Leveraging Artificial Intelligence in Linguistics: Innovations in Language Acquisition and Analysis
Simuzar Shirinova · EuroGlobal Journal of Linguistics and Language Education. · 2025
Artificial Intelligence (AI) has become a transformative tool in the field of linguistics, providing innovative approaches to studying language acquisition and analysis. This article offers a detailed exploration of AI’s applications in linguistics, with a focus on its contributions to understanding language learning and processing. Using methods such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL), researchers are uncovering new perspectives on linguistic phenomena and advancing the study of language. NLP, ML, and DL have enabled the automation of linguistic data analysis with remarkable accuracy and efficiency. NLP techniques allow researchers to process and analyze natural language text through tasks like part-of-speech tagging, syntactic parsing, named entity recognition, and sentiment analysis. Meanwhile, ML algorithms facilitate the development of predictive models for language acquisition and usage by leveraging large linguistic datasets. Additionally, DL models, particularly neural networks, have shown exceptional capabilities in identifying complex linguistic patterns and capturing semantic relationships. In the context of language acquisition research, AI is instrumental in modeling the cognitive processes involved in learning a language. By employing computational simulations and models, researchers can examine how learners acquire phonology, morphology, syntax, and semantics. AI methods also provide valuable tools for studying language development trajectories, analyzing learner productions, and identifying error patterns, offering deeper insights into the mechanisms of language acquisition.