A stacked, voted, stacked model for named entity recognition
Published 1 January 2003Open access
Dekai Wu, Grace Ngai, Marine Carpuat
Citations52
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TL;DR
This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task, and demonstrates several effective approaches, culminating in a model that achieves error rate reductions on the development and test sets.
Abstract
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.
Keywords
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