{"url":"/sota/column-type-annotation-on-viznet-sato-full","task":{"name":"Column Type Annotation","url":"/task/column-type-annotation","note":null},"dataset":{"name":"VizNet-Sato-Full","url":"/dataset/viznet-sato"},"category":"Natural Language Processing","categories":["Knowledge Base","Natural Language Processing"],"category_note":null,"description":"**Column type annotation** (CTA) refers to the task of predicting the semantic type of a table column and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation). The labels that are usually used in a CTA problem are semantic types from vocabularies like DBpedia, Schema.org or WikiData. Some examples are: *Book*, *Country*, *LocalBusiness* etc.\r\n\r\nCTA can be either treated as a multi-class classification problem where a column is annotated by only one semantic type or as multi-label classification problem where a column can be annotated using multiple semantic types.","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":["Macro-F1","Weighted-F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Macro-F1":"higher","Weighted-F1":"higher"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Watchog","metrics":{"Macro-F1":"85.63"},"uses_additional_data":false,"paper_date":"2023-12-12","paper":"/paper/watchog-a-light-weight-contrastive-learning","paper_url":"https://dl.acm.org/doi/10.1145/3626766","paper_title":"Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"DODUO","metrics":{"Macro-F1":"84.6"},"uses_additional_data":false,"paper_date":"2021-04-05","paper":"/paper/annotating-columns-with-pre-trained-language","paper_url":"https://arxiv.org/abs/2104.01785v2","paper_title":"Annotating Columns with Pre-trained Language Models","code":"https://github.com/megagonlabs/doduo","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Sato","metrics":{"Macro-F1":"75.6","Weighted-F1":"90.2"},"uses_additional_data":false,"paper_date":"2019-11-14","paper":"/paper/sato-contextual-semantic-type-detection-in","paper_url":"https://arxiv.org/abs/1911.06311v3","paper_title":"Sato: Contextual Semantic Type Detection in Tables","code":"https://github.com/megagonlabs/sato","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":9,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"TaBERT","metrics":{"Weighted-F1":"97.2"},"uses_additional_data":false,"paper_date":"2021-05-06","paper":"/paper/tabbie-pretrained-representations-of-tabular","paper_url":"https://arxiv.org/abs/2105.02584v1","paper_title":"TABBIE: Pretrained Representations of Tabular Data","code":"https://github.com/SFIG611/tabbie","n_code_links":2,"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":2,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":1,"n_unverified":12,"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":1,"n_unverified":12,"n_samples":13,"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"}}}