Paper: Augmented Mixture Models For Lexical Disambiguation

ACL ID W02-1005
Title Augmented Mixture Models For Lexical Disambiguation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002

This paper investigates several augmented mixture models that are competitive alternatives to standard Bayesian models and prove to be very suitable to word sense disambiguation and related classifica- tion tasks. We present a new classification correc- tion technique that successfully addresses the prob- lem of under-estimation of infrequent classes in the training data. We show that the mixture models are boosting-friendly and that both Adaboost and our original correction technique can improve the re- sults of the raw model significantly, achieving state- of-the-art performance on several standard test sets in four languages. With substantially different out- put to Naïve Bayes and other statistical methods, the investigated models are also shown to be effective participants in classif...