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Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2013)
Atlanta, GA, USA USA
Nov. 17, 2013 to Nov. 20, 2013
pp: 154-158
ABSTRACT
Story Link Detection (SLD) is known as a sub-task of Topic Detection and Tracking (TDT). SLD aims to specify whether two randomly selected stories discuss the same topic or not. This sub-task drew special attention within the TDT research community as many tasks in TDT are thought to be solved automatically once SLD performs as expected. In this study, performance tests were carried out on the BilCol-2005 Turkish news corpus composed of approximately 209,000 news items using vector space model (VSM) and relevance model (RM) methods with respect to varied index term counts. Accordingly, best results obtained were as follows: the VSM method performed best with 30 terms (F-measure=0.2970) while RM method did with 4 terms (F-measure=0.1910). Furthermore, the combination of two methods using the AND and OR functions increased the precision ratio by 7.9% and recall ratio by 1.2%, respectively, indicating that retrieval performance of SLD algorithms can be increased to some extent by employing both VSM and RM models.
INDEX TERMS
relevance model, story link detection, topic detection and tracking, vector space model
CITATION
Guven Kose, Yasar Tonta, Hamid Ahmadlouei, Aydin Can Polatkan, "Story Link Detection in Turkish Corpus", Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, vol. 01, no. , pp. 154-158, 2013, doi:10.1109/WI-IAT.2013.23
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