Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Le

时间 2021-01-02

标签 NLP 中文NER python 机器学习 nlp 深度学习栏目 Microsoft Office 繁體版

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简介文章主要贡献将训练分词的模型(单向LSTM-CRF)与NER的训练合并来试图提高NER模型的性能。论文地址: https://arxiv.org/pdf/1603.00786.pdf 代码gitlab地址: https://github.com/hltcoe/golden-horse 模型结构模型的总体结构如下：左边是中文分词的模型结构(LSTM+CRF)，右边是NER的模型结构，NE

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1. 《A Multi-task Approach for Named Entity Recognition in Social Media Data》论文笔记
2. F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media 论文翻译
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》论文解读
6. 【论文笔记】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. 论文笔记-Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
10. 【论文笔记】A Survey on Deep Learning for Named Entity Recognition

>>更多相关文章<<