Revisiting the Exit from Nuclear Energy in Germany with NLP

Sebastian Haunss, André Blessing
8/25/2024
cs.CL

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets

80% match2024

NLP for Knowledge Discovery and Information Extraction from Energetics Corpora

79% match2024

Automated Annotation of Evolving Corpora for Augmenting Longitudinal Network Data: A Framework Integrating Large Language Models and Expert Knowledge

78% match2025

Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks

78% match2 repos262023

Political Events using RAG with LLMs

78% match2025

Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification

78% match1 repo62023

Cite this paper

@article{haunss2024revisiting,
  title  = {Revisiting the Exit from Nuclear Energy in Germany with NLP},
  author = {Sebastian Haunss and André Blessing},
  year   = {2024},
  eprint = {2408.13810},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.13810v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2408.13810.svg)](https://codeofpaper.com/paper/2408.13810)

Discussion