Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)

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Research on Chinese Medical Named Entity Recognition Based on ALBERT and IDCNN

Authors
Ziyue Zhang1, Li Jin1, *, Yan Huang1, Weilin Li1
1School of Software, Xi’an Jiao-tong University, Xi’an, Shaanxi, China
*Corresponding author. Email: jin_li@xjtu.edu.cn
Corresponding Author
Li Jin
Available Online 20 December 2022.
DOI
10.2991/978-94-6463-030-5_96How to use a DOI?
Keywords
Electronic Medical Record; Named Entity Recognition; Deep Learning
Abstract

BERT (Bidirectional Encoder Representations from Transformers) as a pre-training model has been widely used in the field of natural language processing, of course, it also covers the field of Chinese medical text. In the process of actually dealing with Chinese tasks, BERT also has its own shortcomings, including the lack of Chinese word segmentation. This is because BERT is segmented based on the granularity of words. In addition, the amount of pre-training parameters of the BERT model is too large, which will also cause some problem of poor model performance caused by excessive computing power requirements, long training time, and excessive parameters. To solve the above problems, this paper proposes a Chinese medical named entity recognition model based on ALBERT and IDCNN. Experiments show that the ALBERT-IDCNN-CRF model constructed in this paper has a good performance on the Chinese electronic medical record named entity recognition task, and effectively solves the problems of polysemy and word recognition completion in Chinese electronic medical record named entity recognition. On the CCKS 2017 dataset the model effect F1 value reached 94.51%, and on the CCKS 2019 dataset, the model effect F1 value reached 88.61%.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
20 December 2022
ISBN
10.2991/978-94-6463-030-5_96
ISSN
2589-4919
DOI
10.2991/978-94-6463-030-5_96How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ziyue Zhang
AU  - Li Jin
AU  - Yan Huang
AU  - Weilin Li
PY  - 2022
DA  - 2022/12/20
TI  - Research on Chinese Medical Named Entity Recognition Based on ALBERT and IDCNN
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 977
EP  - 986
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_96
DO  - 10.2991/978-94-6463-030-5_96
ID  - Zhang2022
ER  -