基于LDA话题演化研究方法综述

现实生活中不断有新话题的产生和旧话题的衰减,同时话题的内容也会随着时间发生变化。自动探测话题随时间的演化越来越受到人们的关注。Latent Dirichlet Allocation模型是近年提出的概率话题模型,已经在话题演化领域得到较为广泛的应用。该文提出了话题演化的两个方面 内容演化和强度演化,总结了基于LDA话题模型的话题演化方法,根据引入时间的不同方式将目前的研究方法分为三类 将时间信息结合到LDA模型、对文本集合后离散和先离散方法。在详细叙述这三种方法的基础上,针对时间粒度、是否在线等多个特征进行了对比,并且简要描述了目前广泛应用的话题演化评测方法。文章最后分析了目前存在的挑战,并且对该研究方向进行了展望。

A Survey of Topic Evolution Based on LDA

With topics evolve over time, new topics emerge and old ones decay. Many researches are devoted to detect the topic evolution automatically. Latent Dirichlet Allocation (LDA), as a recently emerged probabilistic topic model, has been widely used in the research of topic evolution. This paper discusses two aspects of evolution on topic, i.e. the content and the topic intensity. It summarizes three methods in LDA based topic evolution detection according to the dealing with timejoining the time to LDA model, post-discretizing or pre-discretizing methods. The three methods are also compared in several featuresthe time granularity, on-line or off-line, etc. In addition, the evaluation methods for topic evolution are introduced. Finally, the paper gives some analysis and suggestions for future researches on topic evolution based on LDA.
Key wordstopic model;topic evolution;Latent Dirichlet Allocation