KEYWORDS: Data modeling, Neural networks, Deep learning, Adversarial training, Convolution, Convolutional neural networks, Performance modeling, Medical research, Feature extraction, Education and training
Current work on Chinese medical name entity recognition focuses on extracting flattened entities, and when there are nested entities in the entities, the entity recognition is prone to errors and cannot accurately identify all the entities in the statement. In this paper, we construct an ALBIG model based on multilayer neural network and adversarial learning method. The model uses a pre-trained model RoBERTa-wwm stablish word embedding and connects IDCNN model to extract text features. Adds adversarial learning method to increase the robustness of the model and uses Global Pointer method based on Rotary Position Embedding as the output layer to obtain the final result. On the CMeEE dataset, the F1 values of the ALBIG model obtained higher scores compared to other experimental models, proving the validity of the model.
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