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Volumn , Issue , 2010, Pages 729-732

Constraint-driven rank-based learning for information extraction

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL BOTTLENECKS; DATA SETS; GRAPHICAL MODEL; INFORMATION EXTRACTION; LEARNING FRAMEWORKS; PRIOR KNOWLEDGE; SEMI-SUPERVISED LEARNING; STANDARD INFORMATION; UNLABELED DATA;

EID: 84863337853     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (10)
  • 1
    • 80053165082 scopus 로고    scopus 로고
    • Alternating projections for learning with expectation constraints
    • Kedar Bellare, Gregory Druck, and Andrew McCallum. Alternating projections for learning with expectation constraints. In UAI, 2009.
    • (2009) UAI
    • Bellare, K.1    Druck, G.2    McCallum, A.3
  • 2
    • 57749120009 scopus 로고    scopus 로고
    • Guiding semi-supervision with constraint-driven learning
    • Mingwei Chang, Lev Ratinov, and Dan Roth. Guiding semi-supervision with constraint-driven learning. In ACL, 2007.
    • (2007) ACL
    • Chang, M.1    Ratinov, L.2    Roth, D.3
  • 3
    • 1942515563 scopus 로고    scopus 로고
    • Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithm
    • Michael Collins. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithm. In ACL, 2002.
    • (2002) ACL
    • Collins, M.1
  • 4
    • 84858393901 scopus 로고    scopus 로고
    • First-order probabilistic models for coreference resolution
    • Aron Culotta, Michael Wick, and Andrew McCallum. First-order probabilistic models for coreference resolution. In NAACL/HLT, 2007.
    • (2007) NAACL/HLT
    • Culotta, A.1    Wick, M.2    McCallum, A.3
  • 5
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
    • (2001) ICML
    • Lafferty, J.1    McCallum, A.2    Pereira, F.3
  • 6
    • 78349286102 scopus 로고    scopus 로고
    • On the use of virtual evidence in conditional random fields
    • Xiao Li. On the use of virtual evidence in conditional random fields. In EMNLP, 2009.
    • (2009) EMNLP
    • Li, X.1
  • 7
    • 84859912771 scopus 로고    scopus 로고
    • Generalized expectation criteria for semi-supervised learning of conditional random fields
    • Gideon S. Mann and Andrew McCallum. Generalized expectation criteria for semi-supervised learning of conditional random fields. In ACL, 2008.
    • (2008) ACL
    • Mann, G.S.1    McCallum, A.2
  • 8
    • 84863338363 scopus 로고    scopus 로고
    • FACTORIE: Probabilistic programming via imperatively defined factor graphs
    • Andrew McCallum, Karl Schultz, and Sameer Singh. FACTORIE: probabilistic programming via imperatively defined factor graphs. In NIPS, 2009.
    • (2009) NIPS
    • McCallum, A.1    Schultz, K.2    Singh, S.3
  • 9
    • 84937881994 scopus 로고    scopus 로고
    • Bi-directional joint inference for entity resolution and segmentation using imperatively-defined factor graphs
    • Sameer Singh, Karl Schultz, and Andrew McCallum. Bi-directional joint inference for entity resolution and segmentation using imperatively-defined factor graphs. In ECML/PKDD, 2009.
    • (2009) ECML/PKDD
    • Singh, S.1    Schultz, K.2    McCallum, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.