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<Paper uid="W05-0629">
  <Title>Semantic Role Labeling Using Support Vector Machines</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The CoNLL-2005 shared task (Carreras and M`arquez, 2005) concerns the recognition of automatic semantic roles for the English language. Given a sentence, the task consists of analyzing the propositions expressed by various target verbs of the sentence. The semantic roles of constituents of the sentence are extracted for each target verb. There are semantic arguments such as Agent, Patient, and Instrument and also adjuncts such as Locative and Temporal. We performed the semantic role labeling using Support Vector Machines (SVMs). Systems that used SVMs achieved good performance in the CoNLL-2004 shared task, and we added data on full parses to it. We prepared a feature used by the full parses, and we also categorized words that appeared in the training set and added them as features. Here, we report on systems for automatically labeling semantic roles in a closed challenge in the CoNLL-2005 shared task.</Paragraph>
    <Paragraph position="1"> This paper is arranged as follows. Section 2 describes the SVMs. Our system is described Section 3, where we also describe methods of data representation, feature coding, and the parameters of SVMs. The experimental results and conclusion are presented in Sections 4 and 5.</Paragraph>
  </Section>
class="xml-element"></Paper>