서지주요정보
마코프랜텀필드를 이용한 영어 품사태깅 시스템 = Markov random field based english part-of-speech tagging system
서명 / 저자 마코프랜텀필드를 이용한 영어 품사태깅 시스템 = Markov random field based english part-of-speech tagging system / 정성영.
발행사항 [대전 : 한국과학기술원, 1996].
Online Access 원문보기 원문인쇄

소장정보

등록번호

8007002

소장위치/청구기호

학술문화관(문화관) 보존서고

MCS 96050

휴대폰 전송

도서상태

이용가능

대출가능

반납예정일

등록번호

9003005

소장위치/청구기호

서울 학위논문 서가

MCS 96050 c. 2

휴대폰 전송

도서상태

이용가능

대출가능

반납예정일

리뷰정보

초록정보

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which is assigning the most likely tag to each word in a given sentence, is the one of the problems which can be solved by statistical approach. Many researchers have solved the problem using hidden Markov model (HMM), which is well known as one of statistical model. But it has many difficulties: integrating heterogeneous information, coping with data sparseness problem and adapting to new environments. In this paper, we propose a Markov random field (MRF) model based approach to the tagging problem. The MRF provides the base frame to combine various statistical information with maximum entropy (ME) method. As Gibbs distribution can be used to describe a posteriori probability of tagging, we use it in maximum a posteriori (MAP) estimation of optimizing process. Besides, several tagging models are developed to show the effect of adding information. Experimental results show that the performance of tagger gets improved as we add more statistical information, and that MRF based tagging model is better than HMM based tagging model in data sparseness problem.

서지기타정보

서지기타정보
청구기호 {MCS 96050
형태사항 49 p. : 삽도 ; 26 cm
언어 한국어
일반주기 저자명의 영문표기 : Sung-Young Jung
지도교수의 한글표기 : 최기선
지도교수의 영문표기 : Ki-Sun Choi
학위논문 학위논문(석사) - 한국과학기술원 : 전산학과,
서지주기 참고문헌 : p. 46-49
주제 마코프랜덤필드
최대엔트로피
품사태깅
MRF
Tagging
ME
Markov random field
Maximum entropy
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