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<Paper uid="W98-0706">
  <Title>i Text Classification Using WordNet Hypernyms</Title>
  <Section position="6" start_page="49" end_page="49" type="concl">
    <SectionTitle>
5. Conclusions and future work.
</SectionTitle>
    <Paragraph position="0"> This paper describes a method of incorporating WordNet knowledge into text representation that can lead to significant reductions in error rates on certain types of text classification tasks. The method uses the lexical and semantic knowledge embodied in WordNet to move from a bag-of.words representation to a representation based on hypernym density. The appropriate value for the height of generalization parameter h depends on the characteristics of each classification task. A side benefit of the hypernym density representation is that the classification rules induced are often simpler and more comprehensible than rules induced using the bag-of-words.</Paragraph>
    <Paragraph position="1"> Our experience indicates that the hypernym density representation can work well for texts that use an extended or unusual vocabulary, or are written by multiple authors employing different terminologies.</Paragraph>
    <Paragraph position="2"> It is not likely to work well for text that is guaranteed to be written concisely and efficiently, such as the text in Reuters-21578. In particular, hypernym density is more likely to perform well on classification tasks involving narrowly defined and/or semantically distant classes (such as SONGI and USENETI). In the case of classes that are broadly defined and/or semantically related (such as SONG2 and USENET2) hypernym density does not always outperform bag-of-words.</Paragraph>
  </Section>
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