A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM

Aiming at the problems of missing local context features, single word vector representation, and low entity recognition accuracy, a method for e-medical recording with named entity recognition, which is based on BERT and model fusion, is proposed. First, with the model of BERT for pre-training, the preceding and following contextual information is fused for the enhancement of word semantic representation and alleviation of the problem of polysemy; second, the network of bi-directional long-short term memory is for obtaining the sequence feature matrix, generation of optimal sequence in global sense achieved through the conditional random field model; finally, data enhancement is used to alleviate the class imbalance and improve the model ability in generalization. Results of the experiments find model proposal measured by F1 on CCKS21 data set reaches 0.8548, which is 0.51% and 0.08% higher than models with ID-CNNs-CRF and multi-task RNN. This demonstrates the excellent performance of the method proposed in this paper in improving named entity recognition.