In an approach to generating question answer pairs, one or more computer processors receive a corpus of text. One or more computer processors extract one or more key concepts from the corpus of text. Based on the one or more key concepts, one or more computer processors generate one or more questions associated with the key concepts, where the one or more key concepts are answers to the one or more generated questions. One or more computer processors display the one or more generated questions and the answers to the one or more generated questions.

In an approach to generating question answer pairs, one or more computer processors receive a corpus of text. One or more computer processors extract one or more key concepts from the corpus of text. Based on the one or more key concepts, one or more computer processors generate one or more questions associated with the key concepts, where the one or more key concepts are answers to the one or more generated questions. One or more computer processors display the one or more generated questions and the answers to the one or more generated questions.

Provided is a method for classifying information technology (IT) service request messages. The method may include receiving data associated with an IT service request message, determining a plurality of number values associated with a plurality of characters included in the IT service request message, generating a vector that includes index values, generating a first bitmap based on generating the vector, generating a second bitmap based on the first bitmap, where the second bitmap has a first dimension and a second dimension, and where the first dimension and the second dimension are equal, and determining a classification of the IT service request message using a neural network algorithm. A system and computer program product are also disclosed.

Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

A method and system for determining sentiment of an employee towards an organization comprises extracting structured and unstructured data from one or more structured data sources comprising structured datapoints and one or more unstructured data sources comprising unstructured datapoints. The method comprises building regression model on the plurality of structured datapoints to determine a relationship of structured datapoints amongst the structured datapoints, creating a first profile of the employee based on said relationship, creating a second profile of the employee by selecting one or more words from each unstructured datapoints by using a prestored vocabulary, assigning one or more scores, corresponding to said words, in context of corresponding each of the unstructured datapoints. Method comprises generating a matrix based on the first profile and the second profile indicating sentiments of employee towards organization.

A method and system for determining sentiment of an employee towards an organization comprises extracting structured and unstructured data from one or more structured data sources comprising structured datapoints and one or more unstructured data sources comprising unstructured datapoints. The method comprises building regression model on the plurality of structured datapoints to determine a relationship of structured datapoints amongst the structured datapoints, creating a first profile of the employee based on said relationship, creating a second profile of the employee by selecting one or more words from each unstructured datapoints by using a prestored vocabulary, assigning one or more scores, corresponding to said words, in context of corresponding each of the unstructured datapoints. Method comprises generating a matrix based on the first profile and the second profile indicating sentiments of employee towards organization.

A method and apparatus for automatically generating a meeting summary is disclosed herein. Meeting audio is recorded and converted into a text-based transcript. Handwritten meeting notes are converted into notes text. The transcript and notes text are correlated to provide correlated meeting text. Meeting topics are determined from the correlated meeting text. A meeting summary is generated from the meeting topics.

Disclosed is an electronic apparatus. The electronic apparatus includes: a display, a memory storing at least one instruction, and a processor connected to the memory and the display and configured to control the electronic apparatus, the processor, by executing the at least one instruction, is configured to: based on receiving a command for adding a schedule being input while an image is displayed on the display, obtain a plurality of texts by performing text recognition of the image, obtain main datetime information corresponding to each of a plurality of pieces of schedule information and sub-datetime information corresponding to the main datetime information by causing the plurality of obtained texts to be provided to a first neural network model, and update schedule information of a user based on the obtained datetime information, and the first neural network model is configured to be trained to output main datetime information and sub-datetime information corresponding to the main datetime information based on receiving a plurality of pieces of datetime information.

A document authoring application receives a text input including one or more alphanumeric characters. A glyph extraction module in the application determines graphical data describing glyphs of the alphanumeric characters. From the graphical data, the glyph extraction module extracts visual elements, each visual element including a color or an outline from the glyphs. The glyph extraction module generates a responsive font effect that includes the extracted visual element applied to one or more of an underline effect, a strikethrough effect, a bullet point effect, or a list dot effect. Responsive to a modification of the text input, the glyph extraction module extracts an additional visual element from an additional glyph associated with the modified text. The responsive font effect is modified to include the additional visual element. The user interface is updated to display the responsive font effect or the modified font effect.