4. The method of claim 1 , further comprising: determining that the dialect classifier has not been completed; identifying additional training data by applying the dialect classifier to additional candidate content items; and updating the dialect classifier based on an updated version of the current training data set that includes the additional training data.
1. A method, comprising: selecting, by a computing device, an initial training data set as a current training data set, wherein the initial training data set is selected by: receiving one or more initial content items; and establishing dialect parameters for two or more of the initial content items, wherein the dialect parameters identify a first of the two or more of the initial content items as being composed in a first dialect and identify a second of the two or more of the initial content items as being composed in a second dialect; generating, by the computing device and based on the initial training data set and corresponding dialect parameters, a dialect classifier configured to detect language dialects of content items to be classified as being in one of two or more dialects, the two or more dialects including at least the first dialect and the second dialect; augmenting, by the computing device, the current training data set with additional training data by applying the dialect classifier to candidate content items, wherein at least one of the candidate content items that is in the augmented current training data set was not included in the initial training data set; updating the dialect classifier based on the augmented current training data set; and returning the updated dialect classifier, wherein the updated dialect classifier is configured to identify additional content items that are not in the initial training data and are not in the augmented current training data set as being in one of the two or more dialects.