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<Paper uid="C92-2104">
  <Title>Learning Mechanism in Machine Translation System &amp;quot;PIVOT&amp;quot;</Title>
  <Section position="7" start_page="0" end_page="0" type="evalu">
    <SectionTitle>
4. Experiments
</SectionTitle>
    <Paragraph position="0"> Experiments have been made to evaluate the effect of learning mechanism described in Section 3 by simulation. In the experiments, the instruction iteas were limited to case and dependency.</Paragraph>
    <Paragraph position="1"> k total of 1565 sentences were collected from six kinds of technical manuals, These sentences mere translated with PIVOT/J6. Using the analysis editing function stated previously, correction of mistakes in dependencies and cases were made.</Paragraph>
    <Paragraph position="2"> After all errors in the analysis results of the whole text were corrected, correction information for case end dependency was extracted and put into s file.</Paragraph>
    <Paragraph position="3"> k tool which simulates learning mechanism mus prepared.</Paragraph>
    <Paragraph position="4"> After reading the file which stores the correction inforlation, it counts the number of corrections to be =~e in each of the folloaing eases: no application of the learned data, application with restricted exact matching, application with restricted best matching, application with non-restricted exact matching and with non-restricted best utehing.</Paragraph>
    <Paragraph position="5"> The results are shown in the table and the graph beloa. The value is the sum of the estimated number of the corrections and the estimated number of the recorfactions needed to cancel the secondary effect.</Paragraph>
    <Paragraph position="6">  Non-restricted best matching is the most effective among the five methods.</Paragraph>
  </Section>
class="xml-element"></Paper>