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            <title>Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)</title>
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    <abstract>In this study, named entity recognition is constructed and applied in the medical domain. Data is labeled in BIO format. For example, “muscle” would be labeled “B-BODY” and “I-BODY”, and “cough” would be “B-SYMP” and “I-SYMP”. All words outside the category are marked with “O”. The Chinese HealthNER Corpus contains 30,692 sentences, of which 2531 sentences are divided into the validation set (dev) for this evaluation, and the conference finally provides another 3204 sentences for the test set (test). We use BLSTM_CRF, Roberta+BLSTM_CRF and BERT Classifier to submit three prediction results respectively. Finally, the BERT Classifier system submitted as RUN3 achieved the best prediction performance, with an accuracy of 80.18%, a recall rate of 78.3%, and an F1-score of 79.23.</abstract>
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%0 Conference Proceedings
%T SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier
%A Yang, Tsung-Hsien
%A Su, Ruei-Cyuan
%A Su, Tzu-En
%A Chong, Sing-Seong
%A Su, Ming-Hsiang
%Y Chang, Yung-Chun
%Y Huang, Yi-Chin
%S Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
%D 2022
%8 November
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taipei, Taiwan
%G Chinese
%F yang-etal-2022-scu
%X In this study, named entity recognition is constructed and applied in the medical domain. Data is labeled in BIO format. For example, “muscle” would be labeled “B-BODY” and “I-BODY”, and “cough” would be “B-SYMP” and “I-SYMP”. All words outside the category are marked with “O”. The Chinese HealthNER Corpus contains 30,692 sentences, of which 2531 sentences are divided into the validation set (dev) for this evaluation, and the conference finally provides another 3204 sentences for the test set (test). We use BLSTM_CRF, Roberta+BLSTM_CRF and BERT Classifier to submit three prediction results respectively. Finally, the BERT Classifier system submitted as RUN3 achieved the best prediction performance, with an accuracy of 80.18%, a recall rate of 78.3%, and an F1-score of 79.23.
%U https://aclanthology.org/2022.rocling-1.41
%P 329-334

Markdown (Informal)

[SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier](https://aclanthology.org/2022.rocling-1.41) (Yang et al., ROCLING 2022)

• SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier (Yang et al., ROCLING 2022)

ACL

• Tsung-Hsien Yang, Ruei-Cyuan Su, Tzu-En Su, Sing-Seong Chong, and Ming-Hsiang Su. 2022. SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 329–334, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).