Paper: Modeling Consensus: Classifier Combination For Word Sense Disambiguation

ACL ID W02-1004
Title Modeling Consensus: Classifier Combination For Word Sense Disambiguation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002
Authors

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 mix- ture models). The paper also includes in-depth per- formance analysis sensitive to properties of the fea- ture space and component classifiers. When eval- uated on the standard SENSEVAL1 and 2 data sets on 4 languages (English, Spanish, Basque, and Swedish), classifier combination performance ex- ceeds the best published results on these data sets.