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<Paper uid="P06-2087">
  <Title>Argumentative Feedback: A Linguistically-motivated Term Expansion for Information Retrieval</Title>
  <Section position="8" start_page="679" end_page="680" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have reported on the evaluation of a new linguistically-motivated feedback strategy, which selects highly-content bearing features for expansion based on argumentative criteria. Our simple model is based on four classes, which have been reported very stable in scientific reports of all kinds. Our results suggest that argumentation-driven expansion can improve retrieval effectiveness of search engines by more than 40%. The proposed methods open new research directions and are generally promising for natural language processing applied to information retrieval, whose positive impact is still to be confirmed (Strzalkowski et al., 1998). Finally, the proposed methods are important from a theoretical perspective, if we consider  that it initiates a genre-specific paradigm as opposed to the usual information retrieval typology, which distinguishes between domain-specific and domain-independent approaches.</Paragraph>
  </Section>
class="xml-element"></Paper>