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<Paper uid="P06-1003">
  <Title>Unsupervised Topic Modelling for Multi-Party Spoken Discourse</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification: automatically segmenting multi-party meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods (Galley et al., 2003) while simultaneously extractingtopicswhichratehighlywhenassessed null for coherence by human judges. We also show that this method appears robust in the face of off-topic dialogue and speech recognition errors.</Paragraph>
  </Section>
class="xml-element"></Paper>