@article{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",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.525/",
    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."
}