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2008
Springer

Topic Tracking Based on Keywords Dependency Profile

9 years 4 months ago
Topic Tracking Based on Keywords Dependency Profile
Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is “known” by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.
Wei Zheng, Yu Zhang, Yu Hong, Jili Fan, Ting Liu
Added 01 Jun 2010
Updated 01 Jun 2010
Type Conference
Year 2008
Where AIRS
Authors Wei Zheng, Yu Zhang, Yu Hong, Jili Fan, Ting Liu
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