Learning phonetically and phonologically natural classes through constraint indexation

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Abstract

Phonological processes tend to be defined over natural classes (Chomsky & Halle 1968), but there are some arbitrary and language-specific aspects to class behaviour (e.g., Mielke 2004). This paper shows that it is possible to implement a procedure of finding language specific natural classes using contrast detection (Dresher 2014, Sanstedt 2018), but in standard OT with domain-general methods. Three toy languages are constructed, based on those in Prickett & Jarosz (2021), in which /e/ raises to [i] in the presence of a high vowel and in which /s/ palatalizes to [ʃ] before [i]. In one language, raising feeds palatalization (transparent); in the second, raising counterfeeds palatalization (opaque); in the third, raising applies transparently, but only in certain morphemes (lexically specific). All three languages are learned with a version of Round’s (2017) learner that learns indexed constraints (Pater 2000) that are attached to specific segments in morphemes rather than entire morphemes (cf. Nazarov 2021). This learner is able to find appropriate natural classes for these data, both phonetic natural classes (=traditional natural classes) and what I call phonologically natural classes (classes defined by having certain phonetic properties and undergoing a range of phonological processes), showing the feasibility of this approach.
Original languageEnglish
Title of host publicationProceedings of the 2021 Annual Meeting on Phonology
EditorsPeter Jurgec, Liisa Duncan, Emily Elfner, Yoonjung Kang, Alexei Kochetov, Britney K. O'Neill, Avery Ozburn, Keren Rice, Nathan Sander, Jessamyn Schertz, Nate Shaftoe, Lisa Sullivan
PublisherLinguistic Society of America
Number of pages10
DOIs
Publication statusPublished - 2022
EventAnnual Meeting on Phonology 2021 - University of Toronto (online), Toronto, Canada
Duration: 1 Oct 20213 Oct 2021
https://www.amp2021toronto.com/

Conference

ConferenceAnnual Meeting on Phonology 2021
Abbreviated titleAMP 2021
Country/TerritoryCanada
CityToronto
Period1/10/213/10/21
Internet address

Keywords

  • natural classes
  • phonological opacity
  • exceptionality
  • Optimality Theory
  • learnability
  • indexed constraints

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