TY - GEN
T1 - Medical entity extraction from health insurance documents
AU - Pu, Tianling
AU - Zhang, Qifan
AU - Yao, Junjie
AU - Zhang, Yingjie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - The task of named entity recognition is to identify certain types of entities with special meanings from the text. It is a basic task in natural language processing and the foundation of higher-level tasks such as relation extraction, knowledge graph, and question answering system. The correctness of the entity recognition has a huge influence on the effectiveness of the upper layer application.This paper mainly studies the problem of Chinese named entity recognition in the medical field. By extracting the information about the disease in the insurance text and labeling the entity of disease, treatment, and symptom, the data set for entity recognition is established. On the basis of the BILSTM-CRF model, we use different methods to improve the recognition effectiveness of the model. By incorporating word boundary information and adding attention mechanism in the BiLSTM layer, the effectiveness of entity recognition is further improved.
AB - The task of named entity recognition is to identify certain types of entities with special meanings from the text. It is a basic task in natural language processing and the foundation of higher-level tasks such as relation extraction, knowledge graph, and question answering system. The correctness of the entity recognition has a huge influence on the effectiveness of the upper layer application.This paper mainly studies the problem of Chinese named entity recognition in the medical field. By extracting the information about the disease in the insurance text and labeling the entity of disease, treatment, and symptom, the data set for entity recognition is established. On the basis of the BILSTM-CRF model, we use different methods to improve the recognition effectiveness of the model. By incorporating word boundary information and adding attention mechanism in the BiLSTM layer, the effectiveness of entity recognition is further improved.
KW - Attention Mechanism
KW - Knowledge Graph Completion
KW - Named Entity Recognition
UR - https://www.scopus.com/pages/publications/85092517858
U2 - 10.1109/ICBK50248.2020.00085
DO - 10.1109/ICBK50248.2020.00085
M3 - 会议稿件
AN - SCOPUS:85092517858
T3 - Proceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020
SP - 565
EP - 572
BT - Proceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020
A2 - Chen, Enhong
A2 - Antoniou, Grigoris
A2 - Wu, Xindong
A2 - Kumar, Vipin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference on Knowledge Graph, ICKG 2020
Y2 - 9 August 2020 through 11 August 2020
ER -