Hierarchical bayesian models of verb learning in children

The productivity of language lies in the ability to generalize linguistic knowledge to new situations. To understand how children can learn to use language in novel, productive ways, we must investigate how children can find the right abstractions over their input, and how these abstractions can actually guide generalization. In this thesis, I present a series of hierarchical Bayesian models that provide an explicit computational account of how children can acquire and generalize highly abstract knowledge of the verb lexicon from the language around them. By applying the models to large, naturalistic corpora of child-directed speech, I show that these models capture key behaviours in child language development. These models offer the power to investigate developmental phenomena with a degree of breadth and realism unavailable in existing computational accounts of verb learning. By most accounts, children rely on strong regularities between form and meaning to help them acquire abstract verb knowledge. Using a token-level clustering model, I show that by attending to simple syntactic features of potential verb arguments in the input, children can acquire abstract representations of verb argument structure that can reasonably distinguish the senses of a highly polysemous verb. I develop a novel hierarchical model that acquires probabilistic representations of verb argument structure, while also acquiring classes of verbs with similar overall patterns of usage. In a simulation of verb learning within a broad, naturalistic context, I show how this abstract, probabilistic knowledge of alternations can be generalized to new verbs to support learning. I augment this verb class model to acquire associations between form and meaning in verb argument structure, and to generalize this knowledge appropriately via the syntactic and semantic aspects of verb alternations. The model captures children's ability to use the alternation pattern of a novel verb to infer aspects of the verb's meaning, and to use the meaning of a novel verb to predict the range of syntactic forms in which the verb may participate. These simulations also provide new predictions of children's linguistic development, emphasizing the value of this model as a useful framework to investigate verb learning in a complex linguistic environment.