computational phonology

We show a mismatch between the generative capacity of reduplication and the theories which model it.

A novel method for sampling a class of subsequential string transductions encoding homomorphisms allows rigorous testing of learning models' capacity for compositionality.

This chapter examines the brief but vibrant history of learnability in phonology.

We overview the notion of phonological abstractness, various types of evidence for it, and consequences for linguistics and psychology.

My doctoral dissertation, examining the relationship between abductive inference and algebraically structured hypothesis spaces, giving a general form for grammar learning over arbitrary linguistic structure.

We derive the well-studied subregular classes of formal languages, which computationally characterize natural language typology, purely from the perspective of algorithmic learning problems.

We formalize various iterative prosodic processes including stress, syllabification and epenthesis using logical graph transductions, showing that the necessary use of fixed point operators without quantification restricts them to a structured subclass of subsequential functions.

We present an automata-theoretic analysis of locality in nonlinear phonology and morphology.

We analyze divergences in strong generative capacity between morphological processes that are equivalent in weak generative capacity.

We overview vowel harmony computationally, describing necessary and sufficient conditions on phonotactics, processes, and learning.