@inproceedings{rozhkov-loukachevitch-2026-learning,
    title = "Learning Nested Named Entity Recognition from Flat Annotations",
    author = "Rozhkov, Igor  and
      Loukachevitch, Natalia V",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.50/",
    pages = "649--663",
    ISBN = "979-8-89176-383-8",
    abstract = "Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist abundantly, nested resources remain scarce. We investigate whether models can learn nested structure from flat annotations alone, evaluating four approaches: string inclusions (substring matching), entity corruption (pseudo-nested data), flat neutralization (reducing false negative signal), and a hybrid fine-tuned + LLM pipeline. On NEREL, a Russian benchmark with 29 entity types where 21{\%} of entities are nested, our best combined method achieves 26.37{\%} inner F1, closing 40{\%} of the gap to full nested supervision. Code is available at https://github.com/fulstock/Learning-from-Flat-Annotations."
}