Creating term associations using a hierarchical ART architecture
In this work we address the problem of creating semantic term associations (key words) from a text database. The proposed method uses a hierarchical neural architecture based on the Fuzzy Adaptive Resonance Theory (ART) model. It exploits the specific statistical structure of index terms to extract semantically meaningful term associations; these are asymmetric and one-to-many due to the polysemy phenomenon. The underlying algorithm is computationally appropriate for deployment on large databases. The operation of the system is illustrated with a real database.
Key wordsKnowledge extraction information retrieval neural ART models text databases
Unable to display preview. Download preview PDF.
- 1.G.A. Carpenter, S. Grossberg, and D.B. Rosen. Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, 4:759–771, 1991.Google Scholar
- 2.H. Chen and K.J. Lynch. Automatic construction of networks of concepts characterizing document databases. IEEE Transactions on System, Man and Cybernetics, 22(5):885–902, Sept.–Oct. 1992.Google Scholar
- 3.M. Georgiopoulos, G.L. Heilemann, and J. Huang. Properties of learning related to pattern diversity in ART 1. Neural Networks, 4:751–757, 1991.Google Scholar
- 4.L.M. Gomez, C.C. Lochbaum, and T.K. Landauer. All the right words:finding what you want as a function of richness of indexing vocabulary. Journal of the American Society for Information Science, 41(8):547–559, 1990.Google Scholar
- 5.B. Kosko. Neural networks and fuzzy systems: A dynamical approach to machine intelligence. Prentice Hall, Englewood Cliffs, New Jersey, 1991.Google Scholar
- 6.B. Moore. ART 1 and pattern clustering. In G. Hinton D. Touretzky and T. Sejnowski, editors, Proceedings of the 1988 Connectionist Model Summer School, pages 174–185, San Mateo, C.A., 1989. Morgan Kaufmann.Google Scholar
- 7.V.V. Raghavan and S.K.M. Wong. A critical analysis of vector space model for information retrieval. Journal of the American Society for Information Science, 37(5):100–124, 1986.Google Scholar
- 8.T. Saracevic and P. Kantor. A study of information seeking and retrieving. II. Users, questions, and effectiveness. Journal of the American Society for Information Science, 39(3):177–196, 1988.Google Scholar
- 9.G.K. Zipf. The Psycho-biology of language: An introduction to dynamic philology. M.I.T. Press, Cambridge, Mass., 1965.Google Scholar