[NLP] 实体链接论文阅读—Entity Linking for Chinese Short Texts Based on BERT and Entity Name Embeddings

时间 2020-12-30

标签 NLP 深度学习自然语言处理繁體版

原文原文链接

Entity Linking for Chinese Short Texts Based on BERT and Entity Name Embeddings 写在前面：最近在阅读实体链接论文，实体消歧是实体链接必须有的步骤，而实体链接的前一步为实体识别，对于只想知道本文到底用什么方法进行实体消歧并实体链接的，请直接移步第三部分Model架构的描述。这次直接用raw markdown写了，尝试过

>>阅读原文<<

1. 论文阅读：A Walk-based Model on Entity Graphs for Relation Extraction
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3. 《Joint Learning of Named Entity Recognition and Entity Linking》论文笔记
4. [NLP] 实体消歧论文阅读—Entity Disambiguation by Knowledge and Text Jointly Embedding
5. 论文阅读：A Survey on Deep Learning for Named Entity Recognition
6. 《Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition》论文解读
7. 【论文笔记】A Survey on Deep Learning for Named Entity Recognition
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9. Bert：论文阅读-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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1. 论文阅读：A Walk-based Model on Entity Graphs for Relation Extraction
2. ACL2020《Improving Entity Linking through Semantic Reinforced Entity Embeddings》阅读笔记
3. 《Joint Learning of Named Entity Recognition and Entity Linking》论文笔记
4. [NLP] 实体消歧论文阅读—Entity Disambiguation by Knowledge and Text Jointly Embedding
5. 论文阅读：A Survey on Deep Learning for Named Entity Recognition
6. 《Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition》论文解读
7. 【论文笔记】A Survey on Deep Learning for Named Entity Recognition
8. 阅读分享：BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction
9. Bert：论文阅读-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
10. 【论文阅读笔记】Span-based Joint Entity and Relation Extraction with Transformer Pre-training

>>更多相关文章<<