Original Contribution: Stacked generalization
Neural Networks
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Named entity recognition for Catalan using Spanish resources
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Why nitpicking works: evidence for Occam's Razor in error correctors
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The effect of borderline examples on language learning
Journal of Experimental & Theoretical Artificial Intelligence
NTPC: N-fold templated piped correction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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In this paper, we present a learning method using multiple stacking for named entity recognition. In order to take into account the tags of the surrounding words, we propose a method which employs stacked learners using the tags predicted by the lower level learners. We have applied this approach to the CoNLL-2002 shared task to improve a base system.