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

翻译：我们引入了一个高效端对端实体链接模式,它使用细微的实体类型和实体描述进行连接。该模式将提及检测、细微的实体打字和实体在单一远端传票文件内对所有提及的所有内容进行模糊不清,使其速度超过现有竞争性方法的60倍以上。 ReFinED还超过了将数据集平均连接3.7F1的标准实体的最先进性能。该模式能够向大规模知识库,如维基数据(比维基百科多15倍的实体)和零光实体连接。速度、准确性和规模的结合使ReFED成为从网络规模数据集中提取实体的有效和成本效益高的系统,模型已经成功部署。我们的代码和预培训模型可在https://github.com/alexa/ReFinED查阅。