Associative learning of concepts

• Autorzy: Rockai V. , Kende R.
• Języki publikacji: EN

Abstrakty

EN

Humans find it extremely easy to say if two words are related or if one word is more related to a given word than another one. For example, if we come across two words — "car" and "bicycle", we know they are related since both are means of transport. Also, we easily observe that "bicycle" is more related to "car" than "fork" is. In the paper we describe our approach on quantifying the semantic relatedness of concepts based on the theory of associative learning of concepts.

Słowa kluczowe

EN

semantic surrounding; associative learning; concept similarity; grammar; relatedness

PL

gramatyka; nauka

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2007
• Tom: Vol. 14, No. 4
• Strony: 737--743
• Opis fizyczny: Bibliogr. 6 poz., rys.

Twórcy

autor

Rockai V.

autor

Kende R.

• Technical University of Košice, Department of Cybernetics and Artificial Inteligence, Letna 9/B, 04200 Košice, Slovak Republic

Bibliografia

• [1] C. Fellbaum. WordNet: An Electronic Lexical Database. The MIT Press, Cambridge, MA, 1998.
• [2] D.O. Hebb. The Organization of Behavior. John Wiley, New York, USA, 1949.
• [3] R. Hecht-Nielsen. A theory of cerebral cortex. Proceedings of the International Conference on Neural Information Processing (ICONIP98), 1998.
• [4] R. Kende. Ontology Enabled Information Retrieval, Dissertation Thesis. University of Technology in Kosice, Slovakia, 2006.
• [5] G.N. Lance, W.T. Williams. A general theory of classificatory sorting strategies, 1. Hierarchical systems. Computer Journal, 9: 373-380, 1967.
• [6] V. Rockai. Mining of Concepts and Semantic Relations from Texts in Natural Language, Diploma Thesis. University of Technology in Kosice, Slovakia, 2005.