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13 July 2024 Research on the identification method of Chinese medical named entities
Fan Ding, Wenbo Zhou
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Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132080U (2024) https://doi.org/10.1117/12.3036711
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China
Abstract
Named Entity Recognition (NER) plays a crucial role in Chinese Natural Language Processing, particularly in medical texts containing numerous nested entities. Existing NER techniques often rely on Bidirectional Long Short-Term Memory networks combined with Conditional Random Fields (BiLSTM-CRF) to identify entities. However, their effectiveness in handling nested entities is limited. Therefore, this paper proposes a model based on multilevel convolutional networks and Biaffine networks. While single layer convolutional networks capture local features, they lack the ability to capture long-range dependencies in text. To address this issue, multilevel convolutional networks are utilized to integrate information from multiple scales. Subsequently, nested entities are identified using Biaffine networks. The proposed method demonstrates promising performance on various medical text entity recognition tasks, outperforming existing methods in nested entity recognition, as evidenced by experimental results.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Fan Ding and Wenbo Zhou "Research on the identification method of Chinese medical named entities", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132080U (13 July 2024); https://doi.org/10.1117/12.3036711
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KEYWORDS
Convolution
Convolutional neural networks
Medical research
Biomedical applications
Matrices
Semantics
Statistical modeling
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