{"url":"/sota/entity-disambiguation-on-aida-conll","task":{"name":"Entity Disambiguation","url":"/task/entity-disambiguation","note":null},"dataset":{"name":"AIDA-CoNLL","url":"/dataset/conll-1"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Entity Disambiguation** is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.\n\n\n<span class=\"description-source\">Source: [Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation ](https://arxiv.org/abs/1504.07678)</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":["In-KB Accuracy","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":{"In-KB Accuracy":"higher","Micro-F1":"higher"}},"counts":{"rows":20,"rows_with_code":15,"rows_with_paper_page":20,"rows_dated":19,"rows_using_additional_data":2},"rows":[{"rank_in_archive_order":1,"model":"confidence-order","metrics":{"In-KB Accuracy":"95.0"},"uses_additional_data":false,"paper_date":"2019-09-01","paper":"/paper/pre-training-of-deep-contextualized","paper_url":"https://arxiv.org/abs/1909.00426v5","paper_title":"Global Entity Disambiguation with BERT","code":"https://github.com/studio-ousia/luke","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"DCA-SL + Triples","metrics":{"In-KB Accuracy":"94.94"},"uses_additional_data":false,"paper_date":"2020-08-12","paper":"/paper/evaluating-the-impact-of-knowledge-graph","paper_url":"https://arxiv.org/abs/2008.05190v3","paper_title":"Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models","code":"https://github.com/mulangonando/Impact-of-KG-Context-on-ED","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"DeepType","metrics":{"In-KB Accuracy":"94.88"},"uses_additional_data":false,"paper_date":"2018-02-03","paper":"/paper/deeptype-multilingual-entity-linking-by","paper_url":"http://arxiv.org/abs/1802.01021v1","paper_title":"DeepType: Multilingual Entity Linking by Neural Type System Evolution","code":"https://github.com/openai/deeptype","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":4,"model":"NTEE","metrics":{"In-KB Accuracy":"94.7"},"uses_additional_data":false,"paper_date":"2017-05-06","paper":"/paper/learning-distributed-representations-of-texts","paper_url":"http://arxiv.org/abs/1705.02494v3","paper_title":"Learning Distributed Representations of Texts and Entities from Knowledge Base","code":"https://github.com/studio-ousia/ntee","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"DCA-SL (2019)(et al., [2019c])","metrics":{"In-KB Accuracy":"94.64"},"uses_additional_data":false,"paper_date":"2019-09-04","paper":"/paper/learning-dynamic-context-augmentation-for","paper_url":"https://arxiv.org/abs/1909.02117v1","paper_title":"Learning Dynamic Context Augmentation for Global Entity Linking","code":"https://github.com/YoungXiyuan/DCA","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":6,"model":"Fang et al. (2019) (et al., [2019e])","metrics":{"In-KB Accuracy":"94.3"},"uses_additional_data":false,"paper_date":"2019-02-01","paper":"/paper/joint-entity-linking-with-deep-reinforcement","paper_url":"http://arxiv.org/abs/1902.00330v1","paper_title":"Joint Entity Linking with Deep Reinforcement Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"This work+CtxLSTMs+LDC+MPCM","metrics":{"In-KB Accuracy":"94.0"},"uses_additional_data":false,"paper_date":"2017-12-05","paper":"/paper/neural-cross-lingual-entity-linking","paper_url":"http://arxiv.org/abs/1712.01813v1","paper_title":"Neural Cross-Lingual Entity Linking","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"ReFinED","metrics":{"In-KB Accuracy":"93.9"},"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},{"rank_in_archive_order":9,"model":"Chen et al. (2020) (et al, 2020)","metrics":{"In-KB Accuracy":"93.54"},"uses_additional_data":false,"paper_date":"2020-01-06","paper":"/paper/improving-entity-linking-by-modeling-latent-2","paper_url":"https://arxiv.org/abs/2001.01447v1","paper_title":"Improving Entity Linking by Modeling Latent Entity Type Information","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":10,"model":"GENRE","metrics":{"In-KB Accuracy":"93.3"},"uses_additional_data":false,"paper_date":"2020-10-02","paper":"/paper/autoregressive-entity-retrieval","paper_url":"https://arxiv.org/abs/2010.00904v3","paper_title":"Autoregressive Entity Retrieval","code":"https://github.com/facebookresearch/GENRE","n_code_links":2,"syntology":null},{"rank_in_archive_order":11,"model":"Wikipedia2Vec-GBRT","metrics":{"In-KB Accuracy":"93.1"},"uses_additional_data":false,"paper_date":"2016-01-06","paper":"/paper/joint-learning-of-the-embedding-of-words-and","paper_url":"http://arxiv.org/abs/1601.01343v4","paper_title":"Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation","code":"https://github.com/wikipedia2vec/wikipedia2vec","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"ELDEN","metrics":{"In-KB Accuracy":"93.0"},"uses_additional_data":false,"paper_date":"2018-06-01","paper":"/paper/elden-improved-entity-linking-using-densified","paper_url":"https://aclanthology.org/N18-1167","paper_title":"ELDEN: Improved Entity Linking Using Densified Knowledge Graphs","code":"https://github.com/priyaradhakrishnan0/ELDEN","n_code_links":1,"syntology":null},{"rank_in_archive_order":13,"model":"NER4EL","metrics":{"In-KB Accuracy":"92.5"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/named-entity-recognition-for-entity-linking","paper_url":"https://aclanthology.org/2021.findings-emnlp.220","paper_title":"Named Entity Recognition for Entity Linking: What Works and What’s Next","code":"https://github.com/babelscape/ner4el","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"Global","metrics":{"In-KB Accuracy":"92.22"},"uses_additional_data":false,"paper_date":"2017-04-17","paper":"/paper/deep-joint-entity-disambiguation-with-local","paper_url":"http://arxiv.org/abs/1704.04920v3","paper_title":"Deep Joint Entity Disambiguation with Local Neural Attention","code":"https://github.com/dalab/deep-ed","n_code_links":3,"syntology":null},{"rank_in_archive_order":15,"model":"Wikipedia2Vec","metrics":{"In-KB Accuracy":"91.5"},"uses_additional_data":false,"paper_date":"2016-01-06","paper":"/paper/joint-learning-of-the-embedding-of-words-and","paper_url":"http://arxiv.org/abs/1601.01343v4","paper_title":"Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation","code":"https://github.com/wikipedia2vec/wikipedia2vec","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"KBED","metrics":{"In-KB Accuracy":"90.4"},"uses_additional_data":true,"paper_date":"2022-07-08","paper":"/paper/improving-entity-disambiguation-by-reasoning-1","paper_url":"https://arxiv.org/abs/2207.04106v1","paper_title":"Improving Entity Disambiguation by Reasoning over a Knowledge Base","code":"https://github.com/alexa/refined","n_code_links":3,"syntology":null},{"rank_in_archive_order":17,"model":"Le& Titov (2019) (Le and Titov, 2019)","metrics":{"In-KB Accuracy":"89.66"},"uses_additional_data":false,"paper_date":"2019-06-04","paper":"/paper/boosting-entity-linking-performance-by","paper_url":"https://arxiv.org/abs/1906.01250v1","paper_title":"Boosting Entity Linking Performance by Leveraging Unlabeled Documents","code":"https://github.com/lephong/wnel","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":6,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":18,"model":"Hoffart et al.","metrics":{"In-KB Accuracy":"82.29"},"uses_additional_data":false,"paper_date":"2011-07-01","paper":"/paper/robust-disambiguation-of-named-entities-in","paper_url":"https://aclanthology.org/D11-1072","paper_title":"Robust Disambiguation of Named Entities in Text","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"Bootleg","metrics":{"Micro-F1":"96.8"},"uses_additional_data":false,"paper_date":"2020-10-20","paper":"/paper/bootleg-chasing-the-tail-with-self-supervised","paper_url":"https://arxiv.org/abs/2010.10363v3","paper_title":"Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation","code":"https://github.com/HazyResearch/bootleg","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":20,"model":"BERT-Entity-Sim (local & global) AIDA-B","metrics":{"Micro-F1":"93.54"},"uses_additional_data":false,"paper_date":"2020-01-06","paper":"/paper/improving-entity-linking-by-modeling-latent-2","paper_url":"https://arxiv.org/abs/2001.01447v1","paper_title":"Improving Entity Linking by Modeling Latent Entity Type Information","code":null,"n_code_links":0,"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":4,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":3,"n_unverified":9,"n_samples":12,"n_pointer_only_licence":3,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":3,"n_unverified":9,"n_samples":12,"n_pointer_only_licence":3,"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"}}}