Revisiting the Exit from Nuclear Energy in Germany with NLP

2024/08/25 by Sebastian Haunss, Haunss, Sebastian, André Blessing +1
Engineering · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Nuclear reactor physics and engineering

paper · pdf · doi:10.48550/arxiv.2408.13810

openalex publication_date 2024/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Annotation of political discourse is resource-intensive, but recent developments in NLP promise to automate complex annotation tasks. Fine-tuned transformer-based models outperform human annotators in some annotation tasks, but they require large manually annotated training datasets. In our contribution, we explore to which degree a manually annotated dataset can be automatically replicated with today's NLP methods, using unsupervised machine learning and zero- and few-shot learning.

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