Use of LDA Model in Topic Tracking
Liang Xiao-bo · 2011
As more and more researches are made for the LDA model,its ability of representing and mining has been increased a lot.Topic is an important concept in the LDA model,which is represented as a polynomial distribution of the feature set.Topic tracking is monitoring a stream of news stories to find additional stories on a topic identified by several samples.There are two reasons for using the LDA model in topic tracking:one is to show how the performance of the tracking system using the LDA model is;the other is trying to find whether there is some relation between the LDA topic and the tracked topic.The experimental results indicate that the LDA model is better than the vector space model,the unigram language model and the special event model in a topic tracking system.However,since the granularities of two kinds of topics are different,the relation between the LDA topic and the tracked topic is not about bijection.An adjustable LDA model is needed in our future work.