1. A computer-implemented method comprising: accessing a set of concepts derived from textual metadata of videos; accessing, for each of a plurality of the concepts, a corresponding content-based classifier that inputs content features of a video and outputs a score indicating a likelihood that the video represents the corresponding concept; obtaining a set of concept scores by applying, to content features of each video of a plurality of the videos, the content-based classifiers; identifying, for each concept of a plurality of the concepts, a training set comprising ones of the videos for which the obtained concept scores corresponding to the concept indicate at least a threshold level of likelihood that the videos represent the concept; deriving, from textual metadata of the videos of the identified training sets, textual features; training text-based classifiers for the plurality of the concepts, the text-based classifiers taking as input the textual features and outputting concept scores indicating a likelihood that the videos represent the corresponding concepts; after training the text-based classifiers, receiving a new video to which the text-based classifiers have not previously been applied; obtaining a first score for a first one of the concepts by applying the content-based classifier to the new video; obtaining a second score for the first concept by applying the text-based classifier to the new video; forming a combined score, the forming comprising forming a product of the first score and the second score; and determining whether the new video represents the first concept based on the combined score.