论文解读：Graph Convolutional Networks for Text Classifification
论文解读：Graph Convolutional Networks for Text Classifification 先前的文本分类方法是基于CNN或RNN进行的，只能单独的对文本自身的上下文进行语义提取，而不能够对文本之间的相关信息进行表示。随着图结构在NLP领域的大放光彩，将图引入文本分类是新的思路。 一、简要信息 序号 属性 值 1 模型名称 Text-GCN 2 所属领域 自然语言处
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相关文章
- 1. Graph Convolutional Networks for Text Classification
- 2. Graph Convolutional Networks for Text Classification阅读笔记
- 3. 论文阅读总结（Graph Convolutional Networks for Text Classification）
- 4. 【论文阅读笔记】Graph Convolutional Networks for Text Classification
- 5. 论文笔记：AAAI 2019 Graph Convolutional Networks for Text Classification
- 6. Fully Convolutional Networks for Semantic Segmentation 论文解读
- 7. 《Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification》论文理解
- 8. 论文阅读:ImageNet Classifification with Deep Convolutional Neural Networks
- 9. 《Large-Scale Learnable Graph Convolutional Networks》论文理解
- 10. 论文阅读：Knowledge Graph Convolutional Networks for Recommender Systems