Composition of conditional random fields for transfer learning

HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Volumn , Issue , 2005, Pages 748-754

  Sutton, Charles ^a   McCallum, Andrew ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

DECODING;

CONDITIONAL RANDOM FIELD; JOINT DECODING; LEARNING TASKS; SEQUENCE MODELS; SUBTASKS; TEXT DATA; TRAINING DATA; TRANSFER LEARNING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053277243     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1220575.1220669     Document Type: Conference Paper

References (21)

1. Byrd, R. H., Nocedal, J., & Schnabel, R. B. (1994). Representations of quasi-Newton matrices and their use in limited memory methods. Math. Program., 63, 129-156.
2. Califf, M. E., & Mooney, R. J. (1999). Relational learning of pattern-match rules for information extraction. Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99) (pp. 328-334).
3. Carreras, X. & Marquez, L. (2004). Introduction to the CoNLL-2004 shared task: Semantic role labeling. Proceedings of CoNLL-2004.
4. Carreras, X., & Màrquez, L. (2004). Online learning via global feedback for phrase recognition. In S. Thrun, L. Saul and B. Schölkopf (Eds.), Advances in neural information processing systems 16. Cambridge, MA: MIT Press.
5. Caruana, R. (1997). Multitask learning. Machine Learning, 28, 41-75.
6. Ciravegna, F. (2001). Adaptive information extraction from text by rule induction and generalisation. Proceedings of 17th International Joint Conference on Artificial Intelligence (IJCAI 2001).
7. Cohen, W. W., & Carvalho, V. R. (2005). Stacked sequential learning. International Joint Conference on Artificial Intelligence (pp. 671-676).
8. Dean, T., & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142-150.
9. Florian, R., Hassan, H., Ittycheriah, A., Jing, H., Kambhatla, N., Luo, X., Nicolov, N., Roukos, S., & Zhang, T. (2004). A statistical model for multilingual entity detection and tracking. In HLT/NAACL 2004.
10. Freitag, D. (1998). Machine learning for information extraction in informal domains. Doctoral dissertation, Carnegie Mellon University.
11. Frietag, D., & McCallum, A. (1999). Information extraction with HMMs and shrinkage. AAAI Workshop on Machine Learning for Information Extraction.
12. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proc. 18th International Conf. on Machine Learning.
13. Miller, S., Fox, H., Ramshaw, L. A., & Weischedel, R. M. (2000). A novel use of statistical parsing to extract information from text. ANLP 2000 (pp. 226-233).
14. Mohri, M., Pereira, F., & Riley, M. (2002). Weighted finitestate transducers in speech recognition. Computer Speech and Language, 16, 69-88.
15. Pal, C., Sutton, C., & McCallum, A. (2005). Fast inference and learning with sparse belief propagation (Technical Report IR-433). Center for Intelligent Information Retrieval, University of Massachusetts.
16. Peshkin, L., & Pfeffer, A. (2003). Bayesian information extraction network. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
17. Roth, D., & Wen-tau Yih (2001). Relational learning via propositional algorithms: An information extraction case study. International Joint Conference on Artificial Intelligence (pp. 1257-1263).
18. Sha, F., & Pereira, F. (2003). Shallow parsing with conditional random fields. Proceedings of HLT-NAACL 2003.
19. Soderland, S. (1999). Learning information extraction rules for semi-structured and free text. Machine Learning, 233-272.
20. Sutton, C., Rohanimanesh, K., & McCallum, A. (2004). Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. Proceedings of the Twenty-First International Conference on Machine Learning (ICML).
21. Tjong Kim Sang, E. F., & De Meulder, F. (2003). Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. Proceedings of CoNLL-2003 (pp. 142-147). Edmonton, Canada.