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<Paper uid="P06-2075">
  <Title>Integrating Pattern-based and Distributional Similarity Methods for Lexical Entailment Acquisition</Title>
  <Section position="9" start_page="91904" end_page="91904" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> The main contribution of this paper is a novel integration of the pattern-based and distributional approaches for lexical semantic acquisition, applied to lexical entailment. Our investigation highlights the complementary nature of the two approaches and the information they provide.</Paragraph>
    <Paragraph position="1"> Notably, it is possible to extract pattern-based information that complements the weaker evidence of distributional similarity. Supervised learning was found effective for integrating the different information types, yielding noticeably improved performance. Indeed, our analysis reveals that the integrated approach helps eliminating many error cases typical to each method alone. We suggest that this line of research may be investigated further to enrich and optimize the learning processes and to address additional lexical relationships.</Paragraph>
  </Section>
class="xml-element"></Paper>