Named Entity Recognition for Chinese Healthcare Applications

Cheng-Yen Lee, Ming-Hsiang Su, Matús Pleva, Daniel Hládek, Yuan-Fu Liao

Abstract: 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%

External IDs:dblp:conf/icce-tw/LeeSPHL23