# NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

arXiv: https://arxiv.org/abs/2303.04426 · HTML: https://mlsanity.com/paper/2303.04426/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [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)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [Entity Linking and Discovery via Arborescence-based Supervised Clustering](https://mlsanity.com/paper/2109.01242/) (2021)
- [Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering](https://mlsanity.com/paper/1909.07598/) (2019)
- [Uncovering the Semantics of Wikipedia Categories](https://mlsanity.com/paper/1906.12089/) (2019)
- [Entity Extraction from Wikipedia List Pages](https://mlsanity.com/paper/2003.05146/) (2020)
- [Improving Zero-Shot Entity Retrieval through Effective Dense Representations](https://mlsanity.com/paper/2103.04156/) (2021)
- [Information Extraction From Co-Occurring Similar Entities](https://mlsanity.com/paper/2102.05444/) (2021)
- [EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing](https://mlsanity.com/paper/2205.12570/) (2022)
- [Transformer-based Subject Entity Detection in Wikipedia Listings](https://mlsanity.com/paper/2210.01482/) (2022)

## Built on by (most cited first, 5 of 5)

- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement](https://mlsanity.com/paper/2306.14704/) (2023)
- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)
- [TIGER: Temporally Improved Graph Entity Linker](https://mlsanity.com/paper/2410.09128/) (2024)
- [Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations](https://mlsanity.com/paper/2509.26487/) (2025)

## Abstract (arXiv)

Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.