REL: An Entity Linker Standing on the Shoulders of Giants
Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, Arjen P. de Vries
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ReproduceCode
- github.com/informagi/RELpytorch★ 319
Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| AIDA-CoNLL | van Hulst et al. (2020) | Micro-F1 strong | 80.5 | — | Unverified |
| Derczynski | van Hulst et al. (2020) | Micro-F1 | 41.1 | — | Unverified |
| MSNBC | van Hulst et al. (2020) | Micro-F1 | 72.4 | — | Unverified |