Reappraising Lexical Specificity in Children's Early Syntactic Combinations

The flexibility and unbounded expressivity of our linguistic abilities is unparalleled in the biological world. Explaining how children acquire this fundamental aspect of human language is a key challenge for cognitive science. A recent corpus study by Yang (2013) has cast doubt on the lexical specificity of children’s productivity, as hypothesized by usage-based approaches. Focusing on determiner-noun combinations, he suggests that children possess an adult-like determiner category. In this paper, we show that Yang’s results may depend too heavily on an idealized notion of frequency distributions. We propose that these issues may be resolved by sidestepping sampling considerations and directly modeling children’s actual language processing. We therefore evaluate the abilities of two computational models to capture children's productions of determiner-noun combinations. The first model implements a probabilistic context-free grammar, which acquires statistical information incrementally. A second model, the Chunk-based Learner (CBL), provides a simple instantiation of item-based learning. CBL outperforms the rule-based model, successfully producing the vast majority of the determiner-noun combinations in a dense corpus of child speech. The results thus suggest that the case against lexical specificity in children’s early determiner-noun sequences may be overstated.