移就的词汇化形式与语义演变

移就修辞的词汇化形式表现为完全词汇化、移就造词和准词汇化三种形式.不同的词汇化形式表明移就修辞结构可以造成移用词语的语义演变.移用词语的语义演变是一个渐进的过程.主要表现为义域扩大、词义主观化、词性转移等,移就造词也促使其词义进一步泛化.     

面向CSSCI的学者知识地图构建与分析

为促进学术交流和发展,为学者之间的交叉合作提供可参考的事实依据,并获得可支持具体决策的分析结论,将本体机制引入CSSCI学术资源的知识组织,以面向对象的方式来组织学者知识地图的相关概念,构建面向CSSCI的学者知识地图概念模型;此外,通过学者两两之间关系的关联分析和知识挖掘发现学者之间潜在的合作可能,找出某学科领域中能够对中心作者产生重要学术影响的关联作者,并基于关联明确划分作者群,希望能够加强相关领域的研究合作,实现知识的互补与领域成果的借鉴和启发。     

The semantic mapping of words and co-words in contexts

Meaning can be generated when information is related at a systemic level. Such a system can be an observer, but also a discourse, for example, operationalized as a set of documents. The measurement of semantics as similarity in patterns (correlations) and latent variables (factor analysis) has been enhanced by computer techniques and the use of statistics; for example, in “latent semantic analysis”. This communication provides an introduction, an example, pointers to relevant software, and summarizes the choices that can be made by the analyst. Visualization (“semantic mapping”) is thus made more accessible.     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.     

聋人与听力正常人短时记忆比较研究

本实验以视觉系列呈现方式 ,在形近、音近、义近三种干扰下以再认方法比较了听力正常人与聋人对字频与笔画二种不同水平汉字材料的短时记忆容量及编码方式 ,研究结果表明 :( 1 )聋人与听力正常人短时记忆容量差别有限 ,只存在于低频复杂汉字组中。 ( 2 )高频字中 ,聋人短时记忆编码以形码为主 ,义码次之 ,音码最弱 ;听力正常人则以音码为主 ,义码次之 ,形码作用最弱。 ( 3 )低频字中 ,低频简单组中表现出与高频字相同的编码方式 ,低频复杂组中聋人与听力正常人均表现形码、义码为主 ,音码作用弱的特点     

面向语义Web的本体学习研究

语义Web的创建需要一套共同的标准概念体系,即本体(Ontology)。本体的构造手段仍然是以手工构造为主,效率和准确率都非常低,很容易导致知识获取的瓶颈。本文给出一个半自动化的需人工干预的本体学习体系结构,采用平衡的协作建模方式来构造语义Web中的本体;介绍了基于以上体系结构的本体学习的处理过程,并讨论了领域概念抽取,概念之间关系的抽取等关键技术。     

Rare-aware attention network for image–text matching

Image and text matching bridges visual and textual modality differences and plays a considerable role in cross-modal retrieval. Much progress has been achieved through semantic representation and alignment. However, the distribution of multimedia data is severely unbalanced and contains many low-frequency occurrences, which are often ignored and cause performance degradation, i.e., the long-tail effect. In this work, we propose a novel rare-aware attention network (RAAN), which explores and exploits textual rare content for tackling the long-tail effect of image and text matching. Specifically, we first design a rare-aware mining module, which contains global prior information construction and rare fragment detector for modeling the characteristic of rare content. Then, the rare attention matching utilizes prior information as attention to guide the representation enhancement of rare content and introduces the rareness representation to strengthen the similarity calculation. Finally, we design prior information loss to optimize the model together with the triplet loss. We perform quantitative and qualitative experiments on two large-scale databases and achieve leading performance. In particular, we conduct 0-shot test for rare content and improve rSum by 21.0 and 41.5 on Flickr30K (155,000 image and text pairs) and MSCOCO (616,435 image and text pairs), demonstrating the effectiveness of the proposed method for the long-tail effect.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

浅议图形设计中的《易》理

随在中国众多的经书中,《易经》被推为群经之首,其立道设教的思想是中华民族思维的总脉。《易经》的阴阳论构成了一个科学、完整的逻辑系统,这个系统对今天的图形设计具有一定的理论指导和实践应用的现实意义,通过对现代图形设计案例的分析,找出阴阳鱼太极图对现代图形设计的影响,表明《易经》的思想是现代图形设计创造性思维的来源,同时也是提升图形设计文化品位的重要途径。     

“危”字字义的历时演变

词义演变基本按照扩大、缩小和转移发展。"危"其义项的发展、演变对于古代汉语词义研究与探索有着重要意义。文章把"危"字词义演变的历时层分为五个时期,运用历时语言学的研究手段,根据各种古代文学史料和古文字考据成果和"危"字词义从先秦至元明清时期在不同史料文献中使用频率来研究"危"字词义在各朝代的演变过程和引申义项。