Paper Info
Title
Context modeling for ranking and tagging bursty features in text streams
Abstract
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.
Year
DOI
Venue
2010
10.1145/1871437.1871725
CIKM
Keywords
Field
DocType
tagging bursty feature,semantic context,language modeling approach,proposed context language model,text mining application,context modeling,text stream,rank bursty,newsworthy bursty feature,context model,meaningful tag,bursty feature,text mining,language model,term frequency
Data mining,Information retrieval,Ranking,Computer science,Context model,Language model
Conference
Citations 
PageRank 
References 
4
0.47
8
Authors
6
Name
Order
Citations
PageRank
Wayne Xin Zhao1127566.73
Jing Jiang23843191.63
Jing He353719.00
Dongdong Shan41286.11
Hongfei Yan576335.67
Xiaoming Li6166992.16