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Scene Abstraction framework models situated word meaning using LLMs

Researchers have developed a framework called Scene Abstraction to represent the situated meaning of words, moving beyond simple property-based definitions. This approach uses few-shot prompting of large language models to create structured representations of interpretive scenes, including contextual elements and expression profiles. The framework was validated with a new dataset, COCA-Scenes, and demonstrated improved accuracy in identifying word-contextual scenes compared to existing methods. AI

IMPACT This research could lead to more nuanced and context-aware language models, improving their understanding of subtle word meanings and affective associations.

RANK_REASON The cluster contains an academic paper detailing a new framework and dataset for representing lexical semantics.

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Yejin Cho, Katrin Erk ·

    Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaning

    arXiv:2605.22542v1 Announce Type: new Abstract: Coffee and tea share many properties, yet they evoke strikingly different situations, atmospheres, and affective associations. These situated dimensions of word meaning are real and systematic, but they remain implicit in most compu…

  2. arXiv cs.CL TIER_1 · Katrin Erk ·

    Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaning

    Coffee and tea share many properties, yet they evoke strikingly different situations, atmospheres, and affective associations. These situated dimensions of word meaning are real and systematic, but they remain implicit in most computational representations of lexical meaning. We …