Chinese Named Entity Recognition Based on Hierarchical Hybrid Model

2010 ◽  
pp. 620-624 ◽  
Author(s):  
Zhihua Liao ◽  
Zili Zhang ◽  
Yang Liu
Keyword(s):  
Hybrid Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records

2021 ◽  
Vol 20 (2) ◽  
pp. 1-12
Author(s):  
Yu Wang ◽  
Yining Sun ◽  
Zuchang Ma ◽  
Lisheng Gao ◽  
Yang Xu
Keyword(s):  
Neural Network ◽  
Electronic Medical Records ◽  
Hybrid Model ◽  
Medical Records ◽  
Medical Information ◽  
Short Term Memory ◽  
Clinical Symptoms ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.

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