Chinese medical named entity recognition based on Roberta and multi-model voting fusion

Kai Zhang; Bin Zhu; Guanyu Li

doi:10.1117/12.2641479

10 November 2022 Chinese medical named entity recognition based on Roberta and multi-model voting fusion

Kai Zhang, Bin Zhu, Guanyu Li

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 1234814 (2022) https://doi.org/10.1117/12.2641479
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Named entity recognition in medicine is a very important and popular task, which plays an important role in artificial intelligence of Internet medical treatment, and is also the foundation of medical structure of natural language text information in medical field. Medical named entity recognition is mainly a sequence annotation task. Traditional medical named entity recognition methods are based on rules and dictionaries, which have high construction cost and poor generalization ability. In this paper, the text similarity task of online medical interview sentences is used to fine-tune the pre-training model and inject corresponding medical domain knowledge into the pre-training model. After that, roberta- WWM after Finetune was connected with two-way LSTM network and CRF network, and combined with bert-MRC model, voting fusion was carried out. Under strict indicators, the result was 92.02%, better than the popular baseline scheme and has good generalization ability.

Citation Download Citation

Kai Zhang, Bin Zhu, and Guanyu Li "Chinese medical named entity recognition based on Roberta and multi-model voting fusion", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 1234814 (10 November 2022); https://doi.org/10.1117/12.2641479

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