In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT) - hierarchical groupings of attribute values - to learn compact, comprehensible, and accurate classifiers from data - including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the Na?ve Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. We also show that AVT-NBL is more efficient in its use of training data: AVT-NBL produces classifiers that outperform those produced by NBL using substantially fewer training examples.