{"url":"/dataset/gda","name":"GDA","full_name":"Gene-Disease Associations Corpus","description_markdown":"The gene-disease associations corpus contains 30,192 titles and abstracts from PubMed articles that have been automatically labelled for genes, diseases and gene-disease associations via distant supervision. The test set is comprised of 1000 of these examples. It is common to hold out a random 20% of the examples in the train set as a validation set.","description_withheld":null,"homepage":"https://bitbucket.org/alexwuhkucs/gda-extraction/src/master/","introduced_date":"2019-04-02","introduced_date_note":null,"introduced_by":null,"license":{"name":"GNU Affero General Public License","url":"https://bitbucket.org/alexwuhkucs/gda-extraction/src/master/LICENSE.md"},"modalities":[],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Joint Entity and Relation Extraction","url":"/task/joint-entity-and-relation-extraction","datasets_with_task":"/datasets/task/joint-entity-and-relation-extraction"}],"languages":[],"variants":["GDA"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-extraction-on-gda","task":"Relation Extraction","dataset_variant":"GDA","rows":9,"metrics":["F1"],"first_row_in_archive_order":{"model":"SAISORE+CR+ET-SciBERT","paper":"/paper/sais-supervising-and-augmenting-intermediate","metrics":{"F1":"87.1"},"code_links":[{"title":"xiaoyuxin1002/sais","url":"https://github.com/xiaoyuxin1002/sais"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-gda","task":"Joint Entity and Relation Extraction","dataset_variant":"GDA","rows":1,"metrics":["Relation F1"],"first_row_in_archive_order":{"model":"seq2rel","paper":"/paper/a-sequence-to-sequence-approach-for-document","metrics":{"Relation F1":"55.2"},"code_links":[{"title":"johngiorgi/seq2rel","url":"https://github.com/johngiorgi/seq2rel"},{"title":"JohnGiorgi/seq2rel-ds","url":"https://github.com/JohnGiorgi/seq2rel-ds"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-masked-image-reconstruction-network-for","title":"A Masked Image Reconstruction Network for Document-level Relation Extraction","date":"2022-04-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-sequence-to-sequence-approach-for-document","title":"A sequence-to-sequence approach for document-level relation extraction","date":"2022-04-03","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/a-densely-connected-criss-cross-attention","title":"A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction","date":"2022-03-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/document-level-relation-extraction-with-2","title":"Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning","date":"2022-01-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sais-supervising-and-augmenting-intermediate","title":"SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction","date":"2021-09-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/document-level-relation-extraction-as","title":"Document-level Relation Extraction as Semantic Segmentation","date":"2021-06-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/entity-structure-within-and-throughout","title":"Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction","date":"2021-02-20","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/document-level-relation-extraction-with","title":"Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling","date":"2020-10-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/reasoning-with-latent-structure-refinement","title":"Reasoning with Latent Structure Refinement for Document-Level Relation Extraction","date":"2020-05-13","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":8,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}