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Proceedings Paper

Algebraic learning for language acquisition
Author(s): Kevin R. Farrell; Richard J. Mammone; Allen Gorin
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Paper Abstract

This paper explores the application of new algorithms to the adaptive language acquisition model formulated by Gorin. The new methods consists of incremental approaches for the algebraic learning of statistical associations proposed by Tishby. The incremental methods are evaluated on a text-based natural language experiment, namely the inward call manager task. Performance is evaluated with respect to the alternative methods, namely the smooth mutual information method and the pseudo-inverse solution.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172523
Show Author Affiliations
Kevin R. Farrell, Rutgers Univ. (United States)
Richard J. Mammone, Rutgers Univ. (United States)
Allen Gorin, AT&T Bell Labs. (United States)


Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike M.D.; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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