Artificial Intelligence in Linguistics Research: Applications in Language Acquisition and Analysis.

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Elma Sibonghanoy Groenewald
Sangeeta Rani
Priyanka Singla
Eric Howard
Coenrad Adolph Groenewald


Artificial Intelligence (AI) has emerged as a transformative force in linguistics research, offering innovative methodologies and tools for investigating language acquisition and analysis. This paper provides a comprehensive overview of the applications of AI in linguistics research, with a specific focus on its role in language acquisition and analysis processes. Through the lens of Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques, the paper explores how AI is revolutionizing our understanding of linguistic phenomena. AI techniques, particularly NLP, ML, and DL, have significantly advanced linguistic research by enabling automated analysis of linguistic data. NLP techniques facilitate the processing of natural language text, allowing researchers to perform tasks such as part-of-speech tagging, parsing, named entity recognition, and sentiment analysis with unprecedented accuracy and efficiency. ML algorithms, on the other hand, empower researchers to develop predictive models of language acquisition and usage by learning from large datasets of linguistic data. [1] Furthermore, DL models, such as neural networks, have demonstrated remarkable capabilities in capturing complex linguistic patterns and semantic structures. In the realm of language acquisition studies, AI plays a pivotal role in modeling language development processes. By employing computational modeling and simulation techniques, researchers can simulate the cognitive processes involved in language learning and test theoretical frameworks against empirical data. Additionally, AI techniques enable the analysis of language acquisition processes, including phonology, morphology, syntax, and semantics. ML algorithms have been employed to predict language development trajectories and analyze learners' linguistic productions and errors, providing insights into the mechanisms underlying language acquisition. While AI has revolutionized linguistics research, it also presents challenges such as the dependence on annotated data, biases in AI models, and ethical considerations regarding data privacy and consent. Addressing these challenges and fostering interdisciplinary collaboration are crucial for advancing linguistic theories and harnessing the full potential of AI in linguistics research. Overall, the integration of AI techniques holds immense promise for unlocking new insights into language acquisition and analysis, paving the way for future advancements in the field of linguistics.

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