{"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/end-to-end-neural-entity-linking","title":"End-to-End Neural Entity Linking","arxiv_id":"1808.07699","date":"2018-08-23","proceeding":"CONLL 2018 10","authors":["Nikolaos Kolitsas","Octavian-Eugen Ganea","Thomas Hofmann"],"abstract":"Entity Linking (EL) is an essential task for semantic text understanding and\ninformation extraction. Popular methods separately address the Mention\nDetection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging\ntheir mutual dependency. We here propose the first neural end-to-end EL system\nthat jointly discovers and links entities in a text document. The main idea is\nto consider all possible spans as potential mentions and learn contextual\nsimilarity scores over their entity candidates that are useful for both MD and\nED decisions. Key components are context-aware mention embeddings, entity\nembeddings and a probabilistic mention - entity map, without demanding other\nengineered features. Empirically, we show that our end-to-end method\nsignificantly outperforms popular systems on the Gerbil platform when enough\ntraining data is available. Conversely, if testing datasets follow different\nannotation conventions compared to the training set (e.g. queries/ tweets vs\nnews documents), our ED model coupled with a traditional NER system offers the\nbest or second best EL accuracy.","url_abs":"http://arxiv.org/abs/1808.07699v2","url_pdf":"http://arxiv.org/pdf/1808.07699v2.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":"end-to-end-neural-entity-linking","repo_url":"https://github.com/dalab/end2end_neural_el","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"entity-disambiguation","task_name":"Entity Disambiguation"},{"task_slug":"entity-embeddings","task_name":"Entity Embeddings"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"cg","task_name":"NER"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-linking-on-aida-conll","task":"Entity Linking","dataset":"AIDA-CoNLL","model":"Kolitsas et al. 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(2018)","rank_in_archive_order":4,"of":8,"metrics":{"Micro-F1":"72.4"},"uses_additional_data":false},{"leaderboard":"/sota/entity-linking-on-n3-reuters-128-1","task":"Entity Linking","dataset":"N3-Reuters-128","model":"E2E","rank_in_archive_order":2,"of":5,"metrics":{"Micro-F1":"54.6"},"uses_additional_data":false},{"leaderboard":"/sota/entity-linking-on-oke-2015-1","task":"Entity Linking","dataset":"OKE-2015","model":"E2E","rank_in_archive_order":1,"of":5,"metrics":{"Micro-F1":"66.9"},"uses_additional_data":false},{"leaderboard":"/sota/entity-linking-on-oke-2016-1","task":"Entity Linking","dataset":"OKE-2016","model":"E2E","rank_in_archive_order":2,"of":5,"metrics":{"Micro-F1":"58.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.07699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.07699"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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