{"url":"/dataset/ade-corpus","name":"Adverse Drug Events (ADE) Corpus","full_name":null,"description_markdown":"Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports.\r\n\r\nA significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F₁ score of 0.70 indicating a potential useful application of the corpus.","description_withheld":null,"homepage":"https://pubmed.ncbi.nlm.nih.gov/22554702/","introduced_date":"2012-01-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Text Classification","url":"/task/text-classification","datasets_with_task":"/datasets/task/text-classification"},{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","datasets_with_task":"/datasets/task/named-entity-recognition-ner"},{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"NER","url":"/task/cg","datasets_with_task":"/datasets/task/cg"},{"name":"Clinical Concept Extraction","url":"/task/clinical-concept-extraction","datasets_with_task":"/datasets/task/clinical-concept-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Adverse Drug Events (ADE) Corpus"],"data_loaders":[],"num_papers_in_archive":14,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-extraction-on-ade-corpus","task":"Relation Extraction","dataset_variant":"Adverse Drug Events (ADE) Corpus","rows":15,"metrics":["RE+ Macro F1","RE Macro F1","NER Macro F1"],"first_row_in_archive_order":{"model":"ITER","paper":"/paper/iter-iterative-transformer-based-entity","metrics":{"NER Macro F1":"92.63 ± 0.89","RE+ Macro F1":"85.6 ± 1.42"},"code_links":[{"title":"fleonce/iter","url":"https://github.com/fleonce/iter"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/named-entity-recognition-on-adverse-drug","task":"Named Entity Recognition (NER)","dataset_variant":"Adverse Drug Events (ADE) Corpus","rows":1,"metrics":["NER Macro F1"],"first_row_in_archive_order":{"model":"Spark NLP","paper":"/paper/mining-adverse-drug-reactions-from","metrics":{"NER Macro F1":"91.75"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/text-classification-on-adverse-drug-events","task":"Text Classification","dataset_variant":"Adverse Drug Events (ADE) Corpus","rows":1,"metrics":["F1 - macro"],"first_row_in_archive_order":{"model":"Spark NLP","paper":"/paper/mining-adverse-drug-reactions-from","metrics":{"F1 - macro":"85.96"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/iter-iterative-transformer-based-entity","title":"ITER: Iterative Transformer-based Entity Recognition and Relation Extraction","date":"2024-11-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/an-information-extraction-study-take-in-mind","title":"An Information Extraction Study: Take In Mind the Tokenization!","date":"2023-03-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mining-adverse-drug-reactions-from","title":"Mining Adverse Drug Reactions from Unstructured Mediums at Scale","date":"2022-01-05","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/rebel-relation-extraction-by-end-to-end","title":"REBEL: Relation Extraction By End-to-end Language generation","date":"2021-10-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-entity-and-relation-extraction-from","title":"Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types","date":"2021-09-30","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/imposing-relation-structure-in-language-model","title":"Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning","date":"2021-09-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-partition-filter-network-for-joint-entity","title":"A Partition Filter Network for Joint Entity and Relation Extraction","date":"2021-08-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/two-are-better-than-one-joint-entity-and","title":"Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders","date":"2020-10-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/modeling-dense-cross-modal-interactions-for","title":"Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction","date":"2020-07-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deeper-task-specificity-improves-joint-entity","title":"Deeper Task-Specificity Improves Joint Entity and Relation Extraction","date":"2020-02-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/span-based-joint-entity-and-relation","title":"Span-based Joint Entity and Relation Extraction with Transformer Pre-training","date":"2019-09-17","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/neural-metric-learning-for-fast-end-to-end","title":"Neural Metric Learning for Fast End-to-End Relation Extraction","date":"2019-05-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/adversarial-training-for-multi-context-joint","title":"Adversarial training for multi-context joint entity and relation extraction","date":"2018-08-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-entity-recognition-and-relation","title":"Joint entity recognition and relation extraction as a multi-head selection problem","date":"2018-04-20","rows_on_this_dataset":1,"code_links":6,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":1,"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."}