A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

This application relates to a text classification method. The method includes obtaining, by a computer device, a to-be-classified text, and calculating an original text vector corresponding to the text; determining, by the computer device according to the original text vector, an input text vector corresponding to each channel of a trained text classification model; inputting, by the computer device, the input text vector corresponding to each channel into a convolution layer of the corresponding channel of the trained text classification model, the trained text classification model comprising a plurality of channels, each channel being corresponding to a sub-text classification model, and the trained text classification model being used for determining a classification result according to a sub-classification parameter outputted by each sub-text classification model; and obtaining, by the computer device, a classification result outputted by the trained text classification model, and classifying the text according to the classification result.

A server device receives, from a user device, a session initiation request and information identifying a location of the user device, and receives, from a monitoring device that is separate from the user device, an authentication request and information identifying a location of the monitoring device. The server device processes the session initiation request and the authentication request to authenticate a user of the user device, and determines, based on the location of the user device and the location of the monitoring device, that the user device and the monitoring device are collocated. The server device creates, after authenticating the user of the user device and determining that user device and the monitoring device are collocated, a session token, and sends the session token to the user device to enable the user device to access at least one resource of the server device.

Systems and methods are disclosed for comparing selections of text to show differences between the two selections. The text may be selected from the same source or from two different sources. In one implementation, a system receives a first selection of text for comparison and places the selection in a first buffer. The system receives a second selection of text for comparison and places the second selection in a second buffer. The system compares the first buffer and the second buffer to determine differences and displays the differences. In some embodiments, the system may allow a user to choose two buffers from among a plurality of buffers for comparison.

Systems and methods are disclosed for comparing selections of text to show differences between the two selections. The text may be selected from the same source or from two different sources. In one implementation, a system receives a first selection of text for comparison and places the selection in a first buffer. The system receives a second selection of text for comparison and places the second selection in a second buffer. The system compares the first buffer and the second buffer to determine differences and displays the differences. In some embodiments, the system may allow a user to choose two buffers from among a plurality of buffers for comparison.

Methods for providing enhanced services to users participating in communication sessions (CS), via a virtual assistant, are disclosed. One method receives content that is exchanged by users participating in the CS. The content includes natural language expressions that encode a conversation carried out by users. The method determines content features based on natural language models. The content features indicate intended semantics of the natural language expressions. The method determines a relevance of the content and identifies portions of the content that are likely relevant to the user. Determining the relevance is based on the content features, a context of the CS, a user-interest model, and a content-relevance model of the natural language models. Identifying the likely relevant content is based on the determined relevance of the content and a relevance threshold. A summary of the CS is automatically generated from summarized versions of the likely relevant portions of the content.

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.

There is provided a method and apparatus for generating a competition commentary based on artificial intelligence, and a storage medium. The method comprises: obtaining commentator's words commentaries and structured data of historical competitions; generating a commentating model according to obtained information; during live broadcast of a competition, determining a corresponding words commentary according to the commentating model with respect to the structured data obtained each time.