當GNN遇見NLP(六)：Text Level Graph Neural Network for Text Classification，EMNLP2019
我是目錄 Method Building Text Graph Message Passing Mechanism Experiments Datasets & Baselines Experimental Results Ablation Study Conclusion 本文作者來自北大。 以往基於GNN的方法主要面臨的實際問題是固定語料庫級圖結構，不支持在線測試，內存消耗大。因此本文提出一種
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相關文章
- 1. 當GCN遇見NLP(二) Heterogeneous GAN for Semi-supervised Short Text Classification，EMNLP2019
- 2. GNN在文本分類上的應用 | (2) Text Level Graph Neural Network for Text Classification
- 3. 論文閱讀總結(Text Level Graph Neural Network for Text Classification)
- 4. 當GCN遇見NLP(三) Tensor Graph Convolutional Networks for Text Classification，arXiv2020
- 5. 當GNN遇見NLP(五) Sentence-State LSTM for Text Representation，ACL2018
- 6. Recurrent Convolutional Neural Networks for Text Classification
- 7. Recurrent Neural Network for Text Classification with Multi-Task Learning
- 8. A C-LSTM Neural Network for Text Classification
- 9. 《Character-level Convolutional Networks for Text Classification》
- 10. Graph Convolutional Networks for Text Classification