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<Paper uid="P06-1109">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics An All-Subtrees Approach to Unsupervised Parsing</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We investigate generalizations of the all-subtrees &amp;quot;DOP&amp;quot; approach to unsupervised parsing. Unsupervised DOP models assign all possible binary trees to a set of sentences and next use (a large random subset of) all subtrees from these binary trees to compute the most probable parse trees. We will test both a relative frequency estimator for unsupervised DOP and a maximum likelihood estimator which is known to be statistically consistent. We report state-of-the-art results on English (WSJ), German (NEGRA) and Chinese (CTB) data. To the best of our knowledge this is the first paper which tests a maximum likelihood estimator for DOP on the Wall Street Journal, leading to the surprising result that an unsupervised parsing model beats a widely used supervised model (a treebank PCFG).</Paragraph>
  </Section>
class="xml-element"></Paper>