{"url":"/sota/entity-typing-on-aida-conll","task":{"name":"Entity Typing","url":"/task/entity-typing","note":null},"dataset":{"name":"AIDA-CoNLL","url":"/dataset/conll-1"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Entity Typing** is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).\r\n\r\n\r\n<span class=\"description-source\">Source: [Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding ](https://arxiv.org/abs/1602.05307)</span>\r\n\r\n<span class=\"description-source\">Image Credit: [Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding ](https://arxiv.org/abs/1602.05307)</span>","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":["Micro-F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Micro-F1":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"ReFinED","metrics":{"Micro-F1":"84.0"},"uses_additional_data":true,"paper_date":"2022-07-08","paper":"/paper/refined-an-efficient-zero-shot-capable-1","paper_url":"https://arxiv.org/abs/2207.04108v1","paper_title":"ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking","code":"https://github.com/amazon-science/ReFinED","n_code_links":3,"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"}}}