Abstract
Information Extraction (IE), encompassing Named Entity Recognition (NER), Named Entity Linking (NEL), and Relation Extraction (RE), is critical for transforming the rapidly growing volume of scientific publications into structured, actionable knowledge. This need is especially evident in fast-evolving biomedical fields such as the gut-brain axis, where research investigates complex interactions between the gut microbiota and brain-related disorders. Existing biomedical IE benchmarks, however, are often narrow in scope and rely heavily on distantly supervised or automatically generated annotations, limiting their utility for advancing robust IE methods. We introduce GutBrainIE, a benchmark based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. While grounded in the gut-brain axis, the benchmark's rich schema, multiple tasks, and combination of highly curated and weakly supervised data make it broadly applicable to the development and evaluation of biomedical IE systems across domains.
Abstract (translated)
信息抽取（IE）包括命名实体识别（NER）、命名实体链接（NEL）和关系提取（RE），对于将快速增长的科学出版物转化为结构化、可操作的知识至关重要。特别是在快速发展的生物医学领域，如肠-脑轴，研究复杂地探讨了肠道微生物群与大脑相关疾病之间的相互作用，这种需求尤为明显。然而，现有的生物医学IE基准测试通常范围狭窄，并且严重依赖于远程监督或自动产生的注释，这限制了它们在推进稳健IE方法方面的实用性。 我们引入了GutBrainIE，这是一个基于超过1,600篇PubMed摘要的基准测试，这些摘要由生物医学和术语学专家手动标注，包含了细粒度实体、概念级链接以及关系。虽然该基准测试建立在肠-脑轴的基础上，但其丰富的架构、多项任务设置及高度策划与弱监督数据的结合，使其能够广泛应用于跨领域生物医学IE系统的开发与评估。
URL
https://arxiv.org/abs/2602.04320
