Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine
The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaff...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/1640837 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction. In this paper, we proposed a NER method called Bert-MRC-Biaffine, which formulates the NER as an MRC task. The approach of the machine reading comprehension framework is to introduce prior knowledge, the query about entities. The biaffine mechanism scores pair start and end tokens in a sentence so that the model is able to predict named entities accurately. The proposed method outperforms from the electronic medical record dataset, called CCKS2017 data, and the TCM dataset. We also remove components to evaluate the contribution of individual components of our model. Experiments on two datasets demonstrate the effectiveness of our model. |
|---|---|
| ISSN: | 1026-0226 1607-887X |