Abstract: A system, method and computer program is provided for generating customized text representations of audio commands. A first speech recognition module may be used for generating a first text representation of an audio command based on a general language grammar. A second speech recognition module may be used for generating a second text representation of the audio command, the second module including a custom language grammar that may include contacts for a particular user. Entity extraction is applied to the second text representation and the entities are checked against a file containing personal language. If the entities are found in the user-specific language, the two text representations may be fused into a combined text representation and named entity recognition may be performed again to extract further entities.

Abstract: A system, method and computer program is provided for generating customized text representations of audio commands. A first speech recognition module may be used for generating a first text representation of an audio command based on a general language grammar. A second speech recognition module may be used for generating a second text representation of the audio command, the second module including a custom language grammar that may include contacts for a particular user. Entity extraction is applied to the second text representation and the entities are checked against a file containing personal language. If the entities are found in the user-specific language, the two text representations may be fused into a combined text representation and named entity recognition may be performed again to extract further entities.

Abstract: A system, method and computer program is provided for generating customized text representations of audio commands. A first speech recognition module may be used for generating a first text representation of an audio command based on a general language grammar. A second speech recognition module may be used for generating a second text representation of the audio command, the second module including a custom language grammar that may include contacts for a particular user. Entity extraction is applied to the second text representation and the entities are checked against a file containing personal language. If the entities are found in the user-specific language, the two text representations may be fused into a combined text representation and named entity recognition may be performed again to extract further entities.

Abstract: Methods and a natural language processor for processing a natural language query are provided. The processor includes a classifier, a rule-based pre-processor, a rule-based post-processor, a named entity recognizer, and an output module. The method involves receiving a text representation of the natural language query, pre-processing the text representation, applying a classification statistical model to the text representation when pre-processing fails, applying a post-processing rule, and performing name entity recognition.

Abstract: A system, method and computer program is provided for generating customized text representations of audio commands. A first speech recognition module may be used for generating a first text representation of an audio command based on a general language grammar. A second speech recognition module may be used for generating a second text representation of the audio command, the second module including a custom language grammar that may include contacts for a particular user. Entity extraction is applied to the second text representation and the entities are checked against a file containing personal language. If the entities are found in the user-specific language, the two text representations may be fused into a combined text representation and named entity recognition may be performed again to extract further entities.

Abstract: Methods and a natural language processor for processing a natural language query are provided. The processor includes a classifier, a rule-based pre-processor, a rule-based post-processor, a named entity recognizer, and an output module. The method involves receiving a text representation of the natural language query, pre-processing the text representation, applying a classification statistical model to the text representation when pre-processing fails, applying a post-processing rule, and performing name entity recognition.

Abstract: A system, method and computer program is provided for generating customized text representations of audio commands. A first speech recognition module may be used for generating a first text representation of an audio command based on a general language grammar. A second speech recognition module may be used for generating a second text representation of the audio command, the second module including a custom language grammar that may include contacts for a particular user. Entity extraction is applied to the second text representation and the entities are checked against a file containing personal language. If the entities are found in the user-specific language, the two text representations may be fused into a combined text representation and named entity recognition may be performed again to extract further entities.

Abstract: A method, system and non-transitory computer-readable medium are provided for improving a statistical classification system, such as a statistical classification system that accepts natural language voice queries as inputs. A clustering engine may create one or more clusters of queries where the queries in each cluster are related in some way. A reviewing module may be employed to determine whether each cluster relates to an existing category supported by the classification system, a new category that can be supported by the classification system by training statistical models with the data from the cluster, is ambiguous, or is not useful to improve the classification system. For clusters determined to be useful for improving the system, the data in the clusters may be added to an existing training set or used as a training set to train new statistical models.