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PhysBERT: A Text Embedding Model for Physics Scientific Literature

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arxiv 2408.09574 v1 pith:N6RK5ZJL submitted 2024-08-18 physics.comp-ph cs.CL

Thorsten Hellert , João Montenegro , Andrea Pollastro This is my paper · ORCID
classification physics.comp-phcs.CL
keywords physicstextembeddingmodelphysbertinformationlanguagephysics-specific
verification ladder T0 review T1 audit T2 compute T3 formal
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The specialized language and complex concepts in physics pose significant challenges for information extraction through Natural Language Processing (NLP). Central to effective NLP applications is the text embedding model, which converts text into dense vector representations for efficient information retrieval and semantic analysis. In this work, we introduce PhysBERT, the first physics-specific text embedding model. Pre-trained on a curated corpus of 1.2 million arXiv physics papers and fine-tuned with supervised data, PhysBERT outperforms leading general-purpose models on physics-specific tasks including the effectiveness in fine-tuning for specific physics subdomains.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Trajectories of Change: Approaches for Tracking Knowledge Evolution

    cs.CL 2024-12 conditional novelty 5.0 of 10

    A computational framework combining Kullback-Leibler divergence and embedding density estimation traces individual scholars' language against disciplinary knowledge evolution in 20th-century general relativity research.

  2. Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics

    cs.CL 2024-11 conditional novelty 5.0 of 10

    This paper introduces Astro-HEP-BERT, a BERT model adapted to astrophysics and high-energy physics text, plus a large arXiv-based corpus, as a low-cost tool for studying conceptual change in science.

  3. Meaning at the Planck scale? Contextualized word embeddings for doing history, philosophy, and sociology of science

    cs.CL 2024-11 conditional novelty 5.0 of 10

    Domain-adapted BERT models distinguish senses of 'Planck' better than general models, and reveal the rise of the Planck mission meaning in physics papers.

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