Domain adaptation for sequence labeling tasks with a probabilistic language adaptation model

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 1, 2013, Pages 293-301

  Xiao, Min ^a   Guo, Yuhong ^a  

^a Temple University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; TEXT PROCESSING;

COMPARISON METHODS; DISTRIBUTED REPRESENTATION; DOMAIN ADAPTATION; LEARNING TECHNIQUES; NAMED ENTITY RECOGNITION; POS TAGGING SYSTEMS; PROBABILISTIC LANGUAGE; REAL-VALUED REPRESENTATIONS;

PROBABILITY DISTRIBUTIONS;

EID: 84897513800     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Ando, R. and Zhang, T. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research (JMLR), 6:1817-1853, 2005.
2. Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems (NIPS), 2007.
3. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. A theory of learning from different domains. Machine Learning, 79:151-175, 2010.
4. Bengio, Y. and Senécal, J. Quick training of probabilistic neural nets by importance sampling. In Proc. of the conference on Artificial Intelligence and Statistics (AISTATS), 2003.
5. Bengio, Y., Ducharme, R., and Vincent, P. A neural probabilistic language model. In Advances in Neural Information Processing Systems (NIPS), 2000.
6. Bengio, Y., Ducharme, R., Vincent, P., and Janvin, C. A neural probabilistic language model. J. of Machine Learn. Research (JMLR), 3:1137-1155, 2003.
7. Blitzer, J., Weinberger, K., Saul, L., and Pereira, F. Hierarchical distributed representations for statistical language modeling. In Advances in Neural Information Processing Systems (NIPS), 2004.
8. Blitzer, J., McDonald, R., and Pereira, F. Domain adaptation with structural correspondence learning. In Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2006.
9. Blitzer, J., Foster, D., and Kakade, S. Domain adaptation with coupled subspaces. In Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2011.
10. Carreras, X. and Màrquez, L. Introduction to the conll-2005 shared task: semantic role labeling. In Proc. of the Conference on Computational Natural Language Learning (CoNLL), 2005.
11. Collobert, R. and Weston, J. A unified architecture for natural language processing: deep neural networks with multitask learning. In Proc. of the International Conf. on Machine Learning (ICML), 2008.
12. Daumé III, H. Frustratingly easy domain adaptation. In Proc. of the Annual Meeting of the Association of Computational Linguistics (ACL), 2007.
13. Daumé III, H. and Marcu, D. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26:101-126, 2006. (Pubitemid 44452208)
14. Daumé III, H., Kumar, A., and Saha, A. Co-regularization based semi-supervised domain adaptation. In Advances in Neural Information Processing Systems (NIPS), 2010.
15. Gillick, L. and Cox, S. Some statistical issues in the comparison of speech recognition algorithms. In Proc. of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1989.
16. Gutmann, M. and Hyvarinen, A. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
17. Gutmann, M. U. and Hyvärinen, A. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. of Machine Learning Research (JMLR), 13:307-361, 2012.
18. Huang, F. and Yates, A. Distributional representations for handling sparsity in supervised sequence labeling. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), 2009.
19. Huang, F. and Yates, A. Exploring representation-learning approaches to domain adaptation. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), 2010.
20. Jiang, J. and Zhai, C. Instance weighting for domain adaptation in nlp. In Proc. of the Annual Meeting of the Association of Computational Linguistics (ACL), 2007.
21. Maas, A. and Ng, A. A probabilistic model for semantic word vectors. In Advances in Neural Information Processing Systems Wordshop on Deep Learning and Unsupervised Feature Learning, 2010.
22. McClosky, D., Charniak, E., and Johnson, M. Automatic domain adaptation for parsing. In Proc. of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), 2010.
23. Mnih, A. and Hinton, G. Three new graphical models for statistical language modelling. In Proc. of the International Conference on Machine learning (ICML), 2007.
24. Mnih, A. and Hinton, G. A scalable hierarchical distributed language model. In Advances in Neural Information Processing Systems (NIPS), 2009.
25. Mnih, A. and Teh, Y. A fast and simple algorithm for training neural probabilistic language models. In Proc. of the International Conference on Machine Learning (ICML), 2012.
26. Socher, R., Lin, C., Ng, A., and Manning, C. Parsing natural scenes and natural language with recursive neural networks. In Proc. of the International Conference on Machine Learning (ICML), 2011.
27. Turian, J., Ratinov, L., and Bengio, Y. Word representations: a simple and general method for semi-supervised learning. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), 2010.