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<Paper uid="W05-0628">
  <Title>Semantic Role Labeling as Sequential Tagging</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper we present a semantic role labeling system submitted to the CoNLL-2005 shared task. The system makes use of partial and full syntactic information and converts the task into a sequential BIO-tagging. As a result, the labeling architecture is very simple . Building on a state-of-the-art set of features, a binary classifier for each label is trained using AdaBoost with fixed depth decision trees. The final system, which combines the outputs of two base systems performed F1=76.59 on the official test set. Additionally, we provide results comparing the system when using partial vs. full parsing input information.</Paragraph>
  </Section>
class="xml-element"></Paper>