Understanding EFL Linguistic Models through Relationship between Natural Language Processing and Artificial Intelligence Applications

Abstract

Natural Language Processing (NLP) platforms have recently reported a higher adoption rate of Artificial Intelligence (AI) applications. The purpose of this research is to examine the relationship between NLP and AI in the application of linguistic tasks related to morphology, parsing, and semantics. To achieve this objective, a theoretical framework was designed to investigate the direct and indirect impact of the relationship between NLP platforms and AI applications, such as machine learning and deep learning. Theoretically, this study contributes to examining the relationship between NLP platforms and AI applications through selected linguistic models from the English as a Foreign Language (EFL) perspective. Practical implications are derived from syntactic and semantic variables when AI applications are used. The results of this study suggest that AI applications can use to support NLP tasks, particularly the adaptation of deep learning applications that can prove useful in extracting analytical inferences and enhancing NLP approaches applied to EFL texts. The conclusion drawn of this study is that if NLP caters to knowledge-rich AI techniques, it can make significant advances in the linguistics disciplines of morphology, parsing, and semantics.