Abstract: In some examples, a system for generating a topic model includes a processor that can process a set of documents to generate training data, wherein each document in the set of documents is associated with one or more users. The processor can also generate a plurality of topic models using the training data, such that each topic model includes a different number of topics. The processor can also generate an evaluation score for each of the topic models based on information about the users associated with the documents included in the training data. The evaluation score describes a percentage of topics that exhibit a specified level of interest from a specified number of users. The processor can also identify a final topic model based on the evaluation scores and store the final topic model to be used in natural language processing.

Abstract: Balancing an imbalanced dataset, by: Receiving a balancing policy and the imbalanced dataset. Performing initial adjustment of the imbalanced dataset to comply with the balancing policy, by: oversampling one or more underrepresented classes, and, if one or more of the classes are overrepresented, undersampling them. Operating a generative machine learning model to generate samples for the one or more underrepresented classes, based on the initially-adjusted dataset. Operating a machine learning classification model to label the generated samples with class labels corresponding to the one or more underrepresented classes. Selecting some of the generated samples which, according to the labeling, have a relatively high probability of preserving their class labels.

Abstract: Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.

Abstract: Balancing an imbalanced dataset, by: Receiving a balancing policy and the imbalanced dataset. Performing initial adjustment of the imbalanced dataset to comply with the balancing policy, by: oversampling one or more underrepresented classes, and, if one or more of the classes are overrepresented, undersampling them. Operating a generative machine learning model to generate samples for the one or more underrepresented classes, based on the initially-adjusted dataset. Operating a machine learning classification model to label the generated samples with class labels corresponding to the one or more underrepresented classes. Selecting some of the generated samples which, according to the labeling, have a relatively high probability of preserving their class labels.

Abstract: The present invention provides a method, computer program product, and system of generating prioritized list. In an embodiment, the method, computer program product, and system include receiving, by a computer system, target user identification data identifying a target user, target action data, social network content for the one or more users, and social network activity data for the one or more users, analyzing, by a computer system, social network links between the source user and the target user and the social network activity data for the one or more users, determining, by a computer system, a prioritized list of probabilistic action paths that could result in the target user performing the target action on the content based on the analyzing, and outputting the prioritized list to the source user.

Abstract: Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.

Abstract: In some examples, a system for generating a topic model includes a processor that can process a set of documents to generate training data, wherein each document in the set of documents is associated with one or more users. The processor can also generate a plurality of topic models using the training data, such that each topic model includes a different number of topics. The processor can also generate an evaluation score for each of the topic models based on information about the users associated with the documents included in the training data. The evaluation score describes a percentage of topics that exhibit a specified level of interest from a specified number of users. The processor can also identify a final topic model based on the evaluation scores and store the final topic model to be used in natural language processing.

Abstract: The present invention provides a method, computer program product, and system of generating prioritized list. In an embodiment, the method, computer program product, and system include receiving, by a computer system, target user identification data identifying a target user, target action data, social network content for the one or more users, and social network activity data for the one or more users, analyzing, by a computer system, social network links between the source user and the target user and the social network activity data for the one or more users, determining, by a computer system, a prioritized list of probabilistic action paths that could result in the target user performing the target action on the content based on the analyzing, and outputting the prioritized list to the source user.

Abstract: A method comprising using at least one hardware processor for: computing a tree edit distance between two medical treatment plans; and displaying an output based on the computed tree edit distance. The two medical treatment plans are optionally a recommended treatment plan and an executed treatment plan. The output is optionally indicative of compliance of the executed treatment plan with the recommended treatment plan.

Abstract: Machines, systems and methods for supporting clinical decisions comprises providing a graphical user interface (GUI) to facilitate selection of one or more treatment plans (TPs) for one or more clinical presentations (CPs), wherein data records for the TPs and the CPs are implemented over a data structure that defines one or more relationship between the CPs and the TPs, according to medical guidelines or clinical data, wherein interaction with the GUI allows for filtering through TPs associated with one or more CPs, or filtering through CPs associated with one or more TPs, wherein selecting a CP from among a plurality of the CPs results in display of one or more TPs associated with the selected CP, and wherein cross-referencing between results displayed in response to the selection of the selected CP and TP provides details that help determine one or more relevant TPs for a target CP.