Early lexical development in a self-organizing neural network

Authors:
Ping Li;Igor Farkas;Brian MacWhinney
Affiliations:
University of Richmond, Richmond, VA;Comenius University, Bratislava, Slovak Republic;Carnegie Mellon University, Pittsburgh, PA
Venue:
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Year:
2004

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Abstract

In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on lexical development, and have significant implications for models of language acquisition based on self-organizing neural networks.