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<Paper uid="E06-1044">
  <Title>Modelling Semantic Role Plausibility in Human Sentence Processing</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present the psycholinguistically motivatedtask ofpredictinghuman plausibility judgements for verb-role-argument triples and introduce a probabilistic model that solves it. We also evaluate our model on the related role-labelling task, and compare it with a standard role labeller. For both tasks, our model benefits from class-based smoothing, which allows it to make correct argument-specific predictions despite a severe sparse data problem. The standard labeller suffers from sparse data and a strong reliance on syntactic cues, especially in the prediction task.</Paragraph>
  </Section>
class="xml-element"></Paper>