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<Paper uid="P05-1060">
  <Title>Multi-Field Information Extraction and Cross-Document Fusion</Title>
  <Section position="8" start_page="489" end_page="489" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper has presented new experimental methodologies and results for cross-document information fusion, focusing on the task of biographic fact extraction and has proposed a new method for cross-field bootstrapping. In particular, we have shown that automatic annotation can be used effectively to train statistical information extractors such Na&amp;quot;ive Bayes and CRFs, and that CRF extraction accuracy can be improved by 5% with a negative example model. We looked at cross-document fusion and demonstrated that voting outperforms choosing the highest confidence extracted information by 2% to 20%. Finally, we introduced a cross-field bootstrapping method that improved average accuracy by 7%.</Paragraph>
  </Section>
class="xml-element"></Paper>