| A00-2009 | joint probability found by the | Naive Bayesian | classifier . However , a preliminary |
| A00-2009 | approach here is to group the 81 | Naive Bayesian | classifiers into general categories |
| A00-2009 | begins with an introduction to the | Naive Bayesian | classifier . The features used |
| A00-2009 | these words with an ensemble of | Naive Bayesian | classifiers are shown to rival |
| A00-2009 | 2 Naive Bayesian Classifiers A | Naive Bayesian | classifier assumes that all the |
| A00-2009 | sentence b-o-w in Table 6 . When the | Naive Bayesian | classifier is evaluated words |
| A00-2009 | context . The latter utilize a | Naive Bayesian | classifier . In both cases context |
| A00-2009 | Approach to Building Ensembles of | Naive Bayesian | Classifiers for Word Sense Disambiguation |
| A00-2009 | disambiguation is performed with a | Naive Bayesian | classifier . The work in this |
| A00-2009 | finds that none outperform the | Naive Bayesian | classifier , which attains accuracy |
| A00-2009 | the disambiguation accuracy of a | Naive Bayesian | classifier , a content vector |
| A00-2009 | average accuracy of the individual | Naive Bayesian | classifiers across the five folds |
| A00-2009 | Four folds were used to train the | Naive Bayesian | classifier while the remaining |
| A00-2009 | Naive Bayesian classifiers The | Naive Bayesian | classifier has emerged as a consistently |
| A00-2009 | show that the accuracy of the | Naive Bayesian | ensemble is comparable to that |
| A00-2009 | decision tree learner ( 78 % ) and a | Naive Bayesian | classifier ( 74 % ) are most |
| A00-2009 | methods in this study proved to be a | Naive Bayesian | classifier ( 72 % ) and a perceptron |
| A00-2009 | approach is to train a separate | Naive Bayesian | classifier for each of the 81 |
| A00-2009 | , a win dow of context . For a | Naive Bayesian | classifier , the joint probability |
| A00-2009 | disambiguation that builds an ensemble of | Naive Bayesian | classifiers , each of which is |
