Statistical classifiers for spoken language understanding and command/control scenarios

• US 20040148170A1
• Filed: 05/30/2003
• Published: 07/29/2004
• Est. Priority Date: 01/23/2003
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Huttenhower, Curtis, Cipollone, Domenic, Chelba, Ciprian, Acero, Alejandro, Calcagno, Michael, Wong, Leon, Shahani, Ravi, Wang, YeYi
• Application Number:
  US10/449,708
• Publication Number:
  US 20040148170A1
• Time in Patent Office: Days
• US Class Current: 704/257
• CPC Class Codes: G06F 40/216 using statistical methods

Abstract

The present invention involves using one or more statistical classifiers in order to perform task classification on natural language inputs. In another embodiment, the statistical classifiers can be used in conjunction with a rule-based classifier to perform task classification. In one application, a statistical classifier is used in order ascertain if an input is a search query or a natural-language input.

Claims

1. A text classifier in a natural language interface that receives a natural language user input, the text classifier comprising:

a feature extractor extracting a feature vector from a textual input indicative of the natural language user input;

a statistical classifier coupled to the feature extractor outputting a class identifier identifying a target class associated with the textual input based on the feature vector.

2. The text classifier of claim 1 wherein the statistical classifier comprises:

a plurality of statistical classification components each outputting a class identifier.

3. The text classifier of claim 2 wherein the statistical classifier comprises:

a class selector coupled to the plurality of statistical classification components and selecting one of the class identifiers as identifying the target class.

4. The text classifier of claim 3 wherein the class selector comprises a voting component.

5. The text classifier of claim 3 wherein the class selector comprises an additional statistical classifier.

6. The text classifier of claim 1 and further comprising:

a rule-based classifier receiving the textual input and outputting a class identifier; and

a selector selecting at least one of the class identifiers as identifying the target class.

7. The text classifier of claim 1 and further comprising:

a rule-based parser receiving the textual input and the class identifier and outputting a semantic representation of the textual input.

8. The text classifier of claim 7 wherein the semantic representation includes a class having slots, the slots being filled with semantic expressions.

9. The text classifier of claim 1 and further comprising:

a pre-processor identifying words in the textual input having semantic content.

10. The text classifier of claim 9 wherein the preprocessor is configured to remove words from the textual input that have insufficient semantic content.

11. The text classifier of claim 9 wherein the preprocessor is configured to insert tags for words in the textual input, the tags being semantic labels for the words.

12. The text classifier of claim 1 wherein the feature vector is based on words in a vocabulary supported by the natural language interface.

13. The text classifier of claim 12 wherein the feature vector is based on n-grams of the words in the vocabulary.

14. The text classifier of claim 12 wherein the feature vector is based on words in the vocabulary having semantic content.

15. The text classifier of claim 1 wherein the statistical classifier comprises a Naive Bayes Classifier.

16. The text classifier of claim 1 wherein the statistical classifier comprises a support vector machine.

17. The text classifier of claim 1 wherein the statistical classifier comprises a plurality of class-specific statistical language models.

18. The text classifier of claim 1 wherein a number c of classes are supported by the natural language interface and wherein the statistical classifier comprises c class-specific statistical language models.

19. The text classifier of claim 1 and further comprising:

a speech recognizer receiving a speech signal indicative of the natural language input and providing the textual input.

20. The text classifier of claim 1 wherein the statistical classifier identifies a plurality of n-best target classes.

21. The text classifier of claim 20 and further comprising:

an output displaying the n-best target classes for user selection.

22. The text classifier of claim 2 wherein each statistical classifier outputs a plurality of n-best target classes.

23. A computer-implemented method of processing a natural language input for use in completing a task represented by the natural language input, comprising:

performing statistical classification on the natural language input to obtain a class identifier for a target class associated with the natural language input;

identifying rules in a rule-based analyzer based on the class identifier; and

analyzing the natural language input with the rule-based analyzer using the identified rules to fill semantic slots in the target class.

24. The method of claim 23 and further comprising:

prior to performing statistical classification, identifying words in the natural language input that have semantic content.

25. The method of claim 23 wherein the natural language input is represented by a speech signal and further comprising:

performing speech recognition on the speech signal prior to performing statistical classification.

26. The method of claim 23 wherein performing statistical classification comprises:

performing statistical classification on the natural language input using a plurality of different statistical classifiers; and

selecting a class identifier output by one of the statistical classifiers as representing the target class.

27. The method of claim 26 wherein selecting comprises:

performing statistical classification on the class identifiers output by the plurality of statistical classifiers to select the class identifier that represents the target class.

28. The method of claim 26 wherein selecting comprises:

selecting the class identifier output by a greatest number of the plurality of statistical classifiers.

29. The method of claim 23 and further comprising:

performing rule-based analysis on the natural language input to obtain a class identifier; and

identifying the target class based on the class identifier obtained from the statistical classification and the class identifier obtained from the rule-based analysis.

30. A system for identifying a task to be performed by a computer based on a natural language input, comprising:

a feature extractor extracting features from the natural language input; and

a statistical classifier, trained to accommodate unseen data, receiving the extracted features and identifying the task based on the features.

31. The system of claim 30 wherein the statistical classifier and wherein probabilities used by the statistical classifier are smoothed using smoothing data to accommodate for the unseen data.

32. The system of claim 31 wherein smoothing data is obtained using cross-validation data.

33. A text classifier identifying a target class corresponding to a natural language input, comprising:

a feature extractor extracting a set of features from the natural language input; and

a Naï

ve Bayes Classifier receiving the set of features and identifying the target class based on the set of features.

34. The text classifier of claim 33 wherein the target class is indicative of a task to be performed based on the natural language input.

35. The text classifier of claim 34 and further comprising:

a preprocessor identifying content words in the natural language input prior to the feature extractor extracting the set of features.

36. The text classifier of claim 35 wherein the preprocessor identifies the content words by removing from the natural language input words having insufficient semantic content.

37. A text classifier identifying a target class corresponding to a natural language input, comprising:

a feature extractor extracting a set of features from the natural language input; and

a statistical language model classifier receiving the set of features and identifying the target class based on the set of features.

38. The text classifier of claim 37 wherein the set of features includes n-grams.

39. The text classifier of claim 37 and further comprising:

a preprocessor identifying content words in the natural language input prior to the feature extractor extracting the set of features.

40. A text classifier identifying one or more target classes corresponding to a natural language input, comprising:

a feature extractor extracting a set of features from the natural language input; and

a plurality of statistical classifiers receiving the set of features and identifying a target class based on the set of features.

41. The text classifier of claim 40 wherein each statistical classifier outputs a class identifier based on the set of features and further comprising:

a selector receiving the class identifiers from each of the statistical classifiers and selecting the target class as a class identified by at least one of the class identifiers.

42. The text classifier of claim 40 and further comprising:

a preprocessor identifying content words in the natural language input prior to the feature extractor extracting the set of features.

43. A text classifier identifying a target class corresponding to a natural language input, comprising:

a feature extractor extracting a set of features from the natural language input;

a statistical classifier receiving the set of features and outputting a class identifier based on the set of features;

a rules based classifier outputting a class identifier based on the natural language input; and

a selector selecting a target class based on the class identifiers output by the statistical classifier and the rule-based classifier.

44. The text classifier of claim 43 and further comprising:

a preprocessor identifying content words in the natural language input prior to the feature extractor extracting the set of features and prior to the rule-based classifier receiving the natural language input.

45. A text classifier identifying a target task to be completed corresponding to a natural language input, comprising:

a feature extractor extracting a set of features from a textual input indicative of the natural language input;

a statistical classifier receiving the set of features and identifying the target task based on the set of features; and

a rule-based parser receiving the textual input and a class identifier indicative of the identified target task and outputting a semantic representation of the textual input.

46. The text classifier of claim 45 wherein the rule-based parser is configured to identify semantic expressions in the textual input.

47. The text classifier of claim 46 wherein the semantic representation includes a class having slots, the slots being filled with the semantic expressions.

48. The text classifier of claim 45 and further comprising:

a pre-processor identifying words in the textual input having semantic content.

49. The text classifier of claim 48 wherein the preprocessor is configured to remove words from the textual input that have insufficient semantic content.

50. The text classifier of claim 48 wherein the preprocessor is configured to insert tags for words in the textual input, the tags being semantic labels for the words.

51. The text classifier of claim 48 wherein the preprocessor is configured to replace words in the textual input with semantic tags, the semantic tags being semantic labels for the words.

52. A text classifier in a natural language interface that receives a natural language user input, the text classifier comprising:

a statistical classifier configured to receive a textual input and output a class identifier identifying a target class associated with the textual input.

53. The text classifier of claim 52 wherein the statistical classifier is configured to form tokens of the textual input and access a lexicon to ascertain token frequency of each token corresponding to the textual input in order to identify a target class. [LCW1]

54. The text classifier of claim 53 wherein the statistical classifier is configured to calculate a probability that the textual input corresponds to each of a plurality of possible classes based on token frequency of each token corresponding to the textual input.

55. The text classifier of claim 54 wherein the statistical classifier is configured to use a default value for token frequency if a token is not present in the lexicon.

56. The text classifier of claim 54 wherein the statistical classifier is configured to apply a scaling factor to a probability of a class based on whether a token is present in the lexicon.

57. The text classifier of claim 56 wherein the scaling factor varies as a function of the class.

58. The text classifier of claim 57 wherein the scaling factor for a class is a function of how frequently unseen words are encountered for the class.

59. The text classifier of claim 53 wherein tokens in the lexicon comprise words.

60. The text classifier of claim 53 wherein tokens in the lexicon comprise groups of words.

61. The text classifier of claim 53 wherein tokens in the lexicon comprise auxiliary features.

62. The text classifier of claim 53 wherein tokens in the lexicon comprise named entities.

63. The text classifier of claim 53 wherein tokens in the lexicon comprise generalized tokens that represent specific words.

64. The text classifier of claim 53 wherein the statistical classifier is configured to provide a list of class identifiers identifying target classes associated with the textual input.

65. The text classifier of claim 64 wherein the statistical classifier is configured to calculate a probability that the textual input corresponds to each of a plurality of possible classes based on token frequency of each token corresponding to the textual input.

66. The text classifier of claim 65 wherein the statistical classifier is configured to select a target class as a function of comparing calculated probabilities for each possible class.

67. The text classifier of claim 66 wherein the statistical classifier is configured to select a target class as a function of comparing calculated probabilities exceeding a selected threshold.

68. The text classifier of claim 67 wherein the statistical classifier is configured to use a first selected threshold for a first set of classes and a second selected threshold for a second set of classes.

69. The text classifier of claim 67 wherein the statistical classifier is configured to use a first selected threshold for a set of classes when a first class of the set has a greater probability than a second class of the set, and is configured to use a second selected threshold when the second class of the set has a greater probability than the first class of the set.

70. The text classifier of claim 53 wherein the lexicon includes a first class associated with natural language commands and a second class associated with search queries.

71. The text classifier of claim 52 and further comprising an interpretation collection module configured to receive the output from statistical classifier and combine the output with an output from a semantic analyzer analyzing the textual input to form a combined list of possible interpretations.

72. The text classifier of claim 71 wherein the interpretation collection module is configured to remove duplicates in the combined list.

73. The text classifier of claim 72 wherein the interpretation collection module is configured to ascertain if a first interpretation in the combined list is a subset of another interpretation.

74. A computer-implemented method of processing textual input, comprising:

performing statistical classification on the textual input to obtain a target class associated with the textual input; and

forwarding the textual input to a search service if the target class identified relates to the textual input comprising a search query.

75. The computer-implemented method of claim 74 and further comprising:

forwarding the textual input to a statistical classifier if the target class identified relates to the textual input comprising a natural-language command; and

performing statistical classification on the textual input to obtain a target class indicative of a natural language command associated with the textual input.

76. The computer-implemented method of claim 74 wherein the step of performing includes forming tokens of the textual input and accessing a lexicon to ascertain token frequency of each token corresponding to the textual input in order to identify a target class.

77. The computer-implemented method of claim 76 wherein the step of performing includes calculating a probability that the textual input corresponds to each of a plurality of possible classes based on token frequency of each token corresponding to the textual input.

78. The computer-implemented method of claim 77 wherein the step of performing includes providing a list of class identifiers identifying target classes associated with the textual input.

79. The computer-implemented method of claim 78 wherein the step of performing includes selecting a target class for the list as a function of comparing calculated probabilities for each possible class.

80. The computer-implemented method of claim 77 and further comprising taking action as a function of a calculated probability exceeding a selected threshold.

81. A computer-implemented method of processing textual input comprising a natural-language command, comprising:

performing statistical classification on the textual input to obtain a target class and associated interpretation with the textual input; and

combining the interpretation from performing statistical classification with an interpretation from another form of analysis of the textual input to form a combined list of possible interpretations.

82. The computer-implemented method of claim 81 wherein combining includes removing duplicates in the combined list.

83. The computer-implemented method of claim 82 wherein combining includes ascertaining if a first interpretation in the combined list is a subset of another interpretation.

84. The computer-implemented method of claim 83 wherein combining includes removing the first interpretation from the combined list.