論文解讀：Graph Convolutional Networks for Text Classifification
論文解讀：Graph Convolutional Networks for Text Classifification 先前的文本分類方法是基於CNN或RNN進行的，只能單獨的對文本自身的上下文進行語義提取，而不能夠對文本之間的相關信息進行表示。隨着圖結構在NLP領域的大放光彩，將圖引入文本分類是新的思路。 一、簡要信息 序號 屬性 值 1 模型名稱 Text-GCN 2 所屬領域 自然語言處
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相關文章
- 1. Graph Convolutional Networks for Text Classification
- 2. Graph Convolutional Networks for Text Classification閱讀筆記
- 3. 論文閱讀總結（Graph Convolutional Networks for Text Classification）
- 4. 【論文閱讀筆記】Graph Convolutional Networks for Text Classification
- 5. 論文筆記：AAAI 2019 Graph Convolutional Networks for Text Classification
- 6. Fully Convolutional Networks for Semantic Segmentation 論文解讀
- 7. 《Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification》論文理解
- 8. 論文閱讀:ImageNet Classifification with Deep Convolutional Neural Networks
- 9. 《Large-Scale Learnable Graph Convolutional Networks》論文理解
- 10. 論文閱讀：Knowledge Graph Convolutional Networks for Recommender Systems