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<Paper uid="N04-1003">
  <Title>Robust Reading: Identification and Tracing of Ambiguous Names</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> A given entity, representing a person, a location or an organization, may be mentioned in text in multiple, ambiguous ways. Understanding natural language requires identifying whether different mentions of a name, within and across documents, represent the same entity.</Paragraph>
    <Paragraph position="1"> We develop an unsupervised learning approach that is shown to resolve accurately the name identification and tracing problem. At the heart of our approach is a generative model of how documents are generated and how names are &amp;quot;sprinkled&amp;quot; into them. In its most general form, our model assumes: (1) a joint distribution over entities, (2) an &amp;quot;author&amp;quot; model, that assumes that at least one mention of an entity in a document is easily identifiable, and then generates other mentions via (3) an appearance model, governing how mentions are transformed from the &amp;quot;representative&amp;quot; mention. We show how to estimate the model and do inference with it and how this resolves several aspects of the problem from the perspective of applications such as questions answering.</Paragraph>
  </Section>
class="xml-element"></Paper>