Abstract
We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label 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.- Anthology ID:
- 2022.lrec-1.116
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1073–1082
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.116
- DOI:
- Cite (ACL):
- Dongxu Zhang, Sunil Mohan, Michaela Torkar, and Andrew McCallum. 2022. A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1073–1082, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes (Zhang et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.116.pdf
- Code
- chanzuckerberg/chemdisgene + additional community code
- Data
- ChemDisGene