Machine learning in R&D of linguistic projects with artificial intelligence in crisis times

Abstract

Machine learning (ML) is a core element of artificial intelligence (AI) and a key tool for linguistic research during crises. Classical ML ensures interpretability and efficiency for structured data, while neural network approaches are effective for large-scale and unstructured information. In unstable contexts, ML enables automation, adaptability, and rapid analysis of texts. Its applications include natural language processing, machine translation, and corpus-based forecasting of linguistic trends. Despite challenges such as data quality, bias, and resource limitations, hybrid integration of classical and neural methods provides both accuracy and resilience. Thus, ML strengthens the stability and continuity of linguistic R&D projects in times of crisis.