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<Paper uid="W05-0629">
  <Title>Semantic Role Labeling Using Support Vector Machines</Title>
  <Section position="7" start_page="199" end_page="199" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper reported on semantic role labeling using SVMs. Systems that used SVMs achieved good performance in the CoNLL-2004 shared task, and we added data on full parses to it. We applied a token-depth feature to SVM learning and it had a large effect. We also added a semantic-class feature and it had a small effect. Some classes were similar to the named entities, e.g., the PERSON or LOCATION of the named entities. Our semantic class feature also included not only proper names but also common words. For example, &amp;quot;he&amp;quot; and &amp;quot;she&amp;quot; also included the PERSON semantic class. Furthermore, we added a time, number, and money class. The  With DF and SC 71.68% 64.93% 68.14 semantic class feature was manually categorized on the most frequently occurring 1,000 words in the training set. More effort of the categorization may improve the performance of our system.</Paragraph>
  </Section>
class="xml-element"></Paper>