Abstract
Topic tracking (TT) is an important component of topic detection and tracking (TDT) applications. TT algorithms aim to determine all subsequent stories of a certain topic based on a small number of initial sample stories. We propose an alternative similarity measure based on chronological term ranking (CTR) concept to quantify the relatedness among news articles for topic tracking. The CTR approach is based on the fact that in general important issues are presented at the beginning of news articles. By following this observation we modify the traditional Okapi BM25 similarity measure using the CTR concept. Using a large standard test collection we show that our method provides a statistically significantly improvement with respect to the Okapi BM25 measure. The highly successful performance indicates that the approach can be used in real applications.
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References
Can, F., Kocberber, S., Baglioglu, O., Kardas, S., Ocalan, H.C., Uyar, E.: New event detection and topic tracking in Turkish. J. Am. Soc. Inf. Sci. Technol. 61(4), 802–819 (2010)
Troy, A.D., Zhang, G.: Enhancing relevance scoring with chronological term rank. In: Proceedings of the ACM SIGIR’07 Conference, pp. 599–606 (2007)
Allan, J.: Introduction to topic detection and tracking. In: Allan, J. (ed.) Topic Detection and Tracking: Event-based Information Organization, pp. 1–16. Kluwer Academic Publishers, Norwell (2002)
Yang, Y., Pierce, T., Carbonell, J.: A study on retrospective and on-line event detection. In: Proceedings of the ACM SIGIR’98 Conference, pp. 28–36 (1998)
Topic Detection and Tracking Evaluation: NIST Information Access Division. DET-curve plotting software tool. http://www.itl.nist.gov/iad/mig//tests/tdt/ (2007). Accessed 14 April 2012
Can, F., Kocberber, S., Balcik, E., Kaynak, C., Ocalan, H.C., Vursavas, O.M.: Information retrieval on Turkish texts. J. Am. Soc. Inf. Sci. Technol. 59(3), 407–421 (2008)
Fiscus, J.G., Doddington, G.R.: Topic detection and tracking evaluation overview. In: Allan, J. (ed.) Topic Detection and Tracking: Event-based Information Organization, pp. 17–31. Kluwer Academic Publisher, Norwell (2002)
Baglioglu, O.: New event detection using chronological term ranking. Master thesis, Computer Engineering Department, Bilkent University, Ankara, Turkey (2009). http://www.cs.bilkent.edu.tr/canf/bilir_web/theses/ozgurBagliogluThesis.pdf
Zemberek, open source NLP library for Turkic languages. http://code.google.com/p/zemberek/. Accessed 5 Jan 2012
BilTracker Android Application Beta Demo Extended. http://youtu.be/MnyTO8bendU. Accessed 5 May 2012
Acknowledgments
This work is partially supported by the Scientific and Technical Research Council of Turkey (TÜBİTAK) under the grant number 111E030. We thank Süleyman Kardaş of Sabancı University for his helps in performance evaluation.
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Acun, B., Başpınar, A., Oğuz, E., Saraç, M.İ., Can, F. (2013). Topic Tracking Using Chronological Term Ranking. In: Gelenbe, E., Lent, R. (eds) Computer and Information Sciences III. Springer, London. https://doi.org/10.1007/978-1-4471-4594-3_36
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DOI: https://doi.org/10.1007/978-1-4471-4594-3_36
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