{"url":"/task/temporal-information-extraction","name":"Temporal Information Extraction","slug":"temporal-information-extraction","description_markdown":"Temporal information extraction is the identification of chunks/tokens corresponding to temporal intervals, and the extraction and determination of the temporal relations between those. The entities extracted may be temporal expressions (timexes), eventualities (events), or auxiliary signals that support the interpretation of an entity or relation. Relations may be temporal links (tlinks), describing the order of events and times, or subordinate links (slinks) describing modality and other subordinative activity, or aspectual links (alinks) around the various influences aspectuality has on event structure.\r\n\r\nThe markup scheme used for temporal information extraction is well-described in the ISO-TimeML standard, and also on [www.timeml.org](http://www.timeml.org).\r\n\r\n```\r\n<?xml version=\"1.0\" ?>\r\n\r\n<TimeML xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:noNamespaceSchemaLocation=\"http://timeml.org/timeMLdocs/TimeML_1.2.1.xsd\">\r\n<TEXT>\r\n\r\n\r\n PRI20001020.2000.0127 \r\n NEWS STORY \r\n <TIMEX3 tid=\"t0\" type=\"TIME\" value=\"2000-10-20T20:02:07.85\">10/20/2000 20:02:07.85</TIMEX3> \r\n\r\n\r\n The Navy has changed its account of the attack on the USS Cole in Yemen.\r\n Officials <TIMEX3 tid=\"t1\" type=\"DATE\" value=\"PRESENT_REF\" temporalFunction=\"true\" anchorTimeID=\"t0\">now</TIMEX3> say the ship was hit <TIMEX3 tid=\"t2\" type=\"DURATION\" value=\"PT2H\">nearly two hours </TIMEX3>after it had docked.\r\n Initially the Navy said the explosion occurred while several boats were helping\r\n the ship to tie up. The change raises new questions about how the attackers\r\n were able to get past the Navy security.\r\n\r\n\r\n <TIMEX3 tid=\"t3\" type=\"TIME\" value=\"2000-10-20T20:02:28.05\">10/20/2000 20:02:28.05</TIMEX3> \r\n\r\n\r\n\r\n<TLINK timeID=\"t2\" relatedToTime=\"t0\" relType=\"BEFORE\"/>\r\n</TEXT>\r\n</TimeML>\r\n```\r\n\r\nTo avoid leaking knowledge about temporal structure, train, dev and test splits must be made at document level for temporal information extraction.","categories":[{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":86,"papers_with_code":20,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":3,"subtasks":1,"parent_tasks":2},"benchmarks":[{"leaderboard":"/sota/temporal-information-extraction-on-tempeval-3","slug":"temporal-information-extraction-on-tempeval-3","dataset":"TempEval-3","dataset_url":"/dataset/tempeval-3","rows_in_archive":2,"metrics":["Temporal awareness"],"first_row_in_archive_order":{"model":"Ning et al.","paper_title":"A Structured Learning Approach to Temporal Relation Extraction","paper_url":"/paper/a-structured-learning-approach-to-temporal-1","paper_date":"2019-06-12","arxiv_id":"1906.04943","code_links":[],"syntology":null}},{"leaderboard":"/sota/temporal-information-extraction-on-timebank","slug":"temporal-information-extraction-on-timebank","dataset":"TimeBank","dataset_url":"/dataset/timebank","rows_in_archive":2,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"Catena","paper_title":"CATENA: CAusal and TEmporal relation extraction from NAtural language texts","paper_url":"/paper/catena-causal-and-temporal-relation","paper_date":"2016-12-01","arxiv_id":null,"code_links":[{"title":"paramitamirza/CATENA","url":"https://github.com/paramitamirza/CATENA"}],"syntology":null}}],"datasets":[{"url":"/dataset/tempeval-3","name":"TempEval-3","full_name":"TempEval-3: events, times, and temporal relations","num_papers_in_archive":18},{"url":"/dataset/timebank","name":"TimeBank","full_name":"","num_papers_in_archive":7},{"url":"/dataset/thyme-2016","name":"THYME-2016","full_name":"","num_papers_in_archive":2}],"subtasks":[{"url":"/task/temporal-tagging","name":"Temporal Tagging"}],"parent_tasks":[{"url":"/task/information-extraction","name":"Information Extraction"},{"url":"/task/temporal-processing","name":"Temporal Processing"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":20,"of":20,"tagged_in_all":86,"items":[{"url":"/paper/time-expressions-in-mental-health-records-for","title":"Time Expressions in Mental Health Records for Symptom Onset Extraction","date":"2018-10-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/aps-lstm-exploiting-multi-periodicity-and","title":"APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting","date":"2024-12-07","arxiv_id":"2412.06835","repositories_listed":1,"syntology":null},{"url":"/paper/think-step-by-step-chain-of-gesture-prompting","title":"Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos","date":"2024-06-27","arxiv_id":"2406.19217","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-efficient-and-effective-point","title":"Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba","date":"2024-05-09","arxiv_id":"2405.06116","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/softtiger-a-clinical-foundation-model-for","title":"SoftTiger: A Clinical Foundation Model for Healthcare Workflows","date":"2024-03-01","arxiv_id":"2403.00868","repositories_listed":1,"syntology":null},{"url":"/paper/make-an-audio-2-temporal-enhanced-text-to","title":"Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation","date":"2023-05-29","arxiv_id":"2305.18474","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/tieval-an-evaluation-framework-for-temporal","title":"tieval: An Evaluation Framework for Temporal Information Extraction Systems","date":"2023-01-11","arxiv_id":"2301.04643","repositories_listed":1,"syntology":null},{"url":"/paper/improving-event-duration-question-answering","title":"Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-extracting-absolute-event-timelines","title":"Towards Extracting Absolute Event Timelines From English Clinical Reports","date":"2020-09-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/trove-ontology-driven-weak-supervision-for","title":"Ontology-driven weak supervision for clinical entity classification in electronic health records","date":"2020-08-05","arxiv_id":"2008.01972","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/temporal-knowledge-base-completion-new","title":"Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols","date":"2020-05-02","arxiv_id":"2005.05035","repositories_listed":1,"syntology":{"n":12,"n_ran":3,"n_unverified":9,"n_pointer_only":12}},{"url":"/paper/metoomaastricht-building-a-chatbot-to-assist","title":"#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment","date":"2019-09-06","arxiv_id":"1909.02809","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-information-extraction-by-predicting","title":"Temporal Information Extraction by Predicting Relative Time-lines","date":"2018-08-28","arxiv_id":"1808.09401","repositories_listed":1,"syntology":null},{"url":"/paper/inducing-temporal-relations-from-time-anchor","title":"Inducing Temporal Relations from Time Anchor Annotation","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-hand-gesture-recognition-on","title":"Deep Learning for Hand Gesture Recognition on Skeletal Data","date":"2018-05-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/kuleuven-liir-at-semeval-2017-task-12-cross","title":"KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/structured-learning-for-temporal-relation","title":"Structured Learning for Temporal Relation Extraction from Clinical Records","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/catena-causal-and-temporal-relation","title":"CATENA: CAusal and TEmporal relation extraction from NAtural language texts","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gate-time-extraction-of-temporal-expressions","title":"GATE-Time: Extraction of Temporal Expressions and Events","date":"2016-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-baseline-temporal-tagger-for-all-languages","title":"A Baseline Temporal Tagger for all Languages","date":"2015-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":4,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}