Generalized expectation criteria for semi-supervised learning of conditional random fields

ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Volumn , Issue , 2008, Pages 870-878

  Mann, Gideon S ^a   Mccallum, Andrew ^b  

^a GOOGLE INC   (United States)

^b Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL PROBABILITY DISTRIBUTIONS; CONDITIONAL RANDOM FIELD; GIVEN FEATURES; PARAMETER SETTING; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED METHOD; TRAINING METHODS; UNLABELED DATA;

COMPUTATIONAL LINGUISTICS; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES;

CONTENT BASED RETRIEVAL;

EID: 84859912771     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. R. K. Ando and T. Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. JMLR, 6.
2. M.-W. Chang, L. Ratinov, and D. Roth. 2007. Guiding semi-supervision with constraint-driven learning. In ACL.
3. G. Druck, G. Mann, and A. McCallum. 2007. Leveraging existing resources using generalized expectation criteria. In NIPS Workshop on Learning Problem Design.
4. G. Druck, G. S. Mann, and A. McCallum. 2008. Learning from labeled features using generalized expectation criteria. In SIGIR.
5. D. Freitag. 2004. Trained named entity recognition using distributional clusters. In EMNLP.
6. Y. Grandvalet and Y. Bengio. 2004. Semi-supervised learning by entropy minimization. In NIPS.
7. T. Grenager, D. Klein, and C. Manning. 2005. Unsupervised learning of field segmentation models for information extraction. In ACL.
8. A. Haghighi and D. Klein. 2006. Prototype-driver learning for sequence models. In NAACL.
9. 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.
10. Thorsten Joachims. 1999. Transductive inference for text classification using support vector machines. In ICML.
11. S. Kakade, Y-W. Teg, and S.Roweis. 2002. An alternate objective function for markovian fields. In ICML.
12. G. Mann and A. McCallum. 2007. Simple, robust, scalable semi-supervised learning via expectation regularization. In ICML.
13. A. McCallum, G. S. Mann, and G. Druck. 2007. Generalized expectation criteria. Computer science technical note, University of Massachusetts, Amherst, MA.
14. S. Miller, J. Guinness, and A. Zamanian. 2004. Name tagging with word clusters and discriminative training. In ACL.
15. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. 1998. Learning to classify text from labeled and unlabeled documents. In AAAI.
16. A. Quattoni, S. Wang, L-P. Morency, M. Collins, and T. Darrell. 2007. Hidden-state conditional random fields. In PAMI.
17. H. Raghavan, O. Madani, and R. Jones. 2006. Active learning with feedback on both features and instances. JMLR.
18. R. Salakhutdinov, S. Roweis, and Z. Ghahramani. 2003. Optimization with em and expectation-conjugategradient. In ICML.
19. R. Schapire, M. Rochery, M. Rahim, and N. Gupta. 2002. Incorporating prior knowledge into boosting. In ICML.
20. N. Smith and J. Eisner. 2005. Contrastive estimation: Training log-linear models on unlabeled data. In ACL.
21. Martin Szummer and Tommi Jaakkola. 2002. Partially labeled classification with markov random walks. In NIPS, volume 14.
22. X. Zhu and Z. Ghahramani. 2002. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, CMU.
23. X. Zhu. 2005. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison. http://www.cs.wisc.edu/-jerryzhu/pub/sslsurvey.pdf.