论文标题
精制：端到端实体链接的有效的零射击方法
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
论文作者
论文摘要
我们介绍了一种有效的端到端实体链接模型，该模型使用精细的实体类型和实体描述来执行链接。该模型执行提及的检测，细粒度实体键入以及单个向前传球中文档中所有提及的实体歧义，这使其比现有方法的速度快60倍以上。精制还超过了链接数据集的标准实体的最先进性能，平均为3.7 F1。该模型能够将其推广到大规模的知识库，例如Wikidata（其实体的15倍）和零拍的实体链接。速度，准确性和规模的结合使精制成为从网络尺度数据集中提取实体的有效且具有成本效益的系统，该数据集已成功部署该模型。我们的代码和预培训模型可在https://github.com/alexa/refined上找到
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
