美国对外政策话语中的语义意藴案例探析

语言与文化总是密切相关的,离开了文化,语言的意义将无所依附。同一个词语在不同的文化语境中,其语义内涵并不完全一样,这无疑将给阅读和理解带来困难和障碍。文中将以＂American Exceptionalism＂、＂Wake Up＂和＂Morality＂作为案例,探析它们在美国语言历史文化环境中的语义意藴,以便读者在理解美国对外政策话语时能够更好地理解美国对外政策的精神内涵。     

微时代视域下广西政府微博影响力现状及对策研究

随着网络的发展,政府官方微博成为与民众沟通交流的平台,政府通过微博及时掌握民众的心理动态,有效地解决人民的问题。文章对广西政府官方微博舆论影响力的现状进行了研究,目前广西政府微博存在四个方面的特点和问题,针对这些问题提出了广西政府微博发展的对策:第一,为创新社会管理方式,政府应高度重视政务微博的应用;第二,对人民群众关心的问题要做出快速反应并及时解决;第三,打造一支新闻媒介素养过硬的专职微博团队;第四,加大县处级及县处级以下部门微博的建设的力度。     

一种关联多本体的科技奖励检索方法

现有的科技奖励检索都是基于关键词的匹配,忽略了对用户查询的语义理解。针对传统信息检索存在的问题以及结合当前面向实际应用的基于本体的语义检索的特点,提出一种关联多本体的科技奖励检索方法。通过对科技奖励项目的领域本体构建,对概念相似度计算方法的改进及关联多本体概念,使扩展词语更能表达用户检索意图。实验结果表明,该方法对比传统查询检索和单独本体扩展检索获得了更好的召回率和准确率。     

基于语义模型关联规则的局域网数据检索

提出一种基于语义模型关联规则的局域网数据检索方法.首先基于概念树描述语义模型并建立FP-树频集算法,通过概念映射将数据源与语义模型进行语义关联,在此基础上,进行噪声过滤并列出主题关键词,从而提高局域网的数据检索速度和一次查询效率.     

基于无监督学习的部分-整体关系获取

针对面向中文自由文本的部分-整体关系抽取问题,提出一种基于无监督学习的方法. 首先提出子模式提取算法,从领域文本集中获取概念对和概念对所在上下文模式,利用概念对和概念对上下文模式建立分布式语义模型;然后采用协同聚类算法将具有相同语义关系的概念对聚合成簇,通过训练L1正则化逻辑回归模型提取簇的特征并得到代表每个簇语义关系的概念对上下文模式;最后根据模式识别表达部分-整体关系的簇,从而获取部分-整体关系概念对. 实验结果表明,该方法取得较好的性能,F度量达到68.97%,优于传统聚类方法（55.77%）和模式匹配方法（61.95%）.      

Web语义的分类和缓存

针对Web信息查询缺乏对查询概念的相关语义这一问题，引入一种本体的形式化模型，通过引入本体内聚度等概念建立本体中概念间的语义关联，从而便于对常识知识的处理，并作为语义扩展及缓存的依据，由此建立起查询的上下文关联及联想，给查询带来较好的适应性。     

不同Statechart语义比较

Statechart是一种可视的规格描述语言,由于其直观的特性和强大的描述能力,一直得到广泛的重视,Statechart语义的关键在于执行步语义,不同的statechart的变体有着不同的描述能力,而应用于不同的领域.本文以三种应用最为广泛的Statechart语义为对象,以逆时间的方式,从UMLStatechart的语义开始,通过对Statechart功能的讨论,逐步引出了STATEMATE Statechart的语义和Pnueli给出的Statechart的语义,并进行了比较,显示出不同语义的描述能力的差异和不同语义表达的思想.     

山西省交通科学研究院 城市与综合运输规划研究院

<正>一、定位以智能、绿色、低碳、创新为发展理念,为政府、行业和市场提供全方位多层次的具有前瞻性、创新性、战略性决策咨询服务,发展成为具有城市规划甲级资质的研究院。二、业务系统面向政府,做好重大战略规划的决策咨询服务;面向行业,做好综合交通运输体系建设的方案支持;面向市场,承揽规划评价评估、可行性研究等任务。三、主要服务功能通过结构优化、管理提升、技术进步和政策引导等各种途径,创新主要业务功能:1、开展软科学研究,为交通科技创新发展提供支撑;2、开展决策咨询,为政府和行业提供战略规划咨询服务;3、开展政策储备性研究,为省厅行业政策提供技术支持。     

基于语料库的英语中程度副词语义韵对比研究

采用基于语料库的"中介语对比分析法",分别以中国学习者英语语料库(CLEC)和由Mark Davies 开发的BYU-BNC①语料库为基础,利用WordSmith Tools,以节点词rather和quite为例,将两个语料库中显著形容词搭配词的语义特征进行对比分析,调查中国英语学习者在书面语中程度副词与形容词搭配时所呈现出的语义韵特征.研究结果表明,中国大学生在程度副词搭配方面所呈现出的语义韵特征与本族语者的使用惯例之间有显著差异,可归纳为三个特征:1.典型搭配使用频率较低,且随意性较大;2. 语义韵冲突,不和谐;3. 词语搭配类型偏少,语义范围狭窄.     

英汉颜色词中的“黑”“白”之议

颜色词的差异不仅体现在词语的概会意思上，更多的则是表现在语义反映概会时所增添的民族文化色彩上，即形成所谓的国俗语义，这些国俗语义有褒有贬，本文探讨一下不同民族的色彩词所包含的褒贬国俗语义。     

Chinese to Braille translation based on Braille word segmentation using statistical model

Automatic translation of Chinese text to Chinese Braille is important for blind people in China to acquire information using computers or smart phones. In this paper, a novel scheme of Chinese-Braille translation is proposed. Under the scheme, a Braille word segmentation model based on statistical machine learning is trained on a Braille corpus, and Braille word segmentation is carried out using the statistical model directly without the stage of Chinese word segmentation. This method avoids establishing rules concerning syntactic and semantic information and uses statistical model to learn the rules stealthily and automatically. To further improve the performance, an algorithm of fusing the results of Chinese word segmentation and Braille word segmentation is also proposed. Our results show that the proposed method achieves accuracy of 92.81% for Braille word segmentation and considerably outperforms current approaches using the segmentation-merging scheme.     

隐喻、转喻思维与词义引申的认知语言学分析——以Storm及其语义场词语为例

本文从认知语言学的角度分析storm语义场隐含的概念隐喻、概念转喻,探讨其语义理据,揭示隐喻、转喻对词义形成的影响,从而观察概念形成的轨迹,探讨词义引申习得过程中的心理活动和认知路径。     

半监督卷积神经网络的词义消歧

为了解决有标签语料获取困难的问题，提出了一种半监督学习的卷积神经网络(convolutional neural networks, CNN)汉语词义消歧方法. 首先，提取歧义词左右各2个词汇单元的词形、词性和语义类作为消歧特征，利用词向量工具将消歧特征向量化；然后，对有标签语料进行预处理，获取初始化聚类中心和阈值，同时，使用有标签语料对卷积神经网络消歧模型进行训练，利用优化后的卷积神经网络对无标签语料进行语义分类，选取满足阈值条件的高置信度语料添加到训练语料之中，不断重复上述过程，直到训练语料不再扩大为止；最后，使用SemEval-2007:Task#5作为有标签语料，使用哈尔滨工业大学无标注语料作为无标签语料进行实验. 实验结果表明:所提出方法使CNN的消歧准确率提高了3.1%.      

Automatic semantic analysis of software requirements through machine learning and ontology approach

Nowadays, software requirements are still mainly analyzed manually, which has many drawbacks (such as a large amount of labor consumption, inefficiency, and even inaccuracy of the results). The problems are even worse in domain analysis scenarios because a large number of requirements from many users need to be analyzed. In this sense, automatic analysis of software requirements can bring benefits to software companies. For this purpose, we proposed an approach to automatically analyze software requirement specifications (SRSs) and extract the semantic information. In this approach, a machine learning and ontology based semantic role labeling (SRL) method was used. First of all, some common verbs were calculated from SRS documents in the E-commerce domain, and then semantic frames were designed for those verbs. Based on the frames, sentences from SRSs were selected and labeled manually, and the labeled sentences were used as training examples in the machine learning stage. Besides the training examples labeled with semantic roles, external ontology knowledge was used to relieve the data sparsity problem and obtain reliable results. Based on the SemCor and WordNet corpus, the senses of nouns and verbs were identified in a sequential manner through the K-nearest neighbor approach. Then the senses of the verbs were used to identify the frame types. After that, we trained the SRL labeling classifier with the maximum entropy method, in which we added some new features based on word sense, such as the hypernyms and hyponyms of the word senses in the ontology. Experimental results show that this new approach for automatic functional requirements analysis is effective.     

A Semi-automatic Method Based on Statistic for Mandarin Semantic Structures Extraction in Specific Domains

This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallow parsing and semantic labeling. By iteratively extracting new words and clustering words, we get an inital semantic lexicon that groups words of the same semantic meaning together as a class. After that, a bootstrapping algorithm is adopted to extract semantic structures. Then the semantic structures are used to extract new key words and augment the semantic lexicon. The resultant semantic structures are interpreted by persons and are amenable to handediting for refinement. In this experiment, the semi-automatically extracted structures SSA provide recall rate of 84.5%.     

Research on Web Page Classification Method Based on Query Log

Web page classification is an important application in many fields of Internet information retrieval, such as providing directory classification and vertical search. Methods based on query log which is a light weight version of Web page classification can avoid Web content crawling, making it relatively high in efficiency, but the sparsity of user click data makes it difficult to be used directly for constructing a classifier. To solve this problem, we explore the semantic relations among different queries through word embedding, and propose three improved graph structure classification algorithms. To reflect the semantic relevance between queries, we map the user query into the low-dimensional space according to its query vector in the first step. Then, we calculate the uniform resource locator (URL) vector according to the relationship between the query and URL. Finally, we use the improved label propagation algorithm (LPA) and the bipartite graph expansion algorithm to classify the unlabeled Web pages. Experiments show that our methods make about 20% more increase in F1-value than other Web page classification methods based on query log.     

英文船舶命名特点分析

本文以IMO登记的55450条船名作为语料进行分析,认为船名以1-3词为宜,词汇选择以普通名词和形容词为主,地名人名、动植物、神话人物、星辰星座、臆造词、船舶用途命名为辅,极少使用修辞。     

论《法国中尉的女人》中权力话语转变

话语是权力的表现形式,所有权力都是通过话语来实现的,话语是知识传播和权力控制的工具。权力如果争夺不到话语,便不再是权力。本文旨在讨论《法国中尉的女人》中权力话语转变,在文本中宗教权力话语实现了对人的控制,而女主人翁莎拉则利用其边缘话语身份巧妙地绕过宗教权力话语从而争得自己想要的自由。     

高校职工高脂血症的统计分析及对策

某高校职工体检统计显示:高脂血症高达38%。分析原因。饮食结构不合理及运动量少是其主要原因。提倡健康的"金字塔"饮食结构,加强运动是防治高脂血症的基础性措施。