| 摘要: |
| 多标签文本分类旨在为文本分配若干预定义的标签或类别. 为了充分发掘标签间的关联, 目前的方法通常使用标签关系图并结合图神经网络获取标签特征表示. 然而, 这类方法过度依赖初始建图策略, 忽视了当前文本中固有的标签相关性, 使得分类结果更依赖于数据集统计信息, 而容易忽视当前文本段中的标签相关信息. 因此, 提出一种基于特征融合动态图网络的多标签文本分类算法, 设计动态图来建模当前文本中的标签相关性, 并结合特征融合与图神经网络, 形成基于当前文本的标签表示, 并由此形成更为准确的多标签文本结果. 随后, 设计实验进行验证, 在3个数据集实验结果表明, 所提出的模型在多标签分类任务中取得优秀的性能, 验证其有效性和可行性. |
| 关键词: 多标签文本分类 图卷积网络 注意力机制 动态图 |
| DOI：10.13328/j.cnki.jos.007229 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62172226, 62272230); 江苏省“双创博士”人才计划(JSSCBS20210200) |
|
| Multi-label Text Classification Algorithm Based on Feature-fused Dynamic Graph Network |
|
HUANG Jing1, TAO Zhu-Lin2, DU Xiao-Yu1, XIANG Xin-Guang1
|
|
1.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2.School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
|
| Abstract: |
| Multi-label text classification aims to assign several predefined labels or categories to text. To fully explore the correlations among labels, current methods typically utilize a label relation graph and integrate it with graph neural networks to obtain the representations of label features. However, such methods often overly rely on the initial graph construction, overlooking the inherent label correlations in the current text. Consequently, classification results heavily depend on the statistics of datasets and may overlook label-related information within the text. Therefore, this study proposes an algorithm for multi-label text classification based on feature-fused dynamic graph networks. It designs dynamic graphs to model label correlations within the current text and integrates feature fusion with graph neural networks to form label representations based on the current text, thus achieving more accurate multi-label text classifications. Experimental results on three datasets demonstrate the effectiveness and feasibility of the proposed model as it shows excellent performance in multi-label text classifications. |
| Key words: multi-label text classification graph convolutional network (GCN) attention mechanism dynamic graph |
