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.

综述了自 2015 年以来，神经实体链接 (EL) 系统的最新发展，目的是系统化神经实体链接系统的设计特征，并将其性能与常见基准测试中的经典方法进行比较。该文从候选生成、提及 - 上下文编码和实体排名等方面讨论了神经 EL 系统的通用架构，总结了每个方法的突出特点。