Co-training and Self-training for Word Sense Disambiguation
Rada F. Mihalcea · University of North Texas Digital Library (University of North Texas) · 2004
This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.