Phonological theory and computational modelling

The computational modelling of phonology is almost as old as generative phonology itself. Johnson (1972) and Kaplan & Kaye (1981, 1994) showed that SPE rules can be modelled with finite-state machines, after which finite-state modelling became the bedrock of computational phonology, eventually informing computational autosegmental phonology (e.g., Kornai 1991, 1995) and Optimality Theory (e.g., Ellison 1994). More recently, computational modelling has informed phonological theory, following two strands of research: stochastic learning from corpus data and gradient acceptability judgements (e.g., Hayes & Wilson 2008; Albright 2009), and the study of the computational nature of phonological patterns (e.g., Heinz 2007). One can expect that the role computational modelling plays in the explanation of phonological cognition and learning will only increase.