Semantic Logic Field: A Novel Framework for Global Semantic Modeling

抄録

This paper introduces the Semantic Logic Field (SLF), a novel framework for semantic modeling and dimensionality reduction. Inspired by field theory, SLF integrates topological principles with dynamic semantic relationships to bridge the gap between discrete semantic features and continuous transformations. The objective is to address the limitations of traditional methods (e.g., PCA, t-SNE, UMAP) in preserving global semantic structures during dimensionality reduction. SLF quantifies logical features of text, such as topic consistency, semantic coherence, and concept relevance, by modeling semantics as a dynamic field. Experiments on the 20 Newsgroups dataset demonstrate SLF's superior performance in clustering and dimensionality reduction. SLF achieves a Silhouette score of 0.9797 and distance preservation of 0.9994, outperforming traditional methods. The results show that SLF effectively preserves both local and global semantic structures, making it ideal for tasks like text clustering, semantic search, and cross-domain adaptation. In conclusion, SLF provides a robust and interpretable framework for semantic analysis, with potential applications in natural language processing and machine learning. Future work will focus on optimizing SLF's parameters, improving scalability, and integrating it with deep learning architectures.