School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China.
School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China; School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China; Postdoctoral workstation of Dalian Yongjia Electronic Technology Co., Ltd, Liaoning, China.
J Biomed Inform. 2024 Sep;157:104717. doi: 10.1016/j.jbi.2024.104717. Epub 2024 Aug 28.
Biomedical relation extraction aims to reveal the relation between entities in medical texts. Currently, the relation extraction models that have attracted much attention are mainly to fine-tune the pre-trained language models (PLMs) or add template prompt learning, which also limits the ability of the model to deal with grammatical dependencies. Graph convolutional networks (GCNs) can play an important role in processing syntactic dependencies in biomedical texts.
In this work, we propose a biomedical relation extraction model that fuses GCNs enhanced prompt learning to handle limitations in syntactic dependencies and achieve good performance. Specifically, we propose a model that combines prompt learning with GCNs for relation extraction, by integrating the syntactic dependency information analyzed by GCNs into the prompt learning model, by predicting the correspondence with [MASK] tokens labels for relation extraction.
Our model achieved F1 scores of 85.57%, 80.15%, 95.10%, and 84.11% in the biomedical relation extraction datasets GAD, ChemProt, PGR, and DDI, respectively, all of which outperform some existing baseline models.
In this paper, we propose enhancing prompt learning through GCNs, integrating syntactic information into biomedical relation extraction tasks. Experimental results show that our proposed method achieves excellent performance in the biomedical relation extraction task.
生物医学关系抽取旨在揭示医学文本中实体之间的关系。目前，引起广泛关注的关系抽取模型主要是微调预训练语言模型（PLMs）或添加模板提示学习，这也限制了模型处理语法依赖的能力。图卷积网络（GCNs）在处理生物医学文本中的语法依赖方面可以发挥重要作用。
在这项工作中，我们提出了一种融合 GCN 增强提示学习的生物医学关系抽取模型，以处理语法依赖的局限性并取得良好的性能。具体来说，我们提出了一种结合提示学习和 GCN 进行关系抽取的模型，通过将 GCN 分析的语法依赖信息集成到提示学习模型中，通过预测与 [MASK] 标记标签的对应关系进行关系抽取。
我们的模型在生物医学关系抽取数据集 GAD、ChemProt、PGR 和 DDI 上的 F1 得分分别为 85.57%、80.15%、95.10%和 84.11%，均优于一些现有的基线模型。
在本文中，我们提出了通过 GCN 增强提示学习，将语法信息集成到生物医学关系抽取任务中。实验结果表明，我们提出的方法在生物医学关系抽取任务中取得了优异的性能。
