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<Paper uid="W04-2416">
  <Title>Semantic Role Labeling by Tagging Syntactic Chunks</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In semantic role labeling the goal is to group sequences of words together and classify them by using semantic labels. For meaning representation the predicate-argument structure that exists in most languages is used. In this structure a word (most frequently a verb) is specified as a predicate, and a number of word groups are considered as arguments accompanying the word (or predicate).</Paragraph>
    <Paragraph position="1"> In this paper, we select support vector machines (SVMs) (Vapnik, 1995; Burges, 1998) to implement the semantic role classifiers, due to their ability to handle an extremely large number of (overlapping) features with quite strong generalization properties. Support vector machines for semantic role chunking were first used This research was partially supported by the ARDA AQUAINT program via contract OCG4423B and by the NSF via grant IIS-9978025 in (Hacioglu and Ward, 2003) as word-by-word (W-by-W) classifiers. The system was then applied to the constituent-by-constituent (C-by-C) classification in (Hacioglu et al., 2003). In (Pradhan et al., 2003; Pradhan et al., 2004), several extensions to the basic system have been proposed, extensively studied and systematically compared to other systems. In this paper, we implement a system that classifies syntactic chunks (i.e. base phrases) instead of words or the constituents derived from syntactic trees. This system is referred to as the phrase-by-phrase (P-by-P) semantic role classifier. We participate in the &amp;quot;closed challenge&amp;quot; of the CoNLL-2004 shared task and report results on both development and test sets.</Paragraph>
    <Paragraph position="2"> A detailed description of the task, data and related work can be found in (Carreras and M`arquez, 2004).</Paragraph>
  </Section>
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