A system includes at least one non-transitory storage medium storing a set of instructions and at least one processor in communication with the at least one non-transitory storage medium. When executing the set of instructions, the at least one processor may be directed to cause the system to obtain a service request signal sent from a user terminal via wireless communication, wherein the service request signal encodes identifier data, a first departure location, and a first departure time; retrieve one or more historical records related to the identifier data, wherein a historical record includes a historical departure location, historical departure time and a historical destination location; determine, using a pre-stored destination matching algorithm, a selection probability of the one or more historical destination location; determine, based on the selection probability, a suggested destination location, which is the same as the one or more historical destination locations.

The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.

The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

A pictogram repository is created of pictograms including expressions that are mapped to at least a portion of source code that is stored in a separate source code repository. A score is recorded for developers for the source code that is stored in the source code repository. A source code search inquiry of at least one pictograms for search query elements is conducted, in which the at least one pictogram for the search query elements are matched to the pictograms in the repository of pictograms that includes expressions that are mapped to at least a portion of source code that is stored in the separate source code repository. Matching source code have the score for their developer checked against a threshold value. Source code meeting the search query elements and having a score for their developer meeting the threshold value are retrieved.

An apparatus and method for generating a schema, the apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to display, at a graphical control interface, a content field window, receive, as a function of the content field window, a criterion element, and generate a schema as a function of the criterion element.

A data normalization system receives a first string and a second string that are ordered according to an initial string ordering. The data normalization system analyzes, the first string and the second string based on a list of known character sets included in surnames, yielding an analysis, and determines, based on the analysis, that a set of characters in the second string matches a known character set included in the list of known character sets included in surnames. In response to determining that the set of characters in the second string matches a known character set included in the list of known character sets included in surname, the data normalization system orders the first string and the second string according to an updated string ordering.

This disclosure relates to a search result processing method and apparatus, and a storage medium. The method may include acquiring a search result according to a search keyword and obtaining an accurate matching score of the search result relative to the search keyword. The method may further include determining a semantic matching weight vector of the search result, a semantic representation vector of the search keyword, and a semantic representation vector of the search result. The method may further include obtaining a semantic matching score of the search result relative to the search keyword according to the semantic representation vectors and the semantic matching weight vector. The method may further include obtaining a similarity between the search result and the search keyword according to the accurate matching score and the semantic matching score.

Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.

Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.