Topic Tracking Using Chronological Term Ranking
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.
KeywordsFalse Alarm Rate News Article News Story Topic Detection Detection Error Tradeoff
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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