A Contrastive Analysis of English and Chinese Idioms About Color Words & Their Cultural Connotations

There are many idioms related to color words in English and Chinese.The use of color words in idioms adds beauty and vividness to the language.Due to the cultural differences,“color idioms”have gained different cultural connotations with the development of English and Chinese languages.It is of great significance to accurately understand and grasp the meanings and differences of color-related idioms in Chinese and English.This paper intends to analyze and expound the cultural connotations of English and Chinese idioms related to several widely used basic color words with the aim of helping English learners know and use the idioms about color words better.

Translation of Culture-loaded Words in The Golden Cangue under the Perspective of Reception Aesthetics

Good translations play a very important role in cultural exchange.The idea of reception aesthetics think the reader as the center in translation process.This paper will study the translation of culture-loaded words from the perspective of reception aesthetics.It is divided into five parts.The first part mainly introduces the background and theoretical foundation of this paper,the second part introduces the writer’s works,the third part gives examples to analyze the culture-loaded words appearing in The Golden Cangue,the fourth part gives examples of the translation methods,and the fifth part is the conclusion.It aims to provide a reference for the English translation of culture-loaded words.

基于LDA和Word2Vec模型的学位论文评阅意见主题挖掘与分析

选取某高校部分硕士学位论文评阅意见为研究对象,使用自然语言处理和机器学习技术进行自动化的硕士学位论文评阅意见主题挖掘与分析。首先,采用LDA(latent dirichlet allocation)模型对评阅数据进行主题建模,提取文本中的潜在主题,并将评阅意见转化为主题分布向量;其次,结合Word2Vec模型将评阅意见的关键词转化为向量表达;最后,采用TextRank方法提取关键词,以揭示评阅专家的关注核心主题。实验结果表明,所提方法能为高校管理人员提供切实有效的分析工具,有助于他们更好地分析总结评阅意见,同时也为硕士研究生撰写高质量学位论文提供有益借鉴。

基于Word2Vec和LDA主题模型的中国省级五年规划“文化政策”文本研究

运用Word2Vec和LDA相结合的主题模型分析技术,对我国31个省份三个时期五年规划文本中文化政策部分进行主题识别,从时间和空间两个维度进行“文化政策”主题挖掘和演化分析。研究发现,“文化政策”主题在发展趋势、重点转移、政策导向、技术应用等方面随时间推移呈现不同演化趋势;四大区域受经济发展水平、文化资源禀赋、政策导向影响,在企业角色强调程度、地区特色旅游发展以及国家级项目和竞争力方面存在地域差异。

英语word的字词之辨

将所有的English words统称为“英语单词”或“英语单字”都是片面的。这种字词不分的观念是导致中国学生学习和记忆英语单词困难的一个主要原因。英语和汉语一样,也应当区分字和词。英语中的词根(基本单词和黏附词根)应被视为“字”,而由词根派生出来的大量较复杂的单词才是“词”。将英语中少量的“字”与大量的“词”区分开,从认识英语词根开始,以字带词的方式学习,有助于快速识记大量英语词汇。

Python实现Excel文档转换到Word文档的自动化方法

通过研究开发了一种基于Python语言,实现Excel数据自动转换成Word文档并实现排版功能的方法,为科研工作者、数据分析师或报告撰写人员提供一个方便快捷的工具,帮助他们更高效地完成Excel数据转换成Word并实现排版的任务。

基于Word2Vec和决策树的故障定位技术

利用Word2Vec方法对Java源代码进行深层语义编码,生成文件级和行级的语义向量,并将其用作输入数据来训练决策树模型,以实现精确的文件级别和行级别故障定位,优化故障检测过程,构建一个综合文件级别与行级别分析的高效故障定位框架.实验结果表明:该模型在各项目中的故障定位准确率均高于83%.

基于LDA-Word2vec的图书情报领域机器学习研究主题演化与热点主题识别

[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以图书情报领域中2011—2022年Web of Science数据库中的机器学习研究论文为例,融合LDA和Word2vec方法进行主题建模和主题演化分析,引入主题强度、主题影响力、主题关注度与主题新颖性指标识别热点主题与新兴热点主题。[结果/结论]研究结果表明,(1)Word2vec语义处理能力与LDA主题演化能力的结合能够更加准确地识别研究主题,直观展示研究主题的分阶段演化规律;(2)图书情报领域的机器学习研究主题主要分为自然语言处理与文本分析、数据挖掘与分析、信息与知识服务三大类范畴。各类主题之间的关联性较强,且具有主题关联演化特征;(3)设计的主题强度、主题影响力和主题关注度指标及综合指标能够较好地识别出2011—2014年、2015—2018年和2019—2022年3个不同周期阶段的热点主题。

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

基于Word VBA辅助技术文件编制的数字化协同建设的探索

文件编制是设计研发人员日常工作的重要组成部分,其中技术文件的编制涉及大量文件结构和起草规则的应用,基于相关标准中关于文件的编写要求,文章利用WordVBA编程技术辅助技术文件编写工作中的格式编排,实现了Word文档标准格式技术文件的自动化编制,从而保障文件编制符合标准格式要求,有效提高了工作效率。

结合Word2vec和BiLSTM的民航非计划事件分析方法

安全是民航业的核心主题。针对目前民航非计划事件分析严重依赖专家经验及分析效率低下的问题,文章提出一种结合Word2vec和双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络模型的民航非计划事件分析方法。首先采用Word2vec模型针对事件文本语料进行词向量训练,缩小空间向量维度;然后通过BiLSTM模型自动提取特征,获取事件文本的完整序列信息和上下文特征向量;最后采用softmax函数对民航非计划事件进行分类。实验结果表明,所提出的方法分类效果更好,能达到更优的准确率和F 1值,对不平衡数据样本同样具有较稳定的分类性能,证明了该方法在民航非计划事件分析上的适用性和有效性。

基于LSTM+Word2vec的微博评论情感分析

微博作为当今热门的社交平台,其中蕴含着许多具有强烈主观性的用户评论文本。为挖掘微博评论文本中潜在的信息,针对传统的情感分析模型中存在的语义缺失以及过度依赖人工标注等问题,提出一种基于LSTM+Word2vec的深度学习情感分析模型。采用Word2vec中的连续词袋模型(continuous bag of words,CBOW),利用语境的上下文结构及语义关系将每个词语映射为向量空间,增强词向量之间的稠密度;采用长短时记忆神经网络模型实现对文本上下文序列的线性抓取,最后输出分类预测的结果。实验结果的准确率可达95.9%,通过对照实验得到情感词典、RNN、SVM三种模型的准确率分别为52.3%、92.7%、85.7%,对比发现基于LSTM+Word2vec的深度学习情感分析模型的准确率更高,具有一定的鲁棒性和泛化性,对用户个性化推送和网络舆情监控具有重要意义。

A Study on the English Translation of Culture-Loaded Words From the Perspective of Relevance-Theoretic Translation Theory-A Case Study of Yang Xianyi’s The True Story of Ah Q

Culture-loaded words reveal the basic features of a country’s social life,customs and habits,religious beliefs,and so on.The meanings of culture-loaded words contain the profound cultural connotation of a country or a nation.However,due to the influence of different cultures,the original author and the target readers lack a common cognitive context,which means they cannot successfully conduct a cultural communication.Therefore,correctly understanding and translating these culture-loaded words have become a major problem for translators in the translation process of literary works.Under the guidance of the dual ostensive-inferential model in relevance-theoretical translation theory,this paper,by emphasizing the importance of common cognitive context to a successful translation,analyzes the translation process of five categories of culture-loaded words and summarizes the translation strategies that can accurately convey the writing intention of the original author,which provides a new perspective for the translation of culture-loaded words.

The Translation Strategies of Chinese Reduplicated Words From a Skopos Theory Perspective

This paper explores the translation strategies for Chinese reduplication to English from the perspective of Skopos theory. Drawing on previous research on the semantic functions of reduplication, the paper analyzes the three main types of semantic changes in Chinese reduplicated words: intensification of meaning, moderation of meaning, and addition of meaning. The paper then proposes different translation strategies for Chinese reduplication, including same-word repetition, near-synonym repetition, “have a try” form, “noun(pl.) + of” form, and “all”, “each”, and “every” pattern. Finally, the paper emphasizes the importance of understanding the intended communicative function of the reduplicated words and the need for flexible handling in order to achieve a natural translation with better readability.

关于Word2Vec文本分类效果若干影响因素的分析

Word2Vec向量模型参数众多,在不同情景下分类效果不一,分析其影响因素很有必要。从Word2Vec模型基本原理出发,分析讨论了预训练语料、词向量预训练参数以及分类模型参数三大因素对模型分类效果的影响。结果表明限定域预料效果好于广域预料;预训练参数中向量维度越大,效果越好,窗口大小存在最优值,分类算法影响不大;分类模型参数中学习率、激活函数、批次大小对模型分类效果影响较大,训练轮次相对较小。

标准电子文件转WORD校验方法

随着标准电子文件在各个领域的广泛应用,文件格式的转换及其后续校验工作变得日益重要。本研究聚焦于探讨标准文档格式转换为Word格式的过程中所面临的挑战,并提出了一个全面的校验流程,以确保转换的准确性和高效性。阐述了标准电子文件在石油等领域的广泛应用以及转换为Word格式的必要性,强调了校验过程的重要性,旨在确保转换后的文件与原始文件在错误率万分之五以内的一致性。

A Study on German Translation Strategies of Culture-Loaded Words in Documentary Hexi Corridor(Expert)by Adaptation Theory

This paper deals with the translation strategies of Chinese Culture-Loaded Words from the perspective of adaptation theory.It is based on the translation text of the sixth episode“Silk Road”and the seventh episode“Dunhuang”of the documentary Hexi Corridor.Many words with Chinese cultural connotations appear in the subtitles of this documentary.This paper will be divided into four parts.The first part and the second part deal with the basic theories,i.e.,definition of Chinese Culture-Loaded Words and of adaptation theory.The original text is analysed in the third part.This part deals with the background and specifics of the language of the documentary film Hexi Corridor.The fourth part deals with the difficulties encountered by the author in translation practice and the corresponding solutions adopted by the author.The translation difficulties are solved by five translation methods,namely transliteration,loan translation,substitution,interpretation,and adaptation.

基于K-means与Word2vec的哺乳文胸评论主题挖掘研究

目的为了了解消费者在网络平台购买哺乳文胸时的关注侧重点,文章从在线评论中抽取有效关键词构建哺乳文胸主题,并通过计算主题的重要程度协助商家了解消费者关注重点方向。方法选用TF-IDF关键词抽取算法,结合K-means和Word2vec进行语义聚类、主题识别、主题词挖掘及主题重要度计算。结果哺乳文胸评论文本聚类后的主题重要程度排名是:产品品质(45.47%)、产品外观(35.83%)、产品服务(18.79%)。结论通过该方法能够有效的识别和构建哺乳文胸主题及主题词,同时,通过主题的重要程度,能够了解消费者对于网络平台购买哺乳文胸时关注的重点方向,为哺乳内衣企业进行产品改善及生产等提供理论参考。

基于Word2vec的二语教学“基本形式库”构建方法初探

二语教学中,重视表达取向的“基本形式”观致力于构建一个“基本形式库”。本文以微博语料库为例,将其中高频词设置为检索词,依据Word2vec训练的词向量进行检索,围绕检索词查找近似词来构成(准)等义组,继而进一步确定该组的“基本形式”。本文初步提出了一种兼具可操作性和效率性的建设方法,作为人工建设“基本形式库”的辅助工具,并检索出了一部分基本形式(准)等义组作为前人研究的补充,为“基本形式”观理论进一步发展提供工具与思考。

Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

Nowadays,Internet of Things(IoT)is widely deployed and brings great opportunities to change people's daily life.To realize more effective human-computer interaction in the IoT applications,the Question Answering(QA)systems implanted in the IoT services are supposed to improve the ability to understand natural language.Therefore,the distributed representation of words,which contains more semantic or syntactic information,has been playing a more and more important role in the QA systems.However,learning high-quality distributed word vectors requires lots of storage and computing resources,hence it cannot be deployed on the resource-constrained IoT devices.It is a good choice to outsource the data and computation to the cloud servers.Nevertheless,it could cause privacy risks to directly upload private data to the untrusted cloud.Therefore,realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task.In this paper,we present a novel efficient word vector learning scheme over encrypted data.We first design a series of arithmetic computation protocols.Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data.The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy.Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.