Named Entity Recognition of Chinese Electronic Medical Records Based on Multi-Feature Fusion

被引:0
作者
Sun, Zhen [1 ]
Li, Xinfu [1 ]
机构
[1] College of Cyberspace Security and Computer, Hebei University, Hebei, Baoding
关键词
electronic medical records; gated recurrent unit (GRU); multi-feature; named entity recognition;
D O I
10.3778/j.issn.1002-8331.2207-0455
中图分类号
学科分类号
摘要
Named entity recognition is a basic task in natural language processing. Currently, the research on named entity recognition in Chinese electronic medical records does not consider the complex structure of medical texts and the uneven distribution of entity types in data sets. It only migrates the named entity recognition model from the general field to the medical field, and the recognition effect is not good. Aiming at the above problems, this paper proposes a multi-feature fusion named entity recognition model for Chinese electronic medical records. Firstly, the characters, radicals, and quadrilateral vectors are obtained to enrich the semantic representation of medical texts through Chinese characters. Secondly, the entity label labeling strategy is used to label the entity types in the vector to enhance the model’s learning of different text data types. Finally, the fusion vector is fed into the Mogrifier GRU layer to strengthen the relationship between feature representation semantics further, and CRF is used to establish label constraints. The experimental results show that the F1 value of the proposed model reaches 88.72% on the CCKS2019 dataset and 95.44% on the MSRA dataset, which verifies the effectiveness of the model. © 2023 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.
引用
收藏
页码:136 / 144
页数:8
相关论文
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