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Meaning in human language is conventionally treated as a relatively stable function of lexical structure and compositional organization. The present study challenges this assumption by proposing a probabilistic and context-sensitive account of interpretation in which meaning emerges not as a fixed semantic value but as a distribution across possible interpretive realizations. Building on a triadic framework linking utterance (U), meaning-space (M), and contextual mediation (C), the paper models interpretation as a conditional probability structure rather than deterministic semantic retrieval. Using an exploratory annotated corpus of Bangla lexical items alongside a complementary semiotic dataset involving colors, gestures, interface symbols, and affective visual cues, the study demonstrates that contextual conditions systematically redistribute interpretive weighting across discourse environments. Interpretive variability is operationalized through entropy-oriented measures and meaning-spread indices, allowing semantic dispersion to be represented analytically without reducing interpretation to rigid categorical assignment. The findings reveal patterned differences across semantic domains: affective, ideological, and philosophical expressions sustain substantially greater interpretive dispersion than highly constrained functional or technical lexical categories. The study further argues that this variability extends beyond language proper. Parallel patterns observed across non-linguistic semiotic systems suggest that linguistic meaning constitutes a specialized instance of a broader interpretive architecture operating across symbolic, perceptual, and social environments. Meaning, therefore, is approached not as semantic possession but as structured interpretive distribution. By integrating insights from corpus pragmatics, semiotics, probabilistic semantics, and contextual NLP, the paper advances a minimal but extensible framework for analyzing context-conditioned interpretive variability across linguistic and semiotic domains. The proposed U–M–C model contributes to ongoing debates concerning semantic instability, contextual interpretation, and probabilistic meaning formation by foregrounding variability not as residual communicative noise, but as one of the organizational conditions through which meaning becomes socially operational.