Analysis of Neural Network Modules for Named Entity Recognition of Chinese Medical Texts

[Objective] This paper decomposes the named entity recognition models based on neural network for Chinese medical texts. We investigate the impacts of single neural network module and the collaboration of multiple modules on the entity recognition performance. [Methods] First, we chosed the benchmark datasets from CCKS2017, CCKS2019, and IMCS-NER for named entity recognition tasks. Then, we conducted extensive experiments to compare the performance of different single modules of the aforementioned layers. Third, we built and compared entity recognition models based on ensemble, parallel, and serial neural models. [Results] Using hfl/chinese-macbert-base, hfl/chinese-roberta-wwm-ext, hfl/chinese-bert-wwm-ext in the symbolic representation layer significantly improved the performance of entity recognition models, the average F1-scores reached 0.8816, 0.8816 and 0.8812 respectively. Stacking neural models at the context encoding layer improved the performance of the neural network. Moreover, ensembled neural networks could achieve the best performance, the F1-scores reached 0.9330, 0.8211 and 0.9181 respectively. [Limitations] More research is needed to examine our findings with datasets in other languages. [Conclusions] The characteristics of single neural modules and their collaboration could significantly affect the performance of the named entity recognition of Chinese medical texts.