An analysis of active learning strategies for sequence labeling tasks

EMNLP 2008 - 2008 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference: A Meeting of SIGDAT, a Special Interest Group of the ACL

Volumn , Issue , 2008, Pages 1070-1079

  Settles, Burr ^a,b   Craven, Mark ^a,b  

^a University of Wisconsin Madison   (United States)

^b UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

ACTIVE LEARNING STRATEGIES; DOCUMENT SEGMENTATION; EMPIRICAL - COMPARISONS; MULTIPLE CORPORA; NATURAL LANGUAGE PROCESSING; SEQUENCE LABELING; SEQUENCE MODELS; STATE OF THE ART;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053375448     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1613715.1613855     Document Type: Conference Paper

References (30)

1. N. Abe and H. Mamitsuka. 1998. Query learning strategies using boosting and bagging. In Proceedings of the International Conference on Machine Learning (ICML), pages 1-9. Morgan Kaufmann.
2. J. Baldridge and M. Osborne. 2004. Active learning and the total cost of annotation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9-16. ACL Press.
3. V.R. Carvalho and W. Cohen. 2004. Learning to extract signature and reply lines from email. In Proceedings of the Conference on Email and Anti-Spam (CEAS).
4. D. Cohn, L. Atlas, and R. Ladner. 1994. Improving generalization with active learning. Machine Learning, 15(2):201-221.
5. A. Culotta and A. McCallum. 2005. Reducing labeling effort for stuctured prediction tasks. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 746-751. AAAI Press.
6. I. Dagan and S. Engelson. 1995. Committee-based sampling for training probabilistic classifiers. In Proceedings of the International Conference on Machine Learning (ICML), pages 150-157. Morgan Kaufmann.
7. R. Hwa. 2004. Sample selection for statistical parsing. Computational Linguistics, 30(3):73-77.
8. J. Kim, T. Ohta, Y. Tsuruoka, Y. Tateisi, and N. Collier. 2004. Introduction to the bio-entity recognition task at JNLPBA. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA), pages 70-75.
9. 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 Proceedings of Human Language Technology and the North American Association for Computational Linguistics (HLT-NAACL), pages 69-72. ACL Press.
10. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning (ICML), pages 282-289. Morgan Kaufmann.
11. K. Lari and S. J. Young. 1990. The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language, 4:35-56.
12. D. Lewis and J. Catlett. 1994. Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 148-156. Morgan Kaufmann.
13. G. Mann and A. McCallum. 2007. Efficient computation of entropy gradient for semi-supervised conditional random fields. In Proceedings of the North American Association for Computational Linguistics (NAACL), pages 109-112. ACL Press.
14. A. McCallum and K. Nigam. 1998. Employing EM in pool-based active learning for text classification. In Proceedings of the International Conference on Machine Learning (ICML), pages 359-367. Morgan Kaufmann.
15. M. Nyffenegger, J.C. Chappelier, and E. Gaussier. 2006. Revisiting Fisher kernels for document similarities. In Proceedings of the European Conference on Machine Learning (ECML), pages 727-734. Springer.
16. F. Peng and A. McCallum. 2004. Accurate information extraction from research papers using conditional random fields. In Proceedings of Human Language Technology and the North American Association for Computational Linguistics (HLT-NAACL). ACL Press.
17. L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286.
18. L.A. Ramshaw and M.P. Marcus. 1995. Text chunking using transformation-based learning. In Proceedings of the ACL Workshop on Very Large Corpora.
19. N. Roy and A. McCallum. 2001. Toward optimal active learning through sampling estimation of error reduction. In Proceedings of the International Conference on Machine Learning (ICML), pages 441-448. Morgan Kaufmann.
20. E.F.T.K. Sang and F. DeMeulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Conference on Natural Language Learning (CoNLL), pages 142-147.
21. T. Scheffer, C. Decomain, and S. Wrobel. 2001. Active hidden Markov models for information extraction. In Proceedings of the International Conference on Advances in Intelligent Data Analysis (CAIDA), pages 309-318. Springer-Verlag.
22. R. Schwartz and Y.-L. Chow. 1990. The N-best algorithm: an fficient and exact procedure for finding the N most likely sentence hypotheses. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 81-83. IEEE Press.
23. B. Settles, M. Craven, and S. Ray. 2008. Multiple-instance active learning. In Advances in Neural Information Processing Systems (NIPS), volume 20, pages 1289-1296. MIT Press.
24. H.S. Seung, M. Opper, and H. Sompolinsky. 1992. Query by committee. In Proceedings of the ACM Workshop on Computational Learning Theory, pages 287-294.
25. C. E. Shannon. 1948. A mathematical theory of communication. Bell System Technical Journal, 27:379-423,623-656.
26. C. Sutton and A. McCallum. 2006. An introduction to conditional random fields for relational learning. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning. MIT Press.
27. A. Vlachos. 2007. Evaluating and combining biomedical named entity recognition systems. In BioNLP 2007: Biological, translational, and clinical language processing, pages 199-206.
28. A. Yeh, A. Morgan, M. Colosimo, and L. Hirschman. 2005. Biocreative task 1a: gene mention finding evaluation. BMC Bioinformatics, 6(Suppl 1):S2.
29. T. Zhang and F.J. Oles. 2000. A probability analysis on the value of unlabeled data for classification problems. In Proceedings of the International Conference on Machine Learning (ICML), pages 1191-1198. Morgan Kaufmann.
30. X. Zhu, J. Lafferty, and Z. Ghahramani. 2003. Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the ICML Workshop on the Continuum from Labeled to Unlabeled Data, pages 58-65.