Medical Entity Extraction from Health Insurance Documents

Abstract

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.