In this survey, we provide a comprehensive description of recent neural entity linking (EL) systems . We distill their generic architecture that includes candidate generation , entity ranking , and unlinkable mention prediction components. For each of them, we summarize the prominent methods and models, including approaches to mention encoding based on the self-attention architecture. Since many EL models take advantage of entity embeddings to improve their generalization capabilities, we provide an overview of the widely-used entity embedding techniques. We group the variety of EL approaches by several common research directions : joint entity recognition and linking, models for global EL , domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. We also discuss the novel application of EL for enhancing word representation models like BERT. We systemize the critical design features of EL systems and provide their reported evaluation results .
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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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
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Since many EL models take advantage of entity embeddings to improve their generalization capabilities, we provide an overview of the widely-used entity embedding techniques. We group the variety of EL approaches
The vast variety of modifications of this general neural entity linking architecture are grouped
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We also discuss the novel application of EL for enhancing word representation models like BERT. We systemize the critical design features of EL systems and provide their reported evaluation results
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