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Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge.
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2020-04-29 , DOI: 10.1016/j.jbi.2020.103435 Yuan Li 1 , Guodong Du 2 , Yan Xiang 1 , Shaozi Li 2 , Lei Ma 1 , Dangguo Shao 1 , Xiongbin Wang 3 , Haoyu Chen 3
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2020-04-29 , DOI: 10.1016/j.jbi.2020.103435 Yuan Li 1 , Guodong Du 2 , Yan Xiang 1 , Shaozi Li 2 , Lei Ma 1 , Dangguo Shao 1 , Xiongbin Wang 3 , Haoyu Chen 3
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The task of electronic medical record named entity recognition (NER) refers to automatically identify all kinds of named entities in the medical record text. Chinese clinical NER remains a major challenge. One of the main reasons is that Chinese word segmentation will lead to the wrong downstream works. Besides, existing methods only use the information of the general field, not consider the knowledge from field of medicine. To address these issues, we propose a dynamic embedding method based on dynamic attention which combines features of both character and word in embedding layer. Domain knowledge is provided by word vector trained by domain dataset. In addition, spatial attention is added to enable the model to obtain more and more effective context encoding information. Finally, we conduct extensive experiments to demonstrate the effectiveness of our proposed algorithm. Experiments on CCKS2017 and Common dataset shows that the proposed method outperforms the baseline.
更新日期：2020-04-29
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