Synergy of hybrid intelligent technologies in innovative machine linguistics in times of crisis

Abstract

This article analyzes the use of hybrid intelligent technologies in modern machine linguistics in the face of uncertainty and crisis-driven information processes. It is noted that traditional neural network and formal linguistic approaches, despite their high effectiveness, demonstrate limited resilience when working with incomplete, heterogeneous, and dynamic text data. Therefore, the need for hybrid architectures combining artificial intelligence, big data analysis, and formalized knowledge is substantiated. It is shown that the synergistic effect arising from the integration of heterogeneous intelligent components facilitates a more accurate interpretation of content and context, and also improves the adaptability and reliability of NLP systems. The paper formulates key scientific and practical recommendations for implementing hybrid solutions in text analysis and decision support tasks. It concludes that hybrid intelligent technologies hold promise as a key area for the development of machine linguistics in an unstable information environment.