Abstract
This article explores the interdisciplinary issues of natural language studies and the perspectives of doctoral education inthe context of the triad «Intellectualization – Virtualization – Big Data.» Recent advancements in technology have significantly propelled progress in the field of natural language processing (NLP). Modern NLP relies heavily on machine learning and deep learning techniques, enabling computers to learn from extensive datasets and execute tasks with high precision. Key aspects highlighted in the article include the use of neural networks, attention mechanisms, and transfer learning. The primary challenges facing contemporary NLP, such as ambiguity, data scarcity, lack of context, and ethical concerns, are thoroughly discussed. Important research directions include multimodality, explainable neural networks, and the development of NLP systems for low-resource languages. The «Results and Discussion» section emphasizes the technological status of NLP and its significance in the realms of intellectualization, virtualization, and big data analytics. Potential research areas are identified, such as enhancing the intellectualization of NLP systems, creating virtual environments for NLP applications, and leveraging big data analytics to improve the accuracy and efficiency of NLP systems. Additionally, the article examines the training of doctoral students in the context of NLP and big data analytics, highlighting the development of competencies in research data management and the methodological capital of educational data science. The conclusion underscores the importance of integrating ethical considerations into the use of NLP technologies and their relevance to contemporary doctoral education in Ukraine. The study highlights the critical need for an integrated approach to language analysis, virtual environments, and big data for the development of NLP systems and the modernization of doctoral education.
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