Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Abstract: Systems and methods for programmatically causing a machine to classify and extract the meaning of text are discussed herein. Some embodiments may provide for a system including circuitry configured to identify topics associated with a block of text and one or more categories for each of the topics. Each unique category across the one or more categories may be further associated with one or more levels of parent and/or child categories to form an expanded category set of category nodes having parent-child relationships. Based on a number of category nodes connected to each unique category, the circuitry may be configured to determine one or more filtered categories from the unique categories and one or more filtered topics. Filtered topics or categories may be used to programmatically classify text with a more relevant data set than may be possible without the filtering.

Abstract: Systems and methods for programmatically causing a machine to classify and extract the meaning of text are discussed herein. Some embodiments may provide for a system including circuitry configured to identify topics associated with a block of text and one or more categories for each of the topics. Each unique category across the one or more categories may be further associated with one or more levels of parent and/or child categories to form an expanded category set of category nodes having parent-child relationships. Based on a number of category nodes connected to each unique category, the circuitry may be configured to determine one or more filtered categories from the unique categories and one or more filtered topics. Filtered topics or categories may be used to programmatically classify text with a more relevant data set than may be possible without the filtering.

Abstract: Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.