Topic tracking using subject templates and clustering positive training instances
Yoshimi Suzuki, Fumiyo Fukumoto, Yoshihiro Sekiguchi · 2002
Topic tracking, which starts from a few sample stories and finds all subsequent stories that discuss the same topic, is a new challenge for the text categorization task and is useful for timeline-based IR systems. Much previous research on topic tracking use machine learning techniques. However, the small size of the training data, especially positive training stories, presents difficulties in training the parameters of the topic tracking system to produce optimal results. In this paper, we present a method for topic tracking using subject templates and k-means clustering algorithm to select a suitable training set. The method was tested on the TDT1 corpus, and the result shows the effectiveness of the method.