PERSONAL LANGUAGE MODEL FOR INPUT METHOD EDITOR

• US 20150186362A1
• Filed: 08/31/2012
• Published: 07/02/2015
• Est. Priority Date: 08/31/2012
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Li, Mu, Chen, Xi
• Granted Patent: US 9,824,085 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/129 Handling non-Latin characters, e.g. kana-to-kanji conversion
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs
• CPC Class Codes: G06F 40/53 Processing of non-Latin text kana-to-kanji conversion G06F40/129; vowelisation G06F40/232

Abstract

Some examples include generating a personal language model based on linguistic characteristics of one or more files stored at one or more locations in a file system. Further, some implementations include predicting and presenting a non-Latin character string based at least in part on the personal language model, such as in response to receiving a Latin character string via an input method editor interface.

Claims

1. A method comprising:

generating a personal language model based at least in part on linguistic characteristics of one or more files stored at one or more locations in a file system; and

in response to receiving a Latin character string via an input method editor interface, predicting a non-Latin character string based at least in part on the personal language model.

2. The method as recited in claim 1, wherein the one or more locations are identified by a user.

3. The method as recited in claim 1, wherein the predicted non-Latin character string is determined based on the personal language model and a general language model.

4. The method as recited in claim 3, wherein a contribution of the personal language model is determined based on a weighting factor.

5. The method as recited in claim 4, wherein the weighting factor includes a default weighting factor or a user-defined weighting factor.

6. The method as recited in claim 1, further comprising presenting the predicted non-Latin character string via the input method editor interface.

7. The method as recited in claim 1, wherein:

the Latin character string includes a Pinyin character string; and

the predicted non-Latin character string includes a Chinese character string.

8. The method as recited in claim 1, wherein a plurality of non-Latin character strings are associated with the Latin character string received via the input method editor interface.

9. The method as recited in claim 8, wherein a conversion probability is associated with each non-Latin character string of the plurality of non-Latin character strings.

10. The method as recited in claim 9, wherein predicting the non-Latin character string includes identifying the non-Latin character string of the plurality of non-Latin character strings with a highest conversion probability.

11. The method as recited in claim 10, wherein a general language model identifies a first non-Latin character string of the plurality of non-Latin character strings as the non-Latin character string with the highest conversion probability.

12. The method as recited in claim 11, wherein the personal language model identifies a second non-Latin character string of the plurality of non-Latin character strings as the non-Latin character string with the highest conversion probability.

13. The method as recited in claim 12, wherein the first non-Latin character string identified by the general language model is different than the second non-Latin character string identified by the personal language model.

14. The method as recited in claim 1, wherein the personal language model includes an N-gram statistical language model.

15. A computing system comprising:

one or more processors;

one or more computer readable media maintaining instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising;

generating a personal language model based on linguistic characteristics of one or more files stored at one or more locations in a file system; and

in response to receiving a Latin character string via an input method editor interface, predicting a non-Latin character string based at least in part on the personal language model.

16. The computing system as recited in claim 15, the acts further comprising:

detecting that new content has been saved; and

in response to detecting that the new content has been saved, processing the new content to update the personal language model.

17. The computing system as recited in claim 15, the acts further comprising:

periodically monitoring the one or more locations to determine whether new content has been saved to the one or more locations; and

processing the new content to update the personal language model.

18. One or more computer readable media maintaining instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:

generating a personal language model based on linguistic characteristics of one or more files stored at one or more locations in a file system, wherein the one or more locations are identified by a user;

in response to receiving a Latin character string via an input method editor interface;

determining an overall conversion probability of each of a plurality of non-Latin character strings based on a first conversion probability determined based on a general language model and a second conversion probability determined based on the personal language model, wherein a contribution of the second conversion probability to the overall conversion probability is weighted based on a weighting factor;

ordering the plurality of non-Latin character strings based on the overall conversion probability; and

displaying an ordered list of non-Latin character strings via the input method editor interface.

19. One or more computer readable media as recited in claim 18, the acts further comprising:

receiving a user-defined weighting factor; and

modifying the weighting factor from a default weighting factor to the user-defined weighting factor.

20. One or more computer readable media as recited in claim 18, wherein the one or more locations are identified by the user via a location selection interface.