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ABSTRACT

The research described in this paper addresses the problem of integrating analogical reasoning and argumentation into a natural language understanding system. We present an approach to completing an implicit argument-by-analogy as found in a natural language editorial text. The transformation of concepts from one domain to another, which is inherent in this task, is a complex process requiring basic reasoning skills and domain knowledge, as well as an understanding of the structure and use of both analogies and arguments. The integration of knowledge about natural language understanding, argumentation, and analogical reasoning is demonstrated in a proof of concept system called ARIEL. ARIEL is able to detect the presence of an analogy in an editorial text, identify the source and target components, and develop a conceptual representation of the completed analogy in memory. The design of our system is modular in nature, permitting extensions to the existing knowledge base and making the argumentation and analogical reasoning components portable to other understanding systems.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Language parsing and understanding

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis; Language generation

General Terms:
Design, Languages