Автоматизовані методи класифікації документів: сучасні підходи, алгоритми та перспективи застосування в цифрових архівах

Abstract

Automated document classification has become a critical component in the management of digital information flows, particularly within archival and document-intensive domains. Various methods have emerged to address the challenges of categorizing large volumes of heterogeneous data, including rule based systems that operate on fixed keyword sets, statistical models that evaluate term frequencies, and machine learning algorithms capable of both supervised and unsupervised learning. More recent advances involve deep learning architectures, such as BERT and GPT, which offer nuanced, context-aware interpretations of textual content. Hybrid approaches, integrating multiple techniques, further enhance accuracy and adaptability. Together, these technologies significantly advance the efficiency and intelligence of document processing systems.