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
Chung-ling Shih (National Kaohsiung First University of Science and Technology)
저널정보
세종대학교 언어연구소 Journal of Universal Language Journal of Universal Language 제11권 제2호
발행연도
2010.9
수록면
227 - 254 (28page)

이용수

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

초록· 키워드

오류제보하기
This research identifies different controlled English (CE) norms to be followed in technical writing for a variety of purposes and for different machine translation (MT) systems. The results of the investigation show that CE norms for MT application are stricter than those for communicative reading. The primary inference here is that human beings can interpret the meanings of polysemous words, pronouns, prepositional phrases based on the context and easily detect the misspellings, but MT systems fail to do so. In addition, a comparison of CE norms for the application of two MT systems indicates that the corpus-based Google MT is less constrained than rule-based TransWhiz in the lexical area. This phenomenon is attributable to the selection of a highly probabilistic module as the semantic scoring preference for the suggested translation provided by Google MT, not word-for-word translation by TransWhiz. In contrast, Google MT is more constrained than TransWhiz in the syntactic area. The inference is that TransWhiz parses syntactic constructions and transfers the parsing result based on grammatical rules stored in the MT system, so it may modify the original word sequence to make the translation conform to linguistic patterns in the target language. Contrary to this, Google MT depends on fuzzy or exact matches statistically retrieved from the labeled corpus. If no matches can be found, syntactically inappropriate translations will be produced. Seen in this regard, CE norms are never fixed and have to be modified through the evolution of time and MT technology.

목차

Abstract
1. Introduction
2. Literature Review
3. Methodology
4. Findings
5. Discussions
6. Conclusion
References

참고문헌 (18)

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-0101-2015-400-001552253