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<Paper uid="W04-1609">
  <Title>An Unsupervised Approach for Bootstrapping Arabic Sense Tagging</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> To date, there are no WSD systems for Arabic. In this paper we present and evaluate a novel unsupervised approach, SALAAM, which exploits translational correspondences between words in a parallel Arabic English corpus to annotate Arabic text using an English WordNet taxonomy. We illustrate that our approach is highly accurate in a0a2a1a4a3a6a5a8a7a10a9 of the evaluated data items based on Arabic native judgement ratings and annotations. Moreover, the obtained results are competitive with state-of-the-art unsupervised English WSD systems when evaluated on English data.</Paragraph>
  </Section>
class="xml-element"></Paper>