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<Paper uid="P89-1014">
  <Title>ON REPRESENTING GOVERNED PREPOSITIONS AND HANDLING &amp;quot;INCORRECT&amp;quot; AND NOVEL PREPOSITIONS</Title>
  <Section position="20" start_page="115" end_page="115" type="concl">
    <SectionTitle>
5.0 DIRECTIONS FOR FUTURE WORK
</SectionTitle>
    <Paragraph position="0"> In this paper we have described a frequent type of ill-formed input which NLP systems must handle, involving the use of non-standard prepositions to mark arguments. We presented a classification of these errors and described our algorithm for handling some of these error types. The importance of handling such non-standard input will increase as speech recognition becomes more reliable, because spoken input is less formal.</Paragraph>
    <Paragraph position="1"> In the near term, planned enhancements include adjusting the weighting scheme to more accurately reflect the empirical data. A frequency-based model of preposition usage, based on a much larger and broader sampling of text will improve system handling of those errors.</Paragraph>
  </Section>
class="xml-element"></Paper>