Topic Model Based New Event Detection Within Topics

Abstract

Traditional new event detection is first proposed by Topic Detection and Tracking and it is actually first event detection. However, one topic usually consists of many events. The automatic instant detection of each event in one topic, not only the first event but also the second, the third and so on, is very useful for users to correctly understand the main development trend of the topic. In this paper, we address the problem of new event detection in one single topic and propose a novel topic model to detect new events along with the topic evolution. Our topic model treats new event detection as novel semantic aspect identification in one topic, rather than measuring the analog degrees between content items by lexical congruence. Besides, it can automatically determine the appropriate number of aspects needed and can naturally adapt dynamic change in the vocabulary along with the topic evolution. We use a sequential Gibbs sampling algorithm for posterior inference, which well realizes the online new event detection. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.