# Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking

arXiv: https://arxiv.org/abs/2005.14253 · HTML: https://mlsanity.com/paper/2005.14253/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 17 of 17)

- [Robustness Gym: Unifying the NLP Evaluation Landscape](https://mlsanity.com/paper/2101.04840/) (2021)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Understanding Hard Negatives in Noise Contrastive Estimation](https://mlsanity.com/paper/2104.06245/) (2021)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [Fast and Effective Biomedical Entity Linking Using a Dual Encoder](https://mlsanity.com/paper/2103.05028/) (2021)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [Multilingual End to End Entity Linking](https://mlsanity.com/paper/2306.08896/) (2023)
- [Effective Few-Shot Named Entity Linking by Meta-Learning](https://mlsanity.com/paper/2207.05280/) (2022)
- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation](https://mlsanity.com/paper/2505.11683/) (2025)
- [Entity Disambiguation via Fusion Entity Decoding](https://mlsanity.com/paper/2404.01626/) (2024)
- [Unified Examination of Entity Linking in Absence of Candidate Sets](https://mlsanity.com/paper/2404.11061/) (2024)
- [Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark](https://mlsanity.com/paper/2207.13005/) (2022)
- [MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2310.11088/) (2023)
- [Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code](https://mlsanity.com/paper/2603.08195/) (2026)

## 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.