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Phonological opacity as local optimization in Gradient Symbolic Computation
In: Proceedings of the Society for Computation in Linguistics (2018)
Abstract: We present a novel approach to counterbleeding rule interactions in Yokuts (Californian) using Gradient Symbolic Computation (GSC). GSC, a dynamical systems model, optimizes two constraint sets: a set specifying a Harmonic Grammar (HG) and a set of quantization constraints preferring discrete symbolic states. During optimization, quantization strength gradually increases, increasing the relative harmony of discrete symbolic vs. intermediate blend states. The output of the system therefore reflects the dynamics of optimization, not simply grammatical harmony. With appropriate dynamics, relatively high harmony intermediate states can trap optimization near less globally harmonic but locally optimal symbolic candidates; this can model Yokuts counterbleeding.
Keyword: Computational Linguistics; GSC; opacity; Phonetics and Phonology; phonology
URL: https://scholarworks.umass.edu/scil/vol1/iss1/35
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1041&context=scil
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