Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical con...Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical concepts accurately. The so-called ＂word-for-word＂ translation is actually ＂English-word-for- Chinese-character＂ translation. First, the authors of the dictionary made a list of Single Characters with English Equivalents, and then they gave each character of the medical term an English equivalent according to the list. Finally, they made some minor modifications to make the rendering grammatically smoother. Many English terms thus produced are confusing. The defect of the word-for-word literal translation stems from the erroneous idea that a single character constitutes the basic element of meaning corresponding to the notion of ＂word＂ in English, and the meaning of a disyllabic or polysyllabic Chinese word is the simple addition of the constituent characters. Another big mistake is the negligence of the polysemy of Chinese characters. One or two English equivalents can by no means cover all the various meanings of a single character which is a polysemous monosyllabic word. Various examples were cited from this dictionary to illustrate the mistakes.展开更多
The article holds that the problem of the relationship between"words"and"meaning"has always been the philosophical proposition.Through analyzing the relationship between"words"and"me...The article holds that the problem of the relationship between"words"and"meaning"has always been the philosophical proposition.Through analyzing the relationship between"words"and"meaning"and probing into"disoourse","text"and"space-time history",the author thinks that the interpreters'preconceptions,preexistence,pre-structure and different perspectives have resulted in diverse forms of interpretations,and the being of each kind of form has its own rationality.Moreover,the process in which the interpreters interpret"words","discourse","text"and"history"is also the process to interpret themselves.Nevertheless,being able to say and being unable to say are soaked in the whole process of interpretation,and,as a result,human beings will always be confronted with a kind of"say and cannot say"embarrassment.展开更多
文章运用WordSmith 8.0对艾丽斯·沃克小说《紫色》中的关键词和特殊词簇进行分析,揭示了《紫色》在词汇上的整体分布特征,并指出文中所使用的词汇与句式均与主人公非裔女性这一人物形象相吻合。通过Word Smith 8.0检索发现,沃克小...文章运用WordSmith 8.0对艾丽斯·沃克小说《紫色》中的关键词和特殊词簇进行分析,揭示了《紫色》在词汇上的整体分布特征,并指出文中所使用的词汇与句式均与主人公非裔女性这一人物形象相吻合。通过Word Smith 8.0检索发现,沃克小说中的关键词和词簇搭配对于促进故事情节和人物刻画方面有重要作用。研究结果表明,语料库文体学有助于学者发现以往研究中忽视的深层文本含义,是对以往《紫色》文学定性研究结果的再次验证,是定性研究和定量研究的积极结合,也是对学界“经典重读”的积极响应。展开更多
规模自动化工业生产中的集群数控机床因各种故障导致停机而造成生产线效率的下降,若能及时准确地预测故障对数控机床进行预检预修有利于提高全线生产效率。在工业智能制造背景下,以数据驱动为支撑,数控机床积累的大量历史故障报警数据...规模自动化工业生产中的集群数控机床因各种故障导致停机而造成生产线效率的下降,若能及时准确地预测故障对数控机床进行预检预修有利于提高全线生产效率。在工业智能制造背景下,以数据驱动为支撑,数控机床积累的大量历史故障报警数据为依托,设计了一种基于Word2vec和LSTM-SVM的故障报警预测方法对机床未来可能发生的故障进行预测。首先通过词嵌入技术将报警文本向量化,然后将报警向量作为输入构建长短期记忆神经网络(long short term memory network,LSTM)预测模型,并使用支持向量机(support vector machine,SVM)代替传统的softmax作为模型的末端分类器,实验结果表明该方法具有更高的预测准确率。展开更多
文摘Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical concepts accurately. The so-called ＂word-for-word＂ translation is actually ＂English-word-for- Chinese-character＂ translation. First, the authors of the dictionary made a list of Single Characters with English Equivalents, and then they gave each character of the medical term an English equivalent according to the list. Finally, they made some minor modifications to make the rendering grammatically smoother. Many English terms thus produced are confusing. The defect of the word-for-word literal translation stems from the erroneous idea that a single character constitutes the basic element of meaning corresponding to the notion of ＂word＂ in English, and the meaning of a disyllabic or polysyllabic Chinese word is the simple addition of the constituent characters. Another big mistake is the negligence of the polysemy of Chinese characters. One or two English equivalents can by no means cover all the various meanings of a single character which is a polysemous monosyllabic word. Various examples were cited from this dictionary to illustrate the mistakes.
基金the research achievements of 2013 Classroom Teaching Reform Project in Higher Education fiscally aided by Zhejiang province(Grant No.kg2013416)2014 Pre-research for High-Level Project in Humanities and Social Sciences financially supported by Huzhou University(Grant No.2014SKYY07)2015 Monographic Research Project financed by Zhejiang Association of Foreign Languages&Literatures(Grant No.ZWYB2015003)
文摘The article holds that the problem of the relationship between"words"and"meaning"has always been the philosophical proposition.Through analyzing the relationship between"words"and"meaning"and probing into"disoourse","text"and"space-time history",the author thinks that the interpreters'preconceptions,preexistence,pre-structure and different perspectives have resulted in diverse forms of interpretations,and the being of each kind of form has its own rationality.Moreover,the process in which the interpreters interpret"words","discourse","text"and"history"is also the process to interpret themselves.Nevertheless,being able to say and being unable to say are soaked in the whole process of interpretation,and,as a result,human beings will always be confronted with a kind of"say and cannot say"embarrassment.
文摘文章运用WordSmith 8.0对艾丽斯·沃克小说《紫色》中的关键词和特殊词簇进行分析,揭示了《紫色》在词汇上的整体分布特征,并指出文中所使用的词汇与句式均与主人公非裔女性这一人物形象相吻合。通过Word Smith 8.0检索发现,沃克小说中的关键词和词簇搭配对于促进故事情节和人物刻画方面有重要作用。研究结果表明,语料库文体学有助于学者发现以往研究中忽视的深层文本含义,是对以往《紫色》文学定性研究结果的再次验证,是定性研究和定量研究的积极结合,也是对学界“经典重读”的积极响应。
文摘规模自动化工业生产中的集群数控机床因各种故障导致停机而造成生产线效率的下降,若能及时准确地预测故障对数控机床进行预检预修有利于提高全线生产效率。在工业智能制造背景下,以数据驱动为支撑,数控机床积累的大量历史故障报警数据为依托,设计了一种基于Word2vec和LSTM-SVM的故障报警预测方法对机床未来可能发生的故障进行预测。首先通过词嵌入技术将报警文本向量化,然后将报警向量作为输入构建长短期记忆神经网络(long short term memory network,LSTM)预测模型,并使用支持向量机(support vector machine,SVM)代替传统的softmax作为模型的末端分类器,实验结果表明该方法具有更高的预测准确率。
