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<Paper uid="W05-0633">
  <Title>Semantic Role Labeling Using Lexical Statistical Information</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classifier. We use both shallow and deep syntactic information from automatically generated chunks and parse trees, and develop a model for learning the semantic arguments of predicates as a multi-class decision problem. We evaluate the performance on a set of relatively 'cheap' features and report an F1 score of 68.13% on the overall test set.</Paragraph>
  </Section>
class="xml-element"></Paper>