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<Paper uid="W03-1019">
  <Title>Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Discriminative models have been of interest in the NLP community in recent years.</Paragraph>
    <Paragraph position="1"> Previous research has shown that they are advantageous over generative models. In this paper, we investigate how different objective functions and optimization methods affect the performance of the classifiers in the discriminative learning framework. We focus on the sequence labelling problem, particularly POS tagging and NER tasks. Our experiments show that changing the objective function is not as effective as changing the features included in the model.</Paragraph>
  </Section>
class="xml-element"></Paper>