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A pre-trained BERT for Korean medical natural language processingopen access

Authors
Kim, YoojoongKim, Jong-HoLee, Jeong MoonJang, Moon JoungYum, Yun JinKim, SeongtaeShin, UnsubKim, Young-MinJoo, Hyung JoonSong, Sanghoun
Issue Date
16-8월-2022
Publisher
NATURE PORTFOLIO
Citation
SCIENTIFIC REPORTS, v.12, no.1
Indexed
SCIE
SCOPUS
Journal Title
SCIENTIFIC REPORTS
Volume
12
Number
1
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143778
DOI
10.1038/s41598-022-17806-8
ISSN
2045-2322
Abstract
With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score.
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