Original Contribution: Stacked generalization
Neural Networks
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Statistical named entity recognizer adaptation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
SLINERC: the Sydney Language-Independent Named Entity Recogniser and Classifier
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Named entity recognition using a character-based probabilistic approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Boosted decision graphs for NLP learning tasks
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Immune network based ensembles
Neurocomputing
Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
Boosting random subspace method
Neural Networks
Improving the Performance of a NER System by Post-processing and Voting
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Improving the Performance of a NER System by Post-processing, Context Patterns and Voting
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Constructing ensembles of classifiers by means of weighted instance selection
IEEE Transactions on Neural Networks
Extracting person names from diverse and noisy OCR text
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
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This paper presents a named entity classification system that utilises both orthographic and contextual information. The random subspace method was employed to generate and refine attribute models. Supervised and unsupervised learning techniques used in the recombination of models to produce the final results.