<?xml version="1.0" standalone="yes"?>
<Paper uid="W05-0601">
  <Title>Effective use of WordNet semantics via kernel-based learning</Title>
  <Section position="8" start_page="7" end_page="7" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> The introduction of semantic prior knowledge in IR has always been an interesting subject as the examined literature suggests. In this paper, we used the conceptual density function on the Word-Net (WN) hierarchy to define a document similarity metric. Accordingly, we defined a semantic kernel to train Support Vector Machine classifiers.</Paragraph>
    <Paragraph position="1"> Cross-validation experiments over 8 categories of 20NewsGroups and Reuters over multiple samples have shown that in poor training data conditions, the WN prior knowledge can be effectively used to improve (up to 4.5 absolute percent points, i.e. 10%) the TC accuracy.</Paragraph>
    <Paragraph position="2"> These promising results enable a number of future researches: (1) larger scale experiments with different measures and semantic similarity models (e.g. (Resnik, 1997)); (2) improvement of the overall efficiency by exploring feature selection methods over the SK, and (3) the extension of the semantic similarity by a general (i.e. non binary) application of the conceptual density model.</Paragraph>
  </Section>
class="xml-element"></Paper>