{"url":"/sota/joint-entity-and-relation-extraction-on-2","task":{"name":"Joint Entity and Relation Extraction","url":"/task/joint-entity-and-relation-extraction","note":null},"dataset":{"name":"CoNLL04","url":"/dataset/conll04"},"category":"Natural Language Processing","categories":["Medical","Natural Language Processing"],"category_note":null,"description":"Joint Entity and Relation Extraction is the task of extracting entity mentions and semantic relations between entities from unstructured text with a single model.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Entity F1","Relation F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Entity F1":"higher","Relation F1":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"DeepStruct multi-task w/ finetune","metrics":{"Entity F1":"90.7","Relation F1":"78.3"},"uses_additional_data":false,"paper_date":"2022-05-21","paper":"/paper/deepstruct-pretraining-of-language-models-for-1","paper_url":"https://arxiv.org/abs/2205.10475v2","paper_title":"DeepStruct: Pretraining of Language Models for Structure Prediction","code":"https://github.com/cgraywang/deepstruct","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":6,"n_samples":13,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"DeepStruct multi-task","metrics":{"Entity F1":"88.4","Relation F1":"72.8"},"uses_additional_data":false,"paper_date":"2022-05-21","paper":"/paper/deepstruct-pretraining-of-language-models-for-1","paper_url":"https://arxiv.org/abs/2205.10475v2","paper_title":"DeepStruct: Pretraining of Language Models for Structure Prediction","code":"https://github.com/cgraywang/deepstruct","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":6,"n_samples":13,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Deepstruct zero-shot","metrics":{"Entity F1":"48.3","Relation F1":"25.8"},"uses_additional_data":false,"paper_date":"2022-05-21","paper":"/paper/deepstruct-pretraining-of-language-models-for-1","paper_url":"https://arxiv.org/abs/2205.10475v2","paper_title":"DeepStruct: Pretraining of Language Models for Structure Prediction","code":"https://github.com/cgraywang/deepstruct","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":6,"n_samples":13,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":3,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":7,"n_unverified":6,"n_samples":13,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":21,"n_unverified":18,"n_samples":39,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}