Active learning by labeling features

EMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009

Volumn , Issue , 2009, Pages 81-90

  Druck, Gregory ^a   Settles, Burr ^b   McCallum, Andrew ^a  

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

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; NATURAL LANGUAGE PROCESSING SYSTEMS;

ACTIVE LEARNING; ACTIVE LEARNING METHODS; INPUT FEATURES; MULTIPLE FEATURES; PASSIVE LEARNING; PRIOR INFORMATION; SEQUENCE LABELING; USER EXPERIMENTS;

LEARNING SYSTEMS;

EID: 77949519575     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1699510.1699522     Document Type: Conference Paper

References (19)

1. Kedar Bellare, Gregory Druck, and Andrew McCallum. 2009. Alternating projections for learning with expectation constraints. In UAI.
2. David M. Blei, Andrew Y. Ng, Michael I. Jordan, and John Lafferty. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, 3:2003.
3. Ming-Wei Chang, Lev Ratinov, and Dan Roth. 2007. Guiding semi-supervision with constraint-driven learning. In ACL, pages 280-287.
4. Gregory Druck, Gideon Mann, and Andrew McCallum. 2008. Learning from labeled features using generalized expectation criteria. In SIGIR.
5. Joao Graça, Kuzman Ganchev, and Ben Taskar. 2008. Expectation maximization and posterior constraints. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press.
6. Trond Grenager, Dan Klein, and Christopher D. Manning. 2005. Unsupervised learning of field segmentation models for information extraction. In ACL.
7. Aria Haghighi and Dan Klein. 2006. Prototype-driven learning for sequence models. In HTL-NAACL.
8. Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, and Dale Schuurmans. 2006. Semisupervised conditional random fields for improved sequence segmentation and labeling. In ACL, pages 209-216.
9. John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML.
10. David D. Lewis andWilliam A. Gale. 1994. A sequential algorithm for training text classifiers. In SIGIR, pages 3-12, New York, NY, USA. Springer-Verlag New York, Inc.
11. Percy Liang, Michael I. Jordan, and Dan Klein. 2009. Learning from measurements in exponential families. In ICML.
12. Gideon Mann and Andrew McCallum. 2008. Generalized expectation criteria for semi-supervised learning of conditional random fields. In ACL.
13. A. Quattoni, S. Wang, L.-P Morency, M. Collins, and T. Darrell. 2007. Hidden conditional random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29:1848-1852, October.
14. Hema Raghavan and James Allan. 2007. An interactive algorithm for asking and incorporating feature feedback into support vector machines. In SIGIR, pages 79-86.
15. Ruslan Salakhutdinov, Sam Roweis, and Zoubin Ghahramani. 2003. Optimization with em and expectation-conjugate-gradient. In ICML, pages 672-679.
16. Burr Settles and Mark Craven. 2008. An analysis of active learning strategies for sequence labeling tasks. In EMNLP.
17. Burr Settles. 2009. Active learning literature survey. Technical Report 1648, University of Wisconsin-Madison.
18. Vikas Sindhwani, Prem Melville, and Richard D. Lawrence. 2009. Uncertainty sampling and transductive experimental design for active dual supervision. In ICML.
19. Sudheendra Vijayanarasimhan and Kristen Grauman. 2008. Multi-level active prediction of useful image annotations for recognition. In NIPS.