JRNN: Joint Event Extraction via Recurrent Neural Networks

时间 2021-01-02

标签自然语言处理繁體版

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[^2016.2]JRNN是非常具有影响力的一篇文章，它首次把神经网络模型应用到了Joint EE上（后面的许多paper都将该模型作为baseline）。之前已经提到，[Li et. al. 2013]的最大问题是对于unseen word/feature的泛化能力较差，其原因在于只使用了手动制作的离散特征集；而 2015.1 中的DMCNN尽管自动提取了能表达更多语义的特征，解决了泛化能力

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1. Joint Event Extraction via Recurrent Neural Networks
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3. 论文阅读 # Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
4. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
5. 019 Recurrent Neural Networks
6. RNN(Recurrent Neural Networks)
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9. Recurrent Neural Networks VS LSTM
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1. Joint Event Extraction via Recurrent Neural Networks
2. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
3. 论文阅读 # Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
4. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
5. 019 Recurrent Neural Networks
6. RNN(Recurrent Neural Networks)
7. Recurrent Neural Networks
8. (zhuan) Recurrent Neural Network
9. Recurrent Neural Networks VS LSTM
10. Recurrent Neural Networks 简述

>>更多相关文章<<