CONTEXTUAL SPELLER MODELS ON ONLINE SOCIAL NETWORKS

• US 20160299882A1
• Filed: 04/10/2015
• Published: 10/13/2016
• Est. Priority Date: 04/10/2015
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Hegerty, Ian Douglas, Holland-Moritz, Marcus, Sadziak, Rafal Krzysztof, Bernhardt, Daniel
• Granted Patent: US 10,095,683 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 40/232 Orthographic correction, e.g. spell checking or vowelisation
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

In one embodiment, a method includes receiving a search query including one or more n-grams, determining for each n-gram if a contextual speller model indicates the n-gram is misspelled, identifying for each misspelled n-gram one or more variant-tokens based at least on the search query and a contextual speller model, generating one or more unique combinations of the n-grams and variant-tokens, where each unique combination includes 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, generating one or more corrected queries, where each corrected query includes a unique combination having a relevance-score greater than a threshold relevance-score, and sending one or more of the corrected queries to a user for display.

Claims

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, for each n-gram, if a contextual speller model indicates the n-gram is misspelled, wherein the contextual speller model is based at least on a standard language model and 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;

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.

2. The method of claim 1, further comprising:

receiving from the first user a selection of one of the corrected queries;

identifying one or more objects matching the selected query; and

sending, to the client system of the first user, a search-result page responsive to the selected query, the search-results page comprising one or more references to one or more of the identified objects, respectively.

3. The method of claim 1, wherein identifying one or more variant-tokens for each misspelled n-gram comprises:

accessing, for each misspelled n-gram, the contextual speller model to identify the variant-tokens having probabilities of appearing in the search query greater than a threshold probability.

4. The method of claim 1, wherein calculating the relevance-score for each unique combination based at least in part on the search query and the contextual speller model comprises:

accessing, for each variant-token or n-gram of the unique combination, the contextual speller model to retrieve a probability of the variant-token or n-gram appearing in the search query; and

calculating the relevance-score for the unique combination based at least on one or more of the retrieved probabilities.

5. The method of claim 1, wherein calculating the relevance-score for each unique combination based at least in part on the search query and the contextual speller model comprises:

accessing, for each variant-token of the unique combination, the contextual speller model to determine a probability of the variant-token being correctly-spelled; and

calculating the relevance-score for the unique combination based at least on one or more of the determined probabilities corresponding to the variant-tokens of the unique combination.

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.

7. The method of claim 1, wherein the social-networking data comprises a personal language model associated with the first user.

8. The method of claim 7, wherein the personal language model comprises a plurality of n-grams extracted from one or more of:

one or more feed searches of the first user on the online social network;

one or more posts viewed by the first user on the online social network;

one or more posts viewed by a second user on the online social network, wherein the posts as viewed by the second user are associated with the first user of the online social network;

one or more likes of the first user on the online social network;

one or more previous search results of the first user on the online social network;

a profile of the first user on the online social network;

or any combination thereof.

9. The method of claim 7, wherein the personal language model is time-invariant.

10. The method of claim 7, wherein calculating the relevance-score for each unique combination based at least in part on the contextual speller model comprises modifying the calculated relevance-score of each unique combination comprising an n-gram having a frequency of use in the personal language model different from a frequency of use in the standard language model greater than a threshold frequency of use.

11. The method of claim 10, wherein modifying the calculated relevance-score of each unique combination comprising the n-gram having the frequency of use in the personal language model different from the frequency of use in the standard language model greater than the threshold frequency of use comprising:

increasing a probability of the n-gram appearing in the search query, the n-gram having the frequency of use in the personal language model different from the frequency of use in the standard language model greater than or equal to the threshold frequency of use;

or

decreasing the probability of the n-gram appearing in the search query, the n-gram having the frequency of use in the personal language model different from the frequency of use in the standard language model less than the threshold frequency of use.

12. The method of claim 1, wherein the social-networking data comprises a personal language model associated with a first group of users.

13. The method of claim 1, wherein the social-networking data associated with the first user comprises data associated with the first user retrieved from the online social network within a pre-determined time range.

14. The method of claim 1, wherein the social-networking data comprises:

demographic information of the first user;

or

one or more concepts of the online social network connected to the first user.

15. The method of claim 1, wherein the contextual speller model comprises a plurality of speller sub-models.

16. The method of claim 15, wherein one of the plurality of speller sub-models corresponds to social-networking data associated with a particular time context associated with the first user

17. The method of claim 15, wherein one of the plurality of speller sub-models corresponds to social-networking data associated with a particular social context associated with the first user.

18. The method of claim 15, wherein the plurality of speller sub-models are based at least on one or more levels of aggregation, and wherein each level of aggregation differentiates the first user from global users of the online social network.

19. 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, for each n-gram, if a contextual speller model indicates the n-gram is misspelled, wherein the contextual speller model is based at least on a standard language model and 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;

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.

20. 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, for each n-gram, if a contextual speller model indicates the n-gram is misspelled, wherein the contextual speller model is based at least on a standard language model and 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;

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.