Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty...Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.展开更多
Based on the analysis of the known quantitative observations and qualitative measurements in the near wall region of the flow over a smooth wall, the distribution of the skewness factor in the vertical direction has b...Based on the analysis of the known quantitative observations and qualitative measurements in the near wall region of the flow over a smooth wall, the distribution of the skewness factor in the vertical direction has been discussed for the openchannel flow over a fully roughened wall. It is found that when H/ks > 1. 0,S >0 is obtained in the range of y/H <0. 2, which means that there exist the low speed streaks, i. e. the turbulence of the now is produced by the phenomenon called 'burst'. For H/ks <1. 0, which belongs to the large-scale roughness case, the turbulence will be produced in another way. Moreover, the burst period is also discussed in this paper.展开更多
As the Smart city trend especially artificial intelligence,data science,and the internet of things has attracted lots of attention,many researchers have created various smart applications for improving people’s life ...As the Smart city trend especially artificial intelligence,data science,and the internet of things has attracted lots of attention,many researchers have created various smart applications for improving people’s life quality.As it is very essential to automatically collect and exploit information in the era of industry 4.0,a variety of models have been proposed for storage problem solving and efficient data mining.In this paper,we present our proposed system,Trendy Keyword Extraction System(TKES),which is designed for extracting trendy keywords from text streams.The system also supports storing,analyzing,and visualizing documents coming from text streams.The system first automatically collects daily articles,then it ranks the importance of keywords by calculating keywords’frequency of existence in order to find trendy keywords by using the Burst Detection Algorithm which is proposed in this paper based on the idea of Kleinberg.This method is used for detecting bursts.A burst is defined as a period of time when a keyword is continuously and unusually popular over the text stream and the identification of bursts is known as burst detection procedure.The results from user requests could be displayed visually.Furthermore,we create a method in order to find a trendy keyword set which is defined as a set of keywords that belong to the same burst.This work also describes the datasets used for our experiments,processing speed tests of our two proposed algorithms.展开更多
基金Supported by the National High Technology Research and Development Program of China(No.2012AA011005)
文摘Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.
文摘Based on the analysis of the known quantitative observations and qualitative measurements in the near wall region of the flow over a smooth wall, the distribution of the skewness factor in the vertical direction has been discussed for the openchannel flow over a fully roughened wall. It is found that when H/ks > 1. 0,S >0 is obtained in the range of y/H <0. 2, which means that there exist the low speed streaks, i. e. the turbulence of the now is produced by the phenomenon called 'burst'. For H/ks <1. 0, which belongs to the large-scale roughness case, the turbulence will be produced in another way. Moreover, the burst period is also discussed in this paper.
基金The work of Tham Vo is supported by Lac Hong University,and funded by Thu Dau Mot University(No.DT.20-031)The work of Phuc Do is funded by Vietnam National University,Ho Chi Minh City(No.DS2020-26-01).
文摘As the Smart city trend especially artificial intelligence,data science,and the internet of things has attracted lots of attention,many researchers have created various smart applications for improving people’s life quality.As it is very essential to automatically collect and exploit information in the era of industry 4.0,a variety of models have been proposed for storage problem solving and efficient data mining.In this paper,we present our proposed system,Trendy Keyword Extraction System(TKES),which is designed for extracting trendy keywords from text streams.The system also supports storing,analyzing,and visualizing documents coming from text streams.The system first automatically collects daily articles,then it ranks the importance of keywords by calculating keywords’frequency of existence in order to find trendy keywords by using the Burst Detection Algorithm which is proposed in this paper based on the idea of Kleinberg.This method is used for detecting bursts.A burst is defined as a period of time when a keyword is continuously and unusually popular over the text stream and the identification of bursts is known as burst detection procedure.The results from user requests could be displayed visually.Furthermore,we create a method in order to find a trendy keyword set which is defined as a set of keywords that belong to the same burst.This work also describes the datasets used for our experiments,processing speed tests of our two proposed algorithms.
