1. A method comprising, by one or more computing systems: receiving, 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; identifying 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; calculating, 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; determining one or more popular n-grams, 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 selecting 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.
1. A method comprising, by one or more computing systems: receiving, 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; identifying 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; calculating, 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; determining one or more popular n-grams, 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 selecting 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. 6. The method of claim 1 , wherein each of one or more of the search queries in the subset of search queries is further identified based on a search context from which the search query is submitted.