Parametric Semantic Lattices (PSL) offer a new approach to enhancing semantic representation and contextual processing in advanced language modeling architectures. By embedding hierarchical structures that capture both local and global linguistic dependencies, PSL addresses key limitations observed in traditional transformer-based frameworks. The adaptive nature of the lattice configurations enables the dynamic interpretation of syntactic and semantic relationships across diverse linguistic inputs. Quantitative analysis reveals significant reductions in perplexity and marked improvements in semantic coherence when compared to baseline models. Through leveraging multi-dimensional encoding, the PSL framework enhances interpretability while maintaining computational efficiency across long text sequences. Experiments demonstrate its scalability across extensive datasets and various hardware configurations, validating its application to resource-intensive natural language processing tasks. Evaluation under conditions of noisy input highlights its robustness in maintaining contextual fidelity and accuracy. Practical applications span areas such as automated translation, sentiment analysis, and advanced query resolution, showcasing its versatility. The integration of PSL into transformers introduces minimal computational overhead while yielding substantial performance gains. Results further highlight its ability to retain long-range dependencies, a critical factor for tasks requiring deeper contextual understanding. By redefining structural modeling within transformers, PSL opens new avenues for the development of contextually aware artificial intelligence systems. The findings collectively underscore its transformative potential in advancing the state of language modeling and representation.