A maximum-entropy model of phonotactics for Korean male and female names
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초록
This paper examines the phonotactic grammar of Korean male and female first names using a maximum-entropy phonotactics learning model (Hayes and Wilson 2008). An analysis of the 7000 most frequent recent Korean names (registered births in the period of 2008-2020, 3500 for each gender) reveals that the Korean names show gender-dependent phonological characteristics, as observed in other languages. Female names are longer, have more sonorants in onset position, and are more likely to end with a vowel than male names. To further compare female and male phonotactic characteristics, maximum-entropy phonotactic grammars were learned from the training data consisting of 3377 names for each gender. The resulting constraints are categorized into word-edge, CV, and VC constraints, and the constraints that are found to differ in male and female names are compared. The results identify the sound sequences that are biased toward either gender. This paper also shows that the predictions made by the maximum-entropy grammar have significant correlations with human ratings.
Abstract
This paper examines the phonotactic grammar of Korean male and female first names using a maximum-entropy phonotactics learning model (Hayes and Wilson 2008). An analysis of the 7000 most frequent recent Korean names (registered births in the period of 2008-2020, 3500 for each gender) reveals that the Korean names show gender-dependent phonological characteristics, as observed in other languages. Female names are longer, have more sonorants in onset position, and are more likely to end with a vowel than male names. To further compare female and male phonotactic characteristics, maximum-entropy phonotactic grammars were learned from the training data consisting of 3377 names for each gender. The resulting constraints are categorized into word-edge, CV, and VC constraints, and the constraints that are found to differ in male and female names are compared. The results identify the sound sequences that are biased toward either gender. This paper also shows that the predictions made by the maximum-entropy grammar have significant correlations with human ratings.
참고문헌 (33)
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