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

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....

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Bibliographic Details
Published in:Arab world English journal 2019-12, Vol.10 (4), p.251-262
Main Author: Salim Keezhatta, Muhammed
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Software
Correlation
English (Second Language)
Inferences
Morphology (Languages)
Natural Language Processing
Second Language Instruction
Second Language Learning
Semantics
Syntax
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Summary: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.
ISSN:2229-9327
2229-9327
DOI:10.24093/awej/vol10no4.19