A Concepts-of-Sentences Associative Neural Network

Hitoshi Tanaka, Jingtao Huang, Masafumi Hagiwara

Research output: Contribution to journalArticlepeer-review


This paper proposes a new neural network (the concepts-of-sentences associative neural network, CSANN) which performs memory and recall of sentences represented by a semantic network, as an approach toward natural language processing. The semantic network is represented in terms of the triples representation of concepts. The triples representation of concepts consists of concepts linked by relations in sentences represented by natural language. CSANN handles sentence data, which are difficult to handle since the data size is not unique, by decomposing the data into triples representations of concepts and integrating them into the semantic network format. It is verified by computer simulation that the proposed network can store sentences and recall sentences even if the input information is incomplete. Thus, the effectiveness of the proposed network in natural language processing is demonstrated.

Original languageEnglish
Pages (from-to)43-54
Number of pages12
JournalSystems and Computers in Japan
Issue number11
Publication statusPublished - 2003 Oct 1


  • Concept
  • Distributed representation
  • Multiple-winner self-organizing network
  • Natural language
  • Triples representation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics


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