Paper ID: 2408.13810

Revisiting the Exit from Nuclear Energy in Germany with NLP

Sebastian Haunss, André Blessing

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.

Submitted: Aug 25, 2024