ADVANCEMENTS IN NATURAL LANGUAGE PROCESSING FOR MULTILINGUAL INFORMATION RETRIEVAL SYSTEMS

Abstract

Natural Language Processing (NLP) has revolutionized the field of Information Retrieval (IR) by enhancing the ability of machines to understand, process, and respond to human language. With the globalization of digital information, multilingual Information Retrieval (MLIR) systems are essential for retrieving information across different languages. This paper examines the recent advancements in NLP techniques used to improve the performance and efficiency of multilingual information retrieval systems. We explore the challenges faced in multilingual information retrieval, such as language diversity, translation, and semantic understanding. The paper also highlights the integration of advanced NLP technologies, including deep learning models, transformers, and cross-lingual embeddings, to tackle these challenges. Through a detailed review of existing methods and case studies, we present how these advancements have significantly enhanced the ability of MLIR systems to retrieve accurate, contextually relevant information across languages. Finally, we provide insights into the future trends in NLP for multilingual IR, particularly in light of emerging technologies like transfer learning and multilingual BERT.