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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.
Florian et al. (Tue,) studied this question.