Publication Type

Conference Proceeding Article

Publication Date

10-2010

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.

Keywords

bursty features, bursty features ranking, bursty feature tagging, context modeling

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

CIKM 2010: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, 26-30 October 2010, Ontario

First Page

1769

Last Page

1772

ISBN

9781450300995

Identifier

10.1145/1871437.1871725

Publisher

ACM

City or Country

Edmonton, Canada

Additional URL

http://dx.doi.org/10.1145/1871437.1871725

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