BioRED: A Rich Biomedical Relation Extraction Dataset

Understand

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.