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<Paper uid="W04-2421">
  <Title>Semantic Role Labeling Via Generalized Inference Over Classifiers</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Semantic role labeling is a complex task to discover patterns within sentences corresponding to semantic meaning. We believe it is hopeless to expect high levels of performance from either purely manual classifiers or purely learned classifiers. Rather, supplemental linguistic information must be used to support and correct a learning system. The system we present here is composed of two phases.</Paragraph>
    <Paragraph position="1"> First, a set of phrase candidates is produced using two learned classifiers--one to discover beginning positions and one to discover end positions for each argument type.</Paragraph>
    <Paragraph position="2"> Hopefully, this phase discovers a small superset of all phrases in the sentence (for each verb).</Paragraph>
    <Paragraph position="3"> In the second phase, the final prediction is made. First, candidate phrases from the first phase are re-scored using a classifier designed to determine argument type, given a candidate phrase. Because phrases are considered as a whole, global properties of the candidates can be used to discover how likely it is that a phrase is of a given argument type. However, the set of possible role-labelings is restricted by structural and linguistic constraints. We encode these constraints using linear functions and use integer programming to ensure the final prediction is consistent (see Section 4).</Paragraph>
  </Section>
class="xml-element"></Paper>