Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaning

Researchers propose Scene Abstraction, a framework that moves beyond static word embeddings to capture the situated, affective dimensions of lexical meaning through structured scene representations. By decomposing word usage into contextual events, entities, settings, and expression-specific emotional profiles via LLM few-shot prompting, the work addresses a fundamental gap in how computational semantics models the experiential richness of language. This bridges cognitive linguistics and NLP, suggesting that future semantic systems may need to encode not just denotation but the interpretive atmospheres words inhabit across contexts.

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Explainer

The paper's actual contribution is methodological: it uses LLM few-shot prompting as the extraction mechanism for scene components, rather than proposing a novel architecture. This means the framework's quality depends entirely on prompt design and LLM consistency, a constraint the summary doesn't flag.

This work sits in tension with recent findings on embedding evaluation. The 'One prompt is not enough' paper from this week showed that instruction phrasing dramatically shifts embedding model performance, yet Scene Abstraction relies on LLM prompting to decompose meaning into scenes. If prompt sensitivity undermines embedding benchmarks, it likely affects scene extraction fidelity too. The framework addresses a real gap in capturing affective and contextual dimensions of words, but the evaluation methodology needs to account for the prompt brittleness that's now documented in the field.

If the authors release ablations showing scene extraction stability across 10+ prompt variants on the same word-context pairs, that validates the approach's robustness. If they don't, or if downstream tasks show high variance with minor prompt rewording, the framework may suffer from the same instruction sensitivity that plagued prior embedding work.

Coverage we drew on

One prompt is not enough: Instruction Sensitivity Undermines Embedding Model Evaluation · arXiv cs.CL

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MentionsScene Abstraction · COCA

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