A stacked, voted, stacked model for named entity recognition

Dekai Wu, Grace Ngai, Marine Carpuat
2003 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -   unpublished
This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several effective approaches, culminating in a model that achieves error rate reductions on the development and test sets of 63.6% and 55.0% (English) and 47.0% and 51.7% (German) over the CoNLL-2003 standard baseline respectively, and 19.7% over a strong AdaBoost baseline model from CoNLL-2002.
doi:10.3115/1119176.1119209 fatcat:yzaww2hgyjcujlzbrshzbttnr4