Neural entity linking: A survey of models based on deep learning

Özge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the 'deep learning revolution' in natural language processing. Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks. This work distills a generic architecture of a neural EL system and discusses its components, such as candidate generation, mention-context encoding, and entity ranking, summarizing prominent methods for each of them. The vast variety of modifications of this general architecture are grouped by several common themes: joint entity mention detection and disambiguation, 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 represent their meaning, this work also overviews prominent entity embedding techniques. Finally, the survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-Trained masked language models based on the Transformer architecture.

Original languageEnglish
Pages (from-to)527-570
Number of pages44
JournalSemantic Web
Volume13
Issue number3
DOIs
Publication statusPublished - 2022

Keywords

  • deep learning
  • Entity linking
  • knowledge graphs
  • natural language processing
  • neural networks

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