Semantic and episodic associative neural network

Keitaro Kataoka, Masafumi Hagiwara

Research output: Contribution to journalConference articlepeer-review


In this paper, we propose a new neural network model termed semantic and episodic associative neural network (SEANN) for natural language processing. The SEANN can deal with both semantic memory and episodic memory by sentences represented in a form of a semantic network. In this model, both semantic memory and episodic memory are represented in triples-representation of concepts. Our model consists of concepts of sentences associative neural network (CSANN) and MAM using area representation. CSANN can recall sentences in a form of triples-representation, and MAM using area representation can recall plural triples-representations from a word. We have carried out computer experiments to confirm the validity of the SEANN for natural language processing. We have investigated that our model can recall plural semantic memories from one word, and can recall semantic memories concerning with episodic memory.

Original languageEnglish
Pages (from-to)1292-1297
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2003 Nov 24
EventSystem Security and Assurance - Washington, DC, United States
Duration: 2003 Oct 52003 Oct 8


  • Concept
  • Distributed pattern
  • Memory
  • Multi-winner self-organizing neural network
  • Natural language
  • Triples representation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture


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