Proceedings of the Annual Meeting of the Cognitive Science Society

Semantic networks generated from different word corporashow common structural characteristics, including high de-grees of clustering, short average path lengths, and scale freedegree distributions. Previous research has disagreed aboutwhether these features emerge from internally- or externally-driven properties (i.e. words already in the lexicon vs. regu-larities in the external world), mapping onto preferential at-tachment and preferential acquisition accounts, respectively(Steyvers & Tenenbaum, 2005; Hills, Maouene, Maouene,Sheya, & Smith, 2009). Such accounts suggest that inherentsemantic structure shapes new lexical growth. Here we ex-tend previous work by creating semantic networks using theSEEDLingS corpus, a newly collected corpus of linguistic in-put to infants. Using a recently developed LSA-like approach(GLoVe vectors), we confirm the presence of previously re-ported structural characteristics, but only in certain ranges ofsemantic similarity space. Our results confirm the robustnessof certain aspects of network organization, and provide novelevidence in support of preferential acquisition accounts.