Contextual speller models on online social networks

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• receive, from a client system of a first user of an online social network, a search query comprising one or more n-grams;
• determine, 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;
• identify, for each misspelled n-gram, one or more variant-tokens based at least on the search query and the contextual speller model;
• generate 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;
• calculate 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;
• generate one or more corrected queries, each corrected query comprising a unique combination having a relevance-score greater than a threshold relevance-score;
• and
• send, to the client system of the first user for display in response to receiving the search query, one or more of the corrected queries.

A system comprising:

• one or more processors;
• and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• receive, from a client system of a first user of an online social network, a search query comprising one or more n-grams;
• determine, 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;
• identify, for each misspelled n-gram, one or more variant-tokens based at least on the search query and the contextual speller model;
• generate 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;
• calculate 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;
• generate one or more corrected queries, each corrected query comprising a unique combination having a relevance-score greater than a threshold relevance-score;
• and
• send, to the client system of the first user for display in response to receiving the search query, one or more of the corrected queries.