A computer-implemented neural network graph (1) system, comprising a plurality of neurons (2), each represented by a unique addressable node in a dynamic data structure and each containing a plurality of data, and a plurality of axons and dendrites (4) connecting two or more neurons (2) between them in order to represent a relation and transport one or more data contained in a neuron (2) to another neuron. Each axon (4) having at its end a synapse (3) for connecting it to a neuron (2) and at least one intermediate neuron (2) is connected through an intermediate axon (4) or dendrite and its synapse (3) directly to another axon (4) which connects two main neurons (2). The intermediate neuron (2) and intermediate axon (4) being configured for: selecting one or more specific data contained in the main neurons (2) and transmitted between them along their axon (4) or dendrites (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a first combination of data; selecting one or more specific data, different from the first selection, contained in the main neurons (2) and transmitted between them along the axon (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a second combination of data different from the first; creating a graphical representation comprising a graph (1) of said data in which a first abstraction level is defined by said first selection and a second abstraction level is defined by said second selection different from the first.

A computer-implemented neural network graph (1) system, comprising a plurality of neurons (2), each represented by a unique addressable node in a dynamic data structure and each containing a plurality of data, and a plurality of axons and dendrites (4) connecting two or more neurons (2) between them in order to represent a relation and transport one or more data contained in a neuron (2) to another neuron. Each axon (4) having at its end a synapse (3) for connecting it to a neuron (2) and at least one intermediate neuron (2) is connected through an intermediate axon (4) or dendrite and its synapse (3) directly to another axon (4) which connects two main neurons (2). The intermediate neuron (2) and intermediate axon (4) being configured for: selecting one or more specific data contained in the main neurons (2) and transmitted between them along their axon (4) or dendrites (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a first combination of data; selecting one or more specific data, different from the first selection, contained in the main neurons (2) and transmitted between them along the axon (4) in function of a preselected data of the intermediate neuron (2) in such a way to define a second combination of data different from the first; creating a graphical representation comprising a graph (1) of said data in which a first abstraction level is defined by said first selection and a second abstraction level is defined by said second selection different from the first.

A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

The present disclosure provides a word mining method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as natural language processing technologies, deep learning technologies, cloud service technologies, or the like. The word mining method includes: acquiring search data; taking first identification information, a search sentence and second identification information in the search data as nodes, and taking a relationship between the first identification information and the search sentence, a relationship between the first identification information and the second identification information and a relationship between the search sentence and the second identification information as sides to construct a behavior graph; obtaining a label vector of each search sentence in the behavior graph according to a search sentence with a preset label in the behavior graph; determining a target search sentence in the behavior graph according to the label vector; and extracting a target word from the target search sentence, and taking the target word as a word mining result of the search data.

The application discloses a method and device for personalized search of visual media. Semantic analysis is conducted on a visual media query text of a user to obtain visual semantic information, time information and/or location information. Semantic similarity matching is conducted on a result of the semantic analysis and attribute data of each visual medium within a specified search range to obtain a query similarity of the visual medium. The visual medium is an image or a video, and the attribute data include personalized visual semantic information, personalized time information and/or personalized location information. A corresponding visual media query result is generated based on the query similarity. By adopting the application, users are provided with visual media which is a result of a personalized.

The application discloses a method and device for personalized search of visual media. Semantic analysis is conducted on a visual media query text of a user to obtain visual semantic information, time information and/or location information. Semantic similarity matching is conducted on a result of the semantic analysis and attribute data of each visual medium within a specified search range to obtain a query similarity of the visual medium. The visual medium is an image or a video, and the attribute data include personalized visual semantic information, personalized time information and/or personalized location information. A corresponding visual media query result is generated based on the query similarity. By adopting the application, users are provided with visual media which is a result of a personalized.

Disclosed is an electronic device. The electronic device may execute an application for transmitting and receiving at least one of text data or voice data with another electronic device using the communication module, in response to occurrence of at least one event, based on receiving at least one of text data or voice data from the another electronic device, identify that a confirmation is necessary using the digital assistant based on at least one of text data or voice data being generated based on a characteristic of ab utterance using a digital assistant, generate a notification to request confirmation using the digital assistant based on confirmation being necessary, and output the notification using the application.

A method for identifying that a confirmation is necessary may include identifying using voice data or text data that is received from another electronic device using a rule-based or AI algorithm.

When a confirmation is necessary is identified using the AI algorithm, the method may use machine learning, neural network, or a deep learning algorithm.

Disclosed is an electronic device. The electronic device may execute an application for transmitting and receiving at least one of text data or voice data with another electronic device using the communication module, in response to occurrence of at least one event, based on receiving at least one of text data or voice data from the another electronic device, identify that a confirmation is necessary using the digital assistant based on at least one of text data or voice data being generated based on a characteristic of ab utterance using a digital assistant, generate a notification to request confirmation using the digital assistant based on confirmation being necessary, and output the notification using the application.

A method for identifying that a confirmation is necessary may include identifying using voice data or text data that is received from another electronic device using a rule-based or AI algorithm.

When a confirmation is necessary is identified using the AI algorithm, the method may use machine learning, neural network, or a deep learning algorithm.

Systems and methods for selecting and reporting objects based on events are provided. An indication of first and second objects, an indication of first events associated with the first object, and an indication of second events associated with the second object may be received. Based on the first events, it may be determined to include in a textual content a description based on the first events of the first object. Based on the second events, it may be determined not to include in the textual content any description based on the second events of the second object. Data associated with the first events may be analyzed to generate a particular description of the first object. The textual content including the particular description of the first object and not including any description based on the second events of the second object may be generated and provided.

Systems and methods for selecting and reporting objects based on events are provided. An indication of first and second objects, an indication of first events associated with the first object, and an indication of second events associated with the second object may be received. Based on the first events, it may be determined to include in a textual content a description based on the first events of the first object. Based on the second events, it may be determined not to include in the textual content any description based on the second events of the second object. Data associated with the first events may be analyzed to generate a particular description of the first object. The textual content including the particular description of the first object and not including any description based on the second events of the second object may be generated and provided.