当GNN遇见NLP(五) Sentence-State LSTM for Text Representation，ACL2018
本文作者来自Singapore University of Technology and Design以及Department of Computer Science, University of Rochester。 虽然本文中没有提到图神经网络的概念，但是从其实际操作上还是被归类为图的空间方法的一种。本文为了克服了BiLSTM局限于顺序文本的缺点，提出了新的模型S-LSTM，利用Recurre
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相关文章
- 1. 当GNN遇见NLP(六)：Text Level Graph Neural Network for Text Classification，EMNLP2019
- 2. 当GCN遇见NLP(三) Tensor Graph Convolutional Networks for Text Classification，arXiv2020
- 3. 当GCN遇见NLP(二) Heterogeneous GAN for Semi-supervised Short Text Classification，EMNLP2019
- 4. Sentence-State LSTM for Text Representation(论文笔记）
- 5. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
- 6. A C-LSTM Neural Network for Text Classification
- 7. BioBERT: a pre-trained biomedical language representation model for biomedical text mining
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- 10. ACL2018论文笔记1