Nested Named Entity Recognition in Plasma Physics Research Articles

TL;DR

This paper introduces a specialized nested NER method for plasma physics research articles using encoder-transformers and CRFs, improving entity extraction in complex scientific texts.

Contribution

It presents a novel annotated corpus, entity-specific BERT-CRF models, and an optimization process tailored for nested NER in plasma physics literature.

Findings

Achieved effective extraction of complex entities in plasma physics texts.

Enhanced model performance through hyperparameter optimization.

Provided a foundation for advanced literature analysis in plasma physics.

Abstract

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.