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<Paper uid="P04-1040">
  <Title>Enriching the Output of a Parser Using Memory-Based Learning</Title>
  <Section position="10" start_page="0" end_page="0" type="concl">
    <SectionTitle>
10 Conclusions
</SectionTitle>
    <Paragraph position="0"> We presented a method to automatically enrich the output of a parser with information that is not provided by the parser itself, but is available in a treebank. Using the method with two state of the art statistical parsers and the Penn Treebank allowed us to recover functional tags (grammatical and semantic), empty nodes and traces. Thus, we are able to provide virtually all information available in the corpus, without modifying the parser, viewing it, indeed, as a black box.</Paragraph>
    <Paragraph position="1"> Our method allows us to perform a meaningful dependency-based comparison of phrase structure parsers. The evaluation on a dependency corpus derived from the Penn Treebank showed that, after our post-processing, two state of the art statistical parsers achieve 84% accuracy on a fine-grained set of dependency labels.</Paragraph>
    <Paragraph position="2"> Finally, our method for enriching the output of a parser is, to a large extent, independent of a specific parser and corpus, and can be used with other syntactic and semantic resources.</Paragraph>
  </Section>
class="xml-element"></Paper>