상세정보
학술지
Decision-Tree-Based Markov Model for Phrase Break Prediction
- 저자
- 김상훈, 오승신
- 발행일
- 200708
- 출처
- ETRI Journal, v.29 no.4, pp.527-529
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 06MW1800, 신성장동력산업용 대용량 대화형 분산 처리 음성인터페이스 기술개발, 이영직
- 초록
- In this paper, a decision-tree-based Markov model for phrase break prediction is proposed The model takes advantage of the non-homogeneous- features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.
- KSP 제안 키워드
- Break strength, Classification models, Decision Tree(DT), Error reduction, Feature set, Markov model, Proposed model, Reduction rate, Search results, Text Corpus, Tree-based