构建首个医学领域概念隐喻语料库，助力科学隐喻计算研究
The Medical Metaphors Corpus (MCC)
- 整合多源文本构建792条医学隐喻数据，含来源-目标映射与7级评分
- 现有大模型在科学隐喻检测上表现平平，说明领域理解仍有巨大提升空间
- 适合研究科学传播、医疗沟通、语言模型认知能力的学者使用
隐喻是塑造科学认知的基础机制，能有效传递复杂概念，但可能限制范式思维。尽管科学话语中普遍存在修辞性语言，现有隐喻检测资源多集中于通用领域，缺乏专业领域的支持。本文提出医学隐喻语料库（MCC），涵盖792条来自同行评审文献、新闻媒体、社交媒体及众包贡献的医学与生物学领域科学概念隐喻。每条数据包含源域-目标域概念映射及0-7分级的隐喻性判断，经人工标注验证。MCC是首个面向计算科学隐喻研究的标注资源。评估显示，当前先进语言模型在科学隐喻检测任务上表现有限，表明领域内修辞性语言理解仍有巨大提升空间。该语料库可支持隐喻检测基准测试、质量感知生成系统及以患者为中心的沟通工具等应用。
原文摘要 · Abstract (English)
Metaphor is a fundamental cognitive mechanism that shapes scientific understanding, enabling the communication of complex concepts while potentially constraining paradigmatic thinking. Despite the prevalence of figurative language in scientific discourse, existing metaphor detection resources primarily focus on general-domain text, leaving a critical gap for domain-specific applications. In this paper, we present the Medical Metaphors Corpus (MCC), a comprehensive dataset of 792 annotated scientific conceptual metaphors spanning medical and biological domains. MCC aggregates metaphorical expressions from diverse sources including peer-reviewed literature, news media, social media discourse, and crowdsourced contributions, providing both binary and graded metaphoricity judgments validated through human annotation. Each instance includes source-target conceptual mappings and perceived metaphoricity scores on a 0-7 scale, establishing the first annotated resource for computational scientific metaphor research. Our evaluation demonstrates that state-of-the-art language models achieve modest performance on scientific metaphor detection, revealing substantial room for improvement in domain-specific figurative language understanding. MCC enables multiple research applications including metaphor detection benchmarking, quality-aware generation systems, and patient-centered communication tools.
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