Medical Named Entity Recognition Based on Multi-Feature and Co-Attention

Aiming at the situation that the accuracy of entity recognition cannot be effectively improved due to the lack of fusion of unique feature information of medical texts in current Chinese medical named entity recognition, and the problem that single attention mechanism affects the effectiveness of entity classification, a Chinese medical named entity recognition method based on multi-feature fusion and co-attention mechanism is proposed. Firstly, the vector representation of the original medical text is obtained by using the pre-trained model, and then the feature vectors of word granularity are obtained by using the bidirectional gated recurrent neural network (BiGRU). Secondly, combined with the distinctive radical features of medical named entities, iterative dilation convolution neural network (IDCNN) is used to extract radical-level feature vectors. Finally, the co-attention network is used to integrate medical vector features to generate double correlation features of pair, and then conditional random field (CRF) is used to output entity recognition results. The experimental results show that, compared with other entity recognition models, it can achieve higher accuracy, recall and F1 value on the CCKS dataset. At the same time, although the complexity of the recognition model is increased, the performance does not decrease significantly. © 2024 Editorial Department of Scientia Agricultura Sinica. All rights reserved.