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

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arxiv 2504.00343 v1 pith:BSIMEDYS submitted 2025-04-01 cs.CL

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

Timo Spinde , Luyang Lin , Smi Hinterreiter , Isao Echizen This is my paper
classification cs.CL
keywords extractiondefinitiontaxomaticbiasclassificationframeworklanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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