Research on the Actual Sentence Partition and Translation of Chinese Cultural Landscape Discourses Based on Natural Language Processing Technology

Abstract

In recent years, ChatGPT has been extensively applied across various fields, with translation being one of them. The objective of this study is to investigate the influence of Natural Language Processing (NLP) technology on the translation process. The research method involved utilizing the concept of Actual Sentence Partition (ASP) within the framework of Function Sentence Perspective (FSP). ASP, introduced by V. Mathesius, the founder of the Prague School, divides sentences into the point of departure and the nucleus of an utterance, providing insights into their communicative and semantic characteristics. Conversely, Formal Sentence Division (FSD) solely focuses on marking grammatical elements without identifying the correct ASP, resulting in ambiguity and inaccuracy. In Chinese discourse, incomplete sentences are commonly observed, including the omission of known information and the use of multiple clauses. Consequently, when translating from Chinese to English, it is crucial to appropriately visualize the hidden information in Chinese and analyze Chinese discourse using ASP to reconstruct its equivalent in English. The findings of this study emphasize the significance of ASP in translation. By understanding the ASP, translators can better comprehend the intention and meaning behind Chinese discourse, ensuring a more accurate and faithful representation in English. Therefore, the application of ASP in translation serves as a valuable tool for bridging the linguistic and cultural gaps between languages. In conclusion, ASP, as a pivotal concept in FSP, offers a comprehensive framework for understanding and translating complex sentence structures in Chinese discourse. By utilizing ASP, translators can capture the communicative and semantic nuances of Chinese sentences, leading to more accurate and effective translation outcomes..