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

arXiv: https://arxiv.org/abs/2006.00575 · HTML: https://mlsanity.com/paper/2006.00575/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 30 of 46)

- [Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP](https://mlsanity.com/paper/2106.06830/) (2021)
- [Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution](https://mlsanity.com/paper/2310.05634/) (2023)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Visual Named Entity Linking: A New Dataset and A Baseline](https://mlsanity.com/paper/2211.04872/) (2022)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes](https://mlsanity.com/paper/2305.14725/) (2023)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition](https://mlsanity.com/paper/2204.05544/) (2022)
- [Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking](https://mlsanity.com/paper/2302.07189/) (2023)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [Financial data analysis application via multi-strategy text processing](https://mlsanity.com/paper/2204.11394/) (2022)
- [NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker](https://mlsanity.com/paper/2303.04426/) (2023)
- [Leave no Place Behind: Improved Geolocation in Humanitarian Documents](https://mlsanity.com/paper/2309.02914/) (2023)
- [DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2309.07545/) (2023)
- [Posthoc Verification and the Fallibility of the Ground Truth](https://mlsanity.com/paper/2106.07353/) (2021)
- [From Retrieval to Generation: Efficient and Effective Entity Set Expansion](https://mlsanity.com/paper/2304.03531/) (2023)
- [xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization](https://mlsanity.com/paper/2310.11275/) (2023)
- [From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies](https://mlsanity.com/paper/2506.09566/) (2025)
- [Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs](https://mlsanity.com/paper/2511.04473/) (2025)
- [A Unified Approach to Entity-Centric Context Tracking in Social Conversations](https://mlsanity.com/paper/2201.12409/) (2022)
- [Learning Entity Linking Features for Emerging Entities](https://mlsanity.com/paper/2208.03877/) (2022)
- [Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval](https://mlsanity.com/paper/2301.13318/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [From Instructions to ODRL Usage Policies: An Ontology Guided Approach](https://mlsanity.com/paper/2506.03301/) (2025)
- [Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2107.04382/) (2021)
- [Namesakes: Ambiguously Named Entities from Wikipedia and News](https://mlsanity.com/paper/2111.11372/) (2021)
- [OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction](https://mlsanity.com/paper/2207.05817/) (2022)
- [Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature](https://mlsanity.com/paper/2401.07447/) (2024)

## Abstract (arXiv)

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.