This paper argues that semantic degeneracy in natural language imposes fundamental limitations on modern natural language processing systems, including large-scale language models (LLMs). As the complexity of semantic representations increases, the possibility of interpretation increases exponentially, as demonstrated using Kolmogorov complexity. It argues that the classical view that linguistic forms themselves have intrinsic meaning is insufficient due to the computational difficulty of recovering the single intended meaning of complex or ambiguous texts. Instead, it argues that meaning is realized dynamically through observer-dependent interpretation behavior, the nondeterministic nature of which is best described by nonclassical quantum-like logic. Through semantic Bell inequality tests using various LLM agents, we obtain several executions that produce values (e.g., 2.3-2.4) that significantly violate the classical bound ($|S|\leq2$), demonstrating that linguistic interpretation under ambiguity can exhibit nonclassical contextuality. We therefore argue that classical frequentist-based analysis approaches to natural language are necessarily lossy, and instead suggest that a Bayesian-style repeated sampling approach may provide more practical and appropriate properties.
