An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

Abstract

Language translation is highly needed in the increasingly networked world. The writing of English essays is an essential competency for higher education students. For non-English students in China, this is especially important. They often represent large numbers of Chinese English as second language students. Therefore this paper introduced Deep Structured Learning with Natural language processing (DSL-NLP) has been proposed to convert the Chinese language to the English language for student learning. An automatic input tool for writing assistance will be beneficial in building the proposed DSL-NLP method. The relationship between textual attributes and input from human teachers and provide characteristics feedback system. This study explored natural language processing (NLP) to determine the interpretative actions required for a successful understanding of the literary text. NLP attempts to assist in achieving consistency in applied linguistics, improving classroom communication, optimizing learner attitudes and motivation, raising personality, facilitating personal development, and even changing students' perspectives toward life. Thus the experimental results show the DSL-NLP improves student learning in the English language, enhances student performance in education, and effectively increases the student career