Methods and apparatus for classifying text and for building a text classifier
AssigneeMicrosoft Corporation
InventorsSusan T. Dumais, David Heckerman, Eric Horvitz, John Carlton Platt, Mehran Sahami
Life of this patent
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Abstract
A text classifier and building the text classifier by determining appropriate parameters for the text classifier.
Claims44
A method for generating, from a training set of textual information objects, each either belonging to a category or not, parameters of a classifier for determining whether or not a textual information object belongs to the category, the method comprising steps of:
- a) extracting features from the textual information objects to generate, for each textual information object of the training set, an associated feature vector having a number of features;
- b) reducing the number of features of each of the feature vectors to generated, for each textual information object of the training set, an associated reduced feature vector;
- c) determining a weight vector for the classifier by training a support vector machine;
- and
- d) determining a monotonic function to be used by the classifier.
A method for generating, from a set of reduced feature vectors associated with a training set of textual information objects, each either belonging to a category or not, parameters of a classifier for determining whether or not a textual information object belongs to the category, the method comprising steps of:
- a) determining a weight vector for the classifier by training a support vector machine;
- and
- b) determining a monotonic function to be used by the classifier.
A method for generating, from a set of feature vectors associated with a training set of textual information objects, each either belonging to a category or not, parameters of a classifier for determining whether or not a textual information object belongs to the category, the method comprising steps of:
- a) reducing the number of features of each of the feature vectors to generated, for each textual information object of the training set, an associated reduced feature vector;
- b) determining a weight vector for the classifier by training support vector machine;
- and
- c) determining a monotonic function to be used by the classifier.
An apparatus for generating, from a training set of textual information objects, each either belonging to a category or not, parameters of a classifier for determining whether or not a textual information object belongs to the category, the apparatus comprising:
- a) a feature extractor for extracting features from the textual information objects to generate, for each textual information object of the training set, an associated feature vector having a number of features;
- b) a feature reduction facility for reducing the number of features of each of the feature vectors to generated, for each textual information object of the training set, an associated reduced feature vector;
- c) a support vector machine training facility for determining a weight vector for the classifier;
- and
- d) a function generator for determining a monotonic function to be used by the classifier.
The apparatus of claim 17 wherein the function generator includes means for determining parameters of the monotonic function by maximizing a likelihood of the training set of textual information objects given an output of the monotonic function.
An apparatus for generating, from a set of reduced feature vectors associated with a training set of textual information objects, each either belonging to a category or not, parameters of a classifier for determining whether or not a textual information object belongs to the category, the apparatus comprising:
- a) a support vector machine training facility for determining a weight vector for the classifier;
- and
- b) a function generator for determining a monotonic function to be used by the classifier.
An apparatus for generating, from a set of feature vectors associated with a training set of textual information objects, each either belonging to a category or not, parameters of a classifier for determining whether or not a textual information object belongs to the category, the apparatus comprising:
- a) a feature reduction facility for reducing the number of features of each of the feature vectors to generated, for each textual information object of the training set, an associated reduced feature vector;
- b) a support vector machine training facility for determining a weight vector for the classifier;
- and
- c) a function generator for determining a monotonic function to be used by the classifier.
A method for classifying a textual information object as belonging to or not belonging to a category, the method comprising steps of:
- a) extracting features from the textual information object to generate a feature vector associated with the textual information object;
- b) reducing features of the feature vector to generate a reduced feature vector;
- and
- c) applying the reduced feature vector and a weight vector to a classification function defined as:
- f ( i ) = 1 1 + ⅇ A ( w → · x → ) + B .
A method for classifying a reduced feature vector, associated with a textual information object, as belonging to or not belonging to a category, the method comprising steps of:
- a) accepting a weight vector parameter;
- b) accepting a classification function defined as;
- f ( i ) = 1 1 + ⅇ A ( w → · x → ) + B
- and
- c) applying the reduced feature vector and the accepted weight vector parameter to the accepted classification function.
A method for classifying a feature vector, associated with a textual information object, as belonging to or not belonging to a category, the method comprising steps of:
- a) reducing features of the feature vector to generate a reduced feature vector;
- and
- b) applying the reduced feature vector and a weight vector to a classification function defined as:
- f ( i ) = 1 1 + ⅇ A ( w → · x → ) + B .
An apparatus for classifying a textual information object as belonging to or not belonging to a category, the apparatus comprising:
- a) a feature extractor for extracting features from the textual information object to generate a feature vector associated with the textual information object;
- b) a feature reduction facility for reducing features of the feature vector to generate a reduced feature vector;
- and
- c) a classifier for applying the reduced feature vector and a weight vector to a classification function defined as:
- f ( i ) = 1 1 + ⅇ A ( w → · x → ) + B .
An apparatus for classifying a reduced feature vector, associated with a textual information object, as belonging to or not belonging to a category, the apparatus comprising:
- a) a first input for accepting a weight vector parameter;
- b) a second input for accepting a classification function;
- and
- c) a classifier for applying the reduced feature vector and the accepted weight vector parameter to the accepted classification function defined as:
- f ( i ) = 1 1 + ⅇ A ( w → · x → ) + B .
An apparatus for classifying a feature vector, associated with a textual information object, as belonging to or not belonging to a category, the apparatus comprising:
- a) a feature reduction facility for reducing features of the feature vector to generate a reduced feature vector;
- and
- b) a classifier for applying the reduced feature vector and a weight vector to a classification function defined as:
- f ( i ) = 1 1 + ⅇ A ( w → · x → ) + B .
A method for generating, from a training set of objects, each object either belonging to a category or not, parameters of a sigmoid function of a classifier for determining whether or not an object belongs to the category, the method comprising steps of:
- a) determining a target value based on a number of objects of the training set in the category and a number of objects of the training set not in the category;
- and
- b) determining the parameters of the sigmoid function by maximizing a likelihood of the training set of textual information objects given an output of the monotonic function using the determined target value.
An apparatus for generating, from a training set of objects, each object either belonging to a category or not, parameters of a sigmoid function of a classifier for determining whether or not an object belongs to the category, the apparatus comprising:
- a) a target value generator for determining a target value based on a number of objects of the training set in the category and a number of objects of the training set not in the category;
- and
- b) means for determining parameters of the sigmoid functions by maximizing a likelihood of the training set of objects using the determined target value.
Dependencies are inferred from the claim text.