The Medical Metaphors Corpus (MCC)

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.