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<Paper uid="P06-1095">
  <Title>Machine Learning of Temporal Relations</Title>
  <Section position="2" start_page="37" end_page="37" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an over-sampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.</Paragraph>
  </Section>
class="xml-element"></Paper>