1. A computer-implemented method, comprising: obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams: identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising: positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image.