【論文筆記】A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Emb
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding 做者評估了雙向LSTM RNN在POS標記，分塊和NER任務上的使用。輸入是與任務無關的輸入功能：單詞及其大小寫。做者經過限制解碼器輸出標籤的有效序列，從而結合了有關標記任務的先驗知識，並提出了一種學習單詞嵌入的新穎
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相關文章
- 1. (zhuan) Recurrent Neural Network
- 2. 【論文學習】Bidirectional LSTM-CRF Models for Sequence Tagging（論文翻譯）
- 3. Paper: Bidirectional LSTM-CRF Models for Sequence Tagging
- 4. 論文筆記：Recurrent Neural Network Grammars
- 5. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 6. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記
- 7. Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano
- 8. Supervised Sequence Labelling with Recurrent Neural Networks 筆記 -LSTM
- 9. 論文筆記：ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
- 10. 《FOTS：Fast Oriented Text Spotting with a Unified Network》論文筆記