Monte Carlo method for natural language understanding and speech recognition language models

1. A Monte Carlo method of developing a training corpus for use with natural language understanding or speech recognition language models, said method comprising:

identifying at least one phrase embedded in a body of text, said phrase belonging to a phrase class;

determining at least one subject matter attribute corresponding to said identified phrase; and

augmenting the training corpus by copying said body of text and replacing said identified phrase with a different phrase selected from a plurality of phrases, said different phrase belonging to said phrase class and having said determined subject matter attribute.

2. The method of claim 1 , wherein said plurality of phrases are included within a single data source selected from the group consisting of a grammar, selected non-terminal within a grammar, and a list.

3. The method of claim 1 , wherein said plurality of phrases are included within at least two data sources wherein at least one of said data sources is selected from the group consisting of a grammar, selected non-terminals within a grammar, and a list.

4. The method of claim 1 , wherein said subject matter attribute is selected from the group comprising at least one phrase category and at least one boundary condition.

5. The method of claim 1 , wherein said subject matter attribute corresponds to at least one of a date attribute, a time attribute, a geographical attribute, and a name attribute.

6. The method of claim 1 , wherein said different phrase has a probability value which exceeds a predetermined threshold value.

7. A Monte Carlo method of developing a training corpus for use with natural language understanding or speech recognition language models, said method comprising;

identifying at least one phrase embedded within a body of text;

locating a second phrase within a plurality of phrases, said second phrase identically matching said identified phrase, wherein said second phrase belongs to a phrase class and has at least one subject matter attribute corresponding to said phrase class; and

copying said body of text and replacing said identified phrase with a different phrase selected from said plurality of phrases, said different phrase having a subiect matter attribute that matches the subject matter attribute of said second phrase.

8. The method of claim 7 , wherein said plurality of phrases are included within a single data source selected from the group consisting of a grammar, selected non-terminals within a grammar, and a list.

9. The method of claim 7 , wherein said plurality of phrases are included within at least two data sources selected from the group consisting of a grammar, selected non-terminals within a grammar, and a list.

10. The method of claim 7 , wherein said subject matter attribute is selected from the group comprising at least one phrase category and at least one boundary condition.

11. The method of claim 7 , wherein said subject matter attribute corresponds to at least one of a date attribute, a time attribute, a geographical attribute, and a name attribute.

12. The method of claim 7 , wherein said different phrase has a probability value which exceeds a predetermined threshold value.

13. A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of:

identifying at least one phrase embedded in a body of text, said phrase belonging to a phrase class;

determining at least one subject matter attribute corresponding to said identified phrase; and

augmenting the training corpus by copying said body of text and replacing said identified phrase with a different phrase selected from a plurality of phrases, said different phrase belonging to said phrase class and having said determined subject matter attribute.

14. The machine-readable storage of claim 13 , wherein said plurality of phrases are included within a single data source selected from the group consisting of a grammar, selected non-terminals within a grammar, and a list.

15. The machine-readable storage of claim 13 , wherein said plurality of phrases are included within at least two data sources wherein at least one of said data sources is selected from the group consisting of a grammar, selected non-terminal within a grammar, and a list.

16. The machine-readable storage of claim 13 , wherein said subject matter attribute is selected from the group comprising at least one phrase category and at least one boundary condition.

17. The machine-readable storage of claim 13 , wherein said subject matter attribute corresponds to at least one of a date attribute, a time attribute, a geographical attribute, and a name attribute.

18. The machine-readable storage of claim 13 , wherein said different phrase has a probability value which exceeds a predetermined threshold value.

19. A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of:

identifying at least one phrase embedded within a body of text;

locating a second phrase within a plurality of phrases, said second phrase identically matching said identified phrase, wherein said second phrase belongs to a phrase class and has at least one subject matter attribute corresponding to said phrase class; and

copying said body of text and replacing said identified phrase with a different phrase selected from said plurality of phrases, said different phrase having a subject mattet attribute that matches the subject matter attribute of said second phrase.

20. The machine-readable storage of claim 19 , wherein said plurality of phrases are included within a single data source selected from the group consisting of a grammar, selected non-terminals within a grammar, and a list.

21. The machine-readable storage of claim 19 , wherein said plurality of phrases are included within at least two data sources selected from the group consisting of a grammar, selected non-terminals within a grammar, and a list.

22. The machine-readable storage of claim 19 , wherein said subject matter attribute is selected from the group comprising at least one phrase category and at least one boundary condition.

23. The machine-readable storage of claim 19 , wherein said subject matter attribute corresponds to at least one of a date attribute, a time attribute, a geographical attribute, and a name attribute.

24. The machine-readable storage of claim 19 , wherein said different phrase has a probability value which exceeds a predetermined threshold value.

25. A Monte Carlo method of developing a training corpus for use with natural language understanding or speech recognition language models, said method comprising:

identifying at least one phrase embedded in a body of text, said phrase belonging to a phrase class;

determining at least one syntax-independent and semantics-independent subject matter attribute corresponding to said identified phrase; and

augmenting the training corpus by copying said body of text and replacing said identified phrase with a different phrase selected from a plurality of phrases, said different phrase belonging to said phrase class and having said determined subject matter attribute.