Muñoz-Ortiz, AlbertoVilares, DavidCorro, CaioGómez-Rodríguez, Carlos2026-02-102026-02-102025-11Alberto Muñoz-Ortiz, David Vilares, Caio Corro, and Carlos Gómez-Rodríguez. 2025. Nested Named Entity Recognition as Single-Pass Sequence Labeling. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 9993–10002, Suzhou, China. Association for Computational Linguistics. DOI: 10.18653/v1/2025.findings-emnlp.530979-8-89176-335-7https://hdl.handle.net/2183/47342Presented at: Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Suzhou, China, November 4th to November 9th, 2025.[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.eng©2025 Association for Computational LinguisticsAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Nested Named Entity Recognition (NNER)Sequence labelingToken classificationStructured predictionOff-the-shelf sequence labeling libraryconference outputopen access10.18653/v1/2025.findings-emnlp.530