<i>The Syriac Tradition of the Infancy Gospel of Thomas: A Critical Edition and English Translation</i>. By <scp>Tony Burke</scp>

Shoemaker, Stephen J.

doi:10.1093/jts/flaa061

Cited by 2 publications

(4 citation statements)

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“…Everyday English is an informal language in daily life and work, used for the smooth running of ordinary life interaction behavior and work [9]. Everyday English translation corpora should pay attention to the integrity of the whole translation, which can accurately convey the translator's will and meet people's needs, and it has a certain flexible diversity and communicative practice, which is widely used in real life [10][11]. Under the influence and constraints of business activities' nature, content, form, and norms, there are strong tendencies and limitations in using language structures and lexical choices.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Xing,

Rong,

Chen

2023

Applied Mathematics and Nonlinear Sciences

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Based on the formal definition of semantic language and specific natural language, the research adopts the Word2vec model to map words into a low-dimensional dense space and performs semantic modeling by unsupervised learning through a large amount of unlabeled data to realize the vectorized representation of translated text. For the vectorized text, the text features are combined and filtered based on the TextCNN network, and the TextCNN network is implemented under the Keras framework. Based on the TextCNN network of Keras, the stylistic features of Business English and Daily English are compared at three levels: lexical, syntactic and discourse. Business English was rated 23.6 points higher in terms of conciseness of diction compared to everyday English, and 28.7 points improved the modesty of diction compared to everyday English. This study furthers the improvement of various English translation types.

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Section: Introductionmentioning

confidence: 99%

“…Jiawei Xing, Xiuli Rong and Guomin Chen. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12] …”

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confidence: 99%

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Xing,

Rong,

Chen

2023

Applied Mathematics and Nonlinear Sciences

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“…A, X. L and B, H. Y proposed a systematic functional language-based approach to the selection of candidate words for professional vocabulary in the process of English-Chinese translation, analyzing the characteristics and differences between the English text and the Chinese text so as to put forward a corresponding translation strategy [13]. Shoemaker, S. J points out the difficulties of translating religious books into English by comparing the original version with the English translation using the text of the Gospel of the Infant Thomas in the Syriac version as an example [14]. Espunya, A presents the concept of abstraction in Spanish-English translation based on a corpus of Spanish and English translations, using the case of attributes denoting nouns to develop a study [15].…”

Section: Introductionmentioning

confidence: 99%

“…Jiao Xue. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] …”

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An Exploration of English Translation Methods for Long Sentences Combined with Modal Analysis from the Perspective of Translation Purpose

Xue

2023

Applied Mathematics and Nonlinear Sciences

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The complex sentence structure and complicated semantic logic of English long sentences are the difficult points that need to be overcome urgently in English translation. This paper outlines the basic direction of machine translation based on the perspective of translation purpose. Based on the grammatical categories of English words, lexical annotation is completed, and regular expressions for matching English long sentences are proposed based on the annotation results. After realizing the preprocessing of English text, the features of the text are extracted and converted into the format required by the CRF model, and the long-sentence slicing is completed through the training of CRF. The cut Encoder encoding and Decoder decoding were utilized to generate the final machine translation result. In the test of the United Nations machine translation corpus, this paper’s method improves the translation performance in the English-Chinese direction by 0.29 Bleu value and the translation performance in the Chinese-English direction by 0.13 Bleu value compared with the method of Xu and Zens. The translation performance is effectively improved by combining long sentence slice translation with modal analysis.

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The Syriac Tradition of the Infancy Gospel of Thomas: A Critical Edition and English Translation. By Tony Burke

Cited by 2 publications

References 0 publications

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

An Exploration of English Translation Methods for Long Sentences Combined with Modal Analysis from the Perspective of Translation Purpose

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