Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx's robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.

翻译：生物医学关系抽取（RE）是一项从自由文本中自动识别并刻画生物医学概念间关系的任务。RE是生物医学自然语言处理（NLP）研究的核心任务，在文献发现、知识图谱构建等下游应用中发挥着关键作用。现有先进方法主要针对单一RE数据集（如蛋白质-蛋白质相互作用、化学诱导疾病关系）训练机器学习模型。然而，人工数据集标注高度昂贵且耗时，需依赖领域知识。现有RE数据集通常具有领域特异性或规模较小，这限制了通用高性能RE模型的发展。本研究提出一种新颖框架，系统性地解决单一数据集的数据异质性，并将其整合为大规模数据集。基于该框架与数据集，我们提出数据驱动的BioREx方法用于关系抽取。评估显示，BioREx在最新发布的BioRED语料库上，F1值从基准系统的74.4%提升至79.6%，创下新最优水平。进一步证明，整合后的数据集可提升五种不同RE任务的性能。此外，BioREx在平均性能上优于当前最佳方法（如迁移学习与多任务学习）。最后，我们在两个未见训练数据的独立RE任务（药物-药物N元组合与文档级基因-疾病RE）中验证了BioREx的鲁棒性与泛化能力。整合数据集与优化方法已打包为独立工具，开源地址：https://github.com/ncbi/BioREx。