Hybrid evolutionary solutions in modern machine linguistics in complex environments

Abstract

This paper reveals the essence and significance of hybrid evolutionary technologies in modern machine linguistics in the conditions of a complex, uncertain and dynamic language environment. It is substantiated that the combination of evolutionary algorithms, neural network models, statistical methods, linguistic rules and formalized knowledge allows to increase the adaptability, stability, accuracy and interpretability of natural language processing systems. The advantages of such solutions in the tasks of machine translation, text classification, entity extraction, semantic analysis, generalization and dialogic interaction are determined. It is shown that hybrid evolutionary approaches are a promising basis for creating intelligent language systems of a new generation, capable of functioning effectively in conditions of noise, data heterogeneity, limited markup and contextual variability.