Dear Mahmoud Adel Al-Feky ,

AI in linguistic analysis faces limitations such as contextual misunderstanding, lack of cultural and emotional nuance, and biases in training data. While AI models excel at pattern recognition and syntactic processing, they often struggle with semantics, irony, and idiomatic expressions, leading to misinterpretations. Additionally, biases in training datasets can perpetuate stereotypes or exclude underrepresented dialects and languages.

Addressing these challenges requires diverse and representative datasets, continuous model refinement, and the integration of human expertise for context validation. Hybrid approaches that combine AI with human linguists, along with transparent model training and ethical oversight, can improve accuracy and inclusivity in linguistic analysis.

Reference

https://www.degruyter.com/document/doi/10.1515/9783111172408-003/pdf?licenseType=restricted

https://www.taylorfrancis.com/chapters/edit/10.1201/9781003433309-2/deep-learning-impacts-field-artificial-intelligence-wasswa-shafik

https://www.igi-global.com/chapter/mobile-learning-and-bring-your-own-device-byod/344309

https://www.igi-global.com/chapter/enhancing-ai-enabled-education-through-mobile-learning-and-bring-your-own-device-byod-integration/351965

AI's limitations in linguistic analysis include context understanding, nuances, and bias, which can be addressed through data diversification, human oversight, and continuous model training and refinement.

Some solutions:

1. Domain Specific Training (Supervise Learning with Language Expert)

2. Reinforcement learning layer where the AI models can rediscover the rules of the language all by themselves

3. Continual learning by interacting with human

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

"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. Keywords: Artificial Intelligence, Linguistics Research, Language Acquisition, Natural Language Processing, Machine Learning, Deep Learning."

Article Artificial Intelligence in Linguistics Research: Application...