Cross-Lingual Semantic Integration: Enhancing Document Retrieval through Vector Space Models (VSM) and Machine Translation

In this research, by combining machine translation with vector space models VSM for cross-lingual semantic integration this research offers a novel method for enhancing document retrieval especially in specialized fields like medical writings although they are good at identifying document similarities traditional VSMs sometimes have trouble effectively collecting semantic subtleties and pertinent terminology across languages we map multilingual materials into a single vector space by utilizing domain-specific machine translation which improves the ability to identify semantic links by removing language barriers and enhancing the retrieval of semantically related content this cross-lingual framework raises the standard of translations in specialized subjects when compared to conventional techniques our model exhibits notable gains in retrieval precision relevance and efficiency when tested on multilingual medical datasets with useful applications in cross-border data systems and multilingual knowledge management the suggested method provides a scalable framework for improving global documentation search and information retrieval.