[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...[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。展开更多
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...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。展开更多
In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in faci...In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.展开更多
On March 21st,Mr.Ai Ping,Vice-President of CAFIU,met in Bejing with a 6-member delegation from the US Word4Asia led by Dr.Gene Wood,Chief Executive Officer of Word4Asia.Mme.Xiao Ningning,Deputy Secretary-General of Ch...On March 21st,Mr.Ai Ping,Vice-President of CAFIU,met in Bejing with a 6-member delegation from the US Word4Asia led by Dr.Gene Wood,Chief Executive Officer of Word4Asia.Mme.Xiao Ningning,Deputy Secretary-General of China NGO Network for International Exchanges,Mr.Gu Chuanyong,Vice-Chair of Board of the Amity Foundation,and Mme.She Hongyu,Deputy Secretary-General of the Amity Foundation,attended the meeting.The two sides exchanged views on deepening people-to-people exchanges and cooperation between China and the United States and on the preparation of the 5th"Amity Cup"International Table Tennis Charity Competition.展开更多
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 cult...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.展开更多
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 fr...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.展开更多
文摘[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。
文摘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。
基金supported in part by the Guangzhou Science and Technology Plan Project under Grants 2024B03J1361,2023B03J1327,and 2023A04J0361in part by the Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control under Grant OHIC2023Y10+3 种基金in part by the Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project under Grant 2023KQNCX036in part by the Special Fund for Science and Technology Innovation Strategy of Guangdong Province(Climbing Plan)under Grant pdjh2024a226in part by the Key Discipline Improvement Project of Guangdong Province under Grant 2022ZDJS015in part by theResearch Fund of Guangdong Polytechnic Normal University under Grants 22GPNUZDJS17 and 2022SDKYA015.
文摘In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.
文摘On March 21st,Mr.Ai Ping,Vice-President of CAFIU,met in Bejing with a 6-member delegation from the US Word4Asia led by Dr.Gene Wood,Chief Executive Officer of Word4Asia.Mme.Xiao Ningning,Deputy Secretary-General of China NGO Network for International Exchanges,Mr.Gu Chuanyong,Vice-Chair of Board of the Amity Foundation,and Mme.She Hongyu,Deputy Secretary-General of the Amity Foundation,attended the meeting.The two sides exchanged views on deepening people-to-people exchanges and cooperation between China and the United States and on the preparation of the 5th"Amity Cup"International Table Tennis Charity Competition.
文摘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.
文摘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.