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<Paper uid="P06-3002">
  <Title>Unsupervised Part-of-Speech Tagging Employing Efficient Graph Clustering</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> An unsupervised part-of-speech (POS) tagging system that relies on graph clustering methods is described. Unlike in current state-of-the-art approaches, the kind and number of different tags is generated by the method itself. We compute and merge two partitionings of word graphs: one based on context similarity of high frequency words, another on log-likelihood statistics for words of lower frequencies. Using the resulting word clusters as a lexicon, a Viterbi POS tagger is trained, which is refined by a morphological component.</Paragraph>
    <Paragraph position="1"> The approach is evaluated on three different languages by measuring agreement with existing taggers.</Paragraph>
  </Section>
class="xml-element"></Paper>