Chinese clinical named entity recognition with variant neural structures based on BERT methods.,Journal of Biomedical informatics

当前位置： X-MOL 学术 › J. Biomed. Inform. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Chinese clinical named entity recognition with variant neural structures based on BERT methods.
Journal of Biomedical informatics ( IF 4.5 ) Pub Date : 2020-04-28 , DOI: 10.1016/j.jbi.2020.103422
Xiangyang Li ₁ , Huan Zhang ₂ , Xiao-Hua Zhou ₃

Affiliation

Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods require high-quality and large-scale labeled clinical data, which is challenging and expensive to obtain, especially data on Chinese clinical records. To tackle the Chinese CNER task, we pre-train BERT model on the unlabeled Chinese clinical records, which can leverage the unlabeled domain-specific knowledge. Different layers such as Long Short-Term Memory (LSTM) and Conditional Random Field (CRF) are used to extract the text features and decode the predicted tags respectively. In addition, we propose a new strategy to incorporate dictionary features into the model. Radical features of Chinese characters are used to improve the model performance as well. To the best of our knowledge, our ensemble model outperforms the state of the art models which achieves 89.56% strict F1 score on the CCKS-2018 dataset and 91.60% F1 score on CCKS-2017 dataset.

更新日期：2020-04-28

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南

全部期刊列表>>