<?xml version="1.0" standalone="yes"?>
<Paper uid="W05-0629">
  <Title>Semantic Role Labeling Using Support Vector Machines</Title>
  <Section position="6" start_page="198" end_page="199" type="evalu">
    <SectionTitle>
4 Results
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="198" end_page="198" type="sub_section">
      <SectionTitle>
4.1 Data
</SectionTitle>
      <Paragraph position="0"> The data provided for the shared task consisted of sections from the Wall Street Journal (WSJ) part of Penn TreeBank II. The training set was WSJ Sections 02-21, the development set was Section 24, and the test set was Section 23 with the addition of fresh sentences. Our experiments were carried out using Sections 15-18 for the training set, because the entire file was too large to learn.</Paragraph>
    </Section>
    <Section position="2" start_page="198" end_page="199" type="sub_section">
      <SectionTitle>
4.2 Experiments
</SectionTitle>
      <Paragraph position="0"> Our final results for the CoNLL-2005 shared task are listed in Table 2. Our system achieved 74.15% precision, 68.25% recall and 71.08 Fa104a98a105 a0 on the overall results of Test WSJ. Table 3 lists the effects of the token-depth and semantic-class features. The token-depth feature had a larger effect than that for the semantic class.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>