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<Paper uid="N03-3001">
  <Title>Semantic Language Models for Topic Detection and Tracking</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this work, we present a new semantic language modeling approach to model news stories in the Topic Detection and Tracking (TDT) task. In the new approach, we build a unigram language model for each semantic class in a news story. We also cast the link detection sub-task of TDT as a two-class classi cation problem in which the features of each sample consist of the generative log-likelihood ratios from each semantic class. We then compute a linear discriminant classi er using the perceptron learning algorithm on the training set. Results on the test set show a marginal improvement over the unigram performance, but are not very encouraging on the whole.</Paragraph>
  </Section>
class="xml-element"></Paper>