Attribute scoring for unstructured content

• US 20040049478A1
• Filed: 09/11/2002
• Published: 03/11/2004
• Est. Priority Date: 09/11/2002
• Status: Active Grant

• Current Assignee: First Data Resources Incorporated (Fiserv Incorporated)
• Original Assignee: Intelligent Results
• Inventors: Meyer, Michael M., Jasper, Robert Joseph, Pennock, Kelly
• Granted Patent: US 7,249,312 B2
• Time in Patent Office: Days
• US Class Current: 707/1
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

The present invention provides for a system and method of assigning attribute scores, including those for abstract and diverse concepts such as sentiment (e.g., happiness, anger, sadness) to documents containing unstructured content (natural language content). Such content may include, but is not limited to, Web pages, e-mails, word processing documents, computer logs, chat logs, audio files, graphical images, text files, books, magazines, articles, etc. The attributes that are scored are specialized views of the unstructured content. Accordingly, attribute scoring denotes the processes of assigning one or more new measures to a document containing unstructured content. The attribute score or measure indicates the degree of the attributes found within the document.

Claims

1. A method for scoring of an attribute exhibited in a corpus of unstructured documents, the method comprising:

decomposing each unstructured document into subdocuments;

determining an attribute score for each subdocument in said corpus corresponding to the level said subdocument exhibits a feature associated with the attribute; and

combining subdocument attribute scores for each document to produce a document attribute score.

2. The method of claim 1, further comprising combining the document attribute scores for each document to produce a corpus attribute score.

3. The method of claim 1, further comprising determining which features of interest in said corpus of unstructured documents affect the document attribute score.

4. The method of claim 3, wherein determining which features of interest affect the document attribute score comprises:

identifying candidate features in a corpus of training documents; and

storing each candidate feature in association with the training document in which it occurs.

5. The method of claim 4, further comprising labeling each training document in which a candidate feature occurs as a positive or negative example for the attribute.

6. The method of claim 5 further comprising two features that are related and such relation alters a weight of one of said candidate features.

7. The method of claim 5, further comprising selecting a test corpus of test documents.

8. The method of claim 4, further comprising weighting said candidate feature.

9. The method of claim 7, further comprising cross validating said candidate feature weights to predict said test document labels.

10. The method of claim 8, wherein weighting said candidate features comprises estimating a model utilizing a maximum likelihood estimation.

11. The method of claim 8, wherein weighting said candidate features comprises calculating a document feature frequency compared to a corpus'"'"' feature frequency.

12. The method of claim 10, wherein said logistic model'"'"'s parameters are selected with a bias against over-fitting.

13. The method of claim 10, wherein weighting said candidate features comprises estimating a model utilizing the method of least squares.

14. The method of claim 13, further comprising determining that said model does not fit and iteratively varying the weighting of said model according to a maximization method to better fit the model.

15. The method of claim 14, wherein said maximization method is Newton'"'"'s method.

16. The method of claim 14, wherein said maximization method is a cyclic ascent method.

17. The method of claim 8, wherein said weighted candidate feature is stored as a weighted feature.

18. The method of claim 17, wherein determining each subdocument'"'"'s attribute score comprises determining if any weighted features are present each subdocument.

19. The method of claim 18, wherein each subdocument'"'"'s attribute score corresponds to a combination of values of weighted features.

21. The computer readable medium of claim 16, further comprising combining the document attribute scores for each document to produce a corpus attribute score.

22. The computer readable medium of claim 16, further comprising determining which features of interest in said corpus of unstructured documents affect the document attribute score.

23. The computer readable medium of claim 18, wherein the learning component determines which features of interest affect the document attribute score by:

identifying training features in a corpus of training documents; and

storing each training feature in association with the training document in which it occurs.

24. The computer readable medium of claim 19, further comprising instruction for labeling each document in which a training feature occurs as a positive or negative example for the attribute.

27. The computer readable medium of claim 22, further comprising instruction for cross validating said training document labels and said training document weights with test document labels and test document weights.

31. The computer readable medium of claim 19, further comprising instructions for weighting said training feature.

32. The computer readable medium of claim 27, wherein said weighted training feature is stored as a weighted feature.

36. The computing apparatus of claim 31, further operative to combine the document attribute scores for each document to produce a corpus attribute score.

37. The computing apparatus of claim 31, further operative to determine which features of interest in said corpus of unstructured documents affect the document attribute score.

42. The computing apparatus of claim 37, further operative to cross validate said training document labels and said training document weights with test document labels and test document weights.

47. The computing apparatus of claim 42, wherein said weighted training feature is stored as a weighted feature.

20. A computer readable medium containing computer readable instructions for scoring of an attribute exhibited in a corpus of unstructured documents comprising

learning a component for learning to what level features are associated with the attribute;

and

scoring a component for determining attribute scores by;

decomposing each unstructured document into subdocuments;

determining an attribute score for each subdocument in said corpus corresponding to the level said subdocument exhibits a feature associated with the attribute; and

combining subdocument attribute scores for each document to produce a document attribute score.

25. The computer readable medium of claim 20 further comprising data representing two training features that are related and such relation alters the weight of one of said training features.

26. The computer readable medium of claim 20, further comprising instructions for selecting a test corpus of test documents.

28. The computer readable medium of claim 20, wherein weighting said training features comprises calculating a document feature frequency compared to a training corpus feature frequency.

29. The computer readable medium of claim 20, wherein weighting said training features comprises estimating a logistic model utilizing a maximum likelihood estimation.

30. The computer readable medium of claim 25, wherein said logistic model'"'"'s parameters are selected with a bias against complexity.

33. The computer readable medium of claim 28, wherein determining each subdocument'"'"'s attribute score comprises determining if any weighted features are exhibited by each subdocument.

34. The computer readable medium of claim 29, wherein each subdocument'"'"'s attribute score corresponds to a combination of values of weighted features.

38. The computing apparatus of claim 33, wherein determining which features of interest affect the document attribute score comprises:

identifying training features in a corpus of training documents; and

storing each training feature in association with the training document in which it occurs.

39. The computing apparatus of claim 34, further operative to label each document in which a training feature occurs as a positive or negative example for the attribute.

46. The computing apparatus of claim 34, further operative to weight said training feature.

51. The computer readable medium of claim 46, wherein said plurality of unstructured documents comprise at least one training document.

52. The computer readable medium of claim 46, wherein said plurality of unstructured documents comprise at least one validation document.

53. The computer readable medium of claim 46, wherein determining to what extent each of said components embodies a desired attribute comprises identifying a feature of said component.

56. The computer readable medium of claim 51, wherein assigning a value comprises combining a plurality of weighted features of a component part.

57. The computer readable medium of claim 52, wherein said contribution is a linear combination.

58. The computer readable medium of claim 53, wherein aggregating said values is a linear aggregation.

35. A computing apparatus, comprising a processor and a memory containing computer executable instructions operative to score an attribute exhibited in a corpus of unstructured documents by:

decomposing each unstructured document into subdocuments;

determining an attribute score for each subdocument in said corpus corresponding to the level said subdocument exhibits a feature associated with the attribute; and

combining subdocument attribute scores for each document to produce a document attribute score.

40. The computing apparatus of claim 35 further operative to determine that two training features are related and that such relation alters the weight of one of said training features.

41. The computing apparatus of claim 35, further operative to select a test corpus of test documents.

43. The computing apparatus of claim 35, wherein weighting said training features comprises calculating a document feature frequency compared to a training corpus feature frequency.

44. The computing apparatus of claim 35, wherein weighting said training features comprises estimating a logistic model utilizing a maximum likelihood estimation.

45. The computing apparatus of claim 40, wherein said logistic model'"'"'s parameters are selected with a bias against complexity.

48. The computing apparatus of claim 43, wherein determining each subdocument'"'"'s attribute score comprises determining if any weighted features are exhibited by each subdocument.

49. The computing apparatus of claim 44, wherein each subdocument'"'"'s attribute score corresponds to a combination of values of weighted features.

54. The computer readable medium of claim 49, further comprising weighting said feature.

50. A computer readable medium containing:

a data structure comprised of;

a plurality of unstructured documents; and

a plurality of attribute scores associated with said unstructured documents,

wherein said attribute scores were derived by;

separating said unstructured documents into component parts;

determining to what extent each of said component parts embodies a desired attribute;

assigning a value corresponding to the extent that each component part embodies said desired attribute; and

aggregating said values to form document values that embody the extent that said unstructured documents embody said desired attribute.

55. The computer readable medium of claim 50, wherein weighting said feature comprises matching said feature to a corresponding weighted feature in a feature/weight database.