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<Paper uid="W06-1647">
  <Title>Lexicon Acquisition for Dialectal Arabic Using Transductive Learning</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We investigate the problem of learning a part-of-speech (POS) lexicon for a resource-poor language, dialectal Arabic.</Paragraph>
    <Paragraph position="1"> Developing a high-quality lexicon is often the rst step towards building a POS tagger, which is in turn the front-end to many NLP systems. We frame the lexicon acquisition problem as a transductive learning problem, and perform comparisons on three transductive algorithms: Transductive SVMs, Spectral Graph Transducers, and a novel Transductive Clustering method. We demonstrate that lexicon learning is an important task in resource-poor domains and leads to signi cant improvements in tagging accuracy for dialectal Arabic.</Paragraph>
  </Section>
class="xml-element"></Paper>