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<Paper uid="W04-3213">
  <Title>Unsupervised Semantic Role Labelling</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present an unsupervised method for labelling the arguments of verbs with their semantic roles.</Paragraph>
    <Paragraph position="1"> Our bootstrapping algorithm makes initial unambiguous role assignments, and then iteratively updates the probability model on which future assignments are based. A novel aspect of our approach is the use of verb, slot, and noun class information as the basis for backing off in our probability model. We achieve 50-65% reduction in the error rate over an informed baseline, indicating the potential of our approach for a task that has heretofore relied on large amounts of manually generated training data.</Paragraph>
  </Section>
class="xml-element"></Paper>