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<Paper uid="W05-0402">
  <Title>Feature Engineering and Post-Processing for Temporal Expression Recognition Using Conditional Random Fields</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present the results of feature engineering and post-processing experiments conducted on a temporal expression recognition task. The former explores the use of different kinds of tagging schemes and of exploiting a list of core temporal expressions during training. The latter is concerned with the use of this list for post-processing the output of a system based on conditional random fields.</Paragraph>
    <Paragraph position="1"> We find that the incorporation of knowledge sources both for training and post-processing improves recall, while the use of extended tagging schemes may help to offset the (mildly) negative impact on precision. Each of these approaches addresses a different aspect of the over-all recognition performance. Taken separately, the impact on the overall performance is low, but by combining the approaches we achieve both high precision and high recall scores.</Paragraph>
  </Section>
class="xml-element"></Paper>