Early lexical development in a self-organizing neural network.
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.
Li
Ping
P
University of Richmond, Richmond, VA 23173, USA. pli@richmond.edu
Farkas
Igor
I
MacWhinney
Brian
B
D001185:Artificial Intelligence; D001245:Association Learning; D002648:Child; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D010700:Phonetics; D012660:Semantics; D049250:Speech Recognition Software