UPDATING POPULATION LANGUAGE MODELS BASED ON CHANGES MADE BY USER CLUSTERS

• US 20140316784A1
• Filed: 04/24/2013
• Published: 10/23/2014
• Est. Priority Date: 04/18/2013
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Bradford, Ethan R., Kay, David J., Corston, Simon, McCray, Donni, Trnka, Keith
• Granted Patent: US 9,672,818 B2
• Time in Patent Office: Days
• US Class Current: 704/245
• CPC Class Codes: G10L 15/18 using natural language modelling

Abstract

Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users'"'"' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user'"'"'s device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Claims

1. A method in a computing system of updating population language models used by language recognition systems, the method comprising:

receiving events associated with local language models that are used by language recognition systems in computing devices of a plurality of users;

receiving information characterizing the computing devices or the plurality of users;

identifying, by the computing system, from the received information, a cluster of users sharing similar characteristics;

generating modifications to a population language model associated with the cluster of users by;

identifying events associated with local language models that were received from users within the cluster; and

aggregating the events to identify modifications to the population model; and

providing the generated modifications to the population language model to a computing device of one or more users in the cluster.

2. The method of claim 1, wherein receiving events associated with local language models comprises receiving information about word usage, responses to word suggestions, addition of words to the local language model, or deletion of words from the local language model.

3. The method of claim 2, wherein information about word usage includes frequencies of use of individual words, word pairs (bigrams), triplets (trigrams), or higher-order n-grams.

4. The method of claim 2, wherein the events are received on a continuous, periodic, or irregular basis.

5. The method of claim 1, wherein the cluster of users share a location.

6. The method of claim 1, wherein the cluster of users share a common interest.

7. The method of claim 1, wherein identifying a cluster of users sharing similar characteristics further comprises identifying similar events associated with local language models.

8. The method of claim 7, wherein identifying similar events comprises determining word or n-gram associations.

9. The method of claim 1, wherein generating modifications to a population language model associated with the cluster of users comprises creating a new population language model associated with the cluster.

10. The method of claim 1, wherein generating modifications to a population language model associated with the cluster of users comprises adding words or n-grams to the population language model or removing words or n-grams from the population language model.

11. The method of claim 1, wherein generating modifications to a population language model associated with the cluster comprises modifying weighting of words or n-grams in the population language model.

12. The method of claim 1, wherein providing generated modifications to the computing device of one or more users in the cluster further comprises providing the associated population language model to a computing device of a user that is newly identified as having a connection to the cluster.

13. The method of claim 1, wherein providing generated modifications to a computing device of one or more users in the cluster comprises providing updates to a local population language model in a computing device of a user.

14. The method of claim 1, wherein aggregating the events to identify modifications to the population model comprises prioritizing events based on frequency of event.

15. The method of claim 1, wherein aggregating the events to identify modifications to the population model further comprises deleting events found on a blacklist or keeping events found on a whitelist.

16. A computer-readable medium containing instructions that, when executed by a computing system, implement a method of updating a local language model used to predictively complete user input, the method comprising:

receiving information about local language models including a first local language model, wherein the information about local language models includes information about local language model events;

identifying one or more of the local language models having;

local language model events similar to first local language model events; and

additional local language model events;

generating modifications to the first local language model using the additional local language model event information of the identified local language models; and

updating the first local language model with the generated modifications.

17. The computer-readable medium of claim 16, wherein receiving information about local language models comprises receiving a change log.

18. The computer-readable medium of claim 16, wherein receiving information about local language models comprises receiving local language models.

19. The computer-readable medium of claim 16, wherein information about local language model events comprises information about word usage, responses to word suggestions, and addition of words to the local language model or deletion of words from the local language model.

20. The computer-readable medium of claim 19, wherein information about word usage includes frequencies of individual words, word pairs (bigrams), triplets (trigrams), or higher-order n-grams.

21. The computer-readable medium of claim 16, further comprising filtering the generated modifications before updating the first local language model with the generated modifications.