Automated relation extraction (RE) from biomedical literature is critical for
many downstream text mining applications in both research and real-world
settings. However, most existing benchmarking datasets for bio-medical RE only
focus on relations of a single type (e.g., protein-protein interactions) at the
sentence level, greatly limiting the development of RE systems in biomedicine.
In this work, we first review commonly used named entity recognition (NER) and
RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus
with multiple entity types (e.g., gene/protein, disease, chemical) and relation
pairs (e.g., gene-disease; chemical-chemical) at the document level, on a set
of 600 PubMed abstracts. Further, we label each relation as describing either a
novel finding or previously known background knowledge, enabling automated
algorithms to differentiate between novel and background information. We assess
the utility of BioRED by benchmarking several existing state-of-the-art
methods, including BERT-based models, on the NER and RE tasks. Our results show
that while existing approaches can reach high performance on the NER task
(F-score of 89.3%), there is much room for improvement for the RE task,
especially when extracting novel relations (F-score of 47.7%). Our experiments
also demonstrate that such a rich dataset can successfully facilitate the
development of more accurate, efficient, and robust RE systems for biomedicine.
The BioRED dataset and annotation guideline are freely available at
this https URL.

本文提出了一个多实体类型和多关系对的文献级生物医学关系抽取数据集 BioRED，包括新发现和先前已知的信息，旨在评估现有状态下的自动化算法，为更准确、高效和稳健的生物医学关系系统的开发奠定基础。