Abstract
In t opic t racking, a topic is usually described by several stories. How to represent a topic is always an issue and a difficult problem in the research on topic tracking. To emphasis the topic in stories, we provide an improved topic-based tf * idf weighting method to measure the topical importance of the features in the representation model. To overcome the topic drift problem and filter the noise existed in the tracked topic description , a dynamic topic model is proposed based on the static model. It extends the initial topic model with the information from the incoming related stories and filters the noise using the latest unrelated story. The topic tracking systems are implemented on the TDT4 Chinese corpus. The experimental results indicate that both the new weighting method and the dynamic model can improve the tracking performance.
Published Version
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