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<Paper uid="W00-1104">
  <Title>Semantic Indexing using WordNet Senses</Title>
  <Section position="9" start_page="42" end_page="43" type="concl">
    <SectionTitle>
8 Conclusions
</SectionTitle>
    <Paragraph position="0"> The full understanding of text is still an elusive goal. Short of that, semantic indexing offers an improvement over current IR techniques. The key to semantic indexing is fast WSD of large collections of documents.</Paragraph>
    <Paragraph position="1"> In this paper we offer a WSD method for open domains that is fast and accurate. Since only 55% of the words can be disambiguated so far, we use a hybrid indexing approach that combines word-based and sense-based indexing. The senses in WordNet are fine grain and the WSD method has to cope with this. The  WSD algorithm presented here is new for the NLP community and proves to be well suited for a task such as semantic indexing.</Paragraph>
    <Paragraph position="2"> The continuously increasing amount of information available today requires more and more sophisticated IR techniques, and semantic indexing is one of the new trends when trying to improve IR effectiveness. With semantic indexing, the search may be expanded to other forms of semantically related concepts as done by Woods (Woods, 1997). Finally, semantic indexing can have an impact on the semantic Web technology that is under consideration (Hellman, 1999).</Paragraph>
  </Section>
class="xml-element"></Paper>