Entity and relation extraction is the necessary step in structuring medical
text. However, the feature extraction ability of the bidirectional long short
term memory network in the existing model does not achieve the best effect. At
the same time, the language model has achieved excellent results in more and
more natural language processing tasks. In this paper, we present a focused
attention model for the joint entity and relation extraction task. Our model
integrates well-known BERT language model into joint learning through dynamic
range attention mechanism, thus improving the feature representation ability of
shared parameter layer. Experimental results on coronary angiography texts
collected from Shuguang Hospital show that the F1-score of named entity
recognition and relation classification tasks reach 96.89% and 88.51%, which
are better than state-of-the-art methods 1.65% and 1.22%, respectively.

该研究提出了一种基于 BERT 语言模型和聚焦注意力机制的联合实体和关系抽取模型，通过动态范围注意机制，提高了共享参数层的特征表示能力，实验结果表明，该方法在冠状动脉造影文本上的名词实体识别和关系分类任务的 F1 得分分别达到 96.89% 和 88.51%，优于现有技术水平。