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Volumn , Issue , 2008, Pages 670-679

Learning with probabilistic features for improved pipeline models

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053350441     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1613715.1613799     Document Type: Conference Paper
Times cited : (22)

References (17)
  • 3
    • 4043070633 scopus 로고    scopus 로고
    • Three new probabilistic models for dependency parsing: An exploration
    • Copenhagen, Denmark
    • Jason M. Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In Proceedings of the 16th Conference on Computational linguistics, pages 340-345, Copenhagen, Denmark.
    • (1996) Proceedings of the 16th Conference on Computational Linguistics , pp. 340-345
    • Eisner, J.M.1
  • 6
    • 34249852033 scopus 로고
    • Building a large annotated corpus of English: The Penn treebank
    • M. Marcus, B. Santorini, and M. A. Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn treebank. Computational Linguistics, 19(2):313-330.
    • (1993) Computational Linguistics , vol.19 , Issue.2 , pp. 313-330
    • Marcus, M.1    Santorini, B.2    Marcinkiewicz, M.A.3
  • 9
    • 33846985381 scopus 로고    scopus 로고
    • Convergence rates of the Voting Gibbs classifier, with application to bayesian feature selection
    • Williamstown, MA
    • Andrew Y. Ng and Michael I. Jordan. 2001. Convergence rates of the Voting Gibbs classifier, with application to bayesian feature selection. In Proceedings of 18th International Conference on Machine Learning (ICML-2001), pages 377-384, Williamstown, MA.
    • (2001) Proceedings of 18th International Conference on Machine Learning ICML-2001 , pp. 377-384
    • Ng, A.Y.1    Jordan, M.I.2
  • 13
  • 14
    • 14344253846 scopus 로고    scopus 로고
    • Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data
    • Banff, Canada, July
    • Charles Sutton, Khashayar Rohanimanesh, and Andrew McCallum. 2004. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. In Proceedings of 21st International Conference on Machine Learning (ICML2004), pages 783-790, Banff, Canada, July.
    • (2004) Proceedings of 21st International Conference on Machine Learning (ICML2004) , pp. 783-790
    • Sutton, C.1    Rohanimanesh, K.2    McCallum, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.