Learning Meaning Before Syntax

We present a simple computational model that includes semantics for language learning, as motivated by readings in the literature of children's language acquisition and by a desire to incorporate a robust notion of semantics in the field of Grammatical Inference. We argue that not only is it more natural to take into account semantics, but also that semantic information can make learning easier, and can give us a better understanding of the relation between positive data and corrections. We propose a model of meaning and denotation using finite-state transducers, motivated by an example domain of geometric shapes and their properties and relations. We give an algorithm to learn a meaning function and prove that it finitely converges to a correct result under a specific set of assumptions about the transducer and examples.

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