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From linguistic scales to constraints: Fixed-rank vs. stringency-related constraints

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This paper examines how we should convert linguistic scales into constraints. Linguistic scales designate implicational universals, which have been represented in terms of fixed-rank (FR) or stringency-related (SR) constraints in Optimality Theory. Centering on the syllabification of English word-medial consonants, in which the hierarchical sonority values of onset and coda consonants play a significant role, this paper constructs two weighted constraint grammars, one with FR constraints and the other with SR constraints, and compares the goodness of fit of the two grammar models by measuring error rates (Mean Absolute Error and Root Mean Square Error) and the coefficient of determination (R2). The results show that the SR constraint model, which is simpler than the FR model (since it does not need fixed rankings), is lower in error rates and better addresses observed variance, and thus provides a better fit for the given data than the FR model.

Abstract

This paper examines how we should convert linguistic scales into constraints. Linguistic scales designate implicational universals, which have been represented in terms of fixed-rank (FR) or stringency-related (SR) constraints in Optimality Theory. Centering on the syllabification of English word-medial consonants, in which the hierarchical sonority values of onset and coda consonants play a significant role, this paper constructs two weighted constraint grammars, one with FR constraints and the other with SR constraints, and compares the goodness of fit of the two grammar models by measuring error rates (Mean Absolute Error and Root Mean Square Error) and the coefficient of determination (R2). The results show that the SR constraint model, which is simpler than the FR model (since it does not need fixed rankings), is lower in error rates and better addresses observed variance, and thus provides a better fit for the given data than the FR model.

참고문헌 (59)

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