# Nested Named Entity Recognition in Plasma Physics Research Articles

arXiv: https://arxiv.org/abs/2602.11163 · HTML: https://mlsanity.com/paper/2602.11163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [Nested Named Entity Recognition via Second-best Sequence Learning and Decoding](https://mlsanity.com/paper/1909.02250/) (2019)
- [Nested Named Entity Recognition with Partially-Observed TreeCRFs](https://mlsanity.com/paper/2012.08478/) (2020)
- [Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition](https://mlsanity.com/paper/2110.07480/) (2021)
- [Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators](https://mlsanity.com/paper/1810.09073/) (2018)
- [Computer Science Named Entity Recognition in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14579/) (2022)
- [An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition](https://mlsanity.com/paper/2208.04534/) (2022)
- [Rethinking the production and publication of machine-reusable expressions of research findings](https://mlsanity.com/paper/2405.13129/) (2024)

## Abstract (arXiv)

Named Entity Recognition (NER) is an important task in natural language processing that aims to identify and extract key entities from unstructured text. We present a novel application of NER in plasma physics research articles and address the challenges of extracting specialized entities from scientific text in this domain. Research articles in plasma physics often contain highly complex and context-rich content that must be extracted to enable, e.g., advanced search. We propose a lightweight approach based on encoder-transformers and conditional random fields to extract (nested) named entities from plasma physics research articles. First, we annotate a plasma physics corpus with 16 classes specifically designed for the nested NER task. Second, we evaluate an entity-specific model specialization approach, where independent BERT-CRF models are trained to recognize individual entity types in plasma physics text. Third, we integrate an optimization process to systematically fine-tune hyperparameters and enhance model performance. Our work contributes to the advancement of entity recognition in plasma physics and also provides a foundation to support researchers in navigating and analyzing scientific literature.