# Learning Nested Named Entity Recognition from Flat Annotations

arXiv: https://arxiv.org/abs/2603.00840 · HTML: https://mlsanity.com/paper/2603.00840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [A Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [A Family of Pretrained Transformer Language Models for Russian](https://mlsanity.com/paper/2309.10931/) (2023)
- [Morphological Analyzer and Generator for Russian and Ukrainian Languages](https://mlsanity.com/paper/1503.07283/) (2015)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [DaN+: Danish Nested Named Entities and Lexical Normalization](https://mlsanity.com/paper/2105.11301/) (2021)
- [NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://mlsanity.com/paper/2108.13112/) (2021)
- [RuNNE-2022 Shared Task: Recognizing Nested Named Entities](https://mlsanity.com/paper/2205.11159/) (2022)
- [Facilitating large language model Russian adaptation with Learned Embedding Propagation](https://mlsanity.com/paper/2412.21140/) (2024)
- [Impact of Tokenization on LLaMa Russian Adaptation](https://mlsanity.com/paper/2312.02598/) (2023)
- [A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents](https://mlsanity.com/paper/2302.10204/) (2023)
- [Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges](https://mlsanity.com/paper/2211.00301/) (2022)

## Abstract (arXiv)

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.