Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?
Identifying technological trends is a core scientometric task, yet traditional frequency-based approaches struggle to capture substantial meaning shifts of domain-specific terms. We hypothesise that contextual embeddings can complement frequency dynamics to effectively track diachronic semantic change. We compare frequency and embedding-based approaches across Astrophysics and NLP corpora spanning from 2010 to 2024. Candidate terms are extracted using KeyBERT (utilizing SciBERT as its underlying language model) and filtered for significant frequency increases using Fisher's exact test. These terms are then evaluated for genuine semantic shift by domain experts to establish ground-truth la...
| Authors | Jianying Liu, Kim Gerdes, Jean-Marc Deltorn |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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