메뉴 건너뛰기




Volumn , Issue , 2009, Pages 664-672

Posterior vs. Parameter sparsity in latent variable models

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY IMPROVEMENT; CLOSED-CLASS WORDS; DIRICHLET PRIOR; DISCRIMINATIVE MODELS; HIDDEN-MARKOV MODELS; LATENT VARIABLE MODELING; NATURAL LANGUAGES; PART-OF-SPEECH INDUCTIONS; REGULARIZATION FRAMEWORK; VARIATIONAL EM;

EID: 80053292525     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (49)

References (18)
  • 1
    • 84943316320 scopus 로고    scopus 로고
    • Floresta sinta(c)tica: A treebank for portuguese
    • S. Afonso, E. Bick, R. Haber, and D. Santos. Floresta Sinta(c)tica: a treebank for Portuguese. In In Proc. LREC, pages 1698-1703, 2002.
    • (2002) Proc. LREC , pp. 1698-1703
    • Afonso, S.1    Bick, E.2    Haber, R.3    Santos, D.4
  • 2
    • 80053165082 scopus 로고    scopus 로고
    • Alternating projections for learning with expectation constraints
    • K. Bellare, G. Druck, and A. McCallum. Alternating projections for learning with expectation constraints. In In Proc. UAI, 2009.
    • (2009) Proc. UAI
    • Bellare, K.1    Druck, G.2    McCallum, A.3
  • 4
    • 80053359625 scopus 로고    scopus 로고
    • A comparison of Bayesian estimators for unsupervised hidden Markov model POS taggers
    • Honolulu, Hawaii, October ACL
    • Jianfeng Gao and Mark Johnson. A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers. In In Proc. EMNLP, pages 344-352, Honolulu, Hawaii, October 2008. ACL.
    • (2008) Proc. EMNLP , pp. 344-352
    • Gao, J.1    Johnson, M.2
  • 5
    • 84859881696 scopus 로고    scopus 로고
    • Em can find pretty good hmm pos-taggers (when given a good start)
    • Y. Goldberg, M. Adler, and M. Elhadad. Em can find pretty good hmm pos-taggers (when given a good start). In Proc. ACL, pages 746-754, 2008.
    • (2008) Proc. ACL , pp. 746-754
    • Goldberg, Y.1    Adler, M.2    Elhadad, M.3
  • 6
    • 84860525845 scopus 로고    scopus 로고
    • A fully Bayesian approach to unsupervised part-of-speech tagging
    • S. Goldwater and T. Griffiths. A fully bayesian approach to unsupervised part-of-speech tagging. In In Proc. ACL, volume 45, page 744, 2007.
    • (2007) Proc. ACL , vol.45 , pp. 744
    • Goldwater, S.1    Griffiths, T.2
  • 7
    • 85162012703 scopus 로고    scopus 로고
    • Expectation maximization and posterior constraints
    • MIT Press
    • J. Graça, K. Ganchev, and B. Taskar. Expectation maximization and posterior constraints. In In Proc. NIPS. MIT Press, 2008.
    • (2008) Proc. NIPS
    • Graça, J.1    Ganchev, K.2    Taskar, B.3
  • 8
    • 70049102734 scopus 로고    scopus 로고
    • Prototype-driven learning for sequence models
    • A. Haghighi and D. Klein. Prototype-driven learning for sequence models. In In Proc. NAACL, pages 320-327, 2006.
    • (2006) Proc. NAACL , pp. 320-327
    • Haghighi, A.1    Klein, D.2
  • 9
    • 80053381171 scopus 로고    scopus 로고
    • Why doesn't EM find good HMM POS-taggers
    • M Johnson. Why doesn't EM find good HMM POS-taggers. In In Proc. EMNLP-CoNLL, 2007.
    • (2007) Proc. EMNLP-conll
    • Johnson, M.1
  • 10
    • 71149098112 scopus 로고    scopus 로고
    • Learning from measurements in exponential families
    • P. Liang, M. I. Jordan, and D. Klein. Learning from measurements in exponential families. In In proc. ICML, 2009.
    • (2009) Proc. ICML
    • Liang, P.1    Jordan, M.I.2    Klein, D.3
  • 11
    • 56449123826 scopus 로고    scopus 로고
    • Simple, robust, scalable semi-supervised learning via expectation regularization
    • G. Mann and A. McCallum. Simple, robust, scalable semi-supervised learning via expectation regularization. In Proc. ICML, 2007.
    • (2007) Proc. ICML
    • Mann, G.1    McCallum, A.2
  • 12
    • 84859912771 scopus 로고    scopus 로고
    • Generalized expectation criteria for semi-supervised learning of conditional random fields
    • G. Mann and A. McCallum. Generalized expectation criteria for semi-supervised learning of conditional random fields. In In Proc. ACL, pages 870 - 878, 2008.
    • (2008) Proc. ACL , pp. 870-878
    • Mann, G.1    McCallum, A.2
  • 13
    • 34249852033 scopus 로고
    • Building a large annotated corpus of English: The penn treebank
    • M.P. Marcus, M.A. Marcinkiewicz, and B. Santorini. Building a large annotated corpus of English: The Penn Treebank. Computational linguistics, 19(2):313-330, 1993.
    • (1993) Computational Linguistics , vol.19 , Issue.2 , pp. 313-330
    • Marcus, M.P.1    Marcinkiewicz, M.A.2    Santorini, B.3
  • 14
    • 84867119745 scopus 로고
    • Tagging English text with a probabilistic model
    • B. Merialdo. Tagging English text with a probabilistic model. Computational linguistics, 20(2):155-171, 1994.
    • (1994) Computational Linguistics , vol.20 , Issue.2 , pp. 155-171
    • Merialdo, B.1
  • 15
    • 84859916726 scopus 로고    scopus 로고
    • Minimized models for unsupervised part-of-speech tagging
    • Sujith Ravi and Kevin Knight. Minimized models for unsupervised part-of-speech tagging. In In Proc. ACL, 2009.
    • (2009) Proc. ACL
    • Ravi, S.1    Knight, K.2
  • 17
    • 84859905260 scopus 로고    scopus 로고
    • Contrastive estimation: Training log-linear models on unlabeled data
    • N.A. Smith and J. Eisner. Contrastive estimation: Training log-linear models on unlabeled data. In In Proc. ACL, pages 354-362, 2005.
    • (2005) Proc. ACL , pp. 354-362
    • Smith, N.A.1    Eisner, J.2
  • 18
    • 79551484033 scopus 로고    scopus 로고
    • A Bayesian LDA-based model for semi-supervised part-of-speech tagging
    • K. Toutanova and M. Johnson. A Bayesian LDA-based model for semi-supervised part-of-speech tagging. In Proc. NIPS, 20, 2007.
    • Proc. NIPS , vol.20 , pp. 2007
    • Toutanova, K.1    Johnson, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.