Method and System for Text Message Normalization Based on Character Transformation and Web Data

• US 20120303355A1
• Filed: 05/27/2011
• Published: 11/29/2012
• Est. Priority Date: 05/27/2011
• Status: Abandoned Application

• Current Assignee: Robert Bosch GmbH, Robert Bosch Tool Corporation (Robert Bosch GmbH)
• Original Assignee: Robert Bosch GmbH
• Inventors: Weng, Fuliang, Liu, Fei
• Application Number:
  US13/117,330
• Publication Number:
  US 20120303355A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/126 Character encoding
• CPC Class Codes: G06F 40/232 Orthographic correction, e.g. spell checking or vowelisation
• CPC Class Codes: G06F 40/274 Converting codes to words; Guess-ahead of partial word inputs

Abstract

A method for generating non-standard tokens that correspond to standard tokens used in speech synthesis systems has been developed. The method includes selecting a standard token from a plurality of standard tokens stored in memory, using a random field model to select a predetermined operation to perform on each character in the selected token, performing the selected operation on each character to generate an output token, and storing the output token in the memory in association with the selected token. The output token is different from each token in the plurality of standard tokens.

Claims

1. A method for generating non-standard tokens from a standard token stored in a memory comprising:

selecting a standard token from a plurality of standard tokens stored in the memory, the selected token having a plurality of input characters;

selecting an operation from a plurality of predetermined operations in accordance with a random field model for each input character in the plurality of input characters;

performing the selected operation on each input character to generate an output token that is different from each token in the plurality of standard tokens; and

storing the output token in the memory in association with the selected token.

2. The method of claim 1, the operation performed on each input character being one of:

providing the input character in the output token;

replacing the input character with one different character in the output token;

replacing the input character with a plurality of different characters in the output token; and

not providing the input character in the output token.

3. The method of claim 1 wherein the random field model is a conditional random field model.

4. The method of claim 3 further comprising:

generating a plurality of operational parameters for the conditional random field model prior to generating the output token, the generation of the plurality of operational parameters for the conditional random field model comprising;

comparing each token in a second plurality of tokens stored in the memory to the standard tokens in the plurality of standard tokens;

identifying a first token in the second plurality of tokens as being a non-standard token in response to the first token being different from each of the tokens in the plurality of standard tokens;

identifying a second token in the second plurality of tokens as being a context token in response to the second token providing contextual information for the first token;

generating at least one database query, the at least one database query including the first token and the second token;

querying a database with the at least one generated database query; and

identifying a result token corresponding to the first token from a result obtained from the database.

5. The method of claim 4, wherein the database is a search engine, the first token and the second token being search terms for the search engine.

6. The method of claim 4, the generation of the plurality of operational parameters for the conditional random field model further comprising:

aligning each character in the result token with at least one character in the non-standard token;

identifying at least one feature in the result token corresponding to each character in the result token;

identifying an operation in the plurality of predetermined operations that generates at least one character in the non-standard token from a corresponding aligned character in the result token; and

updating the operational parameters of the conditional random field model with reference to the identified operation and the at least one feature of the aligned character in the result token.

7. The method of claim 4 further comprising:

generating a plurality of non-standard tokens for the selected standard token, at least some of the plurality of non-standard tokens being different from each token in the second plurality of tokens; and

storing the plurality of non-standard tokens in the memory in association with the selected standard token.

8. The method of claim 1, further comprising:

identifying a non-standard token in a text message having at least one token, the non-standard token corresponding to a non-standard token stored in the memory;

obtaining a standard token that is associated with the non-standard token from the memory;

replacing the non-standard token in the text message with the standard token; and

synthesizing speech corresponding to the at least one standard token in the text message.

9. The method of claim 8 further comprising:

identifying a plurality of standard tokens stored in the memory that are associated with the non-standard token;

applying a rank to each of the standard tokens that are associated with the non-standard token, the rank being a probability of each standard token appearing in the text message; and

replacing the non-standard token with a standard token in the plurality of standard tokens having a highest rank.

10. A method for generating operational parameters for use in a random field model comprising:

comparing each token in a first plurality of tokens stored in a memory to a plurality of standard tokens stored in the memory;

identifying a first token in the first plurality of tokens as a non-standard token in response to the first token being different from each standard token in the plurality of standard tokens;

identifying a second token in the first plurality of tokens as a context token in response to the second token providing contextual information for the first token;

generating a database query including the first token and the second token;

querying a database with the generated query;

identifying a result token corresponding to the first token from a result obtained from the database; and

storing the result token in association with the first token in a memory.

11. The method of claim 10, the result token being different from the second token.

12. The method of claim 10, the identification of the result token further comprising:

identifying a first longest common sequence of characters in the first token and in a candidate token in the result obtained from the database;

identifying a second longest common sequence of characters in the second token and in the candidate token; and

identifying the candidate token as the result token in response to the first longest common sequence of characters having a greater number of characters than the second longest common sequence characters.

13. The method of claim 10 further comprising:

identifying a first candidate token corresponding to the first token in the result obtained from the database, the first candidate token being a non-standard token;

identifying a second candidate token corresponding to the first candidate token, the second candidate token matching a token in the second plurality of standard tokens stored in the memory; and

storing the second candidate token in association with the first token in the memory.

14. A system for generating non-standard tokens from standard tokens comprising:

a memory, the memory storing a plurality of standard tokens and a plurality of operational parameters for a random field model; and

a processing module operatively connected to the memory, the processing module being configured to;

obtain the operational parameters for the random field model from the memory;

generate the random field model from the operational parameters;

select a standard token from the plurality of standard tokens in the memory, the selected standard token having a plurality of input characters;

select an operation from a plurality of predetermined operations in accordance with the random field model for each input character in the plurality of input characters for the selected standard token;

perform the selected operation on each input character in the selected standard token to generate an output token that is different from each standard token in the plurality of standard tokens; and

store the output token in the memory in association with the selected standard token.

15. The system of claim 14, the selected operation being one of:

providing the input character to the output token;

replacing the input character with one different character in the output token;

replacing the input character with a plurality of different characters in the output token; and

deleting the input character in the output token.

16. The system of claim 14 wherein the random field model is a conditional random field model.

17. The system of claim 16 further comprising:

a training module configured to generate the operational parameters for the conditional random field model, the training module being operatively connected to the memory and configured to;

compare each token in a second plurality of tokens stored in the memory to the standard tokens in the plurality of standard tokens stored in the memory;

identify a first token in the second plurality of tokens as a non-standard token in response to the first token being different from each standard token in the plurality of standard tokens;

identify a second token in the second plurality of tokens as a context token in response to the second token providing contextual information for the first token;

generate a database query including the first token and the second token;

query a database with the generated database query;

identify a result token corresponding to the first token from a result obtained from the database in response to the database query; and

store the first token in the memory in association with the result token.

18. The system of claim 17, the training module being further configured to query a search engine with the generated database query.

19. The system of claim 17, the training module being further configured to:

align each character in the result token with at least one character in the first token;

identify at least one feature in the result token corresponding to each character in the result token;

identify an operation in the plurality of predetermined operations that generates at least one character in the first token from a corresponding aligned character in the result token; and

update the operational parameters of the conditional random field model with reference to the identified operation and the at least one feature of the aligned character in the result token.

20. The system of claim 14 further comprising:

a speech synthesis module; and

a non-standard token identification module operatively connected to the memory and the speech synthesis module, the non-standard token identification module being configured to identify a non-standard token in a text message stored in the memory, the non-standard token in the text message corresponding to a standard token stored in the memory, replace the non-standard token in the text message with the standard token, and provide the text message to the speech synthesis module for speech synthesis.