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<Paper uid="P00-1060">
  <Title>An Information-Theory-Based Feature Type Analysis for the Modelling of Statistical Parsing SUI Zhifang +++ , ZHAO Jun + , Dekai WU + +</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The paper proposes an information-theory-based method for feature types analysis in probabilistic evaluation modelling for statistical parsing. The basic idea is that we use entropy and conditional entropy to measure whether a feature type grasps some of the information for syntactic structure prediction. Our experiment quantitatively analyzes several feature types' power for syntactic structure prediction and draws a series of interesting conclusions.</Paragraph>
  </Section>
class="xml-element"></Paper>