  Relation extraction is a fundamental problem in natural language processing.
Most existing models are defined for relation extraction in the general domain.
However, their performance on specific domains (e.g., biomedicine) is yet
unclear. To fill this gap, this paper carries out an empirical study on
relation extraction in biomedical research articles. Specifically, we consider
both sentence-level and document-level relation extraction, and run a few
state-of-the-art methods on several benchmark datasets. Our results show that
(1) current document-level relation extraction methods have strong
generalization ability; (2) existing methods require a large amount of labeled
data for model fine-tuning in biomedicine. Our observations may inspire people
in this field to develop more effective models for biomedical relation
extraction.
