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<Paper uid="W06-1663">
  <Title>Quality Assessment of Large Scale Knowledge Resources</Title>
  <Section position="7" start_page="540" end_page="540" type="concl">
    <SectionTitle>
6 Conclusions and future work
</SectionTitle>
    <Paragraph position="0"> During the last years the research community has derived a large set of semantic resources using a very different set of methods, tools and corpus, resulting on a different set of new semantic relations between synsets. In fact, each resource has different volume and accuracy figures. Although isolated evaluations have been performed by their developers in different experimental settings, to date no complete evaluation has been carried out in a common framework.</Paragraph>
    <Paragraph position="1"> In order to establish a fair comparison, the quality of each resource has been indirectly evaluated in the same way on a WSD task. The evaluation framework selected has been the Senseval-3 English Lexical Sample Task. The study empirically demonstrates that automatically acquired knowledge bases surpass both in terms of precision and recall to the knowledge bases derived manually, and that the combination of the knowledge contained in these resources is very close to the most frequent sense classifier.</Paragraph>
    <Paragraph position="2"> Once empirically demonstrated that the knowledge resulting from MCR and Topic Signatures acquired from the web is complementary and close to the most frequent sense classifier, we plan to integrate the Topic Signatures acquired from the web (of about 100 million relations) into the MCR.</Paragraph>
    <Paragraph position="3"> This process will be performed by disambiguating the Topic Signatures. That is, trying to obtain word sense vectors instead of word vectors. This will allow to enlarge the existing knowledge bases in several orders of magnitude by fully automatic methods. Other evaluation frameworks such as PP attachment will be also considered.</Paragraph>
  </Section>
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