Computational Models of Learning the Raising-Control Distinction
Research on Language and Computation
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We present a Bayesian model for the representation, acquisition and use of argument structure constructions, which is founded on a novel view of constructions as a mapping of a syntactic form to a probability distribution over semantic features. Our computational experiments demonstrate the feasibility of learning general constructions from individual examples of verb usage, and show that the acquired knowledge generalizes to novel or low-frequency situations in language use.