计算机工程与应用2024，Vol.60Issue(15)：150-160,11.DOI:10.3778/j.issn.1002-8331.2305-0368
融合GAT与头尾标签的多标签文本分类模型
Multi-Label Text Classification Model Integrating GAT and Head-Tail Label
摘要
Abstract
The existing multi-label text classification based on neural networks has two main shortcomings:firstly,there is a long tailed distribution of label text frequency in the existing data,and secondly,the correlation between global labels is rarely obtained from the graph structure.To address the above issues,this paper proposes a multi-label text classification model(GATTN)that integrates GAT and head and tail label classifiers.This model utilizes Bi-LSTM with attention mech-anism to obtain the feature vector representation of the text.Simultaneously,the correlation between different labels is transformed into an edge weighted graph containing global information,and a multi-layer graph attention network is used to learn the correlation between head labels.Then it interacts with the text context semantic information by dot product,and gets the feature representation with text semantics and header tag information to improve the generalization ability of the model.The experimental results on two publicly available English datasets,AAPD,RCV1-V2 and EUR-Lex,demon-strate that this model outperforms other baseline models in multi-label text classification for long tailed distribution of data.关键词
多标签文本分类/图注意力网络/头尾标签/多样本学习Key words
multi-label text classification/graph attention networks(GAT)/head and tail label/many-shot learning分类
信息技术与安全科学引用本文复制引用
杨春霞,黄昱锟,闫晗,吴亚雷..融合GAT与头尾标签的多标签文本分类模型[J].计算机工程与应用,2024,60(15):150-160,11.基金项目
国家自然科学基金(61273229) （61273229）
江苏省青蓝工程资助项目. （）
