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Author ORCID Identifier

https://orcid.org/0000-0002-4452-1436

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Linguistics

Year Degree Awarded

2022

Month Degree Awarded

February

First Advisor

Kyle Johnson

Second Advisor

Rajesh Bhatt

Third Advisor

Ellen Woolford

Fourth Advisor

Luiz Amaral

Subject Categories

Syntax

Abstract

When an item moves, it is usually pronounced once but in some cases, it is pronounced multiple times. So, a question is: What determines whether a moved item gets pronounced in only one of its positions or in multiple positions? This dissertation aims at providing an answer to this question by designing a linearization process that yields the correct phonetic realization of a moved item, with a focus on V(P) movement. In particular, this dissertation provides a detailed analysis of how V(P)-doubling cases are linearized and thus show how a V(P) ends up being pronounced multiple times. Regarding the proposed linearization process in this dissertation, following Kusmer (2019), I assume that the basic linearization process contains Candidates Generator G, which generates a set of precedence relations, and Constraints, which pick the right subset from G. As for the Constraints, I adapt the Totality Constraint and the Asymmetry Constraint from Kayne (1994), the Anti-reflexivity Constraint from Partee, ter Meulen and Wall (1990), and Language Specific Constraints from Wilder (1999) and Kusmer (2019). In addition, I propose an Ordering Deletion rule that gets rid of redundant precedence relations and a Set-to-String algorithm that turns a set of precedence relations into a string. Furthermore, I adopt the idea from Fox and Pesetsky (2005) that linearization of precedence relations is implemented in a cyclic way. Also, I employ the idea behind Nunes (2004)'s morphological reanalysis that a higher level node can be linearized instead of the nodes it contains given certain circumstances. Finally, I present the predictions made by the proposed linearization process, which can be evaluated against more data for future research.

DOI

https://doi.org/10.7275/27078593.0

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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