The child directed speech of twelve English‐speaking motherswas analyzed in terms of utterance‐level constructions. First, the mothers' utterances were categorized in terms of general constructional categories such as Wh‐questions, copulas and transitives. Second, mothers' utterances within these categories were further specified in terms of the initial words that framed the utterance, item‐based phrases such as Are you …, I'll …, It's …, Let's …, What did … The findings were: (i) overall, only about 15% of all maternal utterances had SVO (...) form (most were questions, imperatives, copulas, and fragments); (ii) 51% of all maternal utterances began with one of 52 item‐based phrases, mostly consisting of two words or morphemes (45% began with one of just 17 words); and (iii) children used many of these same item‐based phrases, in some cases at a rate that correlated highly with their own mother's frequency of use. We suggest that analyses of adult–child linguistic interaction should take into account not just general constructional categories, but also the item‐based constructions that adults and children use and the frequency with which they use them. (shrink)

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of (...) verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions—associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions. (shrink)