17. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: receive, from a plurality of client systems of a plurality of users, a plurality of search queries, each search query comprising one or more n-grams; identify a subset of search queries from the plurality of search queries as being queries for visual-media items, each of the search queries in the subset of search queries being identified based on one or more n-grams of the search query being associated with visual-media content; calculate, for each of the n-grams of the search queries of the subset of search queries, a popularity-score based on a count of the search queries in the subset of search queries that include the n-gram, wherein, for each of one or more of the n-grams of the search queries of the subset of search queries, the count of the search queries including the n-gram is a weighted count that weights an occurrence of each search query based on a degree of confidence with which the search query is identified as being a query for visual-media items; determine one or more popular n-gram, wherein each of the popular n-grams is an n-gram of the search queries of the subset of search queries having a popularity-score greater than a threshold popularity-score; and select one or more of the popular n-grams for training a visual-concept recognition system, wherein each of the popular n-grams is selected based on whether it is associated with one or more visual concepts.
17. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: receive, from a plurality of client systems of a plurality of users, a plurality of search queries, each search query comprising one or more n-grams; identify a subset of search queries from the plurality of search queries as being queries for visual-media items, each of the search queries in the subset of search queries being identified based on one or more n-grams of the search query being associated with visual-media content; calculate, for each of the n-grams of the search queries of the subset of search queries, a popularity-score based on a count of the search queries in the subset of search queries that include the n-gram, wherein, for each of one or more of the n-grams of the search queries of the subset of search queries, the count of the search queries including the n-gram is a weighted count that weights an occurrence of each search query based on a degree of confidence with which the search query is identified as being a query for visual-media items; determine one or more popular n-gram, wherein each of the popular n-grams is an n-gram of the search queries of the subset of search queries having a popularity-score greater than a threshold popularity-score; and select one or more of the popular n-grams for training a visual-concept recognition system, wherein each of the popular n-grams is selected based on whether it is associated with one or more visual concepts. 20. The media of claim 17 , wherein the weighted count further weights the occurrence of each search query based on information associated with a user of a client system from which the search query was received.