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Volumn , Issue , 2007, Pages 801-809

Finding good sequential model structures using output transformations

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXTUAL INFORMATION; FIRST-ORDER; HILL CLIMBING; INPUT FEATURES; LINEAR CHAIN; MODEL PARAMETERS; OUTPUT SEQUENCES; PARAMETER SPACES; SECOND-ORDER MODELS; SEQUENTIAL MODEL; SPARSE DATA PROBLEM; VITERBI;

EID: 80053381648     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1)

References (12)
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    • Maximum entropy Markov models for information extraction and segmentation
    • Morgan Kaufmann, San Francisco, CA
    • Andrew McCallum, Dayne Freitag, and Fernando Pereira. 2000. Maximum entropy Markov models for information extraction and segmentation. In Proc. 17th International Conf. on Machine Learning, pages 591-598. Morgan Kaufmann, San Francisco, CA.
    • (2000) Proc. 17th International Conf. on Machine Learning , pp. 591-598
    • McCallum, A.1    Freitag, D.2    Pereira, F.3
  • 4
    • 0348198473 scopus 로고    scopus 로고
    • Finite-state transducers in language and speech processing
    • Mehryar Mohri. 1997. Finite-state transducers in language and speech processing. Computational Linguistics, 23(2):269-311.
    • (1997) Computational Linguistics , vol.23 , Issue.2 , pp. 269-311
    • Mohri, M.1
  • 6
    • 0002711083 scopus 로고
    • Text chunking using transformation-based learning
    • David Yarowsky and Kenneth Church, editors, Somerset, New Jersey, Association for Computational Linguistics
    • Lance Ramshaw and Mitch Marcus. 1995. Text chunking using transformation-based learning. In David Yarowsky and Kenneth Church, editors, Proceedings of the Third Workshop on Very Large Corpora, pages 82-94, Somerset, New Jersey. Association for Computational Linguistics.
    • (1995) Proceedings of the Third Workshop on Very Large Corpora , pp. 82-94
    • Ramshaw, L.1    Marcus, M.2
  • 7
    • 85043116988 scopus 로고    scopus 로고
    • Shallow parsing with conditional random fields
    • Fei Sha and Fernando Pereira. 2003. Shallow parsing with conditional random fields. In Proceedings of HLT-NAACL, pages 134-141.
    • (2003) Proceedings of HLT-NAACL , pp. 134-141
    • Sha, F.1    Pereira, F.2
  • 9
    • 0002297358 scopus 로고
    • Hidden Markov Model induction by Bayesian model merging
    • C. L. Giles, S. J. Hanson, and J. D. Cowan, editors, Morgan Kaufman, San Mateo, Ca
    • Andreas Stolcke and Stephen Omohundro. 1993. Hidden Markov Model induction by Bayesian model merging. In C. L. Giles, S. J. Hanson, and J. D. Cowan, editors, Advances in Neural Information Processing Systems 5. Morgan Kaufman, San Mateo, Ca.
    • (1993) Advances in Neural Information Processing Systems , vol.5
    • Stolcke, A.1    Omohundro, S.2
  • 10
    • 33750032384 scopus 로고    scopus 로고
    • An introduction to conditional random fields for relational learning
    • Lise Getoor and Ben Taskar, editors, MIT Press, To appear
    • Charles Sutton and Andrew McCallum. 2006. An introduction to conditional random fields for relational learning. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. MIT Press. To appear.
    • (2006) Introduction to Statistical Relational Learning
    • Sutton, C.1    McCallum, A.2
  • 11
    • 0013157252 scopus 로고    scopus 로고
    • Representing text chunks
    • Bergen, Association for Computational Linguistics
    • Erik Tjong Kim Sang and Jorn Veenstra. 1999. Representing text chunks. In Proceedings of EACL'99, Bergen. Association for Computational Linguistics.
    • (1999) Proceedings of EACL'99
    • Sang, E.T.K.1    Veenstra, J.2
  • 12
    • 0013066971 scopus 로고    scopus 로고
    • Noun phrase recognition by system combination
    • Tilburg, The Netherlands
    • Erik Tjong Kim Sang. 2000. Noun phrase recognition by system combination. In Proceedings of BNAIC, Tilburg, The Netherlands.
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    • Sang, E.T.K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.