Merge pull request #44 from loujie0822/jielou

add paper resource

README.md

@@ -11,6 +11,7 @@
 - [ACL2020信息抽取相关论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/ACL2020信息抽取相关论文汇总.md)
 - [2019各顶会中的关系抽取论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/2019各顶会中的关系抽取论文]汇总.md)
 - [事件抽取论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/事件抽取论文汇总.md)
+- [历年来NER论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/历年来NER论文汇总.md)
 
 ## Codes
 
@@ -42,13 +43,16 @@
 
 - **CCKS2020-医疗实体抽取**：
 
-(注：测试集与ccks2019一致)
+(注：测试集与ccks2019一致，去除ccks2020训练集中已经在2019测试集中的样本，下列指标未做规则处理和模型融合)
 
-| 方法                                         | f          | p          | r          |
-| -------------------------------------------- | ---------- | ---------- | ---------- |
-| char-bigram + lstm-crf                       | 87.52%     | 87.19%     | 87.85%     |
-| char-bigram-BERT + lstm-crf                  | 92.62%     | **92.46%** | 92.79%     |
-| char-bigram-BERT + lexion-augment + lstm-crf | **92.78%** | 92.36%     | **93.20%** |
+| 方法                                         | f      | p      | r      |
+| -------------------------------------------- | ------ | ------ | ------ |
+| char-bigram + lstm-crf                       | 82.68% | 83.14% | 82.22% |
+| char-bigram + lexion-augment + lstm-crf      | 83.12% | 83.10% | 83.14% |
+| char-bigram-BERT + lstm-crf                  | 83.12% | 83.04% | 83.21% |
+| char-bigram-BERT-RoBerta_wwm + lstm-crf      | 83.66% | 83.76% | 83.56% |
+| char-bigram-BERT-XLNet + lstm-crf            | 84.12% | 83.88% | 84.36% |
+| char-bigram-BERT + lexion-augment + lstm-crf | 84.50% | 84.32% | 84.67% |
 
 - **CCKS2020-面向试验鉴定的命名实体识别任务**：TODO
 

docs/IJCAI2020_信息抽取相关论文合集 .md

@@ -0,0 +1,45 @@
+#### 一、 Entity 相关
+
+1. **Alleviate Dataset Shift Problem in Fine-grained Entity Typing with Virtual Adversarial Training**
+   *Haochen Shi, Siliang Tang, Xiaotao Gu, Bo Chen, Zhigang Chen, Jian Shao, Xiang Ren*
+2. **Attention-based Multi-level Feature Fusion for Named Entity Recognition**
+   *Zhiwei Yang, Hechang Chen, Jiawei Zhang, Jing Ma, Yi Chang*
+3. **Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment**
+   *Hao Nie, Xianpei Han, Le Sun, Chi Man Wong, Qiang Chen, Suhui Wu, Wei Zhang*
+4. **Hierarchical Matching Network for Heterogeneous Entity Resolution**
+   *Cheng Fu, Xianpei Han, Jiaming He, Le Sun*
+5. **Learning with Noise: Improving Distantly-Supervised Fine-grained Entity Typing via Automatic Relabeling**
+   *Haoyu Zhang, Dingkun Long, Guangwei Xu, Muhua Zhu, Pengjun Xie, Fei Huang, Ji Wang*
+6. **Leveraging Document-Level Label Consistency for Named Entity Recognition**
+   *Tao Gui, Jiacheng Ye, Qi Zhang, Yaqian Zhou, Yeyun Gong, Xuanjing Huang*
+7. 
+
+#### 二、Relation 相关
+
+1. **A Relation-Specific Attention Network for Joint Entity and Relation Extraction**
+   *Yue Yuan, Xiaofei Zhou, Shirui Pan, Qiannan Zhu, Zeliang Song, Li Guo*
+2. **Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction**
+   *Tianyang Zhao, Zhao Yan, Yunbo Cao, Zhoujun Li*
+3. **Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction**
+   *Jie Liu, Shaowei Chen, Bingquan Wang, Jiaxin Zhang, Na Li, Tong Xu*
+4. **Learning Latent Forests for Medical Relation Extraction**
+   *Zhijiang Guo, Guoshun Nan, Wei LU, Shay B. Cohen*
+5. **Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction**
+   *Shan Zhao, Minghao Hu, Zhiping Cai, Fang Liu*
+6. **On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification**
+   *Xin Liu, Jiefu Ou, Yangqiu Song, Xin Jiang*
+7. **UniTrans : Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data**
+   *Qianhui Wu, Zijia Lin, Börje F. Karlsson, Biqing Huang, Jian-Guang Lou*
+
+
+
+#### 三、Event相关
+
+1. **Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations**
+   *Jian Liu, Yubo Chen, Jun Zhao*
+
+
+
+#### Reference
+
+https://www.yanxishe.com/postDetail/19588

docs/词汇增强.md

@@ -1,2 +0,0 @@
-# 有效提升NER指标——基于词汇增强的实体抽取方法总结
-