Context modeling for ranking and tagging bursty features in text streams

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 features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Text Mining ...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Wayne Xin Zhao, Jing Jiang, Jing He, Dongdong Shan, Hongfei Yan, Xiaoming Li
• Keywords: Bursty Feature, CIKM 2010, Context Models, Information Technology, Text Mining