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	Context modeling for ranking and tagging bursty features in text streams
	Zhao, Wayne Xin ; Jiang, Jing ; He, Jing ; Shan, Dongdong ; Yan, Hongfei ; Li, Xiaoming
	2010
英文摘要	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. ? 2010 ACM.; EI; 0
语种	英语
DOI标识	10.1145/1871437.1871725
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/295430]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhao, Wayne Xin,Jiang, Jing,He, Jing,et al. Context modeling for ranking and tagging bursty features in text streams. 2010-01-01.

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