Papers › End-to-End Neural Entity Linking
End-to-End Neural Entity Linking
Nikolaos Kolitsas, Octavian-Eugen Ganea, Thomas Hofmann
Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Entity Linking | AIDA-CoNLL | Kolitsas et al. (2018) | Micro-F1 strong | 82.4 | #11 of 17 | Archive leaderboard | report |
| Entity Linking | Derczynski | Kolitsas et al. (2018) | Micro-F1 | 34.1 | #6 of 7 | Archive leaderboard | report |
| Entity Linking | MSNBC | Kolitsas et al. (2018) | Micro-F1 | 72.4 | #4 of 8 | Archive leaderboard | report |
| Entity Linking | N3-Reuters-128 | E2E | Micro-F1 | 54.6 | #2 of 5 | Archive leaderboard | report |
| Entity Linking | OKE-2015 | E2E | Micro-F1 | 66.9 | #1 of 5 | Archive leaderboard | report |
| Entity Linking | OKE-2016 | E2E | Micro-F1 | 58.4 | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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