# Revisiting the Exit from Nuclear Energy in Germany with NLP

arXiv: https://arxiv.org/abs/2408.13810 · HTML: https://mlsanity.com/paper/2408.13810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers](https://mlsanity.com/paper/2310.12575/) (2023)
- [Actor Identification in Discourse: A Challenge for LLMs?](https://mlsanity.com/paper/2402.00620/) (2024)

## Abstract (arXiv)

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.