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Co-training and Self-training for Word Sense Disambiguation

Description: 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.
Date: May 2004
Creator: Mihalcea, Rada, 1974-
Partner: UNT College of Engineering