Capturing gradience in long-distance phonology using probabilistic tier-based strictly local grammars
Connor Mayer · Scholarworks (University of Massachusetts Amherst) · 2021
Phonological processes often exhibit gradience, both in response frequencies and in acceptability judgments. This paper presents a variation of tier-based strictly local grammars, probabilistic tier-based strictly local (pTSL) grammars, which calculate the conditional probability that a given input string has some grammatical projection. pTSL grammars are well-suited to modeling gradience, particularly for long-distance processes, and naturally extend categorical tier-based strictly local grammars by probabilizing the projection function. After describing the formal properties of pTSL, I illustrate its application using data from Hungarian and Uyghur. pTSL is able to capture distance-based decay in these languages without an explicit notion of distance, and provides a unified account of gradient blocking and distance-based decay. I finish by outlining some of the limitations of pTSL, and how further extensions may overcome these.