.png){kind=icon}
返回
LMTCSG: Multilabel Text Classification Combining Sequence-Based and GNN-Based Features
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2024.3465596.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Since multilabel text classification datasets often face the problem of label imbalance, therefore, using either sequence-based deep learning (DL) model or graph neural network (GNN)-based DL model alone will not achieve satisfactory classification results. To solve the above problem, firstly, two coattention networks are constructed to simultaneously obtain the sequence-based and GNN-based eigenvectors. Second, labels are added to the graph as global features, and a graph data augmentation strategy is proposed. When obtaining GNN-based eigenvectors, at first, connection and attention weights are obtained through adjacency matrix and the attention of neighborhoods. Then, node features are updated based on convolution and multihead attention, respectively. Multiple comparison experiments on four benchmark datasets prove that the model constructed in this article achieves the optimal classification results and can solve the label imbalance problem.
Keyword:
Vectors
Correlation
Convolution
Feature extraction
Data models
TV
Sun
Semantics
Recurrent neural networks
Informatics
Graph neural network (GNN)-based deep learning (DL) model
label imbalance problem
multilabel text classification (MLTC)
sequence-based deep learning (DL) model
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
机构
引用论文
Triple Alliance Prototype Orthotist Network for Long-Tailed Multi-Label Text Classification用于长尾多标签文本分类的三重联盟原型Orthotist网络
PREAI
分享收藏
Multi-Aspect co-Attentional Collaborative Filtering for extreme multi-label text classification
PREAI
分享收藏BGNN-XML: Bilateral Graph Neural Networks for Extreme Multi-Label Text ClassificationBgnn-xml: 用于极端多标签文本分类的双边图神经网络
PREAI
分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏
查看更多内容