The BioRED track BioCreative VIII Challenge and Workshop

Biomedical relation extraction is the task of automatically identifying and characterizing relations between biomedical concepts from free text. As a central task in biomedical natural language processing (NLP) research, it plays a critical role in many downstream applications, such as drug discovery and personalized medicine.

While there is a significant body of research on automatic relation extraction, most existing benchmarking datasets for biomedical relation extraction only focus on relations of a single type (e.g., protein–protein interactions) at the sentence level. In response, a new biomedical relation extraction dataset – BioRED [1] – with multiple entity types (e.g., gene/protein, disease, chemical) and relation pairs (e.g., gene–disease; chemical–chemical) at the document level was recently made freely available. Despite multiple attempts, the best performance on the BioRED dataset remains modest, with much room for further improvements.