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

Tracking #: 2699-3913

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 performances to the best classic methods on the common benchmarks. We distill generic architectural components of a neural EL system, like candidate generation and entity ranking summarizing the prominent methods for each of them, such as approaches to mention encoding based on the self-attention architecture. 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 pre-trained entity embeddings to improve their generalization capabilities, we provide an overview of popular entity 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 such as BERT.
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