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<Paper uid="W04-2415">
  <Title>Hierarchical Recognition of Propositional Arguments with Perceptrons</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> We describe a system for the CoNLL-2004 Shared Task on Semantic Role Labeling (Carreras and M`arquez, 2004a). The system implements a two-layer learning architecture to recognize arguments in a sentence and predict the role they play in the propositions. The exploration strategy visits possible arguments bottom-up, navigating through the clause hierarchy. The learning components in the architecture are implemented as Perceptrons, and are trained simultaneously online, adapting their behavior to the global target of the system. The learning algorithm follows the global strategy introduced in (Collins, 2002) and adapted in (Carreras and M`arquez, 2004b) for partial parsing tasks.</Paragraph>
  </Section>
class="xml-element"></Paper>