Named Entity Recognition for Chinese Healthcare Applications

Cheng Yen Lee*, Ming Hsiang Su, Matus Pleva, Daniel Hládek, Yuan Fu Liao

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Named Entity Recognition is a fundamental task in information extraction, which locates and classifies defined named entities in unstructured text. Chinese NER is more difficult than English NER. Since there are no separators between Chinese characters, incorrectly segmented entity boundaries will cause error propagation in NER. In this study, named entity recognition is constructed and applied in the Chinese medical domain, where Chinese medical datasets are labeled in BIO format. The Chinese HealthNER Corpus contains 33,89 sentences, of which 2531 sentences are divided into the validation set and 3204 sentences are divided into the test set. This study uses PyTorch Embedding + BiLSTM + CRF, RoBERTa + BiLSTM + CRF, BERT Classifier, and BERT + BiLSTM + CRF for training and compares their model performance. Finally, the BERT + BiLSTM + CRF achieves the best prediction performance with a precision of 91.30%, recall of 89.46%, and F1 score of 90.53%

原文English
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面749-750
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態Published - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, 台灣
持續時間: 17 7月 202319 7月 2023

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區台灣
城市Pingtung
期間17/07/2319/07/23

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