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<Paper uid="W04-2407">
  <Title>Memory-Based Dependency Parsing</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper reports the results of experiments using memory-based learning to guide a deterministic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, memory-based classifiers for predicting the next action of the parser are constructed. The accuracy of a classifier as such is evaluated on held-out data derived from the treebank, and its performance as a parser guide is evaluated by parsing the held-out portion of the treebank. The evaluation shows that memory-based learning gives a signficant improvement over a previous probabilistic model based on maximum conditional likelihood estimation and that the inclusion of lexical features improves the accuracy even further.</Paragraph>
  </Section>
class="xml-element"></Paper>