Efficient computation of entropy gradient for semi-supervised conditional random fields

NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers

Volumn , Issue , 2007, Pages 109-112

  Mann, Gideon S ^a   McCallum, Andrew ^a  

^a University of Massachusetts Amherst   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RANDOM PROCESSES; SUPERVISED LEARNING;

ASYMPTOTIC TIME COMPLEXITY; CONDITIONAL LIKELIHOOD; DYNAMIC PROGRAMS; EFFICIENT COMPUTATION; ENTROPY GRADIENTS; LINEAR CHAIN; OBJECTIVE FUNCTIONS; REGULARISATION; SEMI-SUPERVISED; UNLABELED DATA;

ENTROPY;

EID: 85119688696     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Y. Grandvalet and Y. Bengio. 2004. Semi-supervised learning by entropy minimization. In NIPS.
2. D. Hernando, V. Crespi, and G. Cybenko. 2005. Efficient computation of the hidden markov model entropy for a given observation sequence. IEEE Trans. on Information Theory, 51:7:2681-2685.
3. F. Jiao, S. Wang, C.-H. Lee, R. Greiner, and D. Schuurmans. 2006. Semi-supervised conditional random fields for improved sequence segmentation and labeling. In COLING/ACL.
4. S. Kim, Y. Song, K. Kim, J.-W. Cha, and G. G. Lee. 2006. Mmr-based active machine learning for bio named entity recognition. In HLT/NAACL.
5. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML, pages 282-289.
6. X. Zhu. 2005. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.