Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract: A method of creating a statistical classification model for a classifier within a natural language understanding system can include processing training data using an existing statistical classification model. Sentences of the training data correctly classified into a selected class of the statistical classification model can be selected. The selected sentences of the training data can be assigned to a fringe group or a core group according to confidence score. The training data can be updated by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class. A new statistical classification model can be built from the updated training data. The new statistical classification model can be output.

Abstract: A method of classifying text input for use with a natural language understanding system can include determining classification information including a primary classification and one or more secondary classifications for a received text input using a statistical classification model (statistical model). A statistical classification sub-model (statistical sub-model) can be selectively built according to a model generation criterion applied to the classification information. The method further can include selecting the primary classification or the secondary classification for the text input as a final classification according to the statistical sub-model and outputting the final classification for the text input.

Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract: A method of processing text within a natural language understanding system can include applying a first tokenization technique to a sentence using a statistical tokenization model. A second tokenization technique using a named entity can be applied to the sentence when the first tokenization technique does not extract a needed token according to a class of the sentence. A token determined according to at least one of the tokenization techniques can be output.

Abstract: A method of creating a statistical classification model for a classifier within a natural language understanding system can include processing training data using an existing statistical classification model. Sentences of the training data correctly classified into a selected class of the statistical classification model can be selected. The selected sentences of the training data can be assigned to a fringe group or a core group according to confidence score. The training data can be updated by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class. A new statistical classification model can be built from the updated training data. The new statistical classification model can be output.

Abstract: A method of extracting information from text within a natural language understanding system can include processing a text input through at least one statistical model for each of a plurality of features to be extracted from the text input. For each feature, at least one value can be determined, at least in part, using the statistical model associated with the feature. One value for each feature can be combined to create a complex information target. The complex information target can be output.

Abstract: A method of classifying text input for use with a natural language understanding system can include determining classification information including a primary classification and one or more secondary classifications for a received text input using a statistical classification model (statistical model). A statistical classification sub-model (statistical sub-model) can be selectively built according to a model generation criterion applied to the classification information. The method further can include selecting the primary classification or the secondary classification for the text input as a final classification according to the statistical sub-model and outputting the final classification for the text input.

Abstract: A method of processing text within a natural language understanding system can include applying a first tokenization technique to a sentence using a statistical tokenization model. A second tokenization technique using a named entity can be applied to the sentence when the first tokenization technique does not extract a needed token according to a class of the sentence. A token determined according to at least one of the tokenization techniques can be output.