ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
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Abstract (arXiv)
We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than 60 times faster than competitive existing approaches. ReFinED also surpasses state-of-the-art performance on standard entity linking datasets by an average of 3.7 F1. The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking. The combination of speed, accuracy and scale makes ReFinED an effective and cost-efficient system for extracting entities from web-scale datasets, for which the model has been successfully deployed. Our code and pre-trained models are available at https://github.com/alexa/ReFinED
Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). Builds on 26 of its 46 references that are in the map. First posted on arXiv 8 Jul 2022.
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26 of 46 references.
References · 26
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- 2020Autoregressive Entity Retrievalcited by 333
- 2019Matching the Blanks: Distributional Similarity for Relation Learningcited by 271
- 2019Zero-Shot Entity Linking by Reading Entity Descriptionscited by 124
- 2018End-to-End Neural Entity Linkingcited by 85
- 2017Deep Joint Entity Disambiguation with Local Neural Attentioncited by 79
- 2020Efficient One-Pass End-to-End Entity Linking for Questionscited by 76
- 2020REL: An Entity Linker Standing on the Shoulders of Giantscited by 61
- 2020Machine Knowledge: Creation and Curation of Comprehensive Knowledge Basescited by 55
14 more
- 2018Improving Entity Linking by Modeling Latent Relations between Mentionscited by 53
- 2020Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linkingcited by 49
- 2018DeepType: Multilingual Entity Linking by Neural Type System Evolutioncited by 41
- 2019Fine-Grained Entity Typing for Domain Independent Entity Linkingcited by 36
- 2019Joint Entity Linking with Deep Reinforcement Learningcited by 27
- 2017Named Entity Disambiguation for Noisy Textcited by 24
- 2020Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguationcited by 24
- 2020Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Modelscited by 22
- 2014Analysis of Named Entity Recognition and Linking for Tweetscited by 20
- 2019Learning Dynamic Context Augmentation for Global Entity Linkingcited by 19
- 2020Empirical Evaluation of Pretraining Strategies for Supervised Entity Linkingcited by 17
- 2018Collective Entity Disambiguation with Structured Gradient Tree Boostingcited by 16
- 2019OpenTapioca: Lightweight Entity Linking for Wikidatacited by 14
- 2021Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowingcited by 11
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Citing papers per year: 2022: 2; 2023: 23; 2024: 22; 2025: 17; 2026: 10.
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50 most cited of 74 All 74 (JSON)
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- 2026H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System8 Aug 2026
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- 2026ATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopedias4 May 2026
- 2026GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons22 Apr 2026
- 2026LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation8 Jan 2026
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