System and method for providing interactive feature selection for training a document classification system

• US 20060212142A1
• Filed: 03/15/2006
• Published: 09/21/2006
• Est. Priority Date: 03/16/2005
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Oath Inc. (Verizon Communications Inc.)
• Inventors: Raghavan, Hema, Madani, Omid, Jones, Rosie
• Application Number:
  US11/376,989
• Publication Number:
  US 20060212142A1
• Time in Patent Office: Days
• US Class Current: 700/49
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/41 Interactive pattern learning with a human teacher

Abstract

A method for facilitating development of a document classification function comprises selecting a feature of a document, the feature being less than an entirety of the document; presenting the feature to a human subject; asking the human subject for a feature relevance value of the feature; and generating a classification function using the feature relevance value. The method may also include the steps of presenting the document to the human subject at the same time as presenting the feature; asking the human subject for document relevance value that measures relevance of the document to a category; and wherein the generating the classification function also uses the document relevance value.

Claims

1. A method for facilitating development of a document classification function, the method comprising:

selecting a feature of a document, the feature being less than an entirety of the document;

presenting the feature to a human subject;

asking the human subject for a feature relevance value of the feature; and

generating a classification function using the feature relevance value.

2. The method of claim 1, wherein the feature includes one of a word choice, a synonym, a date, an event, a person or link information.

3. The method of claim 1, wherein the feature relevance value is a binary variable.

4. The method of claim 1, wherein the feature relevance value is a sliding scale value.

5. The method of claim 1, wherein the feature relevance value is selected from a set of values.

6. The method as recited in claim 1, further comprising:

presenting the document to the human subject at the same time as presenting the feature;

asking the human subject for document relevance value that measures relevance of the document to a category; and

wherein the generating the classification function also uses the document relevance value.

7. The method of claim 6, wherein the document relevance value is a binary value.

8. The method of claim 6, wherein the document relevance value is a sliding scale value.

9. The method of claim 6, wherein the document relevance value is a value selected from a set of values.

10. The method of claim 1, wherein the generating of the classification function includes assuming that the features deemed most relevant according to the feature relevance values are the most relevant features for evaluating relevance of a document to a category.

11. The method of claim 1, wherein the generating the classification function includes generating a feature weight based on the feature relevance value.

12. The method of claim 11, further comprising monitoring user actions, and modifying the feature weight based on the monitoring.

13. A system for facilitating development of a classification function, the system comprising:

a feature selector for presenting a feature of a document to a human subject, the feature being less than an entirety of the document, and for asking the human subject for a feature relevance value of the feature; and

a classification function determining module for generating a classification function using the feature relevance value.

14. The system of claim 13, wherein the feature includes one of a word choice, a synonym, a date, an event, a person or link information.

15. The system of claim 13, wherein the feature relevance value is a binary variable.

16. The system of claim 13, wherein the feature relevance value is a sliding scale value.

17. The system of claim 13, wherein the feature relevance value is selected from a set of values.

18. The system as recited in claim 13, further comprising:

a document selector for presenting a document to the human subject at the same time as presenting the feature, and for asking the human subject for a document relevance value that measures relevance of the document to a category; and

wherein the classification function determining module also uses the document relevance value to generate the classification function.

19. The system of claim 18, wherein the document relevance value is a binary value.

20. The system of claim 18, wherein the document relevance value is a sliding scale value.

21. The system of claim 18, wherein the document relevance value is a value selected from a set of values.

22. The system of claim 13, wherein classification function determining module assumes that the features deemed most relevant according to the feature relevance value are the most relevant features for evaluating relevance of a document to a category.

23. The system of claim 13, wherein the classification function determining module generates a feature weight based on the feature relevance value.

24. The system of claim 13, further comprising a feedback module for monitoring user actions, and modifying the feature weight based on the monitoring.

25. A system for facilitating development of a classification function, the system comprising:

means for presenting a feature of a document to a human subject, the feature being less than an entirety of the document;

means for asking the human subject for a feature relevance value of the feature; and

means for generating a classification function using the feature relevance value.

26. A method for facilitating development of a document classification function, the method comprising:

enabling a human subject to identify a distinguishing feature of a document, the feature being less than an entirety of the document; and

generating a classification function using the distinguishing feature.

27. A method for facilitating development of a document classification function, the method comprising:

selecting a plurality of features of a document, each of the features being less than an entirety of the document;

presenting the features to a human subject;

asking the human subject for feature relevance values of the features; and

generating a classification function using the feature relevance values.

28. The method of claim 27, wherein the presenting includes presenting the features one at a time.

29. The method of claim 27, wherein the presenting includes presenting the features as a list.

30. The method of claim 27, wherein the presenting includes presenting the features with document content information.