메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Paul Lance New, Jr. (University of Texas El Paso)
저널정보
세종대학교 언어연구소 Journal of Universal Language Journal of Universal Language 제23권 제2호
발행연도
2022.9
수록면
23 - 75 (53page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Semantic characters such as 〈원문참조〉 for ‘skiing’ represent meaning rather than sound. For centuries, great minds such as Descartes, Leibniz, Francis Lodowyck, and Rev. John Wilkins have called for the creation of a writing-system comprised completely of semantic characters. Such a writing-system must possess enough characters to represent the universe; But too many characters will pose a challenge for users to learn and remember. This article argues for principles limiting the number of basic, non-derived characters (“radicals”) while maximizing the expressive power coaxed from them. I advance three primary strategies here: First, use of the arrow or other selector to derive related meanings from one radical (e.g., 〈원문참조〉 for ‘skier’ as opposed to 〈원문참조〉 (no arrow) for ‘skiing’); Second, a series of “radical-disqualifiers” requiring most concepts or notions to be derived from one or more radicals, rather than being represented by one radical. Finally, constructing radicals from smaller elements whose meanings are consistent across them (e.g., if we designate the single-shafted arrow to mean ‘change’, then it should appear with that meaning in all radicals dealing with change.). Ample visual examples illustrating these points are given, applying these principles to Egyptian, Chinese, Mayan and some modern constructed systems.

목차

Abstract
1. Introduction: Principles for Maximizing Representation
2. The Arrow/Selector/Indicator to Derive Multiple Meanings from One Radical
3. Relatively Few Radicals but Many Derivations and Combinations: Radical-Disqualifiers
4. Sub-Elements
5. Conclusion
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2023-701-000151922