Grounded Learning of Grammatical Constructions

Nancy C. Chang and Tiago V. Maia

We describe a model of grammar learning in which all linguistic units are grounded in rich conceptual representations, and larger grammatical constructions involve relational mappings between form and meaning that are built up from smaller (e.g., lexical) constructions. The algorithm we describe for acquiring these grammatical constructions consists of three separate but interacting processes: an analysis procedure that uses the current set of constructions to identify mappings between an utterance and its accompanying situation; a hypothesis procedure that creates new constructions to account for remaining correlations between these two domains; and reorganization processes that generalize existing constructions on the basis of similarity and cooccurrence. The algorithm is thus grounded not only in the twin poles of form and meaning but also, more importantly, in the relational mappings between the two.

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