Learning within- and between-word variation in probabilistic OT grammars

Aleksei Nazarov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a novel method of inferring diacritics for representing between-word variation (exceptionality) in Optimality Theoretic (OT) grammars (e.g., Pater 2000, 2010) that makes it possible to infer such diacritics in the face of within-word variation. Existing methods of inferring diacritics in OT (Pater 2010, Becker 2009, Coetzee 2009) are based in categorical grammar learning (Tesar 1995), which makes them unable to handle within-word variation. Existing methods of inferring probabilistic OT grammars (e.g., Boersma 1998) handle within-word variation well, but have no provision to distinguish exceptional from non-exceptional words, and are incompatible with the main idea in Pater (2010). I show that this latter idea can be made compatible with probabilistic grammars based on a case study from Hebrew (Temkin-Martínez 2010), so that both within- and between-word variation can be learned.
Original languageEnglish
Title of host publicationSupplemental Proceedings of the 2017 Annual Meeting on Phonology
EditorsGillian Gallagher, Maria Gouskova, Sora Yin
Place of PublicationWashington, DC
PublisherLinguistic Society of America
Number of pages12
DOIs
Publication statusPublished - Feb 2018
Event5th Annual Meeting on Phonology - New York University, New York, United States
Duration: 15 Sep 201717 Sep 2017
Conference number: 5
https://wp.nyu.edu/amp2017/ (Link to Event Website)

Publication series

Name
ISSN (Electronic)2377-3324

Conference

Conference5th Annual Meeting on Phonology
Abbreviated titleAMP 2017
CountryUnited States
CityNew York
Period15/09/1717/09/17
Internet address

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  • Cite this

    Nazarov, A. (2018). Learning within- and between-word variation in probabilistic OT grammars. In G. Gallagher, M. Gouskova, & S. Yin (Eds.), Supplemental Proceedings of the 2017 Annual Meeting on Phonology Washington, DC: Linguistic Society of America. https://doi.org/10.3765/amp.v5i0.4253