  We adapt the classical notion of learning from text to computable structure
theory. Our main result is a model-theoretic characterization of the
learnability from text for classes of structures. We show that a family of
structures is learnable from text if and only if the structures can be
distinguished in terms of their theories restricted to positive infinitary
$\Sigma_2$ sentences.
