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<Paper uid="W06-2923">
  <Title>LingPars, a Linguistically Inspired, Language-Independent Machine Learner for Dependency Treebanks</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents a Constraint Grammarinspired machine learner and parser, Ling Pars, that assigns dependencies to morpho logically annotated treebanks in a functioncentred way. The system not only bases at tachment probabilities for PoS, case, mood, lemma on those features' function probabili ties, but also uses topological features like function/PoS n-grams, barrier tags and daughter-sequences. In the CoNLL shared task, performance was below average on at tachment scores, but a relatively higher score for function tags/deprels in isolation suggests that the system's strengths were not fully exploited in the current architecture.</Paragraph>
  </Section>
class="xml-element"></Paper>