Revisiting the Exit from Nuclear Energy in Germany with NLP
<|reference_start|>Revisiting the Exit from Nuclear Energy in Germany with NLP: 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.<|reference_end|>
@article{haunss2024revisiting,
    title={Revisiting the Exit from Nuclear Energy in Germany with NLP},
    author={Sebastian Haunss, Andr'e Blessing},
    journal={arXiv preprint arXiv:2408.13810},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13810},
    primaryClass={cs.CL}
}