Nested Named Entity Recognition as Single-Pass Sequence Labeling

Muñoz-Ortiz, Alberto; Vilares, David; Corro, Caio; Gómez-Rodríguez, Carlos

Use this link to cite:

https://hdl.handle.net/2183/47342

Nested Named Entity Recognition as Single-Pass Sequence Labeling

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MunozOrtiz_Alberto_2025_Nested_Named_Entity_Recognition_as_Single_Pass_Sequence_Labeling.pdf (258.09 KB)

Identifiers

URI: https://hdl.handle.net/2183/47342

DOI: 10.18653/v1/2025.findings-emnlp.530

Publication date

2025-11

Authors

Muñoz-Ortiz, Alberto

Vilares, David

Corro, Caio

Gómez-Rodríguez, Carlos

Bibliographic citation

Alberto 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.530

Abstract

[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.