arXiv Computation and Language By Jianying Liu (STL, BETA, CEIPI), Kim Gerdes (LISN, Qatent, STL), Jean-Marc Deltorn (CEIPI)

Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?

Read the original on arXiv Computation and Language →

The study investigates whether contextual embeddings can detect meaning changes in scientific terminology beyond traditional frequency counts. Using Astrophysics and NLP corpora from 2010 to 2024, the authors extract candidate terms with KeyBERT, filter for significant frequency rises, and then evaluate semantic drift via multiple embedding‑based metrics. Results show that frequency methods slightly outperform embedding metrics in aligning with expert judgments, yet embedding‑only detections (e.g., "primordial black holes") reveal critical conceptual shifts missed by frequency alone, suggesting complementary value.

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