{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/refined-an-efficient-zero-shot-capable-1","title":"ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking","arxiv_id":"2207.04108","date":"2022-07-08","proceeding":"NAACL (ACL) 2022 7","authors":["Tom Ayoola","Shubhi Tyagi","Joseph Fisher","Christos Christodoulopoulos","Andrea Pierleoni"],"abstract":"We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than 60 times faster than competitive existing approaches. ReFinED also surpasses state-of-the-art performance on standard entity linking datasets by an average of 3.7 F1. The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking. The combination of speed, accuracy and scale makes ReFinED an effective and cost-efficient system for extracting entities from web-scale datasets, for which the model has been successfully deployed. Our code and pre-trained models are available at https://github.com/alexa/ReFinED","url_abs":"https://arxiv.org/abs/2207.04108v1","url_pdf":"https://arxiv.org/pdf/2207.04108v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"refined-an-efficient-zero-shot-capable-1","repo_url":"https://github.com/alexa/refined","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"refined-an-efficient-zero-shot-capable-1","repo_url":"https://github.com/amazon-research/ReFinED","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"refined-an-efficient-zero-shot-capable-1","repo_url":"https://github.com/amazon-science/ReFinED","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"entity-disambiguation","task_name":"Entity Disambiguation"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"entity-typing","task_name":"Entity Typing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-disambiguation-on-ace2004","task":"Entity Disambiguation","dataset":"ACE2004","model":"ReFinED","rank_in_archive_order":3,"of":6,"metrics":{"Micro-F1":"91.6"},"uses_additional_data":true},{"leaderboard":"/sota/entity-disambiguation-on-aida-conll","task":"Entity Disambiguation","dataset":"AIDA-CoNLL","model":"ReFinED","rank_in_archive_order":8,"of":20,"metrics":{"In-KB Accuracy":"93.9"},"uses_additional_data":true},{"leaderboard":"/sota/entity-disambiguation-on-aquaint","task":"Entity Disambiguation","dataset":"AQUAINT","model":"ReFinED","rank_in_archive_order":3,"of":6,"metrics":{"Micro-F1":"91.8"},"uses_additional_data":true},{"leaderboard":"/sota/entity-disambiguation-on-msnbc","task":"Entity Disambiguation","dataset":"MSNBC","model":"ReFinED","rank_in_archive_order":3,"of":6,"metrics":{"Micro-F1":"94.4"},"uses_additional_data":true},{"leaderboard":"/sota/entity-disambiguation-on-wned-cweb","task":"Entity Disambiguation","dataset":"WNED-CWEB","model":"ReFinED","rank_in_archive_order":1,"of":7,"metrics":{"Micro-F1":"79.4"},"uses_additional_data":true},{"leaderboard":"/sota/entity-disambiguation-on-wned-wiki","task":"Entity Disambiguation","dataset":"WNED-WIKI","model":"ReFinED","rank_in_archive_order":3,"of":7,"metrics":{"Micro-F1":"88.7"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-aida-conll","task":"Entity Linking","dataset":"AIDA-CoNLL","model":"ReFinED","rank_in_archive_order":8,"of":17,"metrics":{"Micro-F1 strong":"84.0"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-derczynski-1","task":"Entity Linking","dataset":"Derczynski","model":"ReFinED","rank_in_archive_order":4,"of":7,"metrics":{"Micro-F1":"50.7","Micro-F1 strong":"50.7"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-kore50","task":"Entity Linking","dataset":"KORE50","model":"ReFinED","rank_in_archive_order":3,"of":4,"metrics":{"Micro-F1":"65.9","Micro-F1 strong":"64.7"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-msnbc-1","task":"Entity Linking","dataset":"MSNBC","model":"ReFinED","rank_in_archive_order":7,"of":8,"metrics":{"Micro-F1":"71.8","Micro-F1 strong":"71.8"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-n3-reuters-128-1","task":"Entity Linking","dataset":"N3-Reuters-128","model":"ReFinED","rank_in_archive_order":1,"of":5,"metrics":{"Micro-F1":"58.1","Micro-F1 strong":"58.1"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-oke-2015-1","task":"Entity Linking","dataset":"OKE-2015","model":"ReFinED","rank_in_archive_order":3,"of":5,"metrics":{"Micro-F1":"65.0","Micro-F1 strong":"64.4"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-oke-2016-1","task":"Entity Linking","dataset":"OKE-2016","model":"ReFinED","rank_in_archive_order":1,"of":5,"metrics":{"Micro-F1":"59.5","Micro-F1 strong":"59.1"},"uses_additional_data":true},{"leaderboard":"/sota/entity-linking-on-webqsp-wd","task":"Entity Linking","dataset":"WebQSP-WD","model":"ReFinED","rank_in_archive_order":1,"of":2,"metrics":{"F1":"89.1"},"uses_additional_data":true},{"leaderboard":"/sota/entity-typing-on-aida-conll","task":"Entity Typing","dataset":"AIDA-CoNLL","model":"ReFinED","rank_in_archive_order":1,"of":1,"metrics":{"Micro-F1":"84.0"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.04108","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}