Semi-Supervised Active Learning for Sequence Labeling

ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.

Volumn , Issue , 2009, Pages 1039-1047

  Tomanek, Katrin ^a   Hahn, Udo ^a  

^a FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS;

ACTIVE LEARNING; ACTIVE LEARNING SCHEME; ENTITY RECOGNITION; INTERNAL STRUCTURE; LEARNING APPROACH; RANDOM SELECTION; SEMI-SUPERVISED ACTIVE LEARNING; SEQUENCE LABELING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84964259652     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. A. Blum and T. Mitchell. 1998. Combining labeled and unlabeled data with co-training. In COLT’98 – Proceedings of the 11th Annual Conference on Computational Learning Theory, pages 92–100.
2. A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1):1–38.
3. S. Engelson and I. Dagan. 1996. Minimizing manual annotation cost in supervised training from corpora. In ACL’96 – Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pages 319–326.
4. T. Kristjansson, A. Culotta, and P. Viola. 2004. Interactive information extraction with constrained Conditional Random Fields. In AAAI’04 – Proceedings of 19th National Conference on Artificial Intelligence, pages 412–418.
5. S. Kulick, A. Bies, M. Liberman, M. Mandel, R. T. McDonald, M. S. Palmer, and A. I. Schein. 2004. Integrated annotation for biomedical information extraction. In Proceedings of the HLT-NAACL 2004 Workshop ‘Linking Biological Literature, Ontologies and Databases: Tools for Users’, pages 61–68.
6. J. D. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. In ICML’01 – Proceedings of the 18th International Conference on Machine Learning, pages 282–289.
7. D. D. Lewis and J. Catlett. 1994. Heterogeneous uncertainty sampling for supervised learning. In ICML’94 – Proceedings of the 11th International Conference on Machine Learning, pages 148–156.
8. Linguistic Data Consortium. 2001. Message Understanding Conference (MUC) 7. LDC2001T02. FTP FILE. Philadelphia: Linguistic Data Consortium.
9. A. McCallum and K. Nigam. 1998. Employing EM and pool-based Active Learning for text classification. In ICML’98 – Proceedings of the 15th International Conference on Machine Learning, pages 350–358.
10. I. A. Muslea, S. Minton, and C. A. Knoblock. 2002. Active semi-supervised learning = Robust multi-view learning. In ICML’02 – Proceedings of the 19th International Conference on Machine Learning, pages 435–442.
11. G. Ngai and D. Yarowsky. 2000. Rule writing or annotation: Cost-efficient resource usage for base noun phrase chunking. In ACL’00 – Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pages 117–125.
12. D. Pierce and C. Cardie. 2001. Limitations of co-training for natural language learning from large datasets. In EMNLP’01 – Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, pages 1–9.
13. L. R. Rabiner. 1989. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286.
14. B. Settles and M. Craven. 2008. An analysis of Active Learning strategies for sequence labeling tasks. In EMNLP’08 – Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 1069–1078.
15. B. Settles, M. Craven, and L. Friedland. 2008. Active Learning with real annotation costs. In Proceedings of the NIPS 2008 Workshop on ‘Cost-Sensitive Machine Learning’, pages 1–10.
16. H. S. Seung, M. Opper, and H. Sompolinsky. 1992. Query by committee. In COLT’92 – Proceedings of the 5th Annual Workshop on Computational Learning Theory, pages 287–294.
17. K. Tomanek, J. Wermter, and U. Hahn. 2007. An approach to text corpus construction which cuts annotation costs and maintains corpus reusability of annotated data. In EMNLP-CoNLL’07 – Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Language Learning, pages 486–495.
18. D. Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In ACL’95 – Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189–196.