Databases and Information Systems Commons^™

Context Modeling For Ranking And Tagging Bursty Features In Text Streams, Xin Zhao, Jing Jiang, Jing He, Xiaoming Li, Hongfei Yan, Dongdong Shan

Research Collection School Of Computing and Information Systems

Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty …