  We introduce ChemDisGene, a new dataset for training and evaluating
multi-class multi-label document-level biomedical relation extraction models.
Our dataset contains 80k biomedical research abstracts labeled with mentions of
chemicals, diseases, and genes, portions of which human experts labeled with 18
types of biomedical relationships between these entities (intended for
evaluation), and the remainder of which (intended for training) has been
distantly labeled via the CTD database with approximately 78\% accuracy. In
comparison to similar preexisting datasets, ours is both substantially larger
and cleaner; it also includes annotations linking mentions to their entities.
We also provide three baseline deep neural network relation extraction models
trained and evaluated on our new dataset.
