Papers › REL: An Entity Linker Standing on the Shoulders of Giants
REL: An Entity Linker Standing on the Shoulders of Giants
Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, Arjen P. de Vries
Entity linking is a standard component in modern retrieval system that is often performed by third-party toolkits. Despite the plethora of open source options, it is difficult to find a single system that has a modular architecture where certain components may be replaced, does not depend on external sources, can easily be updated to newer Wikipedia versions, and, most important of all, has state-of-the-art performance. The REL system presented in this paper aims to fill that gap. Building on state-of-the-art neural components from natural language processing research, it is provided as a Python package as well as a web API. We also report on an experimental comparison against both well-established systems and the current state-of-the-art on standard entity linking benchmarks.
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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 | van Hulst et al. (2020) | Micro-F1 strong | 80.5 | #13 of 17 | Archive leaderboard | report |
| Entity Linking | Derczynski | van Hulst et al. (2020) | Micro-F1 | 41.1 | #5 of 7 | Archive leaderboard | report |
| Entity Linking | MSNBC | van Hulst et al. (2020) | Micro-F1 | 72.4 | #5 of 8 | 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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