BioRED: a rich biomedical relation extraction dataset.

ABSTRACT: 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 biomedical 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 a first-of-its-kind biomedical relation extraction dataset (BioRED) 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. Furthermore, we label each relation as describing eithe

SUBMITTER: Luo L 

PROVIDER: S-EPMC9487702 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature