Constructing grammar:
A computational model of the emergence of early constructions

This thesis explores and formalizes the view that grammar learning is driven by meaningful language use in context. We describe a usage-based computational model of how children acquire their earliest multi-unit constructions, or relational form-meaning mappings. The model incorporates key features of child language learning, including rich conceptual structure and pragmatic abilities. We define a construction-based grammar formalism to serve as the hypothesis space, a model of how such constructions can be used to interpret utterances in context, and a model of how such constructions can be learned from a sequence of utterance-situation pairs. Learning biases are designed to facilitate successful communication: new constructions are directly motivated by the language understanding process, either to capture relational mappings present in context but not predicted by the current grammar, or to reorganize the existing set of constructions. Candidate constructions are evaluated using a minimum description length criterion that balances a prior structural bias toward simpler grammars against a data-driven bias toward simpler analyses of the data. When applied to input corpora based on annotated child-directed language, the model exhibits learning behavior compatible with crosslinguistic developmental patterns. It thus offers a cognitively motivated and computationally precise account of how diverse constraints can be integrated to account for the transition from single words to complex utterances.