Training conditional random fields by Periodic Step size Adaptation for large-scale text mining

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2007, Pages 511-516

  Huang, Han Shen ^a   Chang, Yu Ming ^a   Hsu, Chun Nan ^a  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; DECISION SUPPORT SYSTEMS; EDUCATION; GRADIENT METHODS; INFORMATION MANAGEMENT; MINING; PEELING; SEARCH ENGINES;

CONDITIONAL RANDOM FIELDS; INTERNATIONAL CONFERENCES; LEARNING RATES; MEMORY FOOTPRINTS; OFF-LINE ALGORITHMS; OFF-LINE LEARNING; ON-LINE ALGORITHMS; ON-LINE LEARNING; ORDER-OF MAGNITUDES; STEP SIZE ADAPTATION; STOCHASTIC GRADIENT DESCENT; TEXT-MINING; TRAINING DATA SETS;

DATA MINING;

EID: 49749132313     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2007.39     Document Type: Conference Paper

References (18)

1. K. Crammer and Y. Singer. Ultraconservative online algorithms for multiclass problems. Journal of Machine Learning Research, 3:951-991, January 2003.
2. J. N. Darroch and D. Ratcliff. Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics, 43(5):1470-1480, 1972.
3. H.-S. Huang, B.-H. Yang, and C.-N. Hsu. Triple-jump acceleration for the EM algorithm. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM'05), pages 649-652, 2005.
4. M. Jamshidian and R. I. Jennrich. Acceleration of the EM algorithm by using quasi-newton methods. Journal of the Royal Statistical Society, , Series B, 59(3):569-587, 1997.
5. J.-D. Kim, T. Ohta, Y. Tsuruoka, Y. Tateisi, and N. Collier. Introduction to the bio-entity recognition task at JNLPBA. In Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004), pages 70-75, 2004.
6. T. Kudo. CRF++: Yet another CRF toolkit (http://crfpp.sourceforge.net/), 2006.
7. C.-J. Kuo, Y.-M. Chang, H.-S. Huang, K.-T. Lin, B.-H. Yang, Y.-S. Lin, C.-N. Hsu, and I.-F. Chung. Rich feature set, unification of bidirectional parsing and dictionary filtering for high f-score gene mention tagging. In Proceedings of the Second BioCreative Challenge Evaluation Workshop, pages 105-107, 2007.
8. H. J. Kushner and H. Huang. Asymptotic properties of stochastic approximations with constant coefficients. SIAM Journal on Control and Optimization, 19(1):87-105, 1981.
9. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of 18th International Conference on Machine Learning (ICML'01), pages 282-289, 2001.
10. R. Malouf. A comparison of algorithms for maximum entropy parameter estimation. In Proceedings of the Sixth Conference on Natural Language Learning (CoNLL-2002), pages 49-55, 2002.
11. G. J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley Series in Probability and Statistics. Wiley-Interscience, 1997.
12. J. Nocedal and S. J. Wright. Numerical Optimization. Springer, 1999.
13. R. Salakhutdinov and S. Roweis. Adaptive overrelaxed bound optimization methods. In Proceedings of the Twentieth International Conference on Machine Learning (ICML'03), pages 664-671, 2003.
14. E. F. T. K. Sang and S. Buchholz. Introduction to the conll-2000 shared task: Chunking. In Proceedings of Conference on Computational Natural Language Learning (CoNLL-2000), pages 127-132, 2000.
15. B. Settles. Biomedical named entity recognition using conditional random fields and novel feature sets. In Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004), pages 104-107, 2004.
16. F. Sha and F. Pereira. Shallow parsing with conditional random fields. In Proceedings of Human Language Technology, the North American Chapter of the Association for Computational Linguistics (NAACL'03), pages 213-220, 2003.
17. S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. P. Murphy. Accelerated training of conditional random fields with stochastic gradient methods. In Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, June 2006.
18. J. Wilbur, L. Smith, and L. Tanabe. Biocreative 2. gene mention task. In Proceedings of the Second BioCreative Challenge Evaluation Workshop, pages 7-16, 2007.