Semantic and episodic associative neural network

Keitaro Kataoka, Masafumi Hagiwara

研究成果: Conference article査読


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.

ジャーナルProceedings of the IEEE International Conference on Systems, Man and Cybernetics
出版ステータスPublished - 2003 11 24
イベントSystem Security and Assurance - Washington, DC, United States
継続期間: 2003 10 52003 10 8

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ハードウェアとアーキテクチャ


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