A fast boosting-based learner for feature-rich tagging and chunking

CoNLL 2008 - Proceedings of the Twelfth Conference on Computational Natural Language Learning

Volumn , Issue , 2008, Pages 17-24

  Iwakura, Tomoya ^a   Okamoto, Seishi ^a  

^a FUJITSU LABORATORIES LTD   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; SUPPORT VECTOR MACHINES;

ENGLISH POS TAGGING; LEARNING RULES; PART OF SPEECH TAGGING; POLYNOMIAL KERNELS; STATE OF THE ART; TEXT CHUNKING; TRAINING DATA; TRAINING TIME;

LEARNING ALGORITHMS;

EID: 78650474685     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1596324.1596328     Document Type: Conference Paper

References (23)

1. Ando, Rie and Tong Zhang. 2005. A high-performance semisupervised learning method for text chunking. In Proc. of 43rd Meeting of Association for Computational Linguistics, pages 1-9.
2. Collins, Michael and Terry Koo. 2005. Discriminative reranking for natural language parsing. Computational Linguistics, 31(1):25-70.
3. Collins, Michael. 2002. Discriminative training methods for Hidden Markov Models: theory and experiments with perceptron algorithms. In Proc. of the 2002 Conference on Empirical Methods in Natural Language Processing, pages 1-8.
4. Daumé III, Hal and Daniel Marcu. 2005. Learning as search optimization: Approximate large margin methods for structured prediction. In Proc. of 22th International Conference on Machine Learning, pages 169-176.
5. Escudero, Gerard, Lluís Màrquez, and German Rigau. 2000. Boosting applied to word sense disambiguation. In Proc. of 11th European Conference on Machine Learning, pages 129-141.
6. Freund, Yoav and Llew Mason. 1999. The alternating decision tree learning algorithm,. In Proc. of 16th International Conference on Machine Learning, pages 124-133.
7. Giménez, Jesús and Lluís Màrquez. 2003. Fast and accurate part-of-speech tagging: The SVM approach revisited. In Proc. of International Conference Recent Advances in Natural Language Processing 2003, pages 153-163.
8. Iwakura, Tomoya and Seishi Okamoto. 2007. Fast training methods of boosting algorithms for text analysis. In Proc. of International Conference Recent Advances in Natural Language Processing 2007, pages 274-279.
9. Kazama, Jun'ichi and Kentaro Torisawa. 2007. A new perceptron algorithm for sequence labeling with non-local features. In Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 315-324.
10. Kudo, Taku and Yuji Matsumoto. 2001. Chunking with Support Vector Machines. In Proc. of The Conference of the North American Chapter of the Association for Computational Linguistics, pages 192-199.
11. Kudo, Taku and Yuji Matsumoto. 2003. Fast methods for kernel-based text analysis. In Proc. of 41st Meeting of Association for Computational Linguistics, pages 24-31.
12. Kudo, Taku and Yuji Matsumoto. 2004. A boosting algorithm for classification of semi-structured text. In Proc. of the 2004 Conference on Empirical Methods in Natural Language Processing 2004, pages 301-308, July.
13. Kudo, Taku, Jun Suzuki, and Hideki Isozaki. 2005. Boosting-based parse reranking with subtree features. In Proc. of 43rd Meeting of Association for Computational Linguistics, pages 189-196.
14. Marcus, Mitchell P., Beatrice Santorini, and Mary Ann Marcinkiewicz. 1994. Building a large annotated corpus of english: The Penn Treebank. pages 313-330.
15. Morishita, Shinichi. 2002. Computing optimal hypotheses efficiently for boosting. Proc. of 5th International Conference Discovery Science, pages 471-481.
16. Schapire, Robert E. and Yoram Singer. 1999. Improved boosting using confidence-rated predictions. Machine Learning, 37(3):297-336.
17. Schapire, Robert E. and Yoram Singer. 2000. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135-168.
18. Sebastiani, Fabrizio, Alessandro Sperduti, and Nicola Valdambrini. 2000. An improved boosting algorithm and its application to text categorization. In Proc. of International Conference on Information and Knowledge Management, pages 78-85.
19. Shen, Libin, Giorgio Satta, and Aravind Joshi. 2007. Guided learning for bidirectional sequence classification. In Proc. of 45th Meeting of Association for Computational Linguistics, pages 760-767.
20. Suzuki, Jun, Akinori Fujino, and Hideki Isozaki. 2007. Semisupervised structured output learning based on a hybrid generative and discriminative approach. In Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 791-800.
21. Toutanova, Kristina, Dan Klein, Christopher D. Manning, and Yoram Singer. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proc. of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 173-180.
22. Tsuruoka, Yoshimasa and Junichi Tsujii. 2005. Bidirectional inference with the easiest-first strategy for tagging sequence data. In Proc. of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pages 467-474.
23. Zhang, Tong, Fred Damerau, and David Johnson. 2001. Text chunking using regularized winnow. In Proc. of 39th Meeting of Association for Computational Linguistics, pages 539-546.