Structural Differences in the Semantic Networks of Simulated Word Learners

Aida NematzadehUniversity of Toronto
Afsaneh FazlyUniversity of Toronto
Suzanne StevensonUniversity of Toronto

Abstract

A learner’s semantic network represents the learner’s knowledge of words/concepts and the relations among them. The structure of this network is significant as it might reveal aspects of the developmental process that leads to the network. In this work, we use computational modeling to examine the structure of semantic networks of different simulated word learners. We find that the learned semantic knowledge of a learner that simulates a normally-developing child reflects the structural properties found in adult semantic networks of words. In contrast, the network of a late-talking learner — one that simulates a child with a marked delay in vocabulary acquisition — does not exhibit these properties. We discuss the implications of this result for understanding the process of vocabulary acquisition and delay.

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