We address the problem dealing with skewed data, and propose a method for estimating effective training stories for the topic tracking task. For a small number of labelled positive stories, we extract story pairs which consist of positive and its associated stories from bilingual comparable corpora. To overcome the problem of a large number of labelled negative stories, we classify them into some clusters. This is done by using k-means with EM. The results on the TDT corpora show the effectiveness of the method. .
