A Computational Model of Early Argument Structure Acquisition

A Computational Model of Early Argument Structure Acquisition

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 ex...

A Computational Model for Early Argument Structure Acquisition

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 ex...

A Probabilistic Model of Early Argument Structure Acquisition

We present a computational model of usage-based learning of verb argument structure in young children. The model integrates Bayesian classification and prediction to learn from utterances paired with appropriate semantic representations. The model balances item-based and class-based knowledge in language use, demonstrating appropriate word order generalizations, and recovery from overgeneraliza...

A Survey on Computational Models for Argument Structure Acquisition

Towards a Computational Model of Early Syntactic Acquisition

We propose a formal, computational model of early syntactic acquisition, based on stochastic finite-state automata. The model learns from adult and child utterances and generalizes them such that novel utterances can be produced. Unlike much work in computational grammar induction, our model is informed by several well-established psycholinguistic principles, which are implemented as biases in ...