Paper: A Stacked Voted Stacked Model For Named Entity Recognition

ACL ID W03-0433
Title A Stacked Voted Stacked Model For Named Entity Recognition
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

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 ef- fective approaches, culminating in a model that achieves error rate reductions on the develop- ment and test sets of 63.6% and 55.0% (En- glish) 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.