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Automated emotion intensity annotation using LLMs achieves near-human performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to automate the annotation of emotion intensity in text, addressing a key bottleneck in creating datasets for natural language processing tasks. This new approach utilizes large language models to perform comparative annotations, specifically employing a best-worst scaling technique, which proved more reliable than direct rating scales. A transformer regressor fine-tuned on these automated annotations achieved performance nearly on par with models trained on manually annotated data. AI

IMPACT Automating annotation for continuous labels like emotion intensity can accelerate NLP research and model development.

RANK_REASON The cluster describes a research paper detailing a new method for automated annotation of text data for emotion intensity modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Automated emotion intensity annotation using LLMs achieves near-human performance

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The cluster describes a research paper detailing a new method for automated annotation of text data for emotion intensity modeling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa, Roman Klinger ·

    "You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling

    arXiv:2403.17612v3 Announce Type: replace Abstract: Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly for categorical annotations. Some NLP tasks such …

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