跨文化交际与日语课堂教学

通过跨文化交际在日语教学中的必要性探讨,提出了日语教学中必须注重文化教学,以培养学生跨文化交际能力。     

跨文化交际与日语课堂教学

通过跨文化交际在日语教学中的必要性探讨,提出了日语教学中必须注重文化教学,以培养学生跨文化交际能力。     

如何克服跨文化交际中的中国式思维

"中国式日语"在日常交际中屡见不鲜,克服母语语用负迁移的关键在于加强对第二文化的理解,具备足够的跨文化理解能力。传统日语教学中常忽略跨文化交际能力的培养,笔者拟对日语教学中的文化导入提出几点粗浅的建议,以期对日语教学能有所启示。     

关于高职日语教学中日本文化导入的思考

日语教学的最终目的是培养学生的跨文化交际能力。因此,在高职日语教学中进行日本文化的导入是非常必要的。在教授日语语言的同时,还必须重视日语语言所依附的相关日本文化的教育,如果不在日语教学中导入更多的日本文化,那么跨文化的日语教学永远都达不到我们所期待的效果。     

探讨高校日语专业基础阶段跨文化交际能力的培养

学生对跨文化的理解直接影响着他们的语言习得。正确引导学生理解日本文化,是赋予新时期日语教育的重要任务,也是赋予高校日语专业培养国际型人才的教学任务。日语教育不仅是语言教学,还是综合性跨文化教学。从高校日语专业基础阶段开始进行跨文化交际能力的培养是符合新时期国际化社会的发展需要和日语教育的实际。     

对跨文化交际中日语教学的思考

在日语教学过程中,如何提高学生跨文化交际能力是值得重视与研究的课题。而将有关日本语言特征以及文化等方面的知识融入日语教学中,则是培养学生跨文化交际能力的有效方法。     

大学日语跨文化交际能力的培养策略

<正>在经济全球化发展的态势下,我国和各国之间的经济、文化交流日益增多,跨文化交际能力已成为大学生必备的能力之一。语言具有特定的历史背景和文化环境,且不同民族和国家的语言表达方式完全不同。鉴于我国与日本之间的经济文化交流需要,各高校相继设立了日语专业且加强了日语教学,日语教学为跨文化交际发展和跨文化人才培养奠定了坚实的基础。大学日语跨文化交际能力的培养,既需要提高日语的教学水平,也需要增设学生自主学习的平台,其中最重要的是日本文化的渗透和学习。     

日语学习者跨文化意识的培养

语言与文化密切相关,在大学日语教学中通过给学生适时适量介绍日本文化、日语众多变体文化及合理处置本土与异域文化的关系,培养学生的跨文化意识,帮助学生在以日语为媒介的跨文化交际中正确使用语言,从容应对文化差异。     

浅议日语教学与文化导入

语言和文化关系密切,实现语言教学和文化认知教学的统一,提高学生的跨文化交际能力一直是高校外语教学的重要课题之一。本文从日语与日本文化的依存关系入手,阐明了在日语教学中文化导入的必要性,论述了文化导入的方法及注意事项。     

高校日语专业中的日本文学教学与大学生跨文化交际能力的培养

培养具有跨文化交际能力的人才已成为高校日语专业人才培养的目标,传统的日语教学模式已经不能满足这种需要,因此,如何提高学生的跨文化交际能力已经成为如今教学改革方面的重中之重。结合实际的教学经验,总结了日本文学课程对于培养高校日语专业学生的跨文化交际能力的作用和影响等。     

Zero,single, or multi? Genre of web pages through the users’ perspective

The goal of the study presented in this article is to investigate to what extent the classification of a web page by a single genre matches the users’ perspective. The extent of agreement on a single genre label for a web page can help understand whether there is a need for a different classification scheme that overrides the single-genre labelling. My hypothesis is that a single genre label does not account for the users’ perspective. In order to test this hypothesis, I submitted a restricted number of web pages (25 web pages) to a large number of web users (135 subjects) asking them to assign only a single genre label to each of the web pages. Users could choose from a list of 21 genre labels, or select one of the two ‘escape’ options, i.e. ‘Add a label’ and ‘I don’t know’. The rationale was to observe the level of agreement on a single genre label per web page, and draw some conclusions about the appropriateness of limiting the assignment to only a single label when doing genre classification of web pages. Results show that users largely disagree on the label to be assigned to a web page.     

Kalman滤波器在单运动目标自动跟踪中的应用

在系列帧图像中对运动目标以直方图为模型的模板方法进行匹配,由于模板匹配计算量非常大,要想在整幅图像中对目标进行搜索匹配,同时又要达到实时是不可能的。我们对目标状态进行可靠的估计,可以在相对较小的区域内完成对模板的搜索,Kalman滤波器就是一个对动态系统的状态序列进行线形最小方差估计的算法。通过以动态的状态方程和观测方程来描述系统,它可以将任意一点作为起点开始观测,采用递归滤波的方法计算。该算法具有计算量小、可实时计算的特点。     

中小企业网络营销策略研究

随着网络的普及网络营销也为企业的发展注入了新的血液,但是并非所有的企业开展网络营销的道路都是一帆风顺的,由于受到资金、人才、技术等因素的影响,使得中小企业在发展过程中处处受扰。中小企业在发展如何选择合适的网络营销模式才能与传统营销起到相辅相成的作用、弥补传统营销中的不足,以推动企业的长足发展,因此网络营销的策略选择与研究成为中小企业在发展网络营销时首要考虑的问题。     

Extractive speech summarization using evaluation metric-related training criteria

The purpose of extractive speech summarization is to automatically select a number of indicative sentences or paragraphs (or audio segments) from the original spoken document according to a target summarization ratio and then concatenate them to form a concise summary. Much work on extractive summarization has been initiated for developing machine-learning approaches that usually cast important sentence selection as a two-class classification problem and have been applied with some success to a number of speech summarization tasks. However, the imbalanced-data problem sometimes results in a trained speech summarizer with unsatisfactory performance. Furthermore, training the summarizer by improving the associated classification accuracy does not always lead to better summarization evaluation performance. In view of such phenomena, we present in this paper an empirical investigation of the merits of two schools of training criteria to alleviate the negative effects caused by the aforementioned problems, as well as to boost the summarization performance. One is to learn the classification capability of a summarizer on the basis of the pair-wise ordering information of sentences in a training document according to a degree of importance. The other is to train the summarizer by directly maximizing the associated evaluation score or optimizing an objective that is linked to the ultimate evaluation. Experimental results on the broadcast news summarization task suggest that these training criteria can give substantial improvements over a few existing summarization methods.     

Projects as communicating systems: Creating a culture of innovation and performance

Although often downplayed and instrumental, there is evidence that communication in projects is essential in achieving value creation. Our main interest in this paper is on temporary continuity, a situation where the temporary becomes a permanent condition in social systems. The question that we have address is: What characterizes project communication in a situation with temporary continuity?We argue for the need to transform communication processes into communication capabilities. In a situation with temporary continuity, there is a need to connect to a large number of value-creating processes, and communicating capabilities need to be a part of a communication system, where the aim is to bind together value-creating processes and communication capabilities. We construct a viable system consisting of five sub-systems. To become a viable system, projects in the form of temporary continuity, must handle the potential conflict between a culture of performance and a culture of innovation. This involves developing social mechanisms for coordination and interaction, with a focus on developing communication capabilities, in parallel with focusing on all of the five value-creation processes.     

Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling

Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In the present paper, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on two datasets. The first dataset consists of posts from the online health forum r/Cancer and the second dataset is a standard benchmark for topic modeling which consists of a collection of messages posted to 20 different news groups. When compared to the state-of-the-art generative document models (i.e., ETM and NVDM), pPSO is able to produce interpretable clusters. The results indicate that pPSO is able to capture both common topics as well as emergent topics. Moreover, the topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20NewsGroups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus-specific vocabulary which is used by ETM and NVDM.     

Uncertainty and the inconvenient facts of diagnosis

One common contemporary usage of the term “diagnostic uncertainty” is to refer to cases for which a diagnosis is not, or cannot, be applied to the presenting case. This is a paradoxical usage, as the absence of diagnosis is often as close to a certainty as can be a human judgement. What makes this sociologically interesting is that it represents an “epistemic defence,” or a means of accounting for a failure of medicine’s explanatory system. This system is based on diagnosis, or the classification of individual complaints into recognizable diagnostic categories. Diagnosis is pivotal to medicine’s epistemic setting, for it purports to explain illness via diagnosis, and yet is not always able to do so. This essay reviews this paradoxical use, and juxtaposes it to historical explanations for non-diagnosable illnesses. It demonstrates how representing non-diagnosis as uncertainty protects the epistemic setting by positioning the failure to locate a diagnosis in the individual, rather than in the medical paradigm.     

试论构建与完善我国社会保障法律制度的重要意义

我国的社会保障制度已经运行了很长时间。但是由于社会保障基本法的缺失,社会保障一直未进入真正意义上的法治化进程中来,社会保障法律制度的建立势在必行。然而,一些人对构建社会保障法律制度的重要性以及如何构建还认识不清,这严重妨害了社会保障法律制度的建立。因此,有必要对构建社会保障法律制度的重要意义进行阐述,希望引起大家对构建社会保障法律制度的重视。     

QMOS: Query-based multi-documents opinion-oriented summarization

Sentiment analysis concerns the study of opinions expressed in a text. This paper presents the QMOS method, which employs a combination of sentiment analysis and summarization approaches. It is a lexicon-based method to query-based multi-documents summarization of opinion expressed in reviews.QMOS combines multiple sentiment dictionaries to improve word coverage limit of the individual lexicon. A major problem for a dictionary-based approach is the semantic gap between the prior polarity of a word presented by a lexicon and the word polarity in a specific context. This is due to the fact that, the polarity of a word depends on the context in which it is being used. Furthermore, the type of a sentence can also affect the performance of a sentiment analysis approach. Therefore, to tackle the aforementioned challenges, QMOS integrates multiple strategies to adjust word prior sentiment orientation while also considers the type of sentence. QMOS also employs the Semantic Sentiment Approach to determine the sentiment score of a word if it is not included in a sentiment lexicon.On the other hand, the most of the existing methods fail to distinguish the meaning of a review sentence and user's query when both of them share the similar bag-of-words; hence there is often a conflict between the extracted opinionated sentences and users’ needs. However, the summarization phase of QMOS is able to avoid extracting a review sentence whose similarity with the user's query is high but whose meaning is different. The method also employs the greedy algorithm and query expansion approach to reduce redundancy and bridge the lexical gaps for similar contexts that are expressed using different wording, respectively. Our experiment shows that the QMOS method can significantly improve the performance and make QMOS comparable to other existing methods.     

A mixed methods approach to the posting of benevolent comments online

Cyberbullying by way of malicious online comments has been identified as a critical social issue. As a way to combat cyberbullying, it is important to promote the posting of benevolent comments, especially in response to malicious comments. In this study, we adopted a mixed methods approach in using social exchange theory to explore what motivates people to post benevolent comments online. We first adopted a qualitative study to explore the decision factors involved. These were then used as the basis for development of a theoretical research model to undertake a quantitative study. The results explain how people decide to post benevolent comments online. The study makes a strong theoretical contribution in demonstrating the decision factors underlying the posting of benevolent comments. It also has practical implications by providing guidance on how to combat cyberbullying through promoting the posting of benevolent online comments.