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Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Le
時間 2021-01-02
標籤
NLP
中文NER
python
機器學習
nlp
深度學習
欄目
Microsoft Office
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原文
原文鏈接
簡介 文章主要貢獻將訓練分詞的模型(單向LSTM-CRF)與NER的訓練合併來試圖提高NER模型的性能。 論文地址: https://arxiv.org/pdf/1603.00786.pdf 代碼gitlab地址: https://github.com/hltcoe/golden-horse 模型結構 模型的總體結構如下: 左邊是中文分詞的模型結構(LSTM+CRF),右邊是NER的模型結構,NE
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相關文章
1.
《A Multi-task Approach for Named Entity Recognition in Social Media Data》論文筆記
2.
F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media 論文翻譯
3.
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
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《Neural Architectures for Named Entity Recognition》
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【論文筆記】Neural Architectures for Named Entity Recognition
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