1. A method comprising: receiving training data comprising labeled emails, the labeled emails labeled based on user input salient features selected from a plurality of salient features describing email content; extracting labeled email N-grams from the training data at a critical email classifier comprising a processor; identifying labeled email N-gram features from the labeled email N-grams; extracting labeled email salient features from the training data; training the critical email classifier based on the labeled email N-gram features and the labeled email salient features, the training comprising generating a model having a base classifier and a nonnegative weight associated with the base classifier, the nonnegative weight based on a number of misclassifications by preceding base classifiers; and classifying unlabeled emails at the critical email classifier using the model, the classifying comprising: extracting unlabeled email N-grams from the unlabeled emails; determining that a particular unlabeled email N-gram is an unlabeled email N-gram feature; extracting unlabeled email salient features from the unlabeled emails; and classifying the unlabeled emails based on the unlabeled email N-gram feature and the unlabeled email salient features.