A constraint-based approach to Korean loanwords
Hideyuki Hirano
Abstract
Hideyuki Hirano
Abstract
This paper presents an analysis of Korean loanwords within the framework of Optimality Theory_ It argues that the constraints FAITHFULNESS and MARGIN-R, which condition the preservation of original input forms, play a crucial role in loanword phonology, and that they interact with other constraints describing the unmarked characteristics of the basic syllable structure. It also argues that a system of both dominant constraints and ranked, violable constraints selects forms. This research strongly suggests that a ranking of constraints, BR, M-R》F AITHFULNESS》PARSE》ALIGN-R》FILL》ONS》-COD exists in Korean loanword phonology. Moreover, it is demonstrated that two possible actual forms are characterized by the ranking differences between violable constraints, and that overparsing is a reliable strategy for rescuing unsyllabifiable segments. This analysis shows how Optimality Theory works in loanword phonology.
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This paper presents an analysis of Korean loanwords within the framework of Optimality Theory_ It argues that the constraints FAITHFULNESS and MARGIN-R, which condition the preservation of original input forms, play a crucial role in loanword phonology, and that they interact with other constraints describing the unmarked characteristics of the basic syllable structure. It also argues that a system of both dominant constraints and ranked, violable constraints selects forms. This research strongly suggests that a ranking of constraints, BR, M-R》F AITHFULNESS》PARSE》ALIGN-R》FILL》ONS》-COD exists in Korean loanword phonology. Moreover, it is demonstrated that two possible actual forms are characterized by the ranking differences between violable constraints, and that overparsing is a reliable strategy for rescuing unsyllabifiable segments. This analysis shows how Optimality Theory works in loanword phonology.
Key concepts: Optimality theory, Loanword, Phonology, Syllable, Ranking (information retrieval), Linguistics, Constraint (computer-aided design), Margin (machine learning)