Abstract

We describe a computational model of the acquisition of early grammatical constructions that exploits two essential features of the human language learner: significant prior knowledge of concepts and individual lexical items, and sensitivity to the statistical properties of the input data. Such principles, previously applied to lexical acquisition, are shown to be useful and necessary for learning the structured mappings between form and meaning needed to represent phrasal and clausal constructions. We describe an algorithm based on Bayesian model merging that can induce a set of grammatical constructions based on simpler previously learned constructions in combination with new utterance-situation pairs. The resulting model shows how cognitive and computational constraints can intersect to produce a course of learning consistent with data from studies of child language acquisition.

Citations