用大模型自动提取学术文献中的概念定义，聚焦媒体偏见领域。
Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias
- 基于大模型构建自动定义抽取框架，整合文献筛选与定义提取。
- 在2398篇人工标注文章上验证，Claude-3-sonnet表现最佳。
- 适合需要快速构建领域知识体系的研究者使用。
本文提出TaxoMatic框架，利用大语言模型自动化从学术文献中提取概念定义。聚焦媒体偏见领域，该框架包含数据收集、基于LLM的相关性分类及概念定义提取。在包含2,398篇人工标注文章的数据集上进行评估，结果显示Claude-3-sonnet在相关性分类和定义提取任务中均表现最优。未来工作包括扩展数据集并将其应用于其他领域。
原文摘要 · Abstract (English)
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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