THEORETICAL PRINCIPLES BEHIND ARTIFICIAL INTELLIGENCE, MACHINE TRANSLATION, AND ERROR CORRECTION TECHNOLOGIES.
Keywords:
Artificial Intelligence (AI), Machine Translation (MT), Error Correction Technologies, Natural Language Processing (NLP), Neural Machine Translation (NMT), Deep Learning, Transformer Models, Statistical Language Models, Computational Linguistics, Language Modeling, Sequence-to-Sequence Learning, Contextual Embeddings, Generative Grammar, Syntax and Parsing, Low-Resource Languages, Multilingual Communication, Cognitive Linguistics, Corpus-Based Methods, Explainable AI, Hybrid Language Systems.Abstract
This article reviews the key theoretical ideas that support the development of artificial intelligence, machine translation, and automated error correction systems. It outlines how these technologies evolved from traditional linguistic models to modern AI-driven approaches, with a special focus on machine learning and neural network-based techniques. The study emphasizes how theoretical linguistics and computational methods work together to enhance digital language tools.
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