# Tracing the Development of the Virtual Particle Concept Using Semantic Change Detection

arXiv: https://arxiv.org/abs/2410.16855 · HTML: https://mlsanity.com/paper/2410.16855/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses](https://mlsanity.com/paper/2104.14694/) (2021)
- [Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift](https://mlsanity.com/paper/1912.01072/) (2019)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020)
- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [Building astroBERT, a language model for Astronomy & Astrophysics](https://mlsanity.com/paper/2112.00590/) (2021)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection](https://mlsanity.com/paper/2401.14040/) (2024)
- [PhysBERT: A Text Embedding Model for Physics Scientific Literature](https://mlsanity.com/paper/2408.09574/) (2024)

## Built on by (most cited first, 3 of 3)

- [Meaning at the Planck scale? Contextualized word embeddings for doing history, philosophy, and sociology of science](https://mlsanity.com/paper/2411.14073/) (2024)
- [Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics](https://mlsanity.com/paper/2411.14877/) (2024)
- [Trajectories of Change: Approaches for Tracking Knowledge Evolution](https://mlsanity.com/paper/2501.00391/) (2024)

## Abstract (arXiv)

Virtual particles are peculiar objects. They figure prominently in much of theoretical and experimental research in elementary particle physics. But exactly what they are is far from obvious. In particular, to what extent they should be considered "real" remains a matter of controversy in philosophy of science. Also their origin and development has only recently come into focus of scholarship in the history of science. In this study, we propose using the intriguing case of virtual particles to discuss the efficacy of Semantic Change Detection (SCD) based on contextualized word embeddings from a domain-adapted BERT model in studying specific scientific concepts. We find that the SCD metrics align well with qualitative research insights in the history and philosophy of science, as well as with the results obtained from Dependency Parsing to determine the frequency and connotations of the term "virtual." Still, the metrics of SCD provide additional insights over and above the qualitative research and the Dependency Parsing. Among other things, the metrics suggest that the concept of the virtual particle became more stable after 1950 but at the same time also more polysemous.