1. A method comprising, by one or more computing devices: receiving, from a client system of a first user of an online social network, a search query comprising one or more n-grams; determining, based on a contextual speller model, that at least one n-gram of the one or more n-grams is misspelled, wherein the contextual speller model is based at least on a standard language model and a personal language model customized for the first user based on social-networking data associated with the first user; identifying, for each misspelled n-gram, one or more variant-tokens based at least on the search query and the contextual speller model; generating one or more unique combinations of the n-grams and variant-tokens, wherein each unique combination comprises a variant-token corresponding to each misspelled n-gram; calculating a relevance-score for each unique combination based at least in part on the search query and the contextual speller model, wherein the relevance-score for a unique combination is based on a comparison of a probability associated with the n-grams or variant tokens of the unique combination in the standard language model of the contextual speller model to a probability associated with the n-grams or variant tokens of the unique combination in the personal language model of the contextual speller model; generating one or more corrected queries, each corrected query comprising a unique combination having a relevance-score greater than a threshold relevance-score; and sending, to the client system of the first user for display in response to receiving the search query, one or more of the corrected queries.
1. A method comprising, by one or more computing devices: receiving, from a client system of a first user of an online social network, a search query comprising one or more n-grams; determining, based on a contextual speller model, that at least one n-gram of the one or more n-grams is misspelled, wherein the contextual speller model is based at least on a standard language model and a personal language model customized for the first user based on social-networking data associated with the first user; identifying, for each misspelled n-gram, one or more variant-tokens based at least on the search query and the contextual speller model; generating one or more unique combinations of the n-grams and variant-tokens, wherein each unique combination comprises a variant-token corresponding to each misspelled n-gram; calculating a relevance-score for each unique combination based at least in part on the search query and the contextual speller model, wherein the relevance-score for a unique combination is based on a comparison of a probability associated with the n-grams or variant tokens of the unique combination in the standard language model of the contextual speller model to a probability associated with the n-grams or variant tokens of the unique combination in the personal language model of the contextual speller model; generating one or more corrected queries, each corrected query comprising a unique combination having a relevance-score greater than a threshold relevance-score; and sending, to the client system of the first user for display in response to receiving the search query, one or more of the corrected queries. 6. The method of claim 1 , wherein the standard language model comprises a plurality of n-grams corresponding to social-networking data of all users or entities of the online social network.