Abstract: A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.

Abstract: A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.

Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.

Abstract: A computer-implemented mechanism is disclosed. The mechanism includes receiving a data signal, and comparing the data signal to one or more predefined patterns to determine one or more long/short term predictor scores. A discount factor is generated in response to the long/short term predictor scores. A set of expected rewards is generated. The set of expected rewards correspond to an action set specific to the data signal. The set of expected rewards are generated according to reinforced learning. The set of expected rewards are adjusted based on the discount factor. A selected action is selected from the action set based on the set of expected rewards. The selected action is initiated.

Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.

Abstract: A method, system and computer-usable medium are disclosed for enhancing a classification system to include an automatic classification validation system. The automatic classification validation system takes the classification results and automatically validates them for correctness. More specifically, the automatic classification validation system analyzes the data objects in the categories and, if any outliers are identified, then determines a context of the data object from a plurality of records contained within a certain category to determine the context. The classification validation system then uses the context of the data object to validate the classification.

Abstract: A method, system and computer-usable medium are disclosed for enhancing a classification system to include an automatic classification validation system. The automatic classification validation system takes the classification results and automatically validates them for correctness. More specifically, the automatic classification validation system analyzes the data objects in the categories and, if any outliers are identified, then determines a context of the data object from a plurality of records contained within a certain category to determine the context. The classification validation system then uses the context of the data object to validate the classification.