Abstract
Cognitive modelling is a well-established computational intelligence tool, which is very useful for studying cognitive phenomena, such as young children’s first language acquisition. Specifically, linguistic modelling has recently benefited greatly from complex network theory by modelling large sets of empirical linguistic data as complex networks, thereby illuminating interesting new patterns and trends. In this chapter, we show how simple network analysis techniques can be applied to the study of language acquisition, and we argue that they reveal otherwise hidden information. We also note that a key network parameter – the ranked frequency distribution of the links – provides useful knowledge about the data, even though it had been previously neglected in this domain.
| Original language | English |
|---|---|
| Title of host publication | Computational intelligence |
| Editors | A. Floares |
| Place of Publication | New York, USA |
| Publisher | Nova Science Publishers |
| Pages | 95-106 |
| Number of pages | 12 |
| ISBN (Print) | 9781620819012 |
| Publication status | Published - Dec 2012 |