@inproceedings{palen-michel-etal-2025-openner,
    title = "{O}pen{NER} 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages",
    author = {Palen-Michel, Chester  and
      Pickering, Maxwell  and
      Kruse, Maya  and
      S{\"a}lev{\"a}, Jonne  and
      Lignos, Constantine},
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1708/",
    pages = "33637--33662",
    ISBN = "979-8-89176-332-6",
    abstract = "We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets.OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies.We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER.We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER.We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task.OpenNER is released at https://github.com/bltlab/open-ner."
}