发表机构
University of Colorado Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出无监督方法从语料库提取语言隐喻并聚类为概念隐喻群组，揭示左右翼播客在主题与框架上的差异，如左翼将媒体故事比作武器、右翼将经济比作受垂直变化影响的系统。
AI 中文摘要
概念隐喻通过让我们用更具体或具身的体验（如背负物理负担）来推理更抽象的体验（如缴纳税款），从而指导我们的思维与行动（Lakoff和Johnson，2011）。由此可见，不同的概念隐喻会导致不同的推理方式：将缴纳税款视为对社区的投资而非物理负担，会形成对税收截然不同的看法。因此，识别引导说话者或作者的概念隐喻有助于揭示他们对事件的框架设定。尽管这些隐喻无法直接观察到，但语言中出现的语言隐喻（即隐喻表达）的群组可作为它们的证据。基于此，我们提出一种无监督方法，该方法从语料库中提取语言隐喻，并使用结构化聚类方法形成对应概念隐喻的群组。通过该方法，我们指出了左右翼播客在关键主题与框架设定上的差异，例如左翼播客倾向于将媒体故事概念化为武器，而右翼来源通常将经济讨论为受垂直变化影响的系统。
英文摘要
Conceptual metaphors guide our thinking and actions by allowing us to reason about more abstract experiences (e.g., paying taxes) in terms of more concrete or embodied experiences (e.g., carrying a physical load) (Lakoff and Johnson, 2011). It follows that different conceptual metaphors can result in different reasoning: framing paying taxes as an investment in a community rather than a physical load leads to a very different outlook on taxation. Identifying the conceptual metaphors guiding a speaker or writer thus helps to reveal their framing of events. Though these metaphors can't be observed directly, groups of linguistic metaphors, metaphorical expressions as they appear in language, serve as evidence for them. Motivated by this, we present an unsupervised method that extracts linguistic metaphors from a corpus and uses a structured clustering approach to form groups corresponding to conceptual metaphors. Using this method, we point to key topical and framing differences in left- vs. right-leaning podcasts. For example, left-leaning podcasts tend to conceptualize media stories as a weapon, while right-leaning sources commonly discuss the economy as a system subject to vertical changes.
Comments49 pages (8 main text), 8 figures
