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
申景媄 (高麗大學校) 李彰浩 (協成大學校) 崔圭鉢 (高麗大學校)
저널정보
중국어문학연구회 중국어문학논집 中國語文學論集 第96號
발행연도
2016.2
수록면
99 - 117 (19page)

이용수

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

초록· 키워드

오류제보하기
This paper proposes a method of contrastive Korean and Chinese based on Natural Language Processing(NLP).
There is a rising interest in studying information processing with the development of science and technology. For this reason, there has been a visible change in language and linguistics research. For example, we have often used corpus, information retrieval, machine translation, and voice recognition systems etc. Furthermore, a lot of people use this for studies first or second language. So we began an exploration for new research based on NLP.
There are many problems in Chinese NLP systems, because the Chinese words have various meanings and functions, the computer system keeps generating errors. We need to have a new way of contrasting Korean and Chinese sentence patterns. The first way to contrast them is to use the part of speech(POS) and feature taggers. It is very useful gadgets for helping us to understand what the similarities and differences between word and word are. This is merely a first step for improving the accuracy and efficiency of Computational Language Systems.
The tagging data make us prepare for a Computational Language Systems that we able to understand Korean and Chinese sentence patterns. And we propose to collect the data of POS and features taggers. It’s very important material for researchers to analyze the relationship between Korean and Chinese.
This subject must be researched, but it’s still a work in progress, and we need a good collaborative research project on contrasting of Korean and Chinese.

목차

1. 서론
2. 자연언어처리를기반으로한한·중언어연구의문제점과필요성
3. 자연언어처리를기반으로한한·중대조연구의과제와방법
4. 결론
〈參考文獻〉
〈ABSTRACT〉

참고문헌 (30)

참고문헌 신청

함께 읽어보면 좋을 논문

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

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2016-820-002675986