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 computational representations of lexical meaning. We propose Scene Abstraction, a framework for constructing structured representations of the interpretive scenes that words participate in across usage contexts. Each scene consists of a Contextual Scene (Events, Entities, Setting) and an expression-centered Expression Profi
The paper focuses on developing structured representations of situated meaning, a critical step for more nuanced and context-aware AI understanding, reflecting the current push for advanced AI capabilities beyond simple lexical semantics.
This research addresses a fundamental limitation in current AI models by proposing a framework to capture the 'interpretive scenes' of words, essential for creating more human-like, contextually aware AI agents and systems.
The ability to represent situated meaning systematically improves AI's capacity to understand and generate language that reflects real-world contexts, emotional associations, and implicit nuances.
- · AI agents developers
- · Generative AI companies
- · NLP researchers
- · Human-computer interaction specialists
- · AI models relying solely on statistical word embeddings
- · Systems lacking contextual understanding
- · Developers creating non-situated AI models
More sophisticated and context-aware AI models emerge, capable of deeper language understanding.
AI agents become more effective in complex, multi-modal environments by interpreting subtle cues and situations.
The distinction between human and AI linguistic understanding blurs further, impacting user interfaces and human-AI collaboration paradigms.
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