{"url":"/sota/temporal-relation-classification-on-tddman","task":{"name":"Temporal Relation Classification","url":"/task/temporal-relation-classification","note":null},"dataset":{"name":"TDDMan","url":null},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"Temporal Relation Classification is the task that is concerned with classifying the temporal relation between a pair of temporal entities (traditional events and temporal expressions). Initial approaches aimed to classify the temporal relation in thirteen relation types that were depicted by James Allen in his seminal work \"Maintaining Knowledge about Temporal Intervals\". However, due to the ambiguity in the annotation, recent corpora have been limiting the type of relations to a subset of those relations.\r\n\r\nNotice that although Temporal Relation Classification can be thought of as a subtask of Temporal Relation Extraction, the two tasks can be morphed if one adds a label that indicates the absence of a temporal relation between the entities (e.g. \"no_relation\" or \"vague\") to Temporal Relation Classification.","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":["F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1":"higher"}},"counts":{"rows":3,"rows_with_code":1,"rows_with_paper_page":3,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"DTRE","metrics":{"F1":"56.3"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/dct-centered-temporal-relation-extraction","paper_url":"https://aclanthology.org/2022.coling-1.182","paper_title":"DCT-Centered Temporal Relation Extraction","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"SCS-EERE","metrics":{"F1":"51.1"},"uses_additional_data":false,"paper_date":"2022-04-02","paper":"/paper/selecting-optimal-context-sentences-for-event","paper_url":"https://www.aaai.org/AAAI22Papers/AAAI-3912.ManH.pdf","paper_title":"Selecting Optimal Context Sentences for Event-Event Relation Extraction","code":"https://github.com/hieumdt/SCS-EERE","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"TIMERS","metrics":{"F1":"45.5"},"uses_additional_data":false,"paper_date":"2021-08-01","paper":"/paper/timers-document-level-temporal-relation","paper_url":"https://aclanthology.org/2021.acl-short.67","paper_title":"TIMERS: Document-level Temporal Relation Extraction","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"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":0,"n_unverified":0,"n_samples":0,"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"}}}