# EntQA: Entity Linking as Question Answering

arXiv: https://arxiv.org/abs/2110.02369 · HTML: https://mlsanity.com/paper/2110.02369/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [Efficient Passage Retrieval with Hashing for Open-domain Question Answering](https://mlsanity.com/paper/2106.00882/) (2021)
- [Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking](https://mlsanity.com/paper/2003.05473/) (2020)
- [Understanding Hard Negatives in Noise Contrastive Estimation](https://mlsanity.com/paper/2104.06245/) (2021)
- [A BERT Baseline for the Natural Questions](https://mlsanity.com/paper/1901.08634/) (2019)
- [Joint Learning of Named Entity Recognition and Entity Linking](https://mlsanity.com/paper/1907.08243/) (2019)
- [Analysis of Named Entity Recognition and Linking for Tweets](https://mlsanity.com/paper/1410.7182/) (2014)
- [Word-Entity Duet Representations for Document Ranking](https://mlsanity.com/paper/1706.06636/) (2017)
- [Collective Entity Disambiguation with Structured Gradient Tree Boosting](https://mlsanity.com/paper/1802.10229/) (2018)

## Built on by (most cited first, 30 of 34)

- [Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities](https://mlsanity.com/paper/2302.11154/) (2023)
- [Named Entity and Relation Extraction with Multi-Modal Retrieval](https://mlsanity.com/paper/2212.01612/) (2022)
- [Instructed Language Models with Retrievers Are Powerful Entity Linkers](https://mlsanity.com/paper/2311.03250/) (2023)
- [ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget](https://mlsanity.com/paper/2408.00103/) (2024)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs](https://mlsanity.com/paper/2409.05152/) (2024)
- [Grounding Language Models for Visual Entity Recognition](https://mlsanity.com/paper/2402.18695/) (2024)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2305.03688/) (2023)
- [OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting](https://mlsanity.com/paper/2410.07549/) (2024)
- [AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes](https://mlsanity.com/paper/2305.14725/) (2023)
- [ChatEL: Entity Linking with Chatbots](https://mlsanity.com/paper/2402.14858/) (2024)
- [Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking](https://mlsanity.com/paper/2306.12245/) (2023)
- [EntGPT: Entity Linking with Generative Large Language Models](https://mlsanity.com/paper/2402.06738/) (2024)
- [DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking](https://mlsanity.com/paper/2209.13187/) (2022)
- [Benchmarking Diverse-Modal Entity Linking with Generative Models](https://mlsanity.com/paper/2305.17337/) (2023)
- [ImpRAG: Retrieval-Augmented Generation with Implicit Queries](https://mlsanity.com/paper/2506.02279/) (2025)
- [Contextual Augmentation for Entity Linking using Large Language Models](https://mlsanity.com/paper/2510.18888/) (2025)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Entity Disambiguation via Fusion Entity Decoding](https://mlsanity.com/paper/2404.01626/) (2024)
- [Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling](https://mlsanity.com/paper/2406.08116/) (2024)
- [Unified Examination of Entity Linking in Absence of Candidate Sets](https://mlsanity.com/paper/2404.11061/) (2024)
- [Entity Insertion in Multilingual Linked Corpora: The Case of Wikipedia](https://mlsanity.com/paper/2410.04254/) (2024)
- [Exploring Partial Knowledge Base Inference in Biomedical Entity Linking](https://mlsanity.com/paper/2303.10330/) (2023)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)
- [Evaluation of LLMs on Long-tail Entity Linking in Historical Documents](https://mlsanity.com/paper/2505.03473/) (2025)
- [An Entity Linking Agent for Question Answering](https://mlsanity.com/paper/2508.03865/) (2025)
- [Do Language Models Learn about Legal Entity Types during Pretraining?](https://mlsanity.com/paper/2310.13092/) (2023)

## Abstract (arXiv)

A conventional approach to entity linking is to first find mentions in a given document and then infer their underlying entities in the knowledge base. A well-known limitation of this approach is that it requires finding mentions without knowing their entities, which is unnatural and difficult. We present a new model that does not suffer from this limitation called EntQA, which stands for Entity linking as Question Answering. EntQA first proposes candidate entities with a fast retrieval module, and then scrutinizes the document to find mentions of each candidate with a powerful reader module. Our approach combines progress in entity linking with that in open-domain question answering and capitalizes on pretrained models for dense entity retrieval and reading comprehension. Unlike in previous works, we do not rely on a mention-candidates dictionary or large-scale weak supervision. EntQA achieves strong results on the GERBIL benchmarking platform.