Open Access

English Translation Stylistic Features and Syntax Translation with Application of Knowledge Mapping

   | Nov 20, 2023

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Deng, D., & Xue, N. (2017). Translation divergences in chinese–english machine translation: an empirical investigation. Computational Linguistics, 1-65. Search in Google Scholar

Farahani, M. V. (2022). Review of feng (2020): form, meaning and function in collocation: a corpus study on commercial chinese-to-english translation:. International Journal of Corpus Linguistics, 27(2), 254-259. Search in Google Scholar

Luo, J., & Li, D. (2022). Universals in machine translation?:a corpus-based study of chinese-english translations bywechat translate. International Journal of Corpus Linguistics, 27(1), 31-58. Search in Google Scholar

Arunachalam, S., Syrett, K., & Chen, Y. X. (2016). Lexical disambiguation in verb learning: evidence from the conjoined-subject intransitive frame in english and mandarin chinese. Frontiers in Psychology, 7(7), 138. Search in Google Scholar

Xu, M., Huang, C., & You, X. (2016). Reasoning patterns of undergraduate theses in translation studies: an intercultural rhetoric study. English for Specific Purposes, 41, 68-81. Search in Google Scholar

Nurmi, A., & Skaffari, J. (2021). Managing latin: support and intratextual translation as mediation strategies in the history of english. Text and Talk. Search in Google Scholar

Dai, F., & Zheng, W. (2019). Self‐translation and English‐language creative writing in China. World Englishes, 38(2). Search in Google Scholar

Robin, & Orton. (2016). Gregory of nyssa: contra eunomium iii. an english translation with commentary and supporting studies. proceedings of the 12th international colloquium on gregory of nyssa (leuven, 14–17 september 2010).edited by johan leemans and matthieu cassin. The Journal of Theological Studies. Search in Google Scholar

Lijewska, A., & Baszkowska, H. (2021). Non-identical cognates yield facilitation in translation – does the way foreign vocabulary is learned affect processing?. Poznan Studies in Contemporary Linguistics. Search in Google Scholar

Vanroy, B., Tezcan, A., & Macken, L. (2019). Predicting syntactic equivalence between source and target sentences. Computational Linguistics in the Netherlands Journal, 101-116. Search in Google Scholar

Heyvaert, L., Maekelberghe, C., & Buyle, A. (2018). Nominal and verbal gerunds in present-day english: aspectual features and nominal status. Language Sciences. Search in Google Scholar

Beiler, I. R., & Dewilde, J. (2020). Translation as translingual writing practice in english as an additional language. Modern Language Journal. Search in Google Scholar

Ptasznik, B. (2020). Which defining model contributes to more successful extraction of syntactic class information and translation accuracy?. Lexikos, 30(1). Search in Google Scholar

Crible, L., Abuczki, A., Burkaitien, N., Péter Furkó, & árka Zikánová. (2019). Functions and translations of underspecified discourse markers in ted talks: a parallel corpus study in five languages. Journal of Pragmatics, 142, 139-155. Search in Google Scholar

Jianwei, Z., & Wenjun, F. (2020). Different processes for translating expressive versus informative texts? a computer-assisted study of professionals’ english–chinese translation. Digital Scholarship in the Humanities. Search in Google Scholar

Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., & Glass, J. (2020). On the linguistic representational power of neural machine translation models. Computational Linguistics, 46(1), 1-57. Search in Google Scholar

Lastres-López, & Cristina. (2018). If -insubordination in spoken british english: syntactic and pragmatic properties. Language Sciences, 66, 42-59. Search in Google Scholar

Shen, G., Wang, W., Mu, Q., Pu, Y., Qin, Y., & Yu, M. (2020). Data-driven cybersecurity knowledge graph construction for industrial control system security. Wireless Communications and Mobile Computing. Search in Google Scholar

Ibrahim, W., & Bijiga, L. K. (2021). Neural network method for solving time-fractional telegraph equation. Mathematical Problems in Engineering. Search in Google Scholar

THENMOZHI, D., ARAVINDAN, & CHANDRABOSE. (2016). Paraphrase identification by using clause-based similarity features and machine translation metrics. Computer Journal. Search in Google Scholar

eISSN:
2444-8656
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics