Abstract
Urban publicity translation, as a cross-cultural communication activity, should aim for communication, employ various translation strategies, adapt to the target language’s expression habits, overcome cultural differences, and make the translation easy to accept for target readers. In order to achieve the goal of external promotion, publicity texts should respect and conform to the target culture’s language expression as well as the psychology of the audience during the initial stage of urban publicity translation. This paper analyzes the causes of cultural vacancies in the translation of urban publicity materials, starting with the classification and sorting of cultural vacancies in the translation of publicity materials. This paper focuses on using a computer corpus to reconstruct cross-cultural text for urban publicity translation. An automatic corpus expansion method combined with the EM (expectation-maximization) algorithm is proposed to solve this problem. The model is iteratively trained after the generated single corpus is combined with the original data set to create a parallel corpus. Finally, as another important feature of words, the word cooccurrence degree is incorporated into the interword relationship extraction model to create a new word translation evaluation index. Finally, the experiment demonstrates that the EIWR (extraction of interword relations) has higher accuracy than the VSM (vector space model).
Highlights
As a cross-cultural communication activity, should aim for communication, employ various translation strategies, adapt to the target language’s expression habits, overcome cultural differences, and make the translation easy to accept for target readers
In order to achieve the goal of external promotion, publicity texts should respect and conform to the target culture’s language expression as well as the psychology of the audience during the initial stage of urban publicity translation. is paper analyzes the causes of cultural vacancies in the translation of urban publicity materials, starting with the classification and sorting of cultural vacancies in the translation of publicity materials. is paper focuses on using a computer corpus to reconstruct cross-cultural text for urban publicity translation
Corpus linguistics uses actual language facts as the research object and performs macroscopic and microscopic, qualitative and quantitative statistics and analysis on a large number of corpora using computer tools, revealing the objective laws of language use and the complexity of natural language [2, 3]. e parallel corpus has strong text alignment and high translation accuracy because it is made up of source language texts and translated texts that correspond to the source language texts
Summary
Received 8 December 2021; Revised 26 December 2021; Accepted 28 December 2021; Published 2 February 2022. Is paper proposes a model training method combined with the EM(expectation-maximization) algorithm to solve the problem of cross-cultural text reconstruction in urban publicity translation. E joint EM optimization method is used to learn translation models from source language to target language and from target language to source language and to complete the bilingual dictionary extraction based on the word relation matrix. Because the audience of urban publicity translation is foreign readers, and there are many differences between Chinese and English languages and cultures, it is difficult to achieve the expected publicity effect if mechanical literal translation is adopted. Literature [12, 13] proposed a method of dependency constraint on the target language part of translation knowledge, which effectively improved the translation accuracy. Literature [22] proposes a dependency tree-based method for completing the construction of a context vector and extracting bilingual dictionaries. Literature [23] proposed a method for extracting parallel resources from comparable corpora based on document-level alignment. e basic idea is to use alignment information instead of word context information
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