Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Le
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
- 4. 《Neural Architectures for Named Entity Recognition》
- 5. 《Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition》論文解讀
- 6. 【論文筆記】Neural Architectures for Named Entity Recognition
- 7. [ICLR2018]Deep Active Learning for Named Entity Recognition
- 8. A Survey on Deep Learning for Named Entity Recognition（2020）
- 9. 論文筆記-Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
- 10. 【論文筆記】A Survey on Deep Learning for Named Entity Recognition