Bruce Hayes – University of California, Los Angeles
Course time: Tuesday/Thursday 9:00-10:50 am
2306 Mason Hall
In the grammar architecture of classical Optimality Theory (Prince and Smolensky 1993), constraints are ranked and the grammar generates exactly one winner per input. Phonologists have proposed instead that we should consider models in which the constraints, rather than being ranked, bear weights (real numbers, intuitively related to constraint strength). Weights are employed to calculate probabilities for all members of the candidate set.
Such quantitative grammars open up new research possibilities for constraint-based phonology:
(a) Modeling free variation and the multiple factors that shift the statistical distribution of outputs across contexts;
(b) Modeling gradient intuitions (intermediate well-formedness, ambivalence among output choices);
(c) Modeling quantitative lexical patterns and how they are characteristically mimicked in experiments where native speakers are tested on their phonological knowledge;
(d) Modeling phonological learning: even where in areas where the ambient language doesn’t vary at all, the child’s conception of what is likely to be the correct grammar of it will change (approaching certainty) as more data are taken in; modeling can trace this process.
This course will be an introduction to these models and research areas. It will emphasize learning by doing. Participants will use software tools that embody the theories at hand and will examine and model data from a variety of digital corpora. The course will not cover computational phonology per se, but it will cover enough computation to give participants a good understanding of the tools they are using. Pre-requisite for this course: a course in phonology.