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

Aleksei Nazarov

doi:10.3765/amp.v5i0.4253

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

Aleksei Nazarov

School of Arts and Humanities

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Supplemental Proceedings of the 2017 Annual Meeting on Phonology
Editors	Gillian Gallagher, Maria Gouskova, Sora Yin
Place of Publication	Washington, DC
Publisher	Linguistic Society of America
Number of pages	12
DOIs	https://doi.org/10.3765/amp.v5i0.4253
Publication status	Published - Feb 2018
Event	5th Annual Meeting on Phonology - New York University, New York, United States Duration: 15 Sept 2017 → 17 Sept 2017 Conference number: 5 https://wp.nyu.edu/amp2017/ (Link to Event Website)

Publication series

Name
ISSN (Electronic)	2377-3324

Conference

Conference	5th Annual Meeting on Phonology
Abbreviated title	AMP 2017
Country/Territory	United States
City	New York
Period	15/09/17 → 17/09/17
Internet address	https://wp.nyu.edu/amp2017/ (Link to Event Website)

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10.3765/amp.v5i0.4253Licence: CC BY

Cite this

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title = "Learning within- and between-word variation in probabilistic OT grammars",

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-Mart{\'i}nez 2010), so that both within- and between-word variation can be learned.",

keywords = "Exceptionality, Indexed constraints, Learning, Variation",

author = "Aleksei Nazarov",

year = "2018",

month = feb,

doi = "10.3765/amp.v5i0.4253",

language = "English",

publisher = "Linguistic Society of America",

editor = "Gillian Gallagher and Maria Gouskova and Sora Yin",

booktitle = "Supplemental Proceedings of the 2017 Annual Meeting on Phonology",

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note = "5th Annual Meeting on Phonology, AMP 2017 ; Conference date: 15-09-2017 Through 17-09-2017",

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T1 - Learning within- and between-word variation in probabilistic OT grammars

AU - Nazarov, Aleksei

N1 - Conference code: 5

PY - 2018/2

Y1 - 2018/2

N2 - 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.

AB - 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.

KW - Exceptionality

KW - Indexed constraints

KW - Learning

KW - Variation

U2 - 10.3765/amp.v5i0.4253

DO - 10.3765/amp.v5i0.4253

M3 - Conference contribution

BT - Supplemental Proceedings of the 2017 Annual Meeting on Phonology

A2 - Gallagher, Gillian

A2 - Gouskova, Maria

A2 - Yin, Sora

PB - Linguistic Society of America

CY - Washington, DC

T2 - 5th Annual Meeting on Phonology

Y2 - 15 September 2017 through 17 September 2017

ER -

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

Abstract

Publication series

Conference

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