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
this https URL

本研究介绍了 ReFinED：一个高效的实体链接模型，使用细粒度实体类型和实体描述执行链接。该模型执行单向传递，可在文档中对所有提及进行提及检测、细粒度实体类型划分和实体消歧，使其比竞争性现有方法快 60 倍以上。ReFinED 在标准实体链接数据集上的性能优于最先进性能平均提高了 3.7 个 F1，具有良好的泛化能力和零 - shot 实体链接。该研究的组合可获得速度，精确性和规模，因此是从网络规模数据集中提取实体的一种有效和成本效益的系统。