Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias

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Main Authors: Spinde, Timo, Lin, Luyang, Hinterreiter, Smi, Echizen, Isao
Format: Preprint
Published: 2025
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author Spinde, Timo
Lin, Luyang
Hinterreiter, Smi
Echizen, Isao
author_facet Spinde, Timo
Lin, Luyang
Hinterreiter, Smi
Echizen, Isao
contents This paper introduces TaxoMatic, a framework that leverages large language models to automate definition extraction from academic literature. Focusing on the media bias domain, the framework encompasses data collection, LLM-based relevance classification, and extraction of conceptual definitions. Evaluated on a dataset of 2,398 manually rated articles, the study demonstrates the frameworks effectiveness, with Claude-3-sonnet achieving the best results in both relevance classification and definition extraction. Future directions include expanding datasets and applying TaxoMatic to additional domains.
format Preprint
id arxiv_https___arxiv_org_abs_2504_00343
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias
Spinde, Timo
Lin, Luyang
Hinterreiter, Smi
Echizen, Isao
Computation and Language
This paper introduces TaxoMatic, a framework that leverages large language models to automate definition extraction from academic literature. Focusing on the media bias domain, the framework encompasses data collection, LLM-based relevance classification, and extraction of conceptual definitions. Evaluated on a dataset of 2,398 manually rated articles, the study demonstrates the frameworks effectiveness, with Claude-3-sonnet achieving the best results in both relevance classification and definition extraction. Future directions include expanding datasets and applying TaxoMatic to additional domains.
title Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias
topic Computation and Language
url https://arxiv.org/abs/2504.00343