@inproceedings{peng-etal-2024-different,
    title = "Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations",
    author = "Peng, Siyao  and
      Sun, Zihang  and
      Loftus, Sebastian  and
      Plank, Barbara",
    editor = "Pyatkin, Valentina  and
      Fried, Daniel  and
      Stengel-Eskin, Elias  and
      Liu, Alisa  and
      Pezzelle, Sandro",
    booktitle = "Proceedings of the Third Workshop on Understanding Implicit and Underspecified Language",
    month = mar,
    year = "2024",
    address = "Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.unimplicit-1.7/",
    pages = "73--81",
    abstract = "Named Entity Recognition (NER) is a key information extraction task with a long-standing tradition. While recent studies address and aim to correct annotation errors via re-labeling efforts, little is known about the sources of label variation, such as text ambiguity, annotation error, or guideline divergence. This is especially the case for high-quality datasets and beyond English CoNLL03. This paper studies disagreements in expert-annotated named entity datasets for three varieties: English, Danish, and Bavarian. We show that text ambiguity and artificial guideline changes are dominant factors for diverse annotations among high-quality revisions. We survey student annotations on a subset of difficult entities and substantiate the feasibility and necessity of manifold annotations for understanding named entity ambiguities from a distributional perspective."
}