To my parents, Paul and B.J., for encouraging both wonder and accomplishment. To my wife, Sarah, for her unflagging love, support, and understanding. To all my friends, who have helped in innumerable ways.

We present a computational model of grammar learning that combines domain-general learning mechanisms with rich representations of linguistic knowledge, world knowledge and situational and discourse context. These representations support processes of language understanding and inference (including both constructional analysis and reference resolution) that help the learner make sense of utterances in context. The learner then draws on generalization and statistical induction techniques to form new constructions that better capture correlations between linguistically identified and contextually inferred information. Our work is part of the larger Neural Theory of Language (NTL) project, whose goal is to build models of cognition and language that satisfy convergent constraints from biology, psychology, linguistics and computation (Chang,