Biomedical named entity recognition based on reinforcement learning label correction
Ziqiang Hao, Fang Tian, Ying Tang, Yan Zhang, Weida Zhan, Yanfeng Tang · 2024
Biomedical named entity recognition is of great significance in the field of natural language processing.Deep learning approaches are mainly used at present, and the BERT-BiLSTM-CRF model is one of them. Although the BiLSTM structure in this model can capture bi-directional long-distance dependencies in sentences, there is the problem that the model performance can easily reach the bottleneck by simply adjusting the hyperparameters or changing the network structure, and the quality of its recognition results can hardly be further improved. To address the above problems, this paper adds the label correction process based on deep reinforcement learning to the BERT-BiLSTM-CRF model to form the BERT-BiLSTM-CRF-DRL model. The intelligent body Agent based on deep reinforcement learning is used as a label corrector, and the label correction threshold is set, so that the uncertainty in the annotation results of the BERT-BiLSTMCRF model is greater than the label correction threshold. The labels with uncertainty greater than this threshold in the results are processed for correction. The experimental results show that the F1 values on the datasets BC4CHEMD, BC5CR-CHEM, and NCBI-DISEASE reach 91.07%, 94.02%, and 89.56%,, which are all improved compared with the existing advanced biomedical named entity recognition models.