當GNN遇見NLP(五) Sentence-State LSTM for Text Representation，ACL2018
本文作者來自Singapore University of Technology and Design以及Department of Computer Science, University of Rochester。 雖然本文中沒有提到圖神經網絡的概念，但是從其實際操作上還是被歸類爲圖的空間方法的一種。本文爲了克服了BiLSTM侷限於順序文本的缺點，提出了新的模型S-LSTM，利用Recurre
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相關文章
- 1. 當GNN遇見NLP(六)：Text Level Graph Neural Network for Text Classification，EMNLP2019
- 2. 當GCN遇見NLP(三) Tensor Graph Convolutional Networks for Text Classification，arXiv2020
- 3. 當GCN遇見NLP(二) Heterogeneous GAN for Semi-supervised Short Text Classification，EMNLP2019
- 4. Sentence-State LSTM for Text Representation(論文筆記）
- 5. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
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- 7. BioBERT: a pre-trained biomedical language representation model for biomedical text mining
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- 10. ACL2018論文筆記1