Abstract

In this paper we introduce a connectionist model for early word learning as an important part of language acquisition. In the spirit of previous connectionist models (e.g. [Plunkett et al. 1993]) we evaluate the performance of the model with respect to important phenomena such as over- and underextensions, the comprehension over production asymmetry, and the naming insight leading to vocabulary spurts. The main motivation behind the model -- based on extensions of the well-known competitive learning paradigm -- is the focus on categorization as a major foundation of language and language learning. We give a detailed motivation of this approach and demonstrate how this model can help in identifying the roots of some of the phenomena in word learning. We also discuss the relationship between this modeling approach and more "conventional" connectionist models based on multilayer perceptrons and backpropagation, and how our model can overcome some of the apparent shortcomings of those mode...

Citations