Work

Learning Nested Named Entity Recognition from Flat Annotations

Year: 2026

Type: conference-paper

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.

Source: Conference of the European Chapter of the Association for Computational Linguistics

DOI: 10.18653/v1/2026.eacl-srw.50

Authors: Igor Rozhkov, Natalia V Loukachevitch

Institution: Lomonosov Moscow State University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Russian Science Foundation, Lomonosov Moscow State University

OpenAlex ID: W7140082662