Exploring the Retailer＇s Relational Satisfaction in a Non-western Context： Evidence From the Tunisian Post 2011 ＇s RevolutionSetting

The current research aims to find out whether the model of the retailer＇s relational satisfaction, developed in a western setting is relevant in a different economic and cultural field of investigation： the Tunisian post 2011 revolution＇s emerging context, and in a different industry： the retail industry. It explores the distinctive economic and cultural features that affect the retailer＇s relational satisfaction with its consumer goods manufacturers. The study employs a survey based on a face-to-face administered questionnaire. Two hundred and thirty four responses from a survey with Tunisian retailers as well as international retailers operating in Tunisia were obtained. All constructs are measured using five-point Likert type scales （1 = strongly disagree, 5 = strongly agree）. Analyses are conducted using exploratory factor analysis and reliability analysis. The results support the original model with however some differences derived from the Tunisian economic setting as a whole, the characteristics of the Tunisian consumer goods industry as well as the clan characteristic feature of the Tunisian collectivistic culture. Thus, the amount of discounts obtained, does no longer contribute to the retailer＇s economic satisfaction. Moreover, when it comes to social satisfaction, the respondents get uncomfortable, even to deal with a potential negative interaction with their suppliers. For retail companies involved in internationalization in the Maghreb Region, and especially in Tunisia, it seems that other competitive advantages than prices are to be developed, such as the development of private label or other services that create positive perceived value for the Tunisian customer. From a cultural standpoint, western managers have rather to go away from a formalized channel relationship management, while taking into account the importance of interpersonal influence. The study sheds light on the heterogeneity of the so-called Arab-Muslim world. This is highlighted in the management of channel relationship in Tunisia, that is influenced by： （1） The government＇s price regulation and control, especially in many food products; （2） The relative weak importance of discounts in gaining retailer＇s economic satisfaction; （3） The reshaping of the local retailing sector due to the arrival of international retail companies; （4） The clan feature of the Tunisian collectivistic culture that advocates putting priority on interpersonal influence in channel relationship＇s management.

A Study On the Structural Dimension of Middle Class Relational Identification in Chinese Context

Through in-depth interviews,this study makes an exploratory study on the dimension of the middle class relational identification in Chinese context by using the grounded theory.It is mainly composed of 4 dimensions:relational orientation,relational adaptation,relational evaluation and relational development.According to the four dimension model of middle class relational identification,the middle class relational identification in China is the cognitive and behavioral orientation that in the action of achieving a certain purpose,because of the perception of consistence with each other in role cognition and personal cognition,the middle class is willing to unite and trust,respect and understand and coordinate the conflict to adapt to this relationship,and then to form a positive evaluation of the relationship and a sense of belonging and commitment to this relationship.The construction of the structural dimension of middle class relational identification in China is conducive to explaining the endemic phenomena of Chinese middle class.

基于自适应上下文匹配网络的小样本知识图谱补全

知识图谱在构建过程中需要面对繁杂的现实世界信息,无法建模所有知识,因此需要补全。真实的知识图谱中很多类型的关系通常只有少量的训练实体样本对。因此,如何进行小样本知识图谱补全是一个十分有价值的问题。目前基于嵌入的方法一般通过注意力机制等方法聚合实体上下文信息,通过学习关系嵌入的方式来补全知识图谱,仅考虑关系层面的匹配程度,虽然能够预测未知关系,但往往准确度不高。针对小样本知识图谱补全问题,提出了一个自适应上下文匹配网络(Adaptive Context Matching Network,ACMN)。首先提出一个共性邻居感知编码器,聚合参考集实体上下文,即一跳邻居实体,获得共性邻居感知编码;接着提出一个任务相关实体编码器,挖掘任务实体上下文与共性上下文的相似度信息,区分一跳邻居对当前任务的贡献,增强实体表征;然后提出一个上下文关系编码器获得动态关系表征;最后通过加权求和综合考虑实体上下文和关系的匹配程度,完成补全。ACMN从实体上下文相似度和关系匹配程度两个方面综合评价查询三元组是否成立,能够在小样本的背景下有效提高预测准确性。在两个公共数据集上和其他8个广泛使用的算法进行比较,ACMN在不同规模的小样本情况下,取得了目前最好的补全结果。

融合实体和上下文信息的篇章关系抽取研究

篇章关系抽取旨在识别篇章中实体对之间的关系.相较于传统的句子级别关系抽取,篇章级别关系抽取任务更加贴近实际应用,但是它对实体对的跨句子推理和上下文信息感知等问题提出了新的挑战.本文提出融合实体和上下文信息(Fuse entity and context information,FECI)的篇章关系抽取方法,它包含两个模块,分别是实体信息抽取模块和上下文信息抽取模块.实体信息抽取模块从两个实体中自动地抽取出能够表示实体对关系的特征.上下文信息抽取模块根据实体对的提及位置信息,从篇章中抽取不同的上下文关系特征.本文在三个篇章级别的关系抽取数据集上进行实验,效果得到显著提升.

融合知识和约束图的远程监督关系抽取方法

远程监督关系抽取通过自动标注数据减少人工标注成本,但存在句子标签噪声和关系长尾分布两个问题.为解决上述问题,提出一种融合知识图中实体信息以及实体和关系间约束的关系抽取方法.该方法对目标实体和其邻居实体的属性进行编码,对目标实体和邻居实体构成的邻居图进行编码,对实体类型和关系间约束进行编码,并通过多源融合注意力模块进行信息整合,构建关系抽取模型.该方法在NYT-10数据集上的AUC值为0.524,P@100值为94.8%,长尾指标Hits@K较之前最先进模型均有提升,取得了优异表现,表明该方法融合实体信息和约束信息解决远程监督关系抽取两个主要问题的有效性.

面向属性概念格的概念特征与概念约简

提出面向属性概念协调集和概念约简,定义了包含补二元关系的面向属性概念极小集,讨论了面向属性概念格中核心概念、相对必要概念及不必要概念的概念特征,并研究了三种概念的判别方法.给出了面向属性概念约简的可行方法及其相应算法.

Learning Context-based Embeddings for Knowledge Graph Completion

Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes important.Many previous approaches are static,this posed a notable problem that all meanings of a polysemous entity share one embedding vector.This study aims to propose a polysemous embedding approach,named KG embedding under relational contexts(ContE for short),for missing link prediction.Design/methodology/approach:ContE models and infers different relationship patterns by considering the context of the relationship,which is implicit in the local neighborhood of the relationship.The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors,which represent the contextual information of the relationship.Then,according to the position of the entity,the entity’s polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship.Findings:ContE is a fully expressive,that is,given any ground truth over the triples,there are embedding assignments to entities and relations that can precisely separate the true triples from false ones.ContE is capable of modeling four connectivity patterns such as symmetry,antisymmetry,inversion and composition.Research limitations:ContE needs to do a grid search to find best parameters to get best performance in practice,which is a time-consuming task.Sometimes,it requires longer entity vectors to get better performance than some other models.Practical implications:ContE is a bilinear model,which is a quite simple model that could be applied to large-scale KGs.By considering contexts of relations,ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning,it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks.Originality/value:ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to.It decomposes a relation vector into two vectors,namely,forward impact vector and backward impact vector in order to capture the relational contexts.ContE has the same low computational complexity as TransE.Therefore,it provides a new approach for contextualized knowledge graph embedding.

A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section

Purpose:Researchers frequently encounter the following problems when writing scientific articles:(1)Selecting appropriate citations to support the research idea is challenging.(2)The literature review is not conducted extensively,which leads to working on a research problem that others have well addressed.The study focuses on citation recommendation in the related studies section by applying the term function of a citation context,potentially improving the efficiency of writing a literature review.Design/methodology/approach:We present nine term functions with three newly created and six identified from existing literature.Using these term functions as labels,we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy.BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation.Then the term function information is applied to enhance the performance.Findings:The experiments show that the term function-based methods outperform the baseline methods regarding the recall,precision,and F1-score measurement,demonstrating that term functions are useful in identifying valuable citations.Research limitations:The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section.More recent deep learning models should be performed to future validate the proposed approach.Practical implications:The citation recommendation strategy can be helpful for valuable citation discovery,semantic scientific retrieval,and automatic literature review generation.Originality/value:The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users,improving the transparency,persuasiveness,and effectiveness of recommender systems.

An Aspectual Analysis of Grice’s Maxim of Relation:Compared With the Principle of Relevance

The Cooperative Principle proposed by Herbert Paul Grice in 1967 requires speakers’utterances to comply with the objective of discourses,while receivers construct assumptions by reasoning speakers’implicatures.He set some maxims and sub-maxims to attempt to explain how the hearers get what is meant from what is said by speakers.What interests me is that the Maxim of Relation only includes one simple annotation—Be relevant without any other sub-maxims.Honestly,this is too obscure.So I would like to analyse Grice’s Maxim of Relation from its definition as well as classification,the discourse-topic and the relationship with context.And,more remarkable,the research method of this article is to explore the essence of the Maxim of Relation based on the opposite side as Dan Sperber and Deirdre Susan Moir Wilson’s the Principle of Relevance(Relevance Theory).

Positivism and Variation： Literary Communication and Propagation at Heterogeneous Cultural Context

The study of the international literary relations in comparative literature includes not only the positivistic relations of different nations and different literary systems, but also the relations of variation among different literatures when it comes to the cross-civilization heterogeneity. Therefore, the positivistic approach and that of variation in the study of international literary relations constitute the two indispensable pillars in this field.

语境信息对词汇语用信息认知理解的构建——来自ERP的证据

语言语境即上下文语境,影响词汇的临时性意义构建。当前,具体语境下的词汇意义构建问题是认知语义学及词汇语用学的重要研究内容,已有学者展开理论性研究。事件相关电位技术(ERPs)中的N400指标主要识别语义违反,其作为语义违反特异性ERP组分,是研究语境意义构建过程的重要指标。本研究将同一词语分别放置在字面义倾向语境、引申义倾向语境两种语境中,采用ERP技术,通过对语义敏感成分N400进行分析,试探究词汇语用信息的认知机制。通过对比被试在两种语境下的实时N400指标变化,可知字面义倾向语境下N400效应不明显,未出现明显的语义违反,在引申义倾向语境下,关键词后出现明显的N400效应。实验表明,语境信息作用下的词汇语用信息激活是一个动态的认知过程,激活词汇的引申义需要付出更多认知努力。

学习型搜索中的信息行为概念图构建与研究展望

[目的/意义]厘清学习情境中不同类型的信息行为概念及概念间关系,识别和发现学习型搜索未来可研究的内容。[方法/过程]文章对学习型搜索研究中涉及信息行为的文献进行了分析和综述,抽取其中的关键概念,构建了学习型搜索中的信息行为概念关系图。[结果/结论]本研究提出,学习型搜索中的信息行为概念包含信息需求、信息理解、信息获取和信息使用四个子领域。这四个子领域交叉相关,共同作用于学习者的学习产出。同时,信息获取与信息使用行为过程迭代进行,并不同程度地受到信息需求和信息理解的影响。未来学习型搜索研究还需更多地从整体的视角出发,探索如何优化搜索系统,支持多种类型的信息行为,并基于此提升用户的学习效果。

Granger因果关系时空图推理的群体行为分析

因果关系普遍存在于群体交互行为中,体现出主动体行为对被动体行为的有向影响.因果关系检测的难点在于交互双方的行为具有复杂的时间动态性.现有方法使用循环神经网络,来描述交互关系的时间变化特性,并使用时间注意力机制,来描述时间依赖关系.上述方法忽视了对多人依赖关系的分析,难以区分交互双方中的主动行为者和被动行为者.本文设计了一种基于Granger因果关系的时空图推理模型,来学习交互双方的主动和被动关系.为了实现Granger因果关系检测,该模型对单个个体时序特征进行自回归建模,来描述行为对个体自己的依赖.该模型对两个个体时序特征进行相关回归建模,来描述行为对两个个体的依赖.该模型通过比较自回归误差和相关回归误差,当自回归误差明显大于相关回归误差,则说明相关个体改变了另一方个体的行为特征,从而检测出相关个体为主动个体,另一方为被动个体.相关回归模型考虑了多种时间延迟量的两个个体的时序特征序列,用于学习两个个体之间行为的时间延迟量.该时间延迟量用于将主动个体时间特征与被动个体时间特征进行对齐.时间对齐后的主动个体特征提供了被动个体的时间和空间上下文特征,并与被动个体特征进行通道级的融合.为了充分描述个体之间的外观模式,位置约束,因果关系的交互关系,该模型构建多尺度外观的因果图,并使用图推理学习融合上下文的个体特征和群体特征.本文对Granger因果关系检测进行消融分析,并说明时间延迟量,交互融合通道比例,多尺度图推理,能够有效改善个体特征、群体特征的描述能力.本文方法在Volleyball和Collective Activity数据集上优于现有群体行为识别方法.本文的可视化结果说明Granger因果关系可以捕获群体中关键的交互关系.

技能背景下学习空间的判别方法和技能评估

技能背景是以问题为对象、技能为属性并体现问题与技能关系的一种特殊形式背景,在技能背景下,通过面向问题概念格和面向技能概念格分别得到知识空间和闭包空间.知识状态是知识空间理论的核心概念,它不仅体现个体当下的学习现状,还可以通过非空的内、外边缘指导下一步的学习.但在知识空间和闭包空间下,知识状态可能不存在非空的边缘,而在良级知识结构下,任意知识状态都存在非空边缘.良级知识空间是学习空间,而且良级知识空间和良级闭包空间都是程序性知识评估的重要工具.在技能背景下研究知识空间是学习空间的充要条件以及什么样的闭包空间是良级的问题.首先,基于知识状态的关系提出良好技能背景,并给出技能背景下学习空间的判别方法和良级的闭包空间的充要条件;其次,根据良好技能背景对个体进行技能评估,得到个体的能力范围;最后,提出技能推测关系,同时给出求解技能推测关系和寻找学习路径的方法.

回归经济学语境:东欧新马克思主义对马克思人道主义解读视角的补充

马克思在《1844年经济学哲学手稿》《政治经济学批判大纲》《资本论》等政治经济学著作中探讨了自由竞争时期资本主义社会人的异化生存状态,他从人与生产的关系出发,以“人表现为生产的目的”为尺度批判了资本主义条件下“生产表现为人的目的”的问题,以政治经济学批判深化了哲学批判。20世纪中叶,东欧新马克思主义在经济学语境中再次围绕人与生产的关系重新阐释马克思的这一观点,并着重传达了其中的人道主义价值理念。针对西方人本主义流派对马克思的人道主义解读,东欧新马克思主义认为有必要补充经济学阐释维度,从而超越抽象层面的“浪漫主义”文化批判。东欧新马克思主义不仅挖掘了马克思经济学理论的超经济学特征,而且在新的历史条件下发展了马克思对“经济人”的批判思想。东欧新马克思主义在经济学语境中重新解读马克思“人与生产的关系”,批判了对马克思的断裂式解读,进而构建了对马克思思想的连续性理解;同时,在对马克思经济学理论的解读中还超越了对马克思的经济决定论理解,是对马克思哲学视角的人道主义解读进行一种经济学视角的补充。

关于“东方”概念的几点思考

以西方为参照系研究东方的学问,我们称之为“东方学”。而东方学话语体系中最为核心的概念便是“东方”。然而,这个概念无论是内涵抑或外延,并非不证自明。我们究竟应该如何科学地从本体论的高度去界定“东方”这一概念,这不仅是学理的问题,实质上也交织着话语权力运作的问题。本文拟聚焦构成“东西方”话语体系重要节点的几个概念,尝试性地对东西方形象进行宏观层面的勾勒,对东西方在政治、经济、文化等层面上所表现出来的差异进行一些探讨,借此揭示东西方二元对立僵局的实质,并阐明当代“东方人”应该坚持的态度。

基于大规模语料库的汉语词义相似度计算方法

词义相似度的计算是自然语言处理领域的关键问题之一,它在信息检索中的查询扩展、机器翻译中的模块识别,以及句法分析、词义消歧等任务中都发挥着重要的作用。该文研究了基于大规模语料库的汉语词义相似度计算方法,系统地比较分析了上下文特征权值的选择、向量相似度计算方法、基于窗口和基于依存关系的表征形式、新闻语体和网络语体的差异。实验结果表明,在网络语言语料上,基于窗口选取上下文特征,用互信息PMI来计算权值,采用cosine来计算相似度,取得了最好的词义相似度结果。

基于上下文的领域本体概念和关系的提取

目前本体学习的研究重点在于概念及关系的提取,概念提取领域一致度与领域相关度相结合的方法取得了比较好的效果,而关系提取则主要采用基于关联规则的方法。这种本体概念、关系学习方法由于只考虑词频,提取结果准确性欠缺。针对这种缺陷,在统计的基础上考虑了语义因素,利用词汇上下文计算概念的语义相似度并将其应用到概念与关系提取中。实验结果表明,词汇上下文与传统统计相结合的方法能够有效改进概念和关系提取的准确度。

三元背景基于二元关系不变的约简

针对三元背景,提出了基于二元关系不变的约简问题。根据每个论域中元素对约简所起的不同作用对所有元素进行分类,并对不同类型元素的性质进行讨论。最后研究了约简前后三元概念之间的关系。