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.     

浅谈CorelDRAW中表格的绘制

文章介绍了在CorelDRAW中绘制表格的步骤和操作方法,并用实例加以说明。     

八数码问题的搜索算法比较

搜索策略是人工智能研究的主攻方向之一,采用不同的搜索策略在求解问题的过程中也会存在差异.通过对于八数码的搜索求解分析,采用盲目搜索中的广度优先搜索算法和启发式搜索中的A*算法进行实现,将广度优先搜索算法与A*算法进行比较,从而评价这两种搜索算法的优劣性.     

The effects of first language orthographic features on word recognition processing in English as a second language

This study investigated the possible effects of first language (L1) orthographic characteristics on word recognition in English as a second language (ESL). Case alternation was used to examine the impact of visually distorted words of different types on fluent ESL readers' word recognition in naming. Visual distortion of word shape (i.e., cAsE aLtErNaTiOn) was utilized because, although visually distorted words have lost word-shape cues, they preserve the cue value of words (i.e., spelling patterns). It, therefore, was hypothesized that if one is sensitive to alphabetic orthography, or if one's inner mechanism of processing an alphabetic word is efficient, then the visual disruption of word-shape cues should not affect one's sensitivity to sequences of letters in words. In other words, this study focused on the magnitude of the effect of case alternation in word recognition as an index of the sensitivity to alphabetic words. Results showed that the magnitude of the case alternation effect in a naming task was significantly larger for the ESL participants whose L1 is not alphabetic (i.e., Chinese and Japanese) than the ESL participants whose L1 is alphabetic (i.e., Iranians -- Persian as L1). This result seems to indicate that the Persian speakers, due to the facilitating influence of their L1 orthography, were less influenced by case alternation than the Chinese and Japanese speakers, whose L1 orthographies are not alphabetic. This finding suggests that the first language orthographic features affect the orthographic coding mechanisms (i.e., word recognition mechanisms) in a second language.     

博弈树搜索算法的分析与实现

α-β剪枝算法是博弈树搜索算法中成熟而重要的一枝。本文给出其各种改进策略及实现技术,并给出实验结果分析。B*算法是α-β剪枝算法的挑战者,它给我们以新的视角看待博弈树搜索,并能克服α-β剪枝算法的一些缺陷。     

利用Delphi访问Word文档

本文论述了利用Delphi访问Word文档,包括对Word文档的打开、连接及修改编辑操作,从而实现Delphi与Word文档的完美结合。     

关于中专英语词汇教学

通过新旧单词对比、同义词反义词对比、构词法等教会中专学生记忆英语单词以扩大词汇量激发他们的学习英语的兴趣，同时通过与词的搭配方面的教学，促进学生扩大语汇，学会理解英语词的词义并掌握其用法，这是中专英语词汇教学的有效方法。     

Confirmatory factor analysis of word recognition and process measures in the Colorado Reading Project

Measures of word recognition (REC) and two component skills, phonological coding (PHON) and orthographic coding (ORTH), were subjected to multivariate behavioral genetic analysis. Data were obtained from a sample of identical and fraternal twin pairs wherein at least one member of each pair was reading disabled (RD), and from a sample of twins wherein both members of each pair read in the normal range. Confirmatory factor analysis was used to fit the genetic, common environmental, and specific environmental covariance components for REC, PHON, and ORTH within the RD and normal simples. The resulting heritability estimates for REC, PHON, and ORTH were 0.59, 0.41, and 0.05 in the RD sample, and 0.35, 0.52, and 0.20 in the normal sample. After dropping the nonsignificant common environment parameters from the models, the genetic correlations between REC and PHON and between REC and ORTH were respectively 0.81 and 0.45 in the RD sample, and 0.68 and 0.45 in the normal sample. Differences between the genetic correlations were significant in the RD sample (p<0.005), marginally significant in the normal sample (p<0.10), and highly significant in the combined sample (p<0.001), indicating that genetic influences on individual differences in REC are more strongly related to genetic variance in PHON than in ORTH. These results are consistent with previous demonstrations of substantial genetic covariance between the disabled group's deficits in REC and PHON, but not between REC and ORTH (Olson et al., 1989; Olson and Rack, 1990).     

Word在办公应用中的技巧使用

文章主要从"创建文档"、"编辑文档"、"打印文档"等方面叙述了Word在办公应用中的各种技巧使用,对办公应用人员起到了一定的指导作用,有助于提高电脑办公的质量和效率.     

基于组件对象模型调用Word

探讨了如何在组件对象模型的基础上调用Word应用程序 ,介绍了其中的主要原理 ,并给出了一个简单的实现 .