In this article, we review probabilistic topic models: graphical models that can be used to
summarize a large collection of documents with a smaller number of distributions over …

The aim of this paper is to map the intellectual structure of research in doctoral dissertations
of Library and Information Science in China. By use of Co-word analysis, including cluster …

This work identifies changes in dominant topics in library and information science (LIS) over
time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at …

A Bayesian dynamic model based on multitask learning (MTL) is developed for radar
automatic target recognition (RATR) using high-resolution range profile (HRRP). The aspect …

摘要Dirichlet 过程是一种应用于非参数贝叶斯模型中的随机过程, 特别是作为先验分布应用在
概率图模型中. 与传统的参数模型相比, Dirichlet 过程的应用更加广泛且模型更加灵活 …

Mining cluster evolution from multiple correlated time-varying text corpora is important in
exploratory text analytics. In this paper, we propose an approach called evolutionary …

An extended latent Dirichlet allocation (LDA) model is presented in this paper for patent
competitive intelligence analysis. After part-of-speech tagging and defining the noun phrase …

In recent years social media have become indispensable tools for information
dissemination, operating in tandem with traditional media outlets such as newspapers, and it …

Social media data are produced continuously by a large and uncontrolled number of users.
The dynamic nature of such data requires the sentiment and topic analysis model to be also …

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic
topic modeling, which attracts worldwide interest and touches on many important …