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<Paper uid="C88-2102">
  <Title>Maintaining Consistency and Plausibility in Integra,ted Natm-al Langu~ge Understanding</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Natural language understanding involves lots of hard issues such as various types of ambiguities, indeterrtfinacies caused by ellipses or fragmental utterances, or ill-formedness. Being confronted with these difl\]culties, it does not seen, reason-able toseek for a method of logically deducing the spe~ker's intexMed meaning or p\]an from utterances. Insteaxt, it is much more natural to characterize natural language understandhlg a~q an abductive process of exploring most plausb ble interpretation which can ext)lain given utt el'a/Ices.</Paragraph>
    <Paragraph position="1"> In this paper, we present an abductive in-.</Paragraph>
    <Paragraph position="2"> ference mechanism, called the integrMcd parsing engine, for natural lmlguage lmdcrst;mding. The integrated pea'sing engine is ~ble to: make plausible assunrptions at z,:pproprlate time reason with multiple alternatives based o~ ditferent sets of a~ss~m~ptions switch the sem'ch process to the maximally plausible alternative , detect contradiction resulting from inco~v sistent ~sumpfions and eliminate ~fil conclutions which depends ,',m these assump~ tions * update plausibility factor of each belief based on new observations.</Paragraph>
    <Paragraph position="3"> Thus, the integrated parsing engine is generM enough to carry out hngulstic and nonlingulso. tic inferences in a uniform manner, by drawing information from various sources: syntax, seo mantle, discourse, pragmatics, or real world. In the remainder of this paper, we first de-~ scribe mechanisms for maintaining consistency and plausibility. We then show how these two mechauisms interact to guide the inference pro.. tess. Finally, we use an implemented exam= ple to demonstrate how the integrated parsing engine is used to interpret sentences by taking contextual factors into account.</Paragraph>
  </Section>
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