A Brief Analysis of Ambiguity of Words' Meaning in Translation

As you can see from the title,we try to provide a brief analysis of ambiguity of words' meaning in translation practice,namely,what is ambiguity of a meaning of a word and how it is caused,and how it affects translation.What effective methods are available to help us achieve a successful translation? Although a perfect solution for the problems presented by the ambiguity of words' meaning is difficult to find,we try to do what it can to contribute to the solution and draw people's attention to those problems.

On How to Guess Meanings of Unknown Words in Reading Materials the Importance of Context

While reading plays a very important role in English study, many English language learners find it very difficult to fully comprehend some reading materials on account of many unknown words in them. This essay mainly discusses one technique of guessing meanings of new words according to context clues, thus emphasizing the importance of context.

The Meaning of Words Depends on the Context

This paper starts with stating the importance of context in understanding the meaning of words through the aspects as extra linguistic and linguistic context,which is followed by the analysis of the function of context in defferent time, place, situation and to different people. The purpose is to try and present the actual use of context in our daily life and enhance our awareness of the importance of the role of context.

The Special Meaning of Color Words in Chinese and Western Culture

In Chinese and western culture,color words are both as many as the sands in the Ganges river and stars in the blue sky.And its meanings have something in common and also something that is different.To find out the same points and differentias and to avoid mistakes in intercommunication,the writer will analysis the meanings of some main color words and makes an explain for the differences from the aspect of culture between Chinese and western countries by the means of comparison and analysis.After the analysis,the writer discovers that the meanings of the colors are basically different except the purple,and which are deeply affected by their each culture.And the meanings of them would change in the situation of intercommunication.Therefore,we should use these words properly.

Changes of Word Meaning in English-Generalization/Specialization and Pejoration/Amelioration

Changes of word meanings in English are often achieved by the processes of generalization/specialization and pejoration/amelioration.By generalization or specialization,the literal meanings of a word are broadened or narrowed.While by pejoration or amelioration,the associations of a word go downhill or rise.Trough supplying certain examples,a brief picture about meaning changes of words in English is drawn.

“Words”and“Meaning”in the Horizon of Philosophical Hermeneutics

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.

Some Symbolic Meanings of Color Words in Chinese and English Cultures

In Chinese and English languages, there are very abundant words that can describe different colors. Not only should we pay attention to their basic meanings, but also should pay attention to their profound symbolic meanings. Because the symbolic meanings of color words often have different characteristics in different national languages, some characteristics even form people to the advocating and taboo to the colors after being guided, stretched and transferred by people. The big difference of the symbolic meanings of color between different cultures is due to the different nations, different cultures, different historical backgrounds, and different aesthetic psychologies. It is sanctified by usage in the sediment of history and the development of society. It is a kind of enduring culture phenomenon. From the angle of comparison between Chinese and English cultures, this essay attempts to probe into the development of different cultures has the impact on symbolic meanings of color words.

A Discussion On the Range of Meaning of English Words──An Approach to English Words Learning

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Social and Cultural Influence on Word Meaning

Different nations have different social cultures,and the social culture of every nation is developing with the development of the nation.language is greatly influenced by social culture,which enables language to possess national and historical characteristics.Word is the smallest,independent,meaningful linguistic unit of language.It can well reflect the national and historical characteristics.From synchronic aspect,the social and cultural influence on word meaning is mainly embodied in conceptual and associative meanings of word.The incorrespondence of word meaning is a phenomenon that exists in both conceptual and associative meanings of word among different languages.From diachronic aspect,the development of social culture causes some changes of word meaning to some extent.Therefore,social culture plays a key role in vocabulary learning and mastery of a language.

基于主题词向量中心点的K-means文本聚类算法

K-means由于其时间复杂度低运行速度快一直是最为流行的聚类算法之一,但是该算法在进行聚类时需要预先给出聚类个数和初始类中心点,其选取得合适与否会直接影响最终聚类效果。该文对初始类中心和迭代类中心的选取进行大量研究,根据决策图进行初始类中心的选择,利用每个类簇的主题词向量替代均值作为迭代类中心。实验表明,该文的初始点选取方法能够准确地选取初始点,且利用主题词向量作为迭代类中心能够很好地避免噪声点和噪声特征的影响,很大程度上地提高了K-means算法的性能。

A Look at Meaning Eliminativism

What is the mental realization of our knowledge of the meaning of words？ Does the lexical side of our semantic competence depend on the fact that we have dedicated representations of the semantic properties of lexemes or does it arise from world knowledge, encyclopedic information, and non-linguistic categorization？ According to meaning eliminativism, lexical concepts have no robust psychological reality and our ability to use the words of a language should not be explained in terms of knowledge of their conventional semantic value, conceived as their array of possible senses. To start, I will briefly explain what meaning eliminativism is and illustrate the key arguments that have been offered in its defense. Then, I will argue that such arguments are not as persuasive as they wish, and I will show that there are convincing theoretical and empirical reasons to maintain that meaning eliminativism is not a plausible claim about the nature of our lexical skills.

A Comparative Study on the Metaphorical Meanings and Translations of Green and绿

Language is not only a part of culture,but also the carrier and container of culture.However,due to differences in cultur⁃al background,customs,historical and geographical environment,language expression is also different.As a part of language,col⁃or words are usually endowed with corresponding metaphorical meaning.Therefore,this paper mainly analyzes the differences be⁃tween English and Chinese color words“green”and“绿”,and explores the translation methods of English and Chinese color words.

基于word2vec和LSTM的饮食健康文本分类研究

为了对饮食文本信息高效分类,建立一种基于word2vec和长短期记忆网络(Long-short term memory,LSTM)的分类模型。针对食物百科和饮食健康文本特点,首先利用word2vec实现包含语义信息的词向量表示,并解决了传统方法导致数据表示稀疏及维度灾难问题,基于K-means++根据语义关系聚类以提高训练数据质量。由word2vec构建文本向量作为LSTM的初始输入,训练LSTM分类模型,自动提取特征,进行饮食宜、忌的文本分类。实验采用48 000个文档进行测试,结果显示,分类准确率为98.08%,高于利用tf-idf、bag-of-words等文本数值化表示方法以及基于支持向量机(Support vector machine,SVM)和卷积神经网络(Convolutional neural network,CNN)分类算法结果。实验结果表明,利用该方法能够高质量地对饮食文本自动分类,帮助人们有效地利用健康饮食信息。

基于改进k-means算法的中文词义归纳

汉语中一词多义现象普遍存在,词义归纳就是对在不同语境中具有相同语义的词进行归类,本质上是一聚类问题。目前广泛采用无指导的聚类方法对词义归纳进行研究,提出一种改进的k-means算法,该算法主要从初始簇中心的选取以及簇均值的计算两个方面进行改进,在一定程度上克服了其对"噪声"和孤立点数据的敏感。在特征表示上用同义词词林中词的分类编号来降低特征维度。实验表明改进k-means算法在性能上有较大的提升,F-Score达到了75.8%。

结合语义改进的K-means短文本聚类算法

针对短文本聚类存在的三个主要挑战,特征关键词的稀疏性、高维空间处理的复杂性和簇的可理解性,提出了一种结合语义改进的K-means短文本聚类算法。该算法通过词语集合表示短文本,缓解了短文本特征关键词的稀疏性问题;通过挖掘短文本集的最大频繁词集获取初始聚类中心,有效克服了K-means聚类算法对初始聚类中心敏感的缺点,解决了簇的理解性问题;通过结合TF-IDF值的语义相似度计算文档之间的相似度,避免了高维空间的运算。实验结果表明,从语义角度出发实现的短文本聚类算法优于传统的短文本聚类算法。

基于K-means的机构归一化研究

分析k-means算法的核心思想和基本步骤,借鉴现有基于频繁词集的文本聚类初始中心确定方法,提出一种面向大规模机构名称归一化处理应用的机构聚类方法,详细阐述机构聚类中心的生成、相似度算法的选择以及迭代次数问题,其实验和应用效果表现良好。

基于聚类中心优化的k-means最佳聚类数确定方法

k-means聚类算法是在确定的聚类数k下对数据集进行聚类,通常凭借先验规则假定一个k值,取值具有很大主观性;此外,k-means初始聚类中心的选择一般也是随机的,这使得k-means聚类经常出现聚类指标局部最优化,聚类结果不稳定.针对以上两个问题,结合密度法改进了k-means初始聚类中心点的选择,并在此基础上提出了一种确定k-means最佳聚类数的方法.实验结果证明,该方法可以得到更好的聚类结果,具有更高的准确性、更好的稳定性以及更优的收敛性.

基于k-means聚类的无导词义消歧

无导词义消歧避免了人工词义标注的巨大工作量,可以适应大规模的多义词消歧工作,具有广阔的应用前景。这篇文章提出了一种无导词义消歧的方法,该方法采用二阶context构造上下文向量,使用k-means算法进行聚类,最后通过计算相似度来进行词义的排歧.实验是在抽取术语的基础上进行的,在多个汉语高频多义词的两组测试中取得了平均准确率82·67%和80·87%的较好的效果。

一种改进的k-means聚类视觉词典构造方法

传统词袋(bag of words,BoW)模型在构造视觉词典时一般采用k-means聚类方法实现,但k-means聚类方法的性能在很大程度上依赖于初始点的选择,从而导致生成的视觉词典鲁棒性较差,此外,每次迭代都要计算数据点与中心点的距离,计算复杂度高。针对上述问题,提出了一种改进的k-means聚类视觉词典构造方法,该方法首先对初始值的选取进行了优化,克服了随机选取初始值对聚类性能的影响,其次基于三角形不等式对计算进行了简化,使生成的视觉词典更加稳定,计算复杂度更低,最后引入权值分布对图像进行基于视觉词典的表示,并将基于改进的视觉词典的词袋模型应用于图像分类,提高了分类性能。通过在Caltech 101和Caltech 256两个数据库进行实验,验证了本文方法的有效性,并分析了词典库大小对分类性能的影响。从实验结果可以看出,采用本文方法所得到的分类正确率提高了5%～8%。

多特征组合与自动加权K-Means聚类算法的影像分类技术研究

为了提高遥感影像地物分类精度,提出了一种基于多特征组合与自动加权K-Means聚类算法的影像分类方法。首先提取影像的SIFT,GIST,颜色,Census和Gabor等多种类型特征,然后通过实验分析确定最佳特征组合。针对一般K-Means算法没有考虑各个特征值权重的问题,提出利用自动加权K-Means算法计算不同特征分量的权值,分别对SIFT,GIST和Gabor特征构建了基于权重的影像特征词汇表;然后利用稀疏编码算法进行影像编码;最后使用SVM算法完成影像分类。通过实验表明提出的方法能有效提高遥感影像分类准确性,并且具有较好的稳定性和鲁棒性。