{"url":"/dataset/conll04","name":"CoNLL04","full_name":null,"description_markdown":"The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.","description_withheld":null,"homepage":"https://cogcomp.seas.upenn.edu/page/resource_view/43","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"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"},{"name":"Cross-Domain Named Entity Recognition","url":"/task/cross-domain-named-entity-recognition","datasets_with_task":"/datasets/task/cross-domain-named-entity-recognition"}],"languages":[],"variants":["CoNLL04"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-extraction-on-conll04","task":"Relation Extraction","dataset_variant":"CoNLL04","rows":16,"metrics":["RE+ Macro F1 ","RE+ Micro F1","NER Macro F1","NER Micro F1","RE+ Macro F1"],"first_row_in_archive_order":{"model":"REBEL","paper":"/paper/rebel-relation-extraction-by-end-to-end","metrics":{"RE+ Macro F1 ":"76.65","RE+ Micro F1":"75.4"},"code_links":[{"title":"Babelscape/rebel","url":"https://github.com/Babelscape/rebel"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-2","task":"Joint Entity and Relation Extraction","dataset_variant":"CoNLL04","rows":3,"metrics":["Entity F1","Relation F1"],"first_row_in_archive_order":{"model":"DeepStruct multi-task w/ finetune","paper":"/paper/deepstruct-pretraining-of-language-models-for-1","metrics":{"Entity F1":"90.7","Relation F1":"78.3"},"code_links":[{"title":"cgraywang/deepstruct","url":"https://github.com/cgraywang/deepstruct"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cross-domain-named-entity-recognition-on","task":"Cross-Domain Named Entity Recognition","dataset_variant":"CoNLL04","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"BiLSTM w/ MTL and MoEE","paper":"/paper/zero-resource-cross-domain-named-entity","metrics":{"F1":"70.04"},"code_links":[{"title":"Siddharthss500/zero-resource","url":"https://github.com/Siddharthss500/zero-resource"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/2408-00103","title":"ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget","date":"2024-07-31","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/autoregressive-structured-prediction-with","title":"Autoregressive Structured Prediction with Language Models","date":"2022-10-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepstruct-pretraining-of-language-models-for-1","title":"DeepStruct: Pretraining of Language Models for Structure Prediction","date":"2022-05-21","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/a-trigger-sense-memory-flow-framework-for","title":"A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction","date":"2021-01-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/structured-prediction-as-translation-between-1","title":"Structured Prediction as Translation between Augmented Natural Languages","date":"2021-01-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/named-entity-recognition-and-relation","title":"Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations","date":"2020-10-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/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/zero-resource-cross-domain-named-entity","title":"Zero-Resource Cross-Domain Named Entity Recognition","date":"2020-02-14","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":1,"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/entity-relation-extraction-as-multi-turn","title":"Entity-Relation Extraction as Multi-Turn Question Answering","date":"2019-05-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/end-to-end-neural-relation-extraction-using","title":"End-to-end neural relation extraction using deep biaffine attention","date":"2018-12-29","rows_on_this_dataset":1,"code_links":1,"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},{"paper":"/paper/end-to-end-neural-relation-extraction-with","title":"End-to-End Neural Relation Extraction with Global Optimization","date":"2017-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/modeling-joint-entity-and-relation-extraction","title":"Modeling Joint Entity and Relation Extraction with Table Representation","date":"2014-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":16,"samples_ran":7,"samples_unverified":9,"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."}