Combining labeled and unlabeled data with word-class distribution learning

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2009, Pages 1737-1740

  Qi, Yanjun ^a   Collobert, Ronan ^a   Kuksa, Pavel ^b   Kavukcuoglu, Koray ^c   Weston, Jason ^a  

^a NEC LABORATORIES AMERICA   (United States)

^b RUTGERS UNIVERSITY   (United States)

^c POLYTECHNIC UNIVERSITY   (United States)

Author keywords

Information extraction; Name entity recognition; Semi supervised feature learning; Semi supervised learning

Indexed keywords

FEATURE LEARNING; INFORMATION EXTRACTION; NAME ENTITY RECOGNITION; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING;

CLASSIFICATION (OF INFORMATION); KNOWLEDGE MANAGEMENT; LABELS; SUPERVISED LEARNING;

FEATURE EXTRACTION;

EID: 74549143165     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646218     Document Type: Conference Paper

References (22)

1. R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853, 2005.
2. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory, pages 92-100, New York, NY, USA, 1998. ACM.
3. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning (Adaptive Computation and Machine Learning). MIT Press, 2006.
4. M. Collins and Y. Singer. Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on EMNLP, pages 100-110, 1999.
5. R. Collobert and J. Weston. A unified architecture for nlp: deep neural networks with multitask learning. In ICML '08: Proceedings of the 25th international conference on Machine learning, pages 160-167, 2008.
6. H. Daumé III. Cross-task knowledge-constrained self training. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 680-688, Honolulu, Hawaii, October 2008. ACL.
7. G. Druck, G. Mann, and A. McCallum. Learning from labeled features using generalized expectation criteria. In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 595-602. ACM, 2008.
8. Erik and F. De Meulder. Introduction to the conll-2003 shared task: language-independent named entity recognition. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003, pages 142-147, 2003.
9. R. Florian, A. Ittycheriah, H. Jing, and T. Zhang. Named entity recognition through classifier combination. In W. Daelemans and M. Osborne, editors, Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003, pages 168-171, 2003.
10. Y. Grandvalet and Y. Bengio. Semi-supervised learning by entropy minimization. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 529-536, Cambridge, MA, 2005. MIT Press.
11. T. Joachims. Transductive inference for text classification using support vector machines. In ICML '99: Proceedings of the Sixteenth International Conference on Machine Learning, pages 200-209, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.
12. R. J. Kate and R. J. Mooney. Semi-supervised learning for semantic parsing using support vector machines. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), pages 81-84, 2007.
13. Z. Kozareva, B. Bonev, and A. Montoyo. Self-training and co-training applied to spanish named entity recognition. In MICAI 2005: Advances in Artificial Intelligence, 2005.
14. D. McClosky, E. Charniak, and M. Johnson. Effective self-training for parsing. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pages 152-159, 2006.
15. D. Nadeau and S. Sekine. A survey of named entity recognition and classfication. Linguisticae Investigationes, 30(1):3-26, 2007.
16. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using em. volume 39, pages 103-134, Hingham, MA, USA, 2000. Kluwer Academic Publishers.
17. R. E. Schapire, M. Rochery, M. G. Rahim, and N. Gupta. Incorporating prior knowledge into boosting. In ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 538-545, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc.
18. H. Scudder. Probability of error of some adaptive pattern-recognition machines. IEEE Transactions on Information Theory, 11(3):363-371, 1965.
19. H. Shan and D. Gildea. Self-training and co-training for semantic role labeling: Primary report. Technical Report TR891, University of Rochester, Comp. Sci. Dept., 2006.
20. X. Wu and R. Srihari. Incorporating prior knowledge with weighted margin support vector machines. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 326-333, New York, NY, USA, 2004. ACM.
21. T. Zhang, F. Damerau, and D. Johnson. Text chunking using regularized winnow. In ACL '01: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, pages 539-546, Morristown, NJ, USA, 2001. Association for Computational Linguistics.
22. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML'03: Proceedings of the 20th International Conference on Machine Learning, pages 912-919, 2003.