Knowledge Re-Use for Call Routing

• US 20090259613A1
• Filed: 04/13/2009
• Published: 10/15/2009
• Est. Priority Date: 04/14/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Liu, Ding
• Granted Patent: US 8,732,114 B2
• Time in Patent Office: Days
• US Class Current: 706/47
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue

Abstract

A method is described for semantic classification in human-machine dialog applications, for example, call routing. Utterances in a new training corpus of a new semantic classification application are tagged using a pre-existing semantic classifier and associated pre-existing classification tags trained for an earlier semantic classification application.

Claims

1. A method for semantic classification comprising:

tagging utterances in a new training corpus of a new semantic classification application using a pre-existing semantic classifier and associated pre-existing classification tags trained for an earlier semantic classification application.

2. A method according to claim 1, further comprising:

training a new semantic classifier on the new training corpus to produce a set of new classification tags for the new semantic classifier including a plurality of the pre-existing classification tags.

3. A method according to claim 2, further comprising:

creating a set of application-specific company-generic classification tags from the set of new classification tags.

4. A method according to claim 1, wherein the semantic classifier is a statistical classifier.

5. A method according to claim 1, wherein the semantic classification application is a call routing application.

6. A method according to claim 1, wherein the new semantic classification application and the earlier semantic classification application are in different application domains.

7. A method according to claim 1, wherein the new semantic classification application and the earlier semantic classification application are in the same application domain.

8. A method of abstracting a semantic classifier, the method comprising:

identifying application-specific features associated with a semantic classifier; and

replacing the application-specific features with generic stem rules.

9. A method according to claim 8, further comprising:

using the semantic classifier and the generic stem rules to implement a new semantic classifier.

10. A method according to claim 8, wherein the semantic classifiers are in different application domains.

11. A method according to claim 8, wherein the semantic classifiers are in the same application domain.

12. A method according to claim 8, wherein the semantic classifiers are statistical classifiers.

13. A method according to claim 8, wherein the semantic classifier is a call routing classifier.

14. A computer program product implemented in a computer readable storage medium for semantic classification, the product comprising:

program code for tagging utterances in a new training corpus of a new semantic classification application using a pre-existing semantic classifier and associated pre-existing classification tags trained for an earlier semantic classification application.

15. A product according to claim 14, further comprising:

program code for training a new semantic classifier on the new training corpus to produce a set of new classification tags for the new semantic classifier including a plurality of the pre-existing classification tags.

16. A product according to claim 15, further comprising:

program code for creating a set of application-specific company-generic classification tags from the set of new classification tags.

17. A product according to claim 14, wherein the semantic classifier is a statistical classifier.

18. A product according to claim 14, wherein the semantic classification application is a call routing application.

19. A product according to claim 14, wherein the new semantic classification application and the earlier semantic classification application are in different application domains.

20. A product according to claim 14, wherein the new semantic classification application and the earlier semantic classification application are in the same application domain.

21. A system for developing a semantic classification application based on an earlier semantic classification application, the system comprising:

means for tagging utterances in a new training corpus of a new semantic classification application using a pre-existing semantic classifier and associated pre-existing classification tags trained for an earlier semantic classification application.

22. A system according to claim 21, further comprising:

means for training a new semantic classifier on the new training corpus to produce a set of new classification tags for the new semantic classifier including a plurality of the pre-existing classification tags.

23. A system according to claim 22, further comprising:

means for creating a set of application-specific company-generic classification tags from the set of new classification tags.

24. A system according to claim 21, wherein the semantic classifier is a statistical classifier.

25. A system according to claim 21, wherein the semantic classification application is a call routing application.

26. A system according to claim 21, wherein the new semantic classification application and the earlier semantic classification application are in different application domains.

27. A system according to claim 21, wherein the new semantic classification application and the earlier semantic classification application are in the same application domain.