On the Constituency of Numeral and Classifier in Korean
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Based on the numeral omission and coordination facts, Barrie et al. (2022) claim that Korean is a classifier-for-numeral language, while Chinese is a classifier-for-noun language. This claim implies that a numeral and a classifier forms a constituent in Korean, while a classifier and a noun forms a constituent in Chinese. The purpose of this paper is to investigate the constituency of the numeral, classifier, and noun in Korean numeral classifier constructions. First, this paper shows that the numeral omission and coordination facts of Barrie et al. (2022) are not critical evidence for the classifier-for-numeral analysis. Then, this paper agues that Korean is a classifier-for-noun language, with the classifier forming a constituent with the relevant noun, based on the data of practicing counting cases, extraction facts, and selectional restriction facts, which Barrie et al (2022) face difficulties in accounting for. The current proposal implies that the classifier classification criteria of Little et al. (2022) is in need of slight modification.
Abstract
Based on the numeral omission and coordination facts, Barrie et al. (2022) claim that Korean is a classifier-for-numeral language, while Chinese is a classifier-for-noun language. This claim implies that a numeral and a classifier forms a constituent in Korean, while a classifier and a noun forms a constituent in Chinese. The purpose of this paper is to investigate the constituency of the numeral, classifier, and noun in Korean numeral classifier constructions. First, this paper shows that the numeral omission and coordination facts of Barrie et al. (2022) are not critical evidence for the classifier-for-numeral analysis. Then, this paper agues that Korean is a classifier-for-noun language, with the classifier forming a constituent with the relevant noun, based on the data of practicing counting cases, extraction facts, and selectional restriction facts, which Barrie et al (2022) face difficulties in accounting for. The current proposal implies that the classifier classification criteria of Little et al. (2022) is in need of slight modification.
