JRNN: Joint Event Extraction via Recurrent Neural Networks
[^2016.2]JRNN是非常具有影響力的一篇文章，它首次把神經網絡模型應用到了Joint EE上（後面的許多paper都將該模型作爲baseline）。 之前已經提到，[Li et. al. 2013]的最大問題是對於unseen word/feature的泛化能力較差，其原因在於只使用了手動製作的離散特徵集； 而 2015.1 中的DMCNN儘管自動提取了能表達更多語義的特徵，解決了泛化能力
相關文章
- 1. Joint Event Extraction via Recurrent Neural Networks
- 2. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
- 3. 論文閱讀 # 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 簡述
- 更多相關文章...
- • C# 事件（Event） - C#教程
- • PHP is_uploaded_file() 函數 - PHP參考手冊
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Joint Event Extraction via Recurrent Neural Networks
- 2. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
- 3. 論文閱讀 # 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 簡述