LEARNING LANGUAGE MODELS FROM SCRATCH BASED ON CROWD-SOURCED USER TEXT INPUT

• US 20150309984A1
• Filed: 04/25/2014
• Published: 10/29/2015
• Est. Priority Date: 04/25/2014
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Bradford, Ethan R., McCray, Donni, Corston, Simon, Cross, Ryan N.
• Application Number:
  US14/262,304
• Publication Number:
  US 20150309984A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/263 Language identification
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06F 40/53 Processing of non-Latin text kana-to-kanji conversion G06F40/129; vowelisation G06F40/232

Abstract

Technology is described for developing a language model for a language recognition system from scratch based on aggregating and analyzing text input from multiple users of the language. The technology allows a user to select a language, and if no existing language model is available for the selected language, provides a new language model for the selected language, monitors and collects information about the use of words in the selected language, combines information collected from multiple users of the selected language, and updates the user'"'"'s language model based on the combined information from multiple users of the selected language.

Claims

1. A tangible computer-readable memory having contents configured to cause at least one computer having a processor to perform a method for assisting in building a new language model used by language recognition systems, the method comprising:

initializing a language model for a selected language,

wherein a language recognition system that uses a language model to predict words in a language is ineffective to predict intended words in the selected language;

monitoring use of words in the selected language on various computing devices by multiple users of the selected language;

collecting, in substantially real-time, information about the monitored use of the words in the selected language by the multiple users of the selected language;

generating updates to the language model based on the collected information about the monitored use of the words in the selected language; and

providing to the various computing devices the generated updates to the language model, such that a language recognition system using the language model including the generated updates is more effective to predict intended words in the selected language.

2. The computer-readable memory of claim 1, wherein generating updates to the language model based on the collected information about the monitored use of the words in the selected language includes adding words or n-grams to the language model or removing words or n-grams from the language model, modifying weighting or usage frequency data of words or n-grams in the language model.

3. The computer-readable memory of claim 1, wherein generating updates to the language model based on the collected information about the monitored use of the words in the selected language includes adding a word entered using characters from more than one language or script to the language model, or adding words entered by a first user using Latin characters and words entered by a second user using non-Latin characters to the language model.

4. The computer-readable memory of claim 1, wherein generating updates to the language model based on the collected information about the monitored use of the words in the selected language includes storing words in different character sets in different language models for the language, such that the updates to the language model are based on use of the selected language by users using words in a substantially similar character set.

5. The computer-readable memory of claim 4, wherein storing words in different character sets in different language models for the language includes storing words entered in a non-Latin script in a first language model for the language and storing words entered in a Latin script in a second language model for the language.

6. The computer-readable memory of claim 1, wherein generating updates to the language model based on the collected information about the monitored use of the words in the selected language includes requiring a threshold number or percentage of users to employ a word before adding that word to the language model.

7. The computer-readable memory of claim 6, wherein requiring a threshold number or percentage of users includes setting a lower threshold based on the size of the language model or the number of the multiple users of the selected language.

8. The computer-readable memory of claim 1, wherein generating updates to the language model based on the collected information about the monitored use of the words in the selected language includes filtering the collected information to identify words likely to contain errors, private information, or objectionable words.

9. The computer-readable memory of claim 8, wherein filtering the collected information to identify words likely to contain errors includes determining that the frequency that users of the selected language employ the correct word exceeds the frequency that users of the selected language employ the word containing the error, or treating word forms containing special characters as more authoritative than similar forms without special characters.

10. The computer-readable memory of claim 1, further comprising:

identifying two language models that have significant overlap in their word lists and word frequency distributions; and

aggregating the overlapping language models.

11. The computer-readable memory of claim 1, wherein providing to the various computing devices the generated updates to the language model includes providing the language model including at least some of the generated updates to a computing device of a new user of the language.

12. The computer-readable memory of claim 1, wherein:

initializing a language model for the selected language includes providing an empty language model containing no words in the language;

collecting, in substantially real-time, information about the monitored use of the words in the selected language by the multiple users of the selected language includes obtaining a language model containing about several hundred words in the language from a user; and

providing to the various computing devices the generated updates to the language model includes providing a language model containing about several thousand words in the language.

13. A method in a computing system of assisting in building a new language model used by a language recognition system to predict words in a language, the method comprising:

distinguishing a language;

determining whether a substantially complete language model is available for the distinguished language;

when a substantially complete language model is not available for the distinguished language,

monitoring, on the computing system, use of words in the distinguished language by a user of the computing system substantially in real time;

collecting, in a language model on the computing system, information about the monitored use of the words in the distinguished language;

receiving updates to the language model on the computing system based on additional information about use of words in the distinguished language by other users of the distinguished language monitored substantially in real time; and

predicting in response to user input, by the language recognition system, a word in the distinguished language intended by the user,

wherein the predicting is based on the information in the language model, including the information about the monitored use of words in the distinguished language and the additional information collected from other users of the distinguished language.

14. The method of claim 13, wherein distinguishing a language includes:

obtaining information about the location of the user or computing system;

identifying at least one language used in locations including or near the obtained location; and

automatically determining a language of user text input based on comparing characteristics of the user text input to characteristics of an identified language;

or

providing for user selection, based on the obtained location information and the language identification, the name of at least one identified language and receiving a user selection of a language name.

15. The method of claim 13, wherein distinguishing a language includes:

receiving a user input language name;

comparing the received user input language name to the contents of a data structure containing recognized language names, including names for languages in English and in native scripts;

determining, based on the comparing, that the received user input language name does not correspond to a recognized language name; and

prompting the user to select a name of a language similar to or related to the received user input language name, such that at least one other user has selected the language;

or to provide alternate names of the user input language and to select a keyboard or a character set for the user input language, and adding the received user input language name to the contents of the data structure.

16. The method of claim 15, further comprising associating at least a portion of a language model with a plurality of languages or with a plurality of language names.

17. The method of claim 13, wherein determining that a substantially complete language model is not available for the selected language includes determining that no language model is available for the selected language, that a language model for the selected language has not been completely developed, or that a language model for the selected language contains fewer than about several hundred words.

18. The method of claim 13, further comprising:

initializing a substantially empty language model, downloading a not completely developed language model based on word usage information from other users, downloading a language model containing fewer than about several hundred words, or downloading a language model containing words from a different language; and

providing or designating a keyboard or a character set for the language.

19. The method of claim 18, wherein providing or designating a keyboard or a character set for the language includes:

determining a keyboard chosen by most users of the language or a keyboard edited by a user of the language; and

presenting the determined keyboard as a default choice for the language.

20. The method of claim 13, wherein monitoring, on the computing system, use of words in the distinguished language by a user of the computing system substantially in real time includes:

monitoring words explicitly added to a user dictionary or language model;

or;

receiving a user selection of a block of text and an indication that the text is in the language; and

scanning the selected text, such that the words in the selected text or information about the words in the selected text is collected in the language model for the language on the computing system.

21. The method of claim 13, wherein information about the monitored use of words in the selected language includes words and frequencies of individual words, word pairs (bigrams), triplets (trigrams), or higher-order n-grams, and information about responses to word suggestions and deletions of words from the language model.

22. A system for assisting in building a language model used by a language recognition system to predict words in a language, the system comprising:

at least one memory storing computer-executable instructions of;

a component configured to associate a crowd-sourced language model with the language;

for one of multiple computing devices;

a component configured to identify user input of words on the computing device as use of words in the language;

a component configured to monitor use of words in the language on the computing device substantially in real time;

a component configured to collect, in the crowd-sourced language model, information about the monitored use of the words in the distinguished language on the multiple computing devices;

a component configured to generate updates to the crowd-sourced language model based on the collected information about the monitored use of the words in the language; and

a component configured to provide to each of the multiple devices the generated updates to the language model; and

at least one processor for executing the computer-executable instructions stored in the at least one memory.

23. The system of claim 22, wherein the component configured to collect, in the crowd-sourced language model, information about the monitored use of the words in the distinguished language on the multiple computing devices is configured to receive a language model or information about changes to a language model from each of the multiple computing devices.