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 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.

Cooking recipe display system (100) is provided with database (11), extraction unit (21a), emphasis unit (21b), and output unit (23). Database (11) stores a plurality of cooking recipes each being expressed in natural language sentences. Extraction unit (21a) extracts one or more recipe terms from the natural language sentences constituting one cooking recipe selected from the plurality of cooking recipes. Emphasis unit (21b) determines an emphasis method for the one or more recipe terms. Output unit (23) outputs the one cooking recipe with the one or more recipe terms emphasized according to the emphasis method determined by emphasis unit (21b).

Cooking recipe display system (100) is provided with database (11), extraction unit (21a), emphasis unit (21b), and output unit (23). Database (11) stores a plurality of cooking recipes each being expressed in natural language sentences. Extraction unit (21a) extracts one or more recipe terms from the natural language sentences constituting one cooking recipe selected from the plurality of cooking recipes. Emphasis unit (21b) determines an emphasis method for the one or more recipe terms. Output unit (23) outputs the one cooking recipe with the one or more recipe terms emphasized according to the emphasis method determined by emphasis unit (21b).

An entity recognition method and apparatus, an electronic device, a storage medium, and a computer program product are provided. The method includes: recognizing a to-be-recognized image to determine a preliminary recognition result for entities in the to-be-recognized image; determining, in response to determining that the preliminary recognition result includes a plurality of entities of a same category, image features of the to-be-recognized image and textual features of the plurality of entities; determining whether the plurality of entities is a consecutive complete entity based on the image features and the textual features, to obtain a complete-entity determining result; and obtaining a final recognition result based on the preliminary recognition result and the complete-entity determining result.

An entity recognition method and apparatus, an electronic device, a storage medium, and a computer program product are provided. The method includes: recognizing a to-be-recognized image to determine a preliminary recognition result for entities in the to-be-recognized image; determining, in response to determining that the preliminary recognition result includes a plurality of entities of a same category, image features of the to-be-recognized image and textual features of the plurality of entities; determining whether the plurality of entities is a consecutive complete entity based on the image features and the textual features, to obtain a complete-entity determining result; and obtaining a final recognition result based on the preliminary recognition result and the complete-entity determining result.

Techniques for phased deployment of machine learning models are described. Customers can call a training API to initiate model training, but then must wait while the training completes before the model can be used to perform inference. Depending on the type of model, machine learning algorithm being used for training, size of the training dataset, etc. this training process may take hours or days to complete. This leads to significant downtime where inference requests cannot be served. Embodiments improve upon existing systems by providing phased deployment of custom models. For example, a simple, less accurate model, can be provided synchronously in response to a request for a custom model. At the same time, one or more machine learning models can be trained asynchronously in the background. When the machine learning model is ready for use, the customers' traffic and jobs can be transferred over to the better model.

Techniques for phased deployment of machine learning models are described. Customers can call a training API to initiate model training, but then must wait while the training completes before the model can be used to perform inference. Depending on the type of model, machine learning algorithm being used for training, size of the training dataset, etc. this training process may take hours or days to complete. This leads to significant downtime where inference requests cannot be served. Embodiments improve upon existing systems by providing phased deployment of custom models. For example, a simple, less accurate model, can be provided synchronously in response to a request for a custom model. At the same time, one or more machine learning models can be trained asynchronously in the background. When the machine learning model is ready for use, the customers' traffic and jobs can be transferred over to the better model.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.