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<Paper uid="C00-2175">
  <Title>Comparing two trainable grammatical relations finders</Title>
  <Section position="5" start_page="1149" end_page="1149" type="concl">
    <SectionTitle>
4 Discussion
</SectionTitle>
    <Paragraph position="0"> ORs are important, but different sets of GR,s are useflfl for different imrposes. We have been looking at ways of ilni)roving automatic Oil, finders when one has only a small amount of data with tile desired Oil, mmotations. In this paper, we compared a transformation rule-based systeln with a menlory-based system oi1 a small training corpus. We found that oll GIls that point to verbs, most of the result ditferences can be accounted fbr by ditferences in the representations and information used. The type of GR determines which information is more important. The rule versus memorpbased difference itself only seeins to produce a small result difference. We also find that partitioning the data mw hell) melnory-based learning.</Paragraph>
  </Section>
class="xml-element"></Paper>