{"url":"/sota/document-dating-on-nyt","task":{"name":"Document Dating","url":"/task/document-dating","note":null},"dataset":{"name":"NYT","url":"/dataset/new-york-times-annotated-corpus"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"Document Dating is the problem of automatically predicting the date of a document based on its content. Date of a document, also referred to as the Document Creation Time (DCT), is at the core of many important tasks, such as, information retrieval, temporal reasoning, text summarization, event detection, and analysis of historical text, among others.\r\n\r\nFor example, in the following document, the correct creation year is 1999. This can be inferred by the presence of terms 1995 and Four years after.\r\n\r\nSwiss adopted that form of taxation in 1995. The concession was approved by the govt last September. Four years after, the IOC….\r\n\r\nDescription from [NLP Progress](http://nlpprogress.com/english/temporal_processing.html)","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":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":3,"rows_with_code":1,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"NeuralDater","metrics":{"Accuracy":"58.9"},"uses_additional_data":false,"paper_date":"2019-02-01","paper":"/paper/dating-documents-using-graph-convolution","paper_url":"http://arxiv.org/abs/1902.00175v1","paper_title":"Dating Documents using Graph Convolution Networks","code":"https://github.com/malllabiisc/NeuralDater","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Chambers","metrics":{"Accuracy":"42.3"},"uses_additional_data":false,"paper_date":"2012-07-01","paper":"/paper/labeling-documents-with-timestamps-learning","paper_url":"https://aclanthology.org/P12-1011","paper_title":"Labeling Documents with Timestamps: Learning from their Time Expressions","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"BurstySimDater","metrics":{"Accuracy":"38.5"},"uses_additional_data":false,"paper_date":"2014-07-01","paper":"/paper/a-burstiness-aware-approach-for-document","paper_url":"https://dl.acm.org/doi/abs/10.1145/2600428.2609495","paper_title":"A Burstiness-aware Approach for Document Dating","code":null,"n_code_links":0,"syntology":null}],"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":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"}}}