Novelty detection for topic tracking

Date
2012
Authors
Aksoy, C.
Can, F.
Kocberber, S.
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
Association for Information Science and Technology. Journal
Print ISSN
2330-1635
Electronic ISSN
Publisher
John Wiley & Sons, Inc.
Volume
63
Issue
4
Pages
777 - 795
Language
English
Type
Article
Journal Title
Journal ISSN
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Series
Abstract

Multisource web news portals provide various advantages such as richness in news content and an opportunity to follow developments from different perspectives. However, in such environments, news variety and quantity can have an overwhelming effect. New-event detection and topic-tracking studies address this problem. They examine news streams and organize stories according to their events; however, several tracking stories of an event/topic may contain no new information (i.e., no novelty). We study the novelty detection (ND) problem on the tracking news of a particular topic. For this purpose, we build a Turkish ND test collection called BilNov-2005 and propose the usage of three ND methods: a cosine-similarity (CS)-based method, a language-model (LM)-based method, and a cover-coefficient (CC)-based method. For the LM-based ND method, we show that a simpler smoothing approach, Dirichlet smoothing, can have similar performance to a more complex smoothing approach, Shrinkage smoothing. We introduce a baseline that shows the performance of a system with random novelty decisions. In addition, a category-based threshold learning method is used for the first time in ND literature. The experimental results show that the LM-based ND method significantly outperforms the CS- and CC-based methods, and categorybased threshold learning achieves promising results when compared to general threshold learning. © 2011 ASIS&T.

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Keywords
Dirichlet, Multisources, News content, Novelty detection, Test collection, Threshold learning, Topic tracking, Turkishs, Information services, Information retrieval systems
Citation
Published Version (Please cite this version)