Original Contribution: Stacked generalization
Neural Networks
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging and boosting a treebank parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Noun phrase recognition by system combination
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Named entity chunking techniques in supervised learning for Japanese named entity recognition
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Named entity extraction based on a maximum entropy model and transformation rules
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Japanese Named Entity extraction with redundant morphological analysis
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A named entity extraction using word information repeatedly collected from unlabeled data
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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In this paper, we propose a method for learning a classifier which combines outputs of more than one Japanese named entity extractors. The proposed combination method belongs to the family of stacked generalizers, which is in principle a technique of combining outputs of several classifiers at the first stage by learning a second stage classifier to combine those outputs at the first stage. Individual models to be combined are based on maximum entropy models, one of which always considers surrounding contexts of a fixed length, while the other considers those of variable lengths according to the number of constituent morphemes of named entities. As an algorithm for learning the second stage classifier, we employ a decision list learning method. Experimental evaluation shows that the proposed method achieves improvement over the best known results with Japanese named entity extractors based on maximum entropy models.