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Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR

  • Zhichang Zhang
  • Yu Zhang
  • Tong Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

Abstract

Named entity recognition (NER) in Chinese electronic medical records (EMRs) has become an important task of clinical natural language processing (NLP). However, limited studies have been performed on the clinical NER study in Chinese EMRs. Furthermore, when end-to-end neural network models have improved clinical NER performance, medical knowledge dictionaries such as various disease association dictionaries, which provide rich information of medical entities and relations among them, are rarely utilized in NER model. In this study, we investigate the problem of NER in Chinese EMRs and propose a clinical neural network NER model enhanced with medical knowledge attention by combining the entity mention information contained in external medical knowledge bases with EMR context together. Experimental results on the manually labeled dataset demonstrated that the proposed method can achieve better performance than the previous methods in most cases.

Keywords

Chinese electronic medical record Named entity recognition Deep learning Knowledge attention 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments. The research work is supported by the National Natural Science Foundation of China (No. 61762081, No. 61662067) and the Key Research and Development Project of Gansu Province (No. 17YF1GA016).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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