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<Paper uid="N06-2043">
  <Title>Illuminating Trouble Tickets with Sublanguage Theory</Title>
  <Section position="4" start_page="170" end_page="171" type="relat">
    <SectionTitle>
RELATED TO THE 21 ON E38ST TICKET 9999
</SectionTitle>
    <Paragraph position="0"> Each of these related tickets usually contains some aspects of the trouble (Figure 3), but current analytic approaches never brought them together to create a complete picture of the problem, which may provide for useful associations. Semantic component related-ticket is expressed through predictable linguistic patterns that can be used as linguistic clues for automatic grouping of related tickets for further analysis.</Paragraph>
    <Section position="1" start_page="170" end_page="171" type="sub_section">
      <SectionTitle>
4.2 Classification experiments
</SectionTitle>
      <Paragraph position="0"> The analysis of Trouble Type distribution revealed, much to the company's surprise, that 18% of tick- null ets had the Miscellaneous (MSE) Type and, thus, remained out-of-scope for any analysis of associations between Trouble Types and semantic components that would reveal trends. A number of reasons may account for this, including uniqueness of a problem or human error. Review of a sample of MSE tickets showed that some of them should have a more specific Trouble Type. For example (Figure 4), both tickets, each initially assigned the MSE type, describe the WL problem, but only one ticket later receives this code.</Paragraph>
      <Paragraph position="1">  Results of n-gram analyses (Liddy et al., 2006), supported our hypothesis that different Trouble Types have distinct linguistic features. Next, we investigated if knowledge of these type-dependent linguistic patterns can help with assigning specific Types to MSE tickets. The task was conceptualized as a multi-label classification, where the system is trained on complaint sections of tickets belonging to specific Trouble Types and then tested on tickets belonging either to these Types or to the MSE Type. Experiments were run using the Extended LibSVM tool (Chang and Lin, 2001), modified for another project of ours (Yilmazel et al., 2005).</Paragraph>
      <Paragraph position="2"> Promising results of classification experiments, with precision and recall for known Trouble Types exceeding 95% (Liddy et al., 2006), can, to some extent, be attributed to the fairly stable and distinct language - a sublanguage - of the trouble tickets.</Paragraph>
    </Section>
  </Section>
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