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<Paper uid="C80-1013">
  <Title>HIERARCHICAL MEANING REPRESENTATION AND ANALYSIS OF NATURAL LANGUAGE DOCUMENTS</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper attempts to systematize natural language analysis process by (I) use of a partitioned semantic network formalism as the meaning representation and (2) stepwise translation based on Montague Grammar. The meaning representation is obtained in two steps. The first step translates natural language into logical expression. The second step interprets logical expression to generate network structure. We have implemented set of programs which performs the stepwise translation.</Paragraph>
    <Paragraph position="1"> Experiments are in progress for machine translation and question answering.</Paragraph>
    <Paragraph position="2"> i. Introduction Conventional AI systems dealing with natural languages paid much efforts on the problem, how to translate natural language input into the internal knowledge structure such as micro PLANNER statements\[14\], semantic networks\[6\], frames\[l\], etc. Most of these systems directly translate input sentences into task oriented internal structure. The architecture of these systems will be much simplified if systematic meaning representation and analysis method based on a formal theory is incorpolated.</Paragraph>
    <Paragraph position="3"> This paper proposes a stepwise translation system based on Montague Grammar (MG for short)\[3\]. Partitioned semantic network\[6\] is employed as a meaning representatin. Input sentence is firstly translated into logical expression and then semantic network is generated by interpreting it. Semantic network is the output of the natural language analyzer. This will be further compiled into task oriented representations to be used by a task oriented problem solver. This paper concentrates on the natural language analyzer. The following is a summary of our approach:</Paragraph>
  </Section>
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