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<Paper uid="W96-0402">
  <Title>Learning Micro-Planning Rules for Preventative Expressions*</Title>
  <Section position="8" start_page="71" end_page="71" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we have discussed the use of machine learning techniques for the automatic construction of micro-planning sub-networks. We demonstrated this for the case of preventative expressions in instructional text.</Paragraph>
    <Paragraph position="1"> We noted that because the automatic derivation of useful, well-defined features for corpus analysis is beyond the current state of the art, the painstaking process of corpus analysis must still be performed manually. As an example of how this can be done, we presented an analysis of English preventative expressions. We intend to continue this part of the work by addressing more preventative forms, addressing ensurative forms, and by extending the analysis to other languages.</Paragraph>
    <Paragraph position="2"> Although the analysis cannot be fully automated, we noted that the derivation of decision networks from coded corpus examples can. This greatly simplifies the tasks of building and testing text planning resources for new domains. We intend to continue this part of the work by applying the technique to larger portions of the planning resources.</Paragraph>
  </Section>
class="xml-element"></Paper>