# A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes Source: [A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:06:48.603Z Authors: Dongxu Zhang, Sunil Mohan, Michaela Torkar, Andrew McCallum Published: 2022-04-13T18:02:05.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Document-level Relation Extraction on ChemDisGene (test)](): Document-level Relation Extraction; ChemDisGene (test); F1 Score: 44.6. - [Relation Extraction on ChemDisGene All relationships (test)](): Relation Extraction; ChemDisGene All relationships (test); F1 Score: 43.8. ## Original sources and implementation [Original paper]() [PDF]()