<?xml version="1.0" standalone="yes"?>
<Paper uid="W06-0204">
  <Title>Improving Semi-Supervised Acquisition of Relation Extraction Patterns</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents a novel approach to the semi-supervised learning of Information Extraction patterns. The method makes use of more complex patterns than previous approaches and determines their similarity using a measure inspired by recent work using kernel methods (Culotta and Sorensen, 2004). Experiments show that the proposed similarity measure out-performs a previously reported measure based on cosine similarity when used to perform binary relation extraction.</Paragraph>
  </Section>
class="xml-element"></Paper>