Semi-supervised sequential labeling and segmentation using giga-word scale unlabeled data

ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Volumn , Issue , 2008, Pages 665-673

  Suzuki, Jun ^a   Isozaki, Hideki ^a  

^a Nippon Telegraph and Telephone Corporation   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRIMINATIVE MODELS; NAMED ENTITY RECOGNITION; NATURAL LANGUAGE PROCESSING; PART OF SPEECH TAGGING; PERFORMANCE IMPROVEMENTS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; TEST COLLECTION; UNLABELED DATA;

COMPUTATIONAL LINGUISTICS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84859884966     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. R. Ando and T. Zhang. 2005. A High-Performance Semi-Supervised Learning Method for Text Chunking. In Proc. of ACL-2005, pages 1-9.
2. R. Ando and T. Zhang. 2007. Two-view Feature Generation Model for Semi-supervised Learning. In Proc. of ICML-2007, pages 25-32.
3. A. Blum and T. Mitchell. 1998. Combining Labeled and Unlabeled Data with Co-Training. In Conference on Computational Learning Theory 11.
4. 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, Series B, 39:1-38.
5. R. Florian, A. Ittycheriah, H. Jing, and T. Zhang. 2003. Named Entity Recognition through Classifier Combination. In Proc. of CoNLL-2003, pages 168-171.
6. T. Grenager, D. Klein, and C. Manning. 2005. Unsupervised Learning of Field Segmentation Models for Information Extraction. In Proc. of ACL-2005, pages 371-378.
7. T. Kudo and Y. Matsumoto. 2001. Chunking with Support Vector Machines. In Proc. of NAACL 2001, pages 192-199.
8. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. of ICML-2001, pages 282-289.
9. W. Li and A. McCallum. 2005. Semi-Supervised Sequence Modeling with Syntactic Topic Models. In Proc. of AAAI-2005, pages 813-818.
10. M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz. 1994. Building a Large Annotated Corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313-330.
11. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. 2000. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39:103- 134.
12. F. Sha and F. Pereira. 2003. Shallow Parsing with Conditional Random Fields. In Proc. of HLT/NAACL-2003, pages 213-220.
13. L. Shen, G. Satta, and A. Joshi. 2007. Guided Learning for Bidirectional Sequence Classification. In Proc. of ACL-2007, pages 760-767.
14. C. Sutton, M. Sindelar, and A. McCallum. 2006. ReducingWeight Undertraining in Structured Discriminative Learning. In Proc. of HTL-NAACL 2006, pages 89-95.
15. J Suzuki, A Fujino, and H Isozaki. 2007. Semi- Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach. In Proc. of EMNLP-CoNLL, pages 791-800.
16. E. F. Tjong Kim Sang and S. Buchholz. 2000. Introduction to the CoNLL-2000 Shared Task: Chunking. In Proc. of CoNLL-2000 and LLL-2000, pages 127-132.
17. E. T. Tjong Kim Sang and F. De Meulder. 2003. Introduction to the CoNLL-2003 Shared Task: Language- Independent Named Entity Recognition. In Proc. of CoNLL-2003, pages 142-147.
18. K. Toutanova, D. Klein, C.D. Manning, and Y. Yoram Singer. 2003. Feature-rich Part-ofspeech Tagging with a Cyclic Dependency Network. In Proc. of HLT-NAACL-2003, pages 252-259.
19. T. Zhang, F. Damerau, and D. Johnson. 2002. Text Chunking based on a Generalization of Winnow. Machine Learning Research, 2:615-637.