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.     

Coupled matrix factorization and topic modeling for aspect mining

Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.     

DOI在科技信息资源搜索与利用中的应用

在数字环境下,科学家做研究和研究成果交流的方式都发生了很大变化,技术发展和观念更新给科技信息资源共享同时带来机遇和挑战.在数字环境下,科技信息资源具有分布广泛、数量巨大、增长迅速、内容丰富、形式多样等特征.与此同时,网络环境下科研工作者却表现出了强烈的对更高层次信息的需求,例如对西文文献、原始科学数据、图片、视频、音频的直接获取等.因此,如何在网络环境下解决这一矛盾已成为信息科学的重要任务.本文拟通过数字对象唯一标识符(DOI)在科技文献与科学数据网络获取的应用实例来探讨相关的科技信息资源共享解决方案.     

近年来邓小平现代化理论研究综述

邓小平的现代化理论是邓小平理论的一个重要组成部分，它直接指导着中国社会主义现代化建设。近年来，学者们从不同角度对它进行了深入研究，并取得了丰硕的成果。将这些研究成果进行综合归纳，将有利于学者们更深入地研究这一课题。对当前的社会主义现代化建设也有重大意义。     

培养差生学习物理的习惯

学生进入高中后,随着学习任务的加重,学习内容的加深,几次考试下来,不少学生对物理学习失去了信心,轮为差生.归其原因主要是:学习物理方法不当,学习习惯不好,学习只是满足课堂能听懂,死记物理概念、定律和公式,不理解其物理意义和应用.为了使这些差生能迅速赶上队伍,我在培养他们学习物理的习惯上做了以下几点尝试.     

《气象学与气候学》教材的分析

本文主要根据系统方法的整体原则、相关原则等分析了高等师范院校地理专业的《气象学与气候学》教材,从而确定教材的目的、特点及其功能,并对该教材提出了自己看法。     

区域信息化发展水平分析及对策研究——以河北省为例

随着科学技术的不断进步,信息化发展水平已成为衡量国家综合国力和国际竞争力的重要标志。区域信息化作为国家信息化的组成部分,对促进区域进步,实现区域协调发展具有重要意义。以京津冀信息化水平为研究对象,旨在了解京津冀一体化背景下,河北省信息化发展水平及现状。采用信息化发展水平评估指标体系,基于因子分析方法,选取关键性指标,对影响河北省信息化发展的关键因素进行研究,并通过对比京津冀三地指标数值,采用文献计量法定量分析河北省信息化研究热点,总结河北省信息化发展现状,发现河北省信息化进程中存在基础设施支持力度不足,科技创新能力低等问题,并提出加大基础设施投资,加速产业信息化等相应对策。     

教师如何写教学反思

教学反思是教师对自己教学行为的内省和剖析，是从自己的经历中学习，从而进行自我调控。它有助于促进教师教学能力的发展和专业水平的提高。教师写教学反思必须实话实说，成败得失记录在案；及时反馈师生两方面的情况；综合分析教与学的关系；找准要点，写出活动中的某些特点；捕捉灵感，写随机生成的动态性教学资源和遐想；最后，可进行深度反思，写出有效性的建议。     

关于高校《马克思主义政治经济学原理》的教学改革

现行的《马克思主义哲学经济原理》教材，在内容上没有跟上时代发展的步伐，要改变这种状况，必须在教学内容和教学方法上改革，把思想政治理论教育贯穿于教学内容中，以便更好地对学生进行马克思主义理论教育。     

住宅小区无线网络的硬件研究

利用低价格高性能智能模块的嵌入式应用实现较为复杂的网络功能,使得网络设计实现模块化;设计了无线通信接口、路由器、中继器等硬件模块,还解决了总线的冲突,报文丢失等问题,为智能小区开发出廉价的的无线网络平台。