The present disclosure provides a method of processing triple data, a method of training a triple data processing model, an electronic device, and a storage medium. A specific implementation solution includes: performing a triple data extraction on text data to obtain a plurality of field data; normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and verifying a confidence level of the target triple data to obtain a verification result.

Embodiments described herein provide methods and systems for summarizing multiple documents. A system receives a plurality of documents and generates embeddings of the sentences from the plurality of documents. The embedded sentences are clustered in a representation space. Sentences from a reference summary are embedded and aligned with the closest cluster. Sentences from each cluster are summarized with the aligned reference sentences as a target. A loss is computed based on the summarized sentences and the aligned references, and the natural language processing model is updated based on the loss. Sentences may be masked from being used in the summarization by identifying sentences that are contradicted by other sentences within the plurality of documents.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis on document data objects that are associated with an ontology graph. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations on document data objects that are associated with an ontology graph using document embeddings that are generated by graph-embedding-based paragraph vector machine learning models.

Disclosed are system and methods for digitally capturing, labeling, and analyzing data representing shared experiences between a service provider and a customer. The shared experience data is used to identify, test, and implement value-added improvements, enhancements, and augmentations to the shared experience and to monitor and ensure the quality of customer service. The improvements can be implemented as customer service process modifications, precision learning and targeted coaching for agents rendering customer service, process compliance monitoring, and as knowledge curation for a knowledge bot software application that facilitates automation of tasks and provides a natural language interface for accessing historical knowledge bases and solutions.

Systems and methods for client intake and management are disclosed herein. In an embodiment, a method for client intake and management by a first party includes receiving initial client data regarding a second party from at least one user terminal, the initial client data including a name of the second party and at least one selection, determining a conflict rating using hierarchical searching on a matter database using the initial client data and the at least one selection, and causing an adjustment to a user interface on a user terminal based on the conflict rating.

Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

From a set of natural language text documents, a concept tree is constructed. For a node in the concept tree a polarity of the subset represented by the node is scored. A second set of natural language text documents is added to the subset, the adding resulting in a modified subset of natural language text documents having a polarity score within a predefined neutral polarity score range. From the modified subset, a bin of sentences is selected according to a sentence selection parameter, a sentence in the bin of sentences being extracted from a selected document in the modified subset. A sentence having a factuality score below a threshold factuality score is removed from the bin of sentences. From the filtered bin of sentences a new natural language text document corresponding to the filtered bin of sentences is generated using a transformer deep learning narration generation model.

A method for identifying documents that are similar in content to an input document includes receiving a request for identifying similar documents from among a plurality of candidate documents, retrieving document classification attributes for the input document and the candidate documents, where the document classification attributes are document level attributes. The method also includes comparing the document classification attributes of the input document with classification attributes of the candidate documents to identify a subset of the candidate documents having matching document classification attributes, retrieving semantic entities from the input document and from candidate documents in the subset, pairwise comparing the semantic entity attribute of the input document with the semantic entity attribute of the candidate documents in the subset to identify semantic entities having matching semantic attributes, calculating a content similarity score between the semantic entity of the input document and the semantic entity of the candidate document in the subset, calculating a total similarity score for the candidate documents in the subset based on the content similarity score, a number of matching document classification attributes, and weight factors, and selecting similar documents from the subset based on the total similarity score.

Disclosed is a method and system for detecting a duplicate document using vector quantization. A duplicate document detection method may include acquiring, by processing circuitry, a respective vector expression for each of a plurality of documents using a similarity model, the similarity model being trained to output similar vector expressions for semantically similar documents, generating a key by performing a vector quantization on the respective vector expression, the key including a binary character string, and detecting a duplicate document from among the plurality of documents using the key.

Systems and methods for data management using machine learning and artificial intelligence techniques related to topic modeling on text comments are described. The text comments may correspond to a particular transaction conducted by a user. Machine learning text analysis is performed on the text comment to determine one or more topics associated with the text comment. The topic with the highest correlation to the text comment is assigned to the transaction claim. Based on the topic assigned to the transaction claim, various actions may be performed, including remedial actions on a user account. These techniques may be applicable to chargeback fraud, in some embodiments.