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<Paper uid="C04-1162">
  <Title>PageRank on Semantic Networks, with Application to Word Sense Disambiguation</Title>
  <Section position="9" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper, we showed that iterative graph-based ranking algorithms - originally designed for content-independent Web link analysis or for social networks - turn into a useful source of information for natural language tasks when applied on semantic networks. In particular, we proposed and evaluated a new approach for unsupervised knowledge-based word-sense disambiguation that relies on PageRank-style algorithms applied on a WordNet-based concepts graph, and showed that the accuracy achieved through our algorithm exceeds the performance obtained by other knowledge-based algorithms.</Paragraph>
  </Section>
class="xml-element"></Paper>