论文标题
使用基于注意的图形神经网络的多标签文本分类
Multi-Label Text Classification using Attention-based Graph Neural Network
论文作者
论文摘要
在多标签文本分类（MLTC）中，一个样本可以属于多个类。可以观察到大多数MLTC任务，标签之间存在依赖性或相关性。现有方法倾向于忽略标签之间的关系。在本文中，提出了一个基于图的基于图表网络的模型，以捕获标签之间的细心依赖性结构。图形注意力网络使用特征矩阵和相关矩阵来捕获和探索标签和为任务生成分类器之间的关键依赖关系。生成的分类器应用于从文本特征提取网络（BILSTM）获得的句子特征向量，以实现端到端培训。注意使系统可以为每个标签分配不同的权重，从而使其能够隐含地学习标签之间的依赖关系。提出的模型的结果将在五个现实世界的MLTC数据集上进行验证。与以前的最新模型相比，所提出的模型的性能相似或更好。
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relationship among labels. In this paper, a graph attention network-based model is proposed to capture the attentive dependency structure among the labels. The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task. The generated classifiers are applied to sentence feature vectors obtained from the text feature extraction network (BiLSTM) to enable end-to-end training. Attention allows the system to assign different weights to neighbor nodes per label, thus allowing it to learn the dependencies among labels implicitly. The results of the proposed model are validated on five real-world MLTC datasets. The proposed model achieves similar or better performance compared to the previous state-of-the-art models.
