We have presented an ensemble approach to word sense disambiguation ( Pedersen , 2000 ) where multiple Naive Bayesian classifiers , each based on co -- occurrence features from varying sized windows of context , is shown to perform well on the widely studied nouns interest and line .
a simple approach to building ensembles of naive bayesian classifiers for word sense disambiguation
T Pedersen
One of our long-term objectives is to identify a core set of features that will be useful for disambiguating a wide class of words using both supervised and unsupervised methodologies . We have presented an ensemble approach to word sense disambiguation ( Pedersen , 2000 ) where multiple Naive Bayesian classifiers , each based on co -- occurrence features from varying sized windows of context , is shown to perform well on the widely studied nouns interest and line . While the accuracy of this approach was as good as any previously published results , the learned models were complex and difficult to interpret , in effect acting as very accurate black boxes .
We have presented an ensemble approach to word sense disambiguation ( @@CITATION ) where multiple Naive Bayesian classifiers , each based on co -- occurrence features from varying sized windows of context , is shown to perform well on the widely studied nouns interest and line .