A Quantifier-Based Approach to NPI-Licensing Typology:
General Material Designation
[Thesis]
First Statement of Responsibility
Vu, Mai Ha
Title Proper by Another Author
Empirical and Computational Investigations
Subsequent Statement of Responsibility
Bruening, Benjamin T.
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
University of Delaware
Date of Publication, Distribution, etc.
2020
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
265
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
Ph.D.
Body granting the degree
University of Delaware
Text preceding or following the note
2020
SUMMARY OR ABSTRACT
Text of Note
This thesis examines the quantifier-based approach to NPI-licensing (as proposed in (Giannakidou, 2000)) from empirical and computational perspectives. This approach argues that all NPIs can be categorized as either existentially or universally quantified items, and that this difference drives cross-linguistically divergent NPI-behaviors. After providing the necessary background and assumptions, in the first half of the thesis I show that English any-NPIs are existentially quantified, whereas Hungarian se-NPIs are universally quantified. I also demonstrate how this approach can help understand the behavior of NPIs in other languages and language families such as Slavic, Mandarin Chinese, Turkish, and Romance languages. In the second half of the thesis, I analyze the quantifier-based NPI-licensing constraints for computational complexity. I find that except for the constraints that rely on derived c-command, all other constraints can be described with Input-local Tier-based Strictly Local (I-TSL) or Multiple Input-local Tier-based Strictly Local (MITSL) restrictions, which means that tree-languages that satisfy NPI-licensing constraints for the most part fit into a fairly restrictive subregular class of tree-languages. Taken together, this thesis argues that a theoretically informed approach to linguistic phenomena can significantly affect results on their computational complexity.