The Medical Metaphors Corpus (MCC)

TL;DR

The Medical Metaphors Corpus (MCC) is a new annotated dataset of 792 scientific metaphors in medical and biological texts, designed to advance computational metaphor detection and understanding in specialized domains.

Contribution

This paper introduces MCC, the first annotated resource for scientific metaphors in medical and biological texts, with diverse sources and graded metaphoricity scores, facilitating domain-specific metaphor research.

Findings

State-of-the-art models perform modestly on scientific metaphors.

MCC enables benchmarking and development of better metaphor detection tools.

The dataset supports applications in communication and generation systems.

Abstract

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.