Abstract:
Embodiments of a computer-implemented method for automatically analyzing a conversational sequence between multiple users are disclosed. The method includes receiving signals corresponding to a training dataset including multiple conversational sequences; extracting a feature from the training dataset based on predefined feature categories; formulating multiple tasks for being learned from the training dataset based on the extracted feature, each task related to a predefined label; and providing a model for each formulated task, the model including a set of parameters common to the tasks. The set includes an explicit parameter, which is explicitly shared with each of the formulated tasks. The method further includes optimizing a value of the explicit parameter to create an optimized model; creating a trained model for the formulated tasks using the optimized value of the explicit parameter; and assigning predefined labels for the formulated tasks to a live dataset based on the corresponding trained model.
Abstract:
The technical solution under the present disclosure automatically analyzes conversations between users by receiving a training dataset having a text sequence including sentences of a conversation between the users; extracting feature(s) from the training dataset based on features; providing equation(s) for a plurality of tasks, the equation(s) being a mathematical function for calculating value of a parameter for each of the tasks based on the extracted feature; determining value of the parameter for tasks by processing the equation(s); assigning label(s) to each of the sentences based on the determined value of the parameter, a first label being selected from a plurality of first labels, and a second label being selected from a number of second labels; and storing and maintaining with the database a pre-defined value of the parameter, first labels, conversations, second labels, a test dataset, equation(s), and pre-defined features.
Abstract:
Embodiments of a computer-implemented method for automatically analyzing a conversational sequence between multiple users are disclosed. The method includes receiving signals corresponding to a training dataset including multiple conversational sequences; extracting a feature from the training dataset based on predefined feature categories; formulating multiple tasks for being learned from the training dataset based on the extracted feature, each task related to a predefined label; and providing a model for each formulated task, the model including a set of parameters common to the tasks. The set includes an explicit parameter, which is explicitly shared with each of the formulated tasks. The method further includes optimizing a value of the explicit parameter to create an optimized model; creating a trained model for the formulated tasks using the optimized value of the explicit parameter; and assigning predefined labels for the formulated tasks to a live dataset based on the corresponding trained model.
Abstract:
A system, method and non-transitory computer readable medium for labeling a plurality of messages from a customer-agent interaction on a social media service to identify an issue and a response are disclosed. For example, the system includes a conversation interface, a conversation database coupled to the conversation interface, a conversation analysis server coupled to the conversation database and a conversation knowledge repository coupled to the conversation analysis server. The conversation analysis server includes a preprocessing module, a dialogue act analysis module, an issue status analysis module and an issue/response identification module.
Abstract:
A method and non-transitory computer readable medium for generating patient profiles via a social media service are disclosed. For example, the method receives data from the social media service, extracts one or more attributes from the data from the social media service, classifies the one or more attributes to one or more of a plurality of predefined profile attributes, generates a patient profile based on the one or more attributes that are classified, determines a medical action to be executed based on the patient profile and transmits the medical action to be executed to a health administration server.
Abstract:
A method for assigning a topic to a collection of microblog posts may include, by an acquisition module, receiving from at least one messaging service server, a plurality of posts, wherein each of the plurality of posts comprise post content; by a generation module, analyzing the posts and extract, from at least one of the posts, a link with an address to an external document; and, by the acquisition module, accessing the external document that is associated with the address and fetch external content associated with the document. The method may also include by the generation module: analyzing the post content to identify at least one label for each post, for each post that includes a link, analyzing the external content to identify a topic, and using a topic modeling technique to generate a trained topic model comprising a plurality of topics and a plurality of associated words.
Abstract:
A system, method and non-transitory computer readable medium for labeling a plurality of messages from a customer-agent interaction on a social media service to identify an issue and a response are disclosed. For example, the system includes a conversation interface, a conversation database coupled to the conversation interface, a conversation analysis server coupled to the conversation database and a conversation knowledge repository coupled to the conversation analysis server. The conversation analysis server includes a preprocessing module, a dialogue act analysis module, an issue status analysis module and an issue/response identification module.
Abstract:
The technical solution under the present disclosure automatically analyzes conversations between users by receiving a training dataset having a text sequence including sentences of a conversation between the users; extracting feature(s) from the training dataset based on features; providing equation(s) for a plurality of tasks, the equation(s) being a mathematical function for calculating value of a parameter for each of the tasks based on the extracted feature; determining value of the parameter for tasks by processing the equation(s); assigning label(s) to each of the sentences based on the determined value of the parameter, a first label being selected from a plurality of first labels, and a second label being selected from a number of second labels; and storing and maintaining with the database a pre-defined value of the parameter, first labels, conversations, second labels, a test dataset, equation(s), and pre-defined features.
Abstract:
Presented are a method, system, and apparatus for using a specialized computing device managing a contact center to analyze and reduce financial risk on a portfolio of accounts (such as loans, insurance claims, etc.) via determining whether and, if so, when to utilize a communication channel (such as telephone, e-mail, text message, etc.) to contact a customer regarding a monitored account. Variables are received including action history and transactions associated with the monitored account. One or more risk models associated with the monitored account are derived. Risk level is determined for the customer. The derived risk models and the determined risk level are used to generate a risk-driven campaign optimization strategy. A solution maximizing advantage considering the risk-driven campaign optimization strategy is then generated, the solution including a determination of whether to contact the customer and, if so, which communication channel to utilize at which time t.
Abstract:
Presented are a system, method, and apparatus for automatic topic relevant content filtering from social media text streams using weak supervision. A computing device utilizes heuristic rules allowing topic filtering and a data stream data chunk identifier. A plurality of messages are transmitted as streaming message data from a social media network in real-time. The messages are split into a plurality of data stream data chunks according to the data stream data chunk identifier. A rule-based labeled data set L0 is built from one or more data instances in the first stream data chunk. An initial classifier is built based upon features of L0. The initial classifier is applied to a next data stream data chunk to build a labeled data set L1. A subset of representative instances S1 is selected from labeled data set L1. A first representative classifier C1 is constructed from representative instance S1.