- 영문명
- A Study on the Missing Data of Stem-Final Consonant Cluster
- 발행기관
- 어문연구학회
- 저자명
- 정영인
- 간행물 정보
- 『어문연구』어문연구 제43집, 95~119쪽, 전체 25쪽
- 주제분류
- 어문학 > 한국어와문학
- 파일형태
- 발행일자
- 2003.12.01
국문 초록
영문 초록
This research is taken to categorize and measure the data of the dialect collected. Korean Dialect Data, published by the Academy of Korean Studies, is adopted as a useful tool in order to obtain homogeneous materials.
68 items selected as materials could be categorized to several types according to its feature: missing data type, the whole omission type, C1 accomplishment type, rp(V) type, rk(V) type, k type, p type, r(V)k type, r(V)k(V) type, r(V) type, r(C) type, k(C) type, k(V)type, rp type and rk type. The research, however, is mainly focused on analysis of missing data. Even though the debate on 'missing data' could be a very specifIc area among variation of a stem-final consonanant cluster, but it is believed that missing data type alone is enough in the process of dealing with the dialect data.
In this research, the comparative frequency i.e permillage is adopted in order to compare the nationwide data. It was possible to compare the dialect data of each province in the permillage. As a result of that, it was able to understand some aspects of nationwide variation in some specific items. In addition, it enabled us to grasp the deviation between neighboring areas to measure linguistic distance. The result was that the areas showing the biggest deviation are some parts of Islands (including Jeju-do. Wando. Goheung. Namhae. etc.) and some areas of Gangwon Province (including Weonseong. Samcheck. Yeongwol. Chunseong).
Of course, measuring the data does not give all the sufficient answers to us. However, it is likely that we could effectively compare the data and materials concerning not only a stem-final consonant cluster but also other phenomenon of phonological variation if we measure materials according to the same method of this research.
목차
어간말 자음군의 결측자료 연구
1. 서론
2. 자료의 성격
3. 결측 자료의 분류
4. 언어적 거리
5. 결론
참고문헌
Abstract
키워드
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