Computational Learning Theory and Language Acquisition
Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Diﬀerent models of learning can yield radically diﬀerent learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent that such assumptions accurately reﬂect human language learning, a model that invokes them can oﬀer important insights into the formal properties of natural languages, and the way in which their representations might be eﬃciently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be eﬃciently learned from the primary linguistic data (PLD) available to children, through..