# Learn to Not Link: Exploring NIL Prediction in Entity Linking

arXiv: https://arxiv.org/abs/2305.15725 · HTML: https://mlsanity.com/paper/2305.15725/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [Named Entity Disambiguation for Noisy Text](https://mlsanity.com/paper/1706.09147/) (2017)
- [A Lightweight Neural Model for Biomedical Entity Linking](https://mlsanity.com/paper/2012.08844/) (2020)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [One for All: Towards Language Independent Named Entity Linking](https://mlsanity.com/paper/1712.01797/) (2017)

## Built on by (most cited first, 7 of 7)

- [Entity Linking in the Job Market Domain](https://mlsanity.com/paper/2401.17979/) (2024)
- [MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations](https://mlsanity.com/paper/2410.08319/) (2024)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)
- [Argument-Aware Approach To Event Linking](https://mlsanity.com/paper/2403.15097/) (2024)
- [One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail](https://mlsanity.com/paper/2608.14683/) (2026)
- [When Names Cross Scripts: A Source-Grounded Benchmark for Historical Entity Reconciliation in the Mongol World](https://mlsanity.com/paper/2608.23507/) (2026)
- [Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection](https://mlsanity.com/paper/2608.27470/) (2026)

## Abstract (arXiv)

Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features. However, the NIL prediction problem, which aims to identify mentions without a corresponding entity in the knowledge base, has received insufficient attention. We categorize mentions linking to NIL into Missing Entity and Non-Entity Phrase, and propose an entity linking dataset NEL that focuses on the NIL prediction problem. NEL takes ambiguous entities as seeds, collects relevant mention context in the Wikipedia corpus, and ensures the presence of mentions linking to NIL by human annotation and entity masking. We conduct a series of experiments with the widely used bi-encoder and cross-encoder entity linking models, results show that both types of NIL mentions in training data have a significant influence on the accuracy of NIL prediction. Our code and dataset can be accessed at https://github.com/solitaryzero/NIL_EL