Semantic Alignment and Dynamic Word Vector Optimization Algorithm in Korean Corpus

This paper addresses the issues of semantic drift and dynamic word meaning distortion in Korean corpus by proposing a word vector modeling algorithm that integrates semantic alignment with dynamic optimization mechanisms. This algorithm introduces weighted orthogonal mapping and semantic anchor constraints in the semantic alignment phase, and combines contextual feedback and temporal smoothing in the dynamic optimization phase to achieve continuous evolution and stable alignment of the semantic space. Experimental results demonstrate that the proposed model achieves a semantic alignment accuracy of 93.15% on a corpus of millions of words, improves the mean cosine similarity to 0.805, and reduces the word vector drift rate by 34.6%. Compared to traditional models, this method outperforms Korean semantic consistency, cross-temporal stability, and generalization performance, providing new algorithmic insights and theoretical support for dynamic semantic modeling in low-resource languages.