Semantic Networks Generated from Early Linguistic Input

Semantic networks generated from different word corpora show common structural characteristics, including high degrees of clustering, short average path lengths, and scale free degree distributions. Previous research has disagreed about whether these features emerge from internally or externally driven properties (i.e. words already in the lexicon vs. regularities in the external world), mapping onto preferential attachment and preferential acquisition accounts, respectively (Steyvers & Tenenbaum, 2005; Hills, Maouene, Maouene, Sheya, & Smith, 2009). Such accounts suggest that inherent semantic structure shapes new lexical growth. Here we extend previous work by creating semantic networks using the SEEDLingS corpus, a newly collected corpus of linguistic input to infants. Using a recently developed LSA-like approach (GLoVe vectors), we confirm the presence of previously reported structural characteristics, but only in certain ranges of semantic similarity space. Our results confirm the robustness of certain aspects of network organization, and provide novel evidence in support of preferential acquisition accounts.