Nested Named Entity Recognition in Plasma Physics Research Articles

2026-02-13 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Physical Sciences & Chemistry · Depth: Advanced, extended

Summary

Researchers introduce a lightweight Nested Named Entity Recognition (NNER) approach for plasma physics research articles, addressing challenges in extracting specialized, complex, and context-rich entities. The method utilizes encoder-transformers and Conditional Random Fields (CRF), specifically a BERT-CRF architecture, with an entity-specific model specialization where independent BERT-CRF models are trained for 16 individual entity types. A new plasma physics corpus, comprising 30 full-text research papers and 500 patent abstracts (10,272 sentences), was annotated with these 16 classes. The approach integrates Bayesian Optimization (BO) to systematically fine-tune hyperparameters, achieving an F1 score of 0.68 on the plasma physics dataset, comparable to the highest-performing baseline (0.69) with lower architectural complexity. The BO-optimized model also achieved the highest recall (0.74) among compared methods.

Key takeaway

For AI Scientists and Research Scientists working on specialized information extraction, this work demonstrates that a lightweight BERT-CRF architecture, combined with entity-specific model specialization and Bayesian Optimization, can achieve competitive performance in complex domains like plasma physics. You should consider developing domain-specific datasets and employing automated hyperparameter tuning to optimize model performance, especially when dealing with class imbalance and nested entities, rather than relying solely on more complex, computationally expensive architectures.

Key insights

A lightweight BERT-CRF model with entity-specific specialization and Bayesian Optimization effectively extracts nested entities from plasma physics texts.

Principles

• Domain-specific NNER requires specialized datasets.
• Entity-specific models mitigate class imbalance.
• Bayesian Optimization enhances model performance.

Method

The method involves annotating a domain-specific corpus with 16 entity classes, training independent BERT-CRF models for each entity type, and using Bayesian Optimization to fine-tune hyperparameters for optimal F1-score.

In practice

• Annotate domain-specific corpora for NNER.
• Train separate models for each entity type.
• Apply Bayesian Optimization for hyperparameter tuning.

Topics

• Nested Named Entity Recognition
• Plasma Physics
• BERT-CRF Models
• Bayesian Optimization
• Scientific Information Extraction

Best for: AI Scientist, Research Scientist, AI Researcher, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.