ALBIG: a Chinese medical text entity recognition model based on Global Pointer

Yijie Zhang; Yan Gao; Qiong Zeng

doi:10.1117/12.3031421

19 July 2024 ALBIG: a Chinese medical text entity recognition model based on Global Pointer

Yijie Zhang, Yan Gao, Qiong Zeng

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810Y (2024) https://doi.org/10.1117/12.3031421
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Current work on Chinese medical name entity recognition focuses on extracting flattened entities, and when there are nested entities in the entities, the entity recognition is prone to errors and cannot accurately identify all the entities in the statement. In this paper, we construct an ALBIG model based on multilayer neural network and adversarial learning method. The model uses a pre-trained model RoBERTa-wwm stablish word embedding and connects IDCNN model to extract text features. Adds adversarial learning method to increase the robustness of the model and uses Global Pointer method based on Rotary Position Embedding as the output layer to obtain the final result. On the CMeEE dataset, the F1 values of the ALBIG model obtained higher scores compared to other experimental models, proving the validity of the model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yijie Zhang, Yan Gao, and Qiong Zeng "ALBIG: a Chinese medical text entity recognition model based on Global Pointer", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810Y (19 July 2024); https://doi.org/10.1117/12.3031421

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