# REL: An Entity Linker Standing on the Shoulders of Giants

arXiv: https://arxiv.org/abs/2006.01969 · HTML: https://mlsanity.com/paper/2006.01969/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 30 of 61)

- [CM3: A Causal Masked Multimodal Model of the Internet](https://mlsanity.com/paper/2201.07520/) (2022)
- [Robustness Gym: Unifying the NLP Evaluation Landscape](https://mlsanity.com/paper/2101.04840/) (2021)
- [NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media](https://mlsanity.com/paper/2104.05893/) (2021)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge](https://mlsanity.com/paper/2403.01432/) (2024)
- [EntQA: Entity Linking as Question Answering](https://mlsanity.com/paper/2110.02369/) (2021)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Highly Parallel Autoregressive Entity Linking with Discriminative Correction](https://mlsanity.com/paper/2109.03792/) (2021)
- [Instructed Language Models with Retrievers Are Powerful Entity Linkers](https://mlsanity.com/paper/2311.03250/) (2023)
- [UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text](https://mlsanity.com/paper/2108.08614/) (2021)
- [ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget](https://mlsanity.com/paper/2408.00103/) (2024)
- [OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs](https://mlsanity.com/paper/2409.05152/) (2024)
- [Grounding Language Models for Visual Entity Recognition](https://mlsanity.com/paper/2402.18695/) (2024)
- [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021)
- [Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.08597/) (2021)
- [Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing](https://mlsanity.com/paper/2108.10949/) (2021)
- [Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem](https://mlsanity.com/paper/2204.05990/) (2022)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [Entity-aware Transformers for Entity Search](https://mlsanity.com/paper/2205.00820/) (2022)
- [ChatEL: Entity Linking with Chatbots](https://mlsanity.com/paper/2402.14858/) (2024)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Does Knowledge Help General NLU? An Empirical Study](https://mlsanity.com/paper/2109.00563/) (2021)
- [Improving Entity Disambiguation by Reasoning over a Knowledge Base](https://mlsanity.com/paper/2207.04106/) (2022)
- [Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking](https://mlsanity.com/paper/2306.12245/) (2023)
- [Joint Coreference Resolution and Character Linking for Multiparty Conversation](https://mlsanity.com/paper/2101.11204/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Quantifying Gender Biases Towards Politicians on Reddit](https://mlsanity.com/paper/2112.12014/) (2021)
- [Community Question Answering Entity Linking via Leveraging Auxiliary Data](https://mlsanity.com/paper/2205.11917/) (2022)

## Abstract (arXiv)

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.