Nested Named Entity Recognition as Single-Pass Sequence Labeling
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911183294955520 |
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| author | Muñoz-Ortiz, Alberto Vilares, David Corro, Caio Gómez-Rodríguez, Carlos |
| author_facet | Muñoz-Ortiz, Alberto Vilares, David Corro, Caio Gómez-Rodríguez, Carlos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16855 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nested Named Entity Recognition as Single-Pass Sequence Labeling Muñoz-Ortiz, Alberto Vilares, David Corro, Caio Gómez-Rodríguez, Carlos Computation and Language 68T50 I.2.7 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. |
| title | Nested Named Entity Recognition as Single-Pass Sequence Labeling |
| topic | Computation and Language 68T50 I.2.7 |
| url | https://arxiv.org/abs/2505.16855 |