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<Paper uid="W06-2501">
  <Title>Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts</Title>
  <Section position="10" start_page="6" end_page="7" type="concl">
    <SectionTitle>
9 Conclusion
</SectionTitle>
    <Paragraph position="0"> We introduced a new measure of semantic relatedness based on the idea of creating a Gloss Vector that combines dictionary content with corpus based data. We find that this measure correlates extremely well with the results of these human studies, and this is indeed encouraging. We believe that this is due to the fact that the context vector may be closer to the semantic representation of concepts in humans. This measure can be tai- null lored to particular domains depending on the corpus used to derive the co-occurrence matrices, and makes no restrictions on the parts of speech of the concept pairs to be compared.</Paragraph>
    <Paragraph position="1"> We also demonstrated that the Vector measure performs relatively well in an application-oriented setup and can be conveniently deployed in a real world application. It can be easily tweaked and modified to work in a restricted domain, such as bio-informatics or medicine, by selecting a specialized corpus to build the vectors.</Paragraph>
  </Section>
class="xml-element"></Paper>