ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

• URL: http://arxiv.org/abs/2207.04108v1
• Date: Fri, 8 Jul 2022 19:20:42 GMT
• Title: ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
• Authors: Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni
• Abstract summary: ReFinED is an efficient end-to-end entity linking model. It performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass. It surpasses state-of-the-art performance on standard entity linking datasets by an average of 3.7 F1.
• Score: 5.382800665115746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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