Hybrid artificial neural networks for adaptive philology in unstable or crisis conditions

Abstract

This paper explores how hybrid artificial neural networks enhance adaptive and targeted philology during periods of instability or crisis. As language rapidly transforms in volatile social and informational environments, hybrid neural systems–combining deep learning, statistical methods, symbolic linguistics and optimization techniques–provide higher accuracy, flexibility and resilience when processing complex textual data. The study outlines key applications of hybrid artificial neural networks, including large-scale corpus analysis, crisis-oriented discourse monitoring, lexicographic automation, multilingual research and multimodal communication processing. Results show that hybrid models effectively detect emerging linguistic trends, manipulative discourse and emotional signals, supporting information stability and improving philological analytics in turbulent conditions. Despite challenges such as computational cost and model opacity, hybrid artificial neural networks form an essential technological basis for the future development of digital philology.