Purpose:Research dynamics have long been a research interest.It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject.A micro perspective of research dynamics,however,...Purpose:Research dynamics have long been a research interest.It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject.A micro perspective of research dynamics,however,concerning a single researcher or a highly cited paper in terms of their citations and“citations of citations”(forward chaining)remains unexplored.Design/methodology/approach:In this paper,we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance,and topic innovation in each generation of forward chaining.Findings:For highly cited work,scientific influence exists in indirect citations.Topic modeling can reveal how long this influence exists in forward chaining,as well as its influence.Research limitations:This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations.Paraphrasing or semantically similar concept may be neglected in this research.Practical implications:This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining.This can serve as an inspiration on how to adequately evaluate research influence.Originality:The main contributions of this paper are the following three aspects.First,besides research dynamics of topic inheritance and topic innovation,we model topic disappearance by using a cross-collection topic model.Second,we explore the length and character of the research impact through“citations of citations”content analysis.Finally,we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton’s publications and the topic dynamics of forward chaining.展开更多
With the rapid popularization of social applications, various kinds of social media have developed into an important platform for publishing information and expressing opinion. Detecting hidden topics from the huge am...With the rapid popularization of social applications, various kinds of social media have developed into an important platform for publishing information and expressing opinion. Detecting hidden topics from the huge amount of user-generated contents is of great commerce value and social significance. However traditional text analysis approachesonly focus on the statistical correlation between words, but ignore the sentiment tendency and the temporal properties which may have great effects on topic detection results. This paper proposed a Dynamic Sentiment-Topic(DST) model which can not only detect and track the dynamic topics but also analyze the shift of public's sentiment tendency towards certain topic.Expectation-Maximization algorithm was used in DST model to estimate the latent distribution, and we used Gibbs sampling method to sample new document set and update the hyper parameters and distributions.Experiments are conducted on a real dataset and the results show that DST model outperforms the existing algorithms in terms of topic detection and sentiment accuracy.展开更多
利用3 D Mine矿业软件建立矿山原始地形面DTM表面模型,主要用于采剥工程量的计算,矿业地质模型、构造模型、传统和现代地质储量的计算,露天及地下矿山的采矿设计、露天短期进度计划制定等,其中原始地形面的DTM表面模型的建立是测量工程...利用3 D Mine矿业软件建立矿山原始地形面DTM表面模型,主要用于采剥工程量的计算,矿业地质模型、构造模型、传统和现代地质储量的计算,露天及地下矿山的采矿设计、露天短期进度计划制定等,其中原始地形面的DTM表面模型的建立是测量工程师、地质工程师和采矿工程师开展工作的基础,地形面的DTM表面模型建立必须依据矿山测量的原始测量数据进行数据编辑处理,建立数据库,然后建立三维表面。通过建立的DTM表面模型,可以实现对矿山的四维动态管理。展开更多
基金This work is supported by the Programs for the Young Talents of National Science Library,Chinese Academy of Sciences(Grant No.2019QNGR003).
文摘Purpose:Research dynamics have long been a research interest.It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject.A micro perspective of research dynamics,however,concerning a single researcher or a highly cited paper in terms of their citations and“citations of citations”(forward chaining)remains unexplored.Design/methodology/approach:In this paper,we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance,and topic innovation in each generation of forward chaining.Findings:For highly cited work,scientific influence exists in indirect citations.Topic modeling can reveal how long this influence exists in forward chaining,as well as its influence.Research limitations:This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations.Paraphrasing or semantically similar concept may be neglected in this research.Practical implications:This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining.This can serve as an inspiration on how to adequately evaluate research influence.Originality:The main contributions of this paper are the following three aspects.First,besides research dynamics of topic inheritance and topic innovation,we model topic disappearance by using a cross-collection topic model.Second,we explore the length and character of the research impact through“citations of citations”content analysis.Finally,we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton’s publications and the topic dynamics of forward chaining.
基金supported by National Natural Science Foundation of China with granted No.61402045,61370197the Specialized Research Fund for the Doctoral Program of Higher Education with granted No.20130005110011the National High Technology Research and Development Program with granted No.2013AA013301
文摘With the rapid popularization of social applications, various kinds of social media have developed into an important platform for publishing information and expressing opinion. Detecting hidden topics from the huge amount of user-generated contents is of great commerce value and social significance. However traditional text analysis approachesonly focus on the statistical correlation between words, but ignore the sentiment tendency and the temporal properties which may have great effects on topic detection results. This paper proposed a Dynamic Sentiment-Topic(DST) model which can not only detect and track the dynamic topics but also analyze the shift of public's sentiment tendency towards certain topic.Expectation-Maximization algorithm was used in DST model to estimate the latent distribution, and we used Gibbs sampling method to sample new document set and update the hyper parameters and distributions.Experiments are conducted on a real dataset and the results show that DST model outperforms the existing algorithms in terms of topic detection and sentiment accuracy.
文摘利用3 D Mine矿业软件建立矿山原始地形面DTM表面模型,主要用于采剥工程量的计算,矿业地质模型、构造模型、传统和现代地质储量的计算,露天及地下矿山的采矿设计、露天短期进度计划制定等,其中原始地形面的DTM表面模型的建立是测量工程师、地质工程师和采矿工程师开展工作的基础,地形面的DTM表面模型建立必须依据矿山测量的原始测量数据进行数据编辑处理,建立数据库,然后建立三维表面。通过建立的DTM表面模型,可以实现对矿山的四维动态管理。
