Research on medical named entity recognition based on DB-MA-BiLSTM-CRF

Ru Wei; GuoHui Ding

doi:10.1117/12.2640767

13 October 2022 Research on medical named entity recognition based on DB-MA-BiLSTM-CRF

Ru Wei, GuoHui Ding

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871T (2022) https://doi.org/10.1117/12.2640767
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

At present, the uneven distribution of entities and the low frequency of some entities in medical text data leads to the low accuracy of medical named entity recognition. To solve the above problems, a neural network model based on dictionary and mutual attention (DB-MA-BiLSTM-CRF) is proposed. The model includes Bert embedding layer, BiLSTM-CNN layer, mutual attention layer and CRF layer. The medical dictionary is fused in the Bert embedding layer to inject medical vocabulary information; Then it is input into the BiLSTM-CNN network layer to extract the global and local features of the text respectively; The features extracted from BiLSTM-CNN network layer are spliced into the mutual attention layer for weighted extraction of important features. Compared with the two benchmark models, the experimental results show that the model proposed in this paper has strong advantages.

Citation Download Citation

Ru Wei and GuoHui Ding "Research on medical named entity recognition based on DB-MA-BiLSTM-CRF", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871T (13 October 2022); https://doi.org/10.1117/12.2640767

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