A multiobjective simulated annealing approach for classifier ensemble: Named entity recognition in Indian languages as case studies

Expert Systems with Applications

Volumn 38, Issue 12, 2011, Pages 14760-14772

  Ekbal, Asif ^a   Saha, Sriparna ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY PATNA   (India)

Author keywords

Classifier ensemble; Conditional Random Field (CRF); Maximum Entropy (ME); Multiobjective optimization (MOO); Named entity recognition; Natural language processing; Simulated annealing (SA); Support Vector Machine (SVM); Weighted voting

Indexed keywords

CLASSIFIER ENSEMBLES; CONDITIONAL RANDOM FIELD; MAXIMUM ENTROPY (ME); MULTI OBJECTIVE; NAMED ENTITY RECOGNITION; NATURAL LANGUAGE PROCESSING; SUPPORT VECTOR; WEIGHTED VOTING;

COMPUTATIONAL LINGUISTICS; ENTROPY; IMAGE SEGMENTATION; MULTIOBJECTIVE OPTIMIZATION; NATURAL LANGUAGE PROCESSING SYSTEMS; SUPPORT VECTOR MACHINES;

SIMULATED ANNEALING;

EID: 80052037775     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.05.004     Document Type: Article

References (37)

1. T.W. Anderson, and S. Scolve Introduction to the statistical analysis of data 1978 Houghton Mifflin
2. S. Bandyopadhyay, S. Saha, U. Maulik, and K. Deb A simulated annealing based multi-objective optimization algorithm: AMOSA IEEE Transactions on Evolutionary Computation 12 3 2008 269 283
3. Borthwick, A. (1999). Maximum entropy approach to named entity recognition. Ph.D. dissertation, New York University.
4. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
5. Cherkauer K. (1996). Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks. In Working notes of the AAAI workshop on integrating multiple learned models (pp. 15-21).
6. C.A. Coello Coello A comprehensive survey of evolutionary-based multiobjective optimization techniques Knowledge and Information Systems 1 3 1999 129 156
7. J. Darroch, and D. Ratcliff Generalized iterative scaling for log-linear models Annals of Mathematical Statistics 43 1972 1470 1480
8. K. Deb Multi-objective optimization using evolutionary algorithms 2001 John Wiley and Sons, Ltd. England
9. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Transactions on Evolutionary Computation 6 2 2002 181 197
10. T.G. Dietterich Ensemble methods in machine learning J. Kittler, F. Roli, Multiple Classifiers Systems: First International Workshop, Proceedings/ MCS 2000 2000 Springer
11. T.G. Dietterich, and G. Bakiri Solving multiclass learning problems via error correcting output codes Journal of Artificial Intelligence Research 2 1995 263 286
12. Ekbal, A., & Bandyopadhyay, S. (2008a). Web-based Bengali news corpus for lexicon development and POS tagging, POLIBITS (Vol. 37, pp. 20-29). ISSN 1870-9044.
13. Ekbal, A., & Bandyopadhyay, S. (2007). Lexical pattern learning from corpus data for named entity recognition. In Proceedings of the 5th international conference on natural language processing (ICON), India (pp. 123-128).
14. Ekbal, A., & Bandyopadhyay, S. (2008b). Bengali named entity recognition using support vector machine. In Proceedings of workshop on NER for South and South East Asian languages, 3rd international joint conference on natural language processing (IJCNLP), India (pp. 51-58).
15. Ekbal, A., & Bandyopadhyay, S. (2009). Voted NER system using appropriate unlabeled data. In Proceedings of the 2009 named entities workshop: Shared task on transliteration (NEWS 2009), ACL-IJCNLP 2009 (pp. 202-210).
16. A. Ekbal, and S. Bandyopadhyay A web-based Bengali news corpus for named entity recognition Language Resources and Evaluation Journal 42 2 2008 173 182
17. A. Ekbal, and S. Bandyopadhyay A conditional random field approach for named entity recognition in Bengali and Hindi Linguistic Issues in Language Technology (LiLT) 2 1 2009 1 44
18. A. Ekbal, S. Naskar, and S. Bandyopadhyay Named Entity Recognition and Transliteration in Bengali Named Entities: Recognition, Classification and Use, Special Issue of Lingvisticae Investigationes Journal 30 1 2007 95 114
19. Florian, R., Ittycheriah, A., Jing, H., & Zhang, T. (2003). Named entity recognition through classifier combination. In Proceedings of the seventh conference on natural language learning at HLT-NAACL.
20. Freund, Y., & Schapire, R. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the second European conference on computational learning theory, Taipei, Taiwan (pp. 23-37).
21. Gali K., Sharma, H., Vaidya, A., Shisthla, P., & Sharma, D. M. (2008). Aggregrating machine learning and rule-based heuristics for named entity recognition. In Proceedings of the IJCNLP-08 workshop on NER for South and South East Asian languages (pp. 25-32).
22. S. Geman, and D. Geman Stochastic relaxation, gibbs distributions and the Bayesian restoration of images IEEE Transactions on Pattern Analysis and Machine Intelligence 6 6 1984 721 741
23. T. Joachims Making large scale SVM learning practical 1999 MIT Press Cambridge, MA, USA
24. S. Kirkpatrick, C. Gelatt, and M. Vecchi Optimization by simulated annealing Science 220 1983 671 680
25. J.F. Kolen, and J.B. Pollack Back propagation is sensitive to initial conditions Advances in Neural Information Processing Systems 1991 860 867
26. J.D. Lafferty, A. McCallum, and F.C.N. Pereira Conditional random fields: Probabilistic models for segmenting and labeling sequence data ICML 2001 282 289
27. W. Li, and A. McCallum Rapid development of Hindi named entity recognition using conditional random fields and feature induction ACM Transactions on Asian Languages Information Processing 2 3 2004 290 294
28. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbloth, A.H. Teller, and E. Teller Equation of state calculation by fast computing machines Journal of Chemical Physics 21 1953
29. Patel, A., Ramakrishnan, G., & Bhattacharya, P. (2009). Relational learning assisted construction of rule base for indian language NER. In Proceedings of ICON 2009: 7th international conference on natural language processing, India.
30. Sha, F., & Pereira, F. (2003). Shallow parsing with conditional random fields. In Proceedings of NAACL '03, Canada (pp. 134-141).
31. Shishtla P. M., Pingali, P., & Varma, V. (2008). A character n-gram based approach for improved recall in Indian language NER. In Proceedings of the IJCNLP-08 workshop on NER for South and South East Asian languages (pp. 101-108).
32. Srikanth, P., & Murthy, K. N. (2008). Named entity recognition for Telugu. In Proceedings of the IJCNLP-08 workshop on NER for South and South East Asian languages (pp. 41-50).
33. B. Suman, and P. Kumar A survey of simulated annealing as a tool for single and multiobjective optimization Journal of the Operational Research Society 57 10 2006 1143 1160
34. Taira, H., & Haruno, M. (1999). Feature selection in SVM text categorization. In Proceedings of AAAI-99.
35. E.F. Tjong Kim Sang, F. De Meulder, Introduction to the Conll-2003 shared task: Language independent named entity recognition. In Proceedings of the seventh conference on natural language learning at HLT-NAACL 2003 (pp. 142-147).
36. V.N. Vapnik The nature of statistical learning theory 1995 Springer-Verlag New York, Inc. New York, NY, USA
37. D. Wolpert Stacked generalization Neural Networks 5 1992 241 259