Neural Entity Linking: A Survey of Models Based on Deep Learning

Tracking #: 2875-4089

This paper is currently under review
Authors: 
Özge Sevgili
Artem Shelmanov
Mikhail Arkhipov
Alexander Panchenko
Chris Biemann

Responsible editor: 
Guest Editors DeepL4KGs 2021

Submission type: 
Survey Article
Abstract: 
In this survey, we provide a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in NLP. Our goal is to systemize design features of neural entity linking systems and compare their performance to the prominent classic methods on common benchmarks. We distill generic architectural components of a neural EL system, like candidate generation and entity ranking, and summarize prominent methods for each of them. The vast variety of modifications of this general neural entity linking architecture are grouped by several common themes: joint entity recognition and linking, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. Since many neural models take advantage of entity and mention/context embeddings to catch semantic meaning of them, we provide an overview of popular embedding techniques. Finally, we briefly discuss applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models based on the Transformer architecture.
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Under Review