Researchers have utilized transformer-based models to analyze approximately 4,000 hours of parliamentary speech from four Slavic languages: Croatian, Czech, Polish, and Serbian. The study investigated the occurrence and rate of filled pauses (FPs), finding that age and speech rate negatively correlate with FP rate, while gender effects are language-specific. Additionally, sentiment showed a positive association with FP rate, with political orientation and power status modulating these effects within specific parliaments. AI
IMPACT Provides insights into applying transformer models for linguistic analysis in political discourse.
RANK_REASON The cluster contains an academic paper published on arXiv detailing research methodology and findings.
- alphaXiv
- CatalyzeX Code Finder for Papers
- CORE Recommender
- Croatian
- Czech
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Mundlak correction
- Polish
- ScienceCast
- Serbian
- transformers
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