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<Paper uid="P04-1013">
  <Title>Discriminative Training of a Neural Network Statistical Parser</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Discriminative methods have shown signi cant improvements over traditional generative methods in many machine learning applications, but there has been di culty in extending them to natural language parsing. One problem is that much of the work on discriminative methods con ates changes to the learning method with changes to the parameterization of the problem.</Paragraph>
    <Paragraph position="1"> We show how a parser can be trained with a discriminative learning method while still parameterizing the problem according to a generative probability model. We present three methods for training a neural network to estimate the probabilities for a statistical parser, one generative, one discriminative, and one where the probability model is generative but the training criteria is discriminative. The latter model out-performs the previous two, achieving state-of-the-art levels of performance (90.1% F-measure on constituents).</Paragraph>
  </Section>
class="xml-element"></Paper>