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Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking

arXiv 2005.14253 ↗First posted 28 May 2020cs.CL cs.LGcited by 17

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Abstract (arXiv)

In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-art on two commonly used entity linking datasets: 96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand what design choices are important for entity linking, including choices of negative entity candidates, Transformer architecture, and input perturbations. Lastly, we present promising results on more challenging settings such as end-to-end entity linking and entity linking without in-domain training data.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2020.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

17 papers.

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