Learning Nested Named Entity Recognition from Flat Annotations

Igor Rozhkov; Natalia Loukachevitch

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arXiv · 2603.00840

Learning Nested Named Entity Recognition from Flat Annotations

Igor Rozhkov·Natalia Loukachevitch

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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.

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Igor Rozhkov, Natalia Loukachevitch. 2026-02-28. Learning Nested Named Entity Recognition from Flat Annotations. https://arxiv.org/abs/2603.00840

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