This paper offers a computational characterization of tone-to-TBU association processes using a restricted least-fixed point logic. Crucially, least fixed point logics allow recursive definitions which capture output-oriented processes. The added requirement that these definitions are quantifier-free ensures that they are inherently local, a restriction that is well-motivated for phonological processes in general. The typology developed here distinguishes between possible and impossible tone mappings, capturing a wider range of attested tone mappings (left-to-right, right-to-left, edge-in, quality-sensitive) than previous rule-based or optimization approaches, while also explaining why certain unattested mapping patterns (for example center-out association) are impossible. This thus represents a strong first approximation of a definition for output-based local functions over non-linear structures
This paper employs a computational framework to demonstrate that two competing feature-geometric models of tonal representation are notationally equivalent. A model-theoretic analysis of these structures using a low-complexity logic yields two main results. First, the current study demonstrates that the models do not differ in their empirical coverage of assimilatory tone-sandhi processes in Chinese dialects, contrary to previous claims. Second, the models are shown to be bi-interpretable (using a more restrictive definition of bi-interpretability than earlier studies), thus providing a formally rigorous demonstration that the differences between the structures of the models are superficial, rather than substantive. The computational characterisation pursued here is well suited to questions of notational equivalence, because it allows for a principled comparison of the empirical coverage and structural content of two models using a single formalism.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.