/R-drop/graph attention network/normalization分类
信息技术与安全科学引用本文复制引用
杨冬菊,程伟飞..基于关系挖掘和对抗训练的多标签文本分类[J].计算机与数字工程,2024,52(1):18-22,42,6.基金项目
国家自然科学基金重点项目(编号:61832004) （编号:61832004）
广州市科技计划项目-重点研发计划(编号:202206030009)资助. （编号:202206030009）
