1. A method comprising, by one or more computing devices: receiving, from a client device of a user of an online social network, a search query comprising one or more n-grams; determining, by a misspelled classifier component, for each n-gram, if a first bloom filter indicates the n-gram does exist or does not exist in a first set of object names associated with a first vertical; identifying, by a variant-tokens generation component, for each n-gram that does not exist in the first set of object names, one or more variant-tokens based at least on the first bloom filter and the first set of object names; generating, by a phrase selection component, one or more unique combinations of the n-grams and variant-tokens, where each unique combination includes: a token corresponding to each n-gram that does exist in the first set of object names; and a variant-token corresponding to each n-gram that does not exist in the first set of object names for the n-gram; calculating, by a phrase confidence scoring component, a confidence score for each unique combination based at least in part on the search query and whether the unique combination exists in the first set of object names; identifying objects matching each unique combination having a confidence score greater than a threshold confidence score; and sending, to the client device of the user, a search-results page responsive to the search query, the search-results page comprising one or more references to one or more of the identified objects, respectively.
1. A method comprising, by one or more computing devices: receiving, from a client device of a user of an online social network, a search query comprising one or more n-grams; determining, by a misspelled classifier component, for each n-gram, if a first bloom filter indicates the n-gram does exist or does not exist in a first set of object names associated with a first vertical; identifying, by a variant-tokens generation component, for each n-gram that does not exist in the first set of object names, one or more variant-tokens based at least on the first bloom filter and the first set of object names; generating, by a phrase selection component, one or more unique combinations of the n-grams and variant-tokens, where each unique combination includes: a token corresponding to each n-gram that does exist in the first set of object names; and a variant-token corresponding to each n-gram that does not exist in the first set of object names for the n-gram; calculating, by a phrase confidence scoring component, a confidence score for each unique combination based at least in part on the search query and whether the unique combination exists in the first set of object names; identifying objects matching each unique combination having a confidence score greater than a threshold confidence score; and sending, to the client device of the user, a search-results page responsive to the search query, the search-results page comprising one or more references to one or more of the identified objects, respectively. 11. The method of claim 1 , wherein calculating the confidence score for each unique combination is further based on whether the unique combination is relevant to a delivery of advertisements to the user of the online social network.