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Authoritative: http://dx.doi.org/10.1016/j.neunet.2004.07.004   (Publisher's PDF... likely be available here.)

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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.

Keywords: Language acquisition, Lexical development, Self-organizing neural network

@article{li04earlyLexicalDevelopment,
  author={Ping Li and Igor Farkas and Brian MacWhinney},
  title={Early lexical development in a self-organizing neural network},
  journal={Neural Networks},
  year={2004},
  volume={17},
  number={8-9},
  pages={1345-1362},
  doi={10.1016/j.neunet.2004.07.004},
  url={http://groups.lis.illinois.edu/amag/langev/paper/li04earlyLexicalDevelopment.html},
  keywords={Language acquisition,Lexical development,Self-organizing neural network}
}