  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
