文献类型：期刊文献

英文题名：Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

作者：Xue, Kui[1]; Zhou, Yangming[1]; Ma, Zhiyuan[1]; Ruan, Tong[1]; Zhang, Huanhuan[1]; He, Ping[2]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Hospital Development Center, Shanghai, 200041, China

年份：2019

起止页码：892

外文期刊名：Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

收录：EI(收录号：20202008645308)

语种：英文

外文关键词：Natural language processing systems - Text processing - Computational linguistics - Character recognition

摘要：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-scores of named entity recognition and relation classification tasks reach 96.89% and 88.51%, which outperform state-of-the-art methods by 1.65% and 1.22%, respectively. ? 2019 IEEE.