THE EVOLUTION OF THEORETICAL APPROACHES TO TEXT ANALYSIS IN NATURAL LANGUAGE PROCESSING (NLP)
Keywords:
Natural Language Processing (NLP), computational linguistics, rule- based approach, corpus linguistics, statistical models, machine learning, neural networks, transformers, semantic analysis, context.Abstract
The article provides a comprehensive examination of the theoretical and
methodological evolution of Natural Language Processing (NLP), the stages of its formation, and contemporary trends in its development. The study presents an in-depth
theoretical analysis of four major approaches to text analysis: rule-based (generative),
statistical and corpus-based, machine learning, and neural network-based paradigms.
Alongside the scholarly perspectives of international researchers, the achievements of
Russian, Tatar, and Uzbek linguistic schools in this field are comparatively examined,
particularly the challenges of automatic processing of Turkic languages in view of their
agglutinative characteristics. The article substantiates the interrelationship between
empirical and formal modeling of linguistic units and highlights the scientific significance
of addressing semantic ambiguity and contextual information in modern artificial
intelligence systems.
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