Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling

2019 ◽  
pp. 687-699
Author(s):  
Yong Xu ◽  
Qi Lu ◽  
Muhua Zhu
Keyword(s):  
Social Media ◽  
Named Entity Recognition ◽  
Language Modeling ◽  
Entity Recognition ◽  
Named Entity ◽  
Adversarial Training ◽  
Chinese Social Media

scholarly journals Semi-Supervised Noisy Label Learning for Chinese Medical Named Entity Recognition

2021 ◽  
pp. 1-10
Author(s):  
Zhucong Li ◽  
Zhen Gan ◽  
Baoli Zhang ◽  
Yubo Chen ◽  
Jing Wan ◽  
...  
Keyword(s):  
Hybrid System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Final Test ◽  
Named Entity ◽  
The Core ◽  
Category Labels ◽  
Adversarial Training ◽  
Core Idea ◽  
The Impact

Abstract This paper describes our approach for the Chinese Medical named entity recognition(MER) task organized by the 2020 China conference on knowledge graph and semantic computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We construct a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule postprocessing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we use post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

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