<?xml version="1.0" standalone="yes"?>
<Paper uid="P05-1060">
  <Title>Multi-Field Information Extraction and Cross-Document Fusion</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we examine the task of extracting a set of biographic facts about target individuals from a collection of Web pages. We automatically annotate training text with positive and negative examples of fact extractions and train Rote, Na&amp;quot;ive Bayes, and Conditional Random Field extraction models for fact extraction from individual Web pages. We then propose and evaluate methods for fusing the extracted information across documents to return a consensus answer. A novel cross-field bootstrapping method leverages data interdependencies to yield improved performance.</Paragraph>
  </Section>
class="xml-element"></Paper>