@inproceedings{MunVilCorGomEMNLP2025,
    title = "Nested Named Entity Recognition as Single-Pass Sequence Labeling",
    author = "Mu{\~n}oz-Ortiz, Alberto  and
      Vilares, David  and
      Corro, Caio  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.530/",
    doi = "10.18653/v1/2025.findings-emnlp.530",
    pages = "9993--10002",
    ISBN = "979-8-89176-335-7",
    abstract = "We cast nested named entity recognition (NNER) as a sequence labeling task by leveraging prior work that linearizes constituency structures, effectively reducing the complexity of this structured prediction problem to straightforward token classification. By combining these constituency linearizations with pretrained encoders, our method captures nested entities while performing exactly n tagging actions. Our approach achieves competitive performance compared to less efficient systems, and it can be trained using any off-the-shelf sequence labeling library."
}