2018· International Journal of Innovative Research in Humanities & Technology· 0 citations
TL;DR
There is a conclusion that although AI has become an inseparable part of linguistics today, there is a need to establish a balanced approach of using computational methods and knowledge of human linguists to be sustainable and ethical.
Abstract
The high rate of development of artificially intelligence (AI) has significantly transformed the linguistic profession by introducing the use of AI-based language applications. Machine learning, deep learning, and natural language processing (NLP) are the driving forces of these tools redefining the methods of how language is studied, generated, and maintained. Since automated translation and speech recognition systems, AI systems are now at the center of linguistic studies and applications of language in practice. In the given article, we derive detailed research on how AI-based language tools impact the contemporary linguistics. It explores theoretical and historical developments, methodological and practical changes within the subdomains of linguistics. A systematic review of the literature brings out main milestones, trends in the research, and shortcomings of the available methods. The suggested methodology is going to assess AI-based linguistic tools based on both qualitative and quantitative scales, such as accuracy, linguistic validity, scalability, and interpretability. The findings indicate that AI-enabled applications can significantly improve the efficiency of the analytical process and reveal the linguistic patterns that are not available to ordinary analysis. Yet, another problem like bias, explainability and ethics issues is significant. The research provides a conclusion that although AI has become an inseparable part of linguistics today, there is a need to establish a balanced approach of using computational methods and knowledge of human linguists to be sustainable and ethical.
The review concludes that the future of applied linguistics depends on developing human-centered and interdisciplinary approaches that balance technological innovation with human agency, linguistic diversity, educational quality, and ethical responsibility in increasingly AI-mediated language environments.
A. Mohammed, Al Fallah, Al Karmaji· Comprehensive Journal of Sci...· 0 citations
A comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT demonstrates that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.
P. Kalaiselvi· International Journal of Eme...· 0 citations
The study comes to the conclusion that AI is a powerful instrument for confirming frequency-based linguistic theory but does not model the human cognitive journey.
Assis. lect. Batool Abdul-Mohsin Miri· Journal of College of Educat...· 0 citations
The findings show that AI-generated texts exhibit greater lexical diversity and syntactic complexity; however, they often exhibit structural uniformity, overuse of cohesive devices, and limited pragmatic depth, and should not replace professionally designed educational materials.
V. Smaglii, T. Korolova, Svitlana Yukhymets et al.· Arab World English Journal· 0 citations
A statistically significant increase in lexical density and frequency of cohesive markers has been revealed, indicating an increase in information compression and explicit discursive organization of texts, consistent with characteristics described in AI-assisted writing studies.
T. Nedashkivska, I. Varvaruk, M. Podoliak et al.· Journal of Intelligent Decis...· 0 citations
Artificial intelligence (AI) systems, such as large-scale speech recognition systems, are vital for acquiring
linguistic competence and achieving this is essential for their use. Thus, for large-scale speech recognition systems,
acquisition of linguistic competence is essential for usage of the system. Even for the widely spoken language like English,
Mandarin and Spanish, these corpora are vast and have hundreds of billions of tokens, which can be used to power up
virtually all natural-language processing (NLP) tasks. In the online world, however, the Azerbaijani language is far less
well resourced: publicly available language corpora are limited to a small number of high-resource languages. In the
article, the main problems related to corpus scarcity, such as poor quality of machine translation, incorrect morphological
processing, recognition dialects of the Azerbaijan language and failure of chatbots to understand the Azerbaijan language
are discussed.
Raksana Aliashrafova, Namiq Abdurahmanov, Gunel Aghammadova et al.· International Journal of Inn...· 0 citations
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