Modeling consensus

This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over 6 major structurally different base classifiers (enhanced Naïve Bayes, cosine, Bayes Ratio, decision lists, transformation-based learning and maximum variance boosted mixture models). The paper also includes in-depth performance analysis sensitive to properties of the feature space and component classifiers. When evaluated on the standard SENSEVAL 1 and 2 data sets on 4 languages (English, Spanish, Basque, and Swedish), classifier combination performance exceeds the best published results on these data sets.