Extending Hidden Structure Learning: Features, Opacity, and Exceptions
Aleksei Ioulevitch Nazarov · Scholarworks (University of Massachusetts Amherst) · 2021
This dissertation explores new perspectives in phonological hidden structure learning (inferring structure not present in the speech signal that is necessary for phonological analysis; Tesar 1998, Jarosz 2013a, Boersma and Pater 2016), and extends this type of learning towards the domain of phonological features, towards derivations in Stratal OT (Bermúdez-Otero 1999), and towards exceptionality indices in probabilistic OT. Two more specific themes also come out: the possibility of inducing instead of pre-specifying the space of possible hidden structures, and the importance of cues in the data for triggering the use of hidden structure. In chapters 2 and 4, phonological features and exception groupings are induced by an unsupervised procedure that finds units not explicitly given to the learner. In chapters 2 and 3, there is an effect of non-specification or underspecification on the hidden level whenever the data does not give enough cues for that hidden level to be used. When features are hidden structure (chapter 2), they are only used for patterns that generalize across multiple segments. When intermediate derivational levels are hidden structure (chapter 3), the hidden structure necessary for opaque interactions is found more often when additional cues for the stratal affiliation of the opaque process are present in the data.