Revisiting the Exit from Nuclear Energy in Germany with NLP

Sebastian Haunss; André Blessing

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arXiv · 2408.13810

Revisiting the Exit from Nuclear Energy in Germany with NLP

Sebastian Haunss·André Blessing

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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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Sebastian Haunss, André Blessing. 2024-08-25. Revisiting the Exit from Nuclear Energy in Germany with NLP. https://arxiv.org/abs/2408.13810

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