<?xml version="1.0" standalone="yes"?>
<Paper uid="P06-2075">
  <Title>Integrating Pattern-based and Distributional Similarity Methods for Lexical Entailment Acquisition</Title>
  <Section position="2" start_page="91904" end_page="91904" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper addresses the problem of acquiring lexical semantic relationships, applied to the lexical entailment relation. Our main contribution is a novel conceptual integration between the two distinct acquisition paradigms for lexical relations - the pattern-based and the distributional similarity approaches. The integrated method exploits mutual complementary information of the two approaches to obtain candidate relations and informative characterizing features.</Paragraph>
    <Paragraph position="1"> Then, a small size training set is used to construct a more accurate supervised classifier, showing significant increase in both recall and precision over the original approaches.</Paragraph>
  </Section>
class="xml-element"></Paper>