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Contextual embeddings capture semantic shifts in scientific texts

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of contextual embeddings in capturing semantic shifts in scientific terminology over time. The study compares frequency-based methods with embedding-based approaches using corpora from Astrophysics and Natural Language Processing. While frequency methods showed a slight edge in identifying trend-related terms, semantic metrics revealed critical conceptual developments, such as "primordial black holes," that pure frequency analysis missed. The findings suggest that integrating contextual embeddings can enhance scientometric trend analysis by capturing complementary information. AI

IMPACT This research could improve how scientific trends are identified and understood, potentially aiding researchers and policymakers.

RANK_REASON Research paper published on arXiv detailing a novel methodology for analyzing semantic change in scientific texts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Contextual embeddings capture semantic shifts in scientific texts

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Research paper published on arXiv detailing a novel methodology for analyzing semantic change in scientific texts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · 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?

    arXiv:2609.18804v1 Announce Type: new Abstract: 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 complem…

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