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<Paper uid="W06-2602">
  <Title>Constraint Satisfaction Inference: Non-probabilistic Global Inference for Sequence Labelling</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar approach require the base classifier to make probabilistic predictions. In contrast, our method can be used with virtually any type of classifier. This is illustrated by implementing a sequence classifier on top of a (nonprobabilistic) memory-based learner. In a series of experiments, this method is shown to outperform two other methods; one naive baseline approach, and another more sophisticated method.</Paragraph>
  </Section>
class="xml-element"></Paper>