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Entity and relation extraction is the necessary step in structuring medicaltext. However, the feature extraction ability of the bidirectional long shortterm memory network in the existing model does not achieve the best effect. Atthe same time, the language model has achieved excellent results in more andmore natural language processing tasks. In this paper, we present a focusedattention model for the joint entity and relation extraction task. Our modelintegrates well-known BERT language model into joint learning through dynamicrange attention mechanism, thus improving the feature representation ability ofshared parameter layer. Experimental results on coronary angiography textscollected from Shuguang Hospital show that the F1-score of named entityrecognition and relation classification tasks reach 96.89% and 88.51%, whichare better than state-of-the-art methods 1.65% and 1.22%, respectively.

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text