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<Paper uid="W06-2929">
  <Title>Vine Parsing and Minimum Risk Reranking for Speed and Precision[?]</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe our entry in the CoNLL-X shared task.</Paragraph>
    <Paragraph position="1"> The system consists of three phases: a probabilistic vine parser (Eisner and N. Smith, 2005) that produces unlabeled dependency trees, a probabilistic relation-labeling model, and a discriminative minimum risk reranker (D. Smith and Eisner, 2006). The system is designed for fast training and decoding and for high precision. We describe sources of cross-lingual error and ways to ameliorate them. We then provide a detailed error analysis of parses produced for sentences in German (much training data) and Arabic (little training data).</Paragraph>
  </Section>
class="xml-element"></Paper>