Neural and Semantic Approaches for Scalable Multilingual Information Retrieval Systems
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Abstract
In our increasingly interconnected world, accessing and storing information across different regions is a challenge. This article explores technologies and innovations that facilitate the retrieval of valuable information in multiple languages. It explores the complexity of processing multilingual data and investigates new methods to bridge language gaps, thereby enhancing global database accessibility. This work encompasses the field of natural language processing and cross-linguistic information retrieval by examining advances in multilingual indexing, query expansion, and machine translation. Additionally, it scrutinises the role of communication and language representation in enhancing information retrieval systems’ performance. The critical analysis and factual examination presented herein emphasise progress, potential, and challenges in implementing multilingual information retrieval on a global scale.
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