Topic evolution based on the probabilistic topic model： a review  

作　　者：Houkui ZHOU Huimin YU Roland HU 

机构地区：[1]College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China [2]State Key Laboratory of CAD & CG, Hangzhou 310027, China [3]School of Information Engineering, Zhejiang A&F University, Hangzhou 311300, China [4]Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China

出　　处：《Frontiers of Computer Science》2017年第5期786-802,共17页中国计算机科学前沿（英文版）

基　　金：Acknowledgements The authors would like to thank the anonymous reviewers for their constructive comments and suggestions, which significantly contributed to improving the manuscript. This work was supported by the National Key Basic Research Project of China （973 Program） （2012CB316400）, the National Natural Science Foundation of China （Grant Nos. 61471321, 61202400, 31300539, and 31570629）, the Zhejiang Provincial Natural Science Foundation of China （LY15C140005, LY16F010004）, Science and Technology Department of Zhejiang Province Public Welfare Project （2016C31G2010057, 2015C31004）, Fundamental Research Funds for the Central Universities （172210261） and the Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research.

摘　　要：Accurately representing the quantity and characteristics of users＇ interest in certain topics is an important problem facing topic evolution researchers, particularly as it applies to modem online environments. Search engines can provide information retrieval for a specified topic from archived data, but fail to reflect changes in interest toward the topic over time in a structured way. This paper reviews notable research on topic evolution based on the probabilistic topic model from multiple aspects over the past decade. First, we introduce notations, terminology, and the basic topic model explored in the survey, then we summarize three categories of topic evolution based on the probabilistic topic model： the discrete time topic evolution model, the continuous time topic evolution model, and the online topic evolution model. Next, we describe applications of the topic evolution model and attempt to summarize model generalization performance evaluation and topic evolution evaluation methods, as well as providing comparative experimental results for different models. To conclude the review, we pose some open questions and discuss possible future research directions.Accurately representing the quantity and characteristics of users＇ interest in certain topics is an important problem facing topic evolution researchers, particularly as it applies to modem online environments. Search engines can provide information retrieval for a specified topic from archived data, but fail to reflect changes in interest toward the topic over time in a structured way. This paper reviews notable research on topic evolution based on the probabilistic topic model from multiple aspects over the past decade. First, we introduce notations, terminology, and the basic topic model explored in the survey, then we summarize three categories of topic evolution based on the probabilistic topic model： the discrete time topic evolution model, the continuous time topic evolution model, and the online topic evolution model. Next, we describe applications of the topic evolution model and attempt to summarize model generalization performance evaluation and topic evolution evaluation methods, as well as providing comparative experimental results for different models. To conclude the review, we pose some open questions and discuss possible future research directions.

关 键 词：topic evolution probabilistic topic models text corpora evaluation method 

分 类 号：TP303[自动化与计算机技术—计算机系统结构] O242.23[自动化与计算机技术—计算机科学与技术]