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<Paper uid="H89-1013">
  <Title>PORTABILITY IN THE JANUS NATURAL LANGUAGE INTERFACE 1</Title>
  <Section position="3" start_page="0" end_page="112" type="intro">
    <SectionTitle>
INTRODUCTION: MOTIVATION
</SectionTitle>
    <Paragraph position="0"> Portability is measurable by the person-effort expended to achieve a pre-specified degree of coverage, given an application program. Factoring an NL system into domain-dependent and domain-independent modules is now part of the state of the art; therefore, the challenge in portability is reducing the effort needed to create domain-dependent modules. For us, those are the domain-dependent knowledge bases, e.g., lexical syntax, lexical semantics, domain models, and transformations specific to the target application system.</Paragraph>
    <Paragraph position="1"> Our experience in installing our natural language interface as part of DARPA's Fleet Command Center Battle Management Program (FCCBMP) iUustrates the kind of portability needed if NL applications (or products) are to become widespread. We demonstrated broad linguistic coverage across 40 fields of a large Oracle database, the Integrated Data Base (IDB), in August 1986. A conclusion was that the state of the art in understanding was adequate. However, the time and cost needed to cover all 400 fields of the IDB in 1986 and the more than 850 fields today would have been prohibitive without a breakthrough in knowledge acquisition and maintenance tools.</Paragraph>
    <Paragraph position="2"> We have developed a suite of tools to greatly increase our productivity in porting BBN's Janus NL understanding and generation system to new domains. KREME \[Abrett, 1987\] enables creating, browsing, and maintaining of taxonomic knowledge bases. IRACQ \[Ayuso, 1987\] supports learning lexical semantics from examples with only one unknown word. Both of those tools were used in preparing the FCCBMP demonstratior~ in 1986. What was missing was a way to rapidly infer the knowledge bases for the overwhelming majority of words used in accessing fields. Then one could bootstrap using IRACQ to acquire more complex lexical items.</Paragraph>
    <Paragraph position="3"> We have developed and used such a tool called KNACQ (for KNowledge ACQuisition). The efficiency we have experienced results from (1) identifying regularities in expression corresponding to domain model structures and (2) requiring little information from the user to identi~ expressions corresponding to those regularities.</Paragraph>
  </Section>
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