Revisiting the Exit from Nuclear Energy in Germany with NLP
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916368981426176 |
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| author | Haunss, Sebastian Blessing, André |
| author_facet | Haunss, Sebastian Blessing, André |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13810 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting the Exit from Nuclear Energy in Germany with NLP Haunss, Sebastian Blessing, André Computation and Language 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. |
| title | Revisiting the Exit from Nuclear Energy in Germany with NLP |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.13810 |