@inproceedings{blevins-etal-2026-universal,
title = "Universal {NER} v2: Towards a Massively Multilingual Named Entity Recognition Benchmark",
author = "Blevins, Terra and
Mayhew, Stephen and
Suppa, Marek and
Gonen, Hila and
Mirkin, Shachar and
Pais, Vasile and
Dobrovoljc Zor, Kaja and
Giouli, Voula and
Kevin, Jun and
Jang, Eugene and
Kim, Eungseo and
Seo, Jeongyeon and
Gialis, Xenophon and
Pinter, Yuval",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.525/",
doi = "10.63317/4qhcjikvgeda",
pages = "6609--6618",
abstract = "We present Universal NER (UNER) v2, a significant extension of the initial version released in 2024. UNER is a collaborative dataset for multilingual named-entity annotations, built to support research on NER methods in a cross-linguistic setting. UNER v2 adds 11 new datasets in 10 typologically varied languages to the resource, including multiple parallel evaluation benchmarks aligned with each other and other datasets in UNER v1, while maintaining the same annotation guidelines and high standards for inter-annotator agreement. We report detailed statistics for the dataset and benchmark UNER v2 using both encoder-based model architectures and LLMs."
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%0 Conference Proceedings
%T Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark
%A Blevins, Terra
%A Mayhew, Stephen
%A Suppa, Marek
%A Gonen, Hila
%A Mirkin, Shachar
%A Pais, Vasile
%A Dobrovoljc Zor, Kaja
%A Giouli, Voula
%A Kevin, Jun
%A Jang, Eugene
%A Kim, Eungseo
%A Seo, Jeongyeon
%A Gialis, Xenophon
%A Pinter, Yuval
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F blevins-etal-2026-universal
%X We present Universal NER (UNER) v2, a significant extension of the initial version released in 2024. UNER is a collaborative dataset for multilingual named-entity annotations, built to support research on NER methods in a cross-linguistic setting. UNER v2 adds 11 new datasets in 10 typologically varied languages to the resource, including multiple parallel evaluation benchmarks aligned with each other and other datasets in UNER v1, while maintaining the same annotation guidelines and high standards for inter-annotator agreement. We report detailed statistics for the dataset and benchmark UNER v2 using both encoder-based model architectures and LLMs.
%R 10.63317/4qhcjikvgeda
%U https://aclanthology.org/2026.lrec-1.525/
%U https://doi.org/10.63317/4qhcjikvgeda
%P 6609-6618
Markdown (Informal)
[Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark](https://aclanthology.org/2026.lrec-1.525/) (Blevins et al., LREC 2026)
ACL
- Terra Blevins, Stephen Mayhew, Marek Suppa, Hila Gonen, Shachar Mirkin, Vasile Pais, Kaja Dobrovoljc Zor, Voula Giouli, Jun Kevin, Eugene Jang, Eungseo Kim, Jeongyeon Seo, Xenophon Gialis, and Yuval Pinter. 2026. Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6609–6618, Palma de Mallorca, Spain. ELRA Language Resource Association.