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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="3" start_page="0" end_page="121" type="intro">
    <SectionTitle>
2 The probabilistic evaluation model
</SectionTitle>
    <Paragraph position="0"> for statistical syntactic parsing Given a sentence, the task of statistical syntactic parsing is to assign a probability to each candidate parsing tree that conforms to the grammar and select the one with highest probability as the final analysis result. That is:</Paragraph>
    <Paragraph position="2"> where S denotes the given sentence, T denotes the set of all the candidate parsing trees that conform to the grammar, P(T|S) denotes the probability of parsing tree T for the given sentence S.</Paragraph>
    <Paragraph position="3"> The task of probabilistic evaluation model in syntactic parsing is the estimation of P(T|S). In the syntactic parsing model which uses rule-based grammar, the probability of a parsing tree can be defined as the probability of the derivation which generates the current parsing tree for the given sentence. That is,</Paragraph>
    <Paragraph position="5"> ,,, [?]i rrr G16 denotes a derivation rule sequence, h i denotes the partial parsing tree derived from  ,,, [?]i rrr G16 .</Paragraph>
    <Paragraph position="6"> In order to accurately estimate the parameters, we need to select some feature types m</Paragraph>
  </Section>
class="xml-element"></Paper>