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<Paper uid="W04-0845">
  <Title>Semantic Role Labeling with Boosting, SVMs, Maximum Entropy, SNOW, and Decision Lists</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> This paper describes the HKPolyU-HKUST systems which participated in the Senseval-3 Semantic Role Labeling task. The systems represent a diverse array of machine learning algorithms, from decision lists to SVMs to Winnow-type networks.</Paragraph>
    <Paragraph position="1"> Semantic Role Labeling (SRL) is a task that has recently received a lot of attention in the NLP community. The SRL task in Senseval-3 used the Framenet (Baker et al., 1998) corpus: given a sentence instance from the corpus, a system's job would be to identify the phrase constituents and their corresponding role.</Paragraph>
    <Paragraph position="2"> The Senseval-3 task was divided into restricted and non-restricted subtasks. In the non-restricted subtask, any and all of the gold standard annotations contained in the FrameNet corpus could be used.</Paragraph>
    <Paragraph position="3"> Since this includes information on the boundaries of the parse constituents which correspond to some frame element, this effectively maps the SRL task to that of a role-labeling classification task: given a constituent parse, identify the frame element that it belongs to.</Paragraph>
    <Paragraph position="4"> Due to the lack of time and resources, we chose to participate only in the non-restricted subtask. This enabled our systems to take the classification approach mentioned in the previous paragraph.</Paragraph>
  </Section>
class="xml-element"></Paper>