Advancing Chinese Survey Document Retrieval: Multilingual Models and Semantic Understanding for Structured Information Extraction

The rapid digitalization of archival documents in China has generated an overwhelming volume of survey documents, requiring efficient retrieval systems for both research and administrative purposes. This study aims to explore innovative approaches for Chinese survey document retrieval by leveraging multilingual pre-trained models, advanced semantic understanding techniques, and structured data extraction. The research will focus on addressing challenges unique to the Chinese language, such as complex character semantics, idiomatic phrases, and hierarchical content structures in survey documents. By integrating natural language processing (NLP), optical character recognition (OCR), and retrieval models, the study seeks to enhance document accessibility, improve search accuracy, and facilitate information retrieval across diverse Chinese survey datasets.