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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge.

Yuan Li1, Guodong Du2, Yan Xiang1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, PR China.

Journal of Biomedical Informatics
|May 4, 2020
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Summary

This study introduces a novel dynamic embedding method for Chinese clinical named entity recognition (NER), improving accuracy by integrating character and word features with domain-specific medical knowledge.

Keywords:
Chinese electronic medical recordDomain-specific knowledgeDynamic embeddingNamed entity recognition

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Chinese clinical named entity recognition (NER) faces challenges due to word segmentation issues.
  • Existing NER methods lack domain-specific medical knowledge integration.

Purpose of the Study:

  • To develop an improved Chinese clinical NER method.
  • To address limitations of current NER approaches by incorporating domain knowledge and advanced embedding techniques.

Main Methods:

  • A dynamic embedding method utilizing dynamic attention.
  • Integration of character and word features in the embedding layer.
  • Inclusion of domain-specific word vectors and spatial attention for enhanced context encoding.

Main Results:

  • The proposed method demonstrates superior performance compared to baseline approaches.
  • Experiments on CCKS2017 and Common datasets validate the algorithm's effectiveness.

Conclusions:

  • The dynamic embedding method effectively enhances Chinese clinical NER.
  • Integrating domain knowledge and attention mechanisms is crucial for improving medical record analysis.