The Analysis of New Words and Translations of New Words by Google Translate

Main Article Content

Suphattra Promdam
Sudsuang Yutdhana

Abstract

          This research aimed to analyze the characteristics of new words added to an English dictionary and to investigate the translation strategies and the efficiency of Google Translate in translating new words.  The study samples were drawn from a list of new words added in 2023 by the Oxford Learner Dictionary.  The research is qualitative.  The data collection was retrieving the new words, categorizing, analyzing the word origination, and translating the words both at the word level and the sentence level.  The study revealed that 1) the new words were based on social change, technological development, medical health sciences, and general words, 2) Google Translate used three translation strategies to translate new words including word-for-word translation, communicative translation, and adaption, and 3) the efficiency of Google Translate depended on the characteristics of new words; in other words, new words with the references with old words could be translated precisely and communicatively while the new words and the words with functional shift could not be translated accurately which led to miscommunication.

Article Details

How to Cite
Promdam, S. ., & Yutdhana, S. . (2024). The Analysis of New Words and Translations of New Words by Google Translate. Journal of Roi Kaensarn Academi, 9(5), 1–17. retrieved from https://so02.tci-thaijo.org/index.php/JRKSA/article/view/269588
Section
Research Article

References

สุพรรณี ปิ่นมณี. (2562). ภาษา วัฒนธรรมกับการแปล: ไทย-อังกฤษ. สำนักพิมพ์จุฬาลงกรณ์มหาวิทยาลัย.

Amilia, I. K., & Yuwono, D. E. (2020). A study of the translation of Google Translate. Lingua: Journal Ilmiah. 16 (2), 1-21.

Brahmana, R. S., Mohammed, F. A., & Chairuang, K. (2020). Customer segmentation based on RFM model using K-means, K-medoids, and DBSCAN methods. Lontar Komput. J. Ilm. Teknol. Inf. 11 (1), 32.

Crystal, D. (2011). Internet linguistics: A student guide. Routledge.

Deutscher, G. (2010). Through the language glass: Why the world looks different in other languages. Metropolitan books.

Hidayati, D., & Prasatyo, B. A. (2024). Equivalence Challenges in Machine Translation: An Analysis of Google Translate Output through Mona Baker's Theory (2011) and Post-Editing Strategies. INTERNATIONAL JOURNAL OF ECONOMICS, MANAGEMENT, BUSINESS, AND SOCIAL SCIENCE (IJEMBIS). 4 (1), 75-86.

Hutchins, W. J. (1986). Machine translation: past, present, future (p. 66). Chichester: Ellis Horwood.

Knight, K., & Luk, S. K. (1994, July). Building a large-scale knowledge base for machine translation. In AAAI (Vol. 94, pp. 773-778).

Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., ... & Herbst, E. (2007, June). Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th annual meeting of the association for computational linguistics companion volume proceedings of the demo and poster sessions (177-180). Association for Computational Linguistics.

Newmark, P. (1988). A textbook of translation (66, 1-312). New York: Prentice hall.

Rahmannia, M., & Triyono, S. (2019). A Study of Google Translate Translations: An Error Analysis of Indonesian-to-English Texts. International Journal of Linguistics, Literature and Translation (IJLLT). 2 (3), 196-200.

Suhono, S., Zuniati, M., Pratiwi, W., & Hasyim, U. (2020, February). Clarifying google translate problems of Indonesia-English translation of abstract scientific writing. In Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA) 2018, 24-25 January 2018, Padang, Indonesia.