A Comparative Study on the Translation of Automotive Marketing Texts Based on an Automotive English Corpus

This study aims to construct an automotive English corpus to comprehensively compare the differences between English automotive marketing texts and their Chinese translations.The objective is to reveal challenges and opportunities in cultural and contextual translation.The research holds significant importance for understanding the impact of cross-cultural communication in the automotive market and providing more effective translation strategies for multinational automotive manufacturers.Through corpus analysis,focusing on common marketing phrases and text features,employing both quantitative and qualitative analysis methods,and examining the accuracy,naturalness,and cultural adaptability of translated texts,we delve into the similarities and differences in conveying automotive information between the two languages.The study finds that expressive and emotional expressions commonly used in English automotive contexts may encounter challenges in Chinese translations due to language and cultural differences.This necessitates the adoption of more flexible translation strategies.Additionally,Chinese translations tend to emphasize the practicality and safety of products more than their English counterparts,placing a greater emphasis on technical and functional descriptions.The primary conclusion of this research is that the translation of automotive marketing texts requires heightened cross-cultural sensitivity and an understanding of the target audience.When translating automotive advertisements and promotions,translators should consider consumer expectations and cultural values in different contexts to ensure the effectiveness and adaptability of the translation.Furthermore,the formulation of more flexible translation strategies,integrating local culture and market demands,will contribute to enhancing the image and influence of automotive brands in the international market.Through this study,we provide deeper insights for automotive manufacturers,assisting them in leveraging the power of language for successful global market penetration.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

A Corpus Study of Acupuncture from the Perspective of Mass Media Based on the Corpus of Contemporary American English

Based on the Corpus of Contemporary American English(COCA),this study examines acupuncture from the perspective of mass media.Acupuncture has been circulating throughout the Chinese Cultural Circle since the Qin and Han Dynasties,and has since spread directly or indirectly to the rest of the world.The United States boasts the world’s second-largest acupuncture market,with its laws,regulations,industry growth,research,and education all positively influencing the development of acupuncture in other nations.This study uses COCA to analyze the form and content of acupuncture’s dissemination in eight different types of media.The findings show that acupuncture appears in COCA a total of 1,788 times,with the highest frequency in magazines,followed by blogs,and the lowest frequency in fiction.These findings reveal the popularity of acupuncture in mass media in the United States and provide empirical data and insights for the future dissemination and development of acupuncture in the United States.

A Corpus-Based Behavioral Profile Study of the Near-Synonyms: Serious, Severe, Grave, Grievous

Using Corpus of Contemporary American English as the source data,this paper carries out a corpus-based behavioral profile study to investigate four near-synonymous adjectives(serious,severe,grave,and grievous),focusing on their register and the types of nouns they each modify.Although sharing core meaning,these adjectives exhibit variations in formality levels and usage patterns.The identification of fine-grained usage differences complements the current inadequacies in describing these adjectives.Furthermore,the study reaffirms the effectiveness of the corpus-based behavioral profile approach in examining synonym differences.

Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

面向SELL语料库的AI虚拟英语教育训练系统研究

英语语法特点容易造成英语学习者缺乏英语语感,出现口语、写作能力欠佳的问题。选择中国学生英语口语WECCL和英语母语COCA作为语料库,比较分析中国英语学习者和英语母语者在使用中动结构间的差异性,并通过将SELL语料库融合虚拟现实技术来构建英语学习交流在线系统,营造真实的外语学习环境。研究结果表明:在使用动词结构频率方面,中国英语学习者与英语母语者之间较为相似,但英语母语者更倾向于extend、crumble类型词汇,采用零修饰词,通过复杂句式来表达。根据研究获得的中国英语学习者具体学习情况,通过虚拟技术和人工智能构建一个灵活的、可扩充的虚拟交互平台,设计多维虚拟场景和智能AI角色,实现不同场景下AI角色的交流和学习,提升学习者的口语能力和语感知识。

基于语料库的法律英语翻译能力培养模式研究

本文旨在探讨平行语料库和平行文本模式在汉英国际服装外贸跟单翻译中的具体应用,主要从订单的文本信息、词语表达、句法结构及谋篇布局等层面探究其翻译规律,并从术语翻译效率、术语翻译决策、工具资源使用、术语翻译认知四个维度对两组被试进行具体考察。实验表明,学生在翻译法律搭配方面存在严重问题,缺乏语言知识和语用意识,而将法律合同翻译的语料库研究引入跨文化翻译教学有利于全面激发学生的思考与参与,培养学生的翻译思维和跨文化交际意识。本文根据最新实证翻译研究的成果,分析法律平行语料库和专门术语库的建构对翻译初学者法律翻译能力提高和技能习得方面的作用,总结出一些适用于合同类文本汉英翻译的原则及策略。