<?xml version="1.0" standalone="yes"?>
<Paper uid="P89-1014">
  <Title>ON REPRESENTING GOVERNED PREPOSITIONS AND HANDLING &amp;quot;INCORRECT&amp;quot; AND NOVEL PREPOSITIONS</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1.0 INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> It is well known that NLP systems, in order to be robust, must handle ill-formed input. One common type of error involves the use of non-standard prepositions to mark arguments. In this paper, we argue that such errors can be handled in a systematic fashion, and that a system designed to handle them offers other advantages.</Paragraph>
    <Paragraph position="1"> The examples of non-standard prepositions we present in the paper are taken from colloquial language, both written and oral. The type of error these examples represent is quite frequent in colloquial written language. The frequency of such examples rises sharply in evolving sub-languages and in oral colloquial language. In developing an NLP system to be used by various U.S. government customers, we have been sensitized to the need to handle variation and innovation in preposition usage. Handling this type of variation or innovation is part of our overall capability to handle novel predicates, which arc frequent in sublanguage. Novel predicates created for sub-languages arc less &amp;quot;stable&amp;quot; in how they mark arguments (ARGUMENT MAPPING) than general English &amp;quot;core&amp;quot; predicates which speakers learn as children. It can be expected that the eventual advent of successful speech understanding systems will further emphasize the need to handle this and other variation.</Paragraph>
    <Paragraph position="2"> The NLP system under development at SRA incorporates a Natural Language Knowledge Base (NLKB), a major part of which consists of objects representing</Paragraph>
  </Section>
class="xml-element"></Paper>