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Multilingual Information Retrieval (MLIR) plays a crucial role in accessing information across different languages. This paper explores various techniques and tools used in Cross-Language Information Retrieval (CLIR), focusing on query translation, document translation, and hybrid approaches. Query translation employs bilingual dictionaries and machine translation systems to convert user queries from one language to another, whereas document translation involves translating documents into the query language for indexing and retrieval. Hybrid approaches combine these methods to optimize retrieval performance, leveraging the strengths of both to address their individual limitations. Our comparative analysis shows that hybrid systems consistently outperform standalone query or document translation systems, achieving higher precision, recall, and user satisfaction. For instance, hybrid systems in multilingual legal document retrieval tasks achieved precision rates of 88%, recall rates of 82%, and an F1 score of 0.85. These results underscore the effectiveness of hybrid approaches in handling the complexities of MLIR, providing more accurate and comprehensive retrieval outcomes. This study highlights the practical benefits of hybrid CLIR systems and suggests directions for future research in enhancing multilingual access to information.
Junhui Hu (Tue,) studied this question.