The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Relating to the field of artificial intelligence, and specifically relating to the field of natural language processing, a data processing method includes and an apparatus performs: determining original text samples, where masking processing is not performed on the original text samples; and performing mask processing on the original text samples to obtain mask training samples, where the mask processing makes mask proportions of the mask training samples unfixed, and the mask training samples each are used to train a pretrained language model PLM. Training the PLM by using the mask training samples whose mask proportions are unfixed can enhance mode diversity of the training samples of the PLM. Therefore, features learned by the PLM are also diversified, a generalization capability of the PLM can be improved, and a natural language understanding capability of the PLM obtained through training can be improved.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating topic divergence classifications for digital videos based on words from the digital videos and further based on a digital text corpus representing a target topic. Particularly, the disclosed systems utilize a topic-specific knowledge encoder neural network to generate a topic divergence classification for a digital video to indicate whether or not the digital video diverges from a target topic. In some embodiments, the disclosed systems determine topic divergence classifications contemporaneously in real time for livestream digital videos or for stored digital videos (e.g., digital video tutorials). For instance, to generate a topic divergence classification, the disclosed systems generate and compare contextualized feature vectors from digital videos with corpus embeddings from a digital text corpus representing a target topic utilizing a topic-specific knowledge encoder neural network.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating topic divergence classifications for digital videos based on words from the digital videos and further based on a digital text corpus representing a target topic. Particularly, the disclosed systems utilize a topic-specific knowledge encoder neural network to generate a topic divergence classification for a digital video to indicate whether or not the digital video diverges from a target topic. In some embodiments, the disclosed systems determine topic divergence classifications contemporaneously in real time for livestream digital videos or for stored digital videos (e.g., digital video tutorials). For instance, to generate a topic divergence classification, the disclosed systems generate and compare contextualized feature vectors from digital videos with corpus embeddings from a digital text corpus representing a target topic utilizing a topic-specific knowledge encoder neural network.

A method may include presenting a user interface on a display device of a computing device, the user interface including: a search query input element; a plurality of graph type options; a graph level selection element; and a graph presentation area; receiving a search query inputted into the search query input element, the search query identifying a concept object in an ontology; retrieving data associated with the concept object from a graph database based on selections made in the graph type options and the graph level selection element, the data including a set of result objects related to the concept object; and rendering a hierarchical graph in the graph presentation area, the hierarchical graph illustrating the set of result objects and the concept object as interactive nodes.

A system and a method for automatically clustering sandbox analysis reports of similar malware samples. An automated malware analysis process includes receiving from a sandbox server the sandbox analysis reports of the similar malware samples at an application programming interface (API) of the clustering server, clustering similar Uniform Resource Locators (URLs) together and clustering the sandbox analysis reports of events in sandbox reports clusters (1-n) based on the URL clustering, static properties of the malware samples and dynamic properties of the malware samples.

Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.

Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.

An agent system includes a first memory and a first processor coupled to the first memory. The first processor analyzes contents of a verbal question, and carries out pre-processing that replaces vocabulary, which is used in the contents of the question, with homogenized vocabulary, and generates response information based on results of analysis. In a case in which there exists substitution vocabulary that has replaced original vocabulary in the pre-processing, the first processor changes the response information such that it can be recognized that the substitution vocabulary in the response information is synonymous with the original vocabulary, and outputs the response information.