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The paper presents an unsupervised method to extract linguistic metaphors and group them into conceptual metaphors, applying the approach to analyze framing differences in left- vs. right-leaning podcasts.
CAMMAR introduces a representation learning framework that organizes Arabic metaphorical meaning into nested lexical, cultural, and metaphorical subspaces using a staged semantic curriculum, achieving strong metaphor detection (AUC up to 0.84) on a new span-annotated dataset.