Chinese medicine based on word embedding Deberta-BiLSTM-CRF model named entity recognition

Shuang Ji; Tianyu Sun

doi:10.1117/12.3004929

9 October 2023 Chinese medicine based on word embedding Deberta-BiLSTM-CRF model named entity recognition

Shuang Ji, Tianyu Sun

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911D (2023) https://doi.org/10.1117/12.3004929
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In this paper, a Deberta-BiLSTM-CRF model based on the fusion of character and lexical features is proposed to solve the problems of semantic lack and entity nesting in Chinese medical texts in entity recognition tasks. The model uses the Deberta pre-training model and the Soft-lexicon method to dynamically fuse dictionary information and enhance the semantic representation of text. After the learning and decoding of BiLSTM and CRF models, the effective recognition of medical entities is realized. Follow-up work will consider making a large-scale Chinese medical dictionary to improve the recognition effect of the model. The experimental results show that the model proposed in this paper has a greater improvement in the F1 value of entity recognition than the RoBERTa-wwm-BiLSTM-CRF and Bert-BiLSTM-CRF models, and the speed has also improved. Therefore, the model has broad application prospects in Chinese medical entity recognition.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuang Ji and Tianyu Sun "Chinese medicine based on word embedding Deberta-BiLSTM-CRF model named entity recognition", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911D (9 October 2023); https://doi.org/10.1117/12.3004929

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