Abstract: The present specification provides a method and apparatus for determining feature words and a server. The method includes: obtaining text data; extracting a first feature word from the text data; updating a word segmentation library based on the first feature word to obtain an updated word segmentation library, the word segmentation library including a plurality of predetermined feature words for representing predetermined attribute types; and extracting a second feature word from the text data based on the updated word segmentation library and the predetermined feature words.

Abstract: A computer-implemented method includes obtaining, by one or more processing devices, a training sample dataset, wherein the training sample dataset comprises at least two pieces of sample data and a sample label corresponding to each piece of sample data; constructing, by the one or more processing devices, a first relational network of the at least two pieces of sample data, and representing the at least two pieces of sample data using nodes of the first relational network; obtaining, by the one or more processing devices, a vectorized representation of each node in the first relational network based on a predetermined network embedding learning model; and training, by the one or more processing devices, a classification model based on the training sample dataset, wherein the classification model associates the sample label with the vectorized representation of each node in the first relational network.

Abstract: The present specification provides a method and apparatus for determining feature words and a server. The method includes: obtaining text data; extracting a first feature word from the text data; updating a word segmentation library based on the first feature word to obtain an updated word segmentation library, the word segmentation library including a plurality of predetermined feature words for representing predetermined attribute types; and extracting a second feature word from the text data based on the updated word segmentation library and the predetermined feature words.

Abstract: A computer-implemented method includes obtaining, by one or more processing devices, a training sample dataset, wherein the training sample dataset comprises at least two pieces of sample data and a sample label corresponding to each piece of sample data; constructing, by the one or more processing devices, a first relational network of the at least two pieces of sample data, and representing the at least two pieces of sample data using nodes of the first relational network; obtaining, by the one or more processing devices, a vectorized representation of each node in the first relational network based on a predetermined network embedding learning model; and training, by the one or more processing devices, a classification model based on the training sample dataset, wherein the classification model associates the sample label with the vectorized representation of each node in the first relational network.

Abstract: Implementations of the present specification disclose a risk control model training and risk control method, apparatus, and device. In one aspect, the method includes obtaining historical data generated during a specified time period, and partitioning the specified time period into multiple sub time periods; for each sub time period, determining respective features of the historical data in the sub time period; generating a plurality of feature sequences, comprising, for each feature type: sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods based on a corresponding sorting rule; and training a risk management machine learning model by using the plurality of feature sequences as training samples.

Abstract: Embodiments of the present invention relate to an auxiliary method, apparatus, and system for diagnosing a failure of a virtual machine. An operation and maintenance system receives a failure message when a virtual machine fails; obtains running information of the virtual machine according to identifier information carried in the message, where the running information is used for diagnosing the failure of the virtual machine; generates graphic multi-dimensional display information of the virtual machine according to the running information; and sends the display information to a user end for displaying. The present invention provides an auxiliary method for diagnosing a failure of a virtual machine. The method may provide correlation analysis for diagnosing a failure of a virtual machine, allow the user end to diagnose the cause of the failure of the virtual machine in a comprehensive multi-dimensional manner, and improve the convenience and accuracy of diagnosing the virtual machine.

Abstract: Embodiments of the present invention relate to an auxiliary method, apparatus, and system for diagnosing a failure of a virtual machine. An operation and maintenance system receives a failure message when a virtual machine fails; obtains running information of the virtual machine according to identifier information carried in the message, where the running information is used for diagnosing the failure of the virtual machine; generates graphic multi-dimensional display information of the virtual machine according to the running information; and sends the display information to a user end for displaying. The present invention provides an auxiliary method for diagnosing a failure of a virtual machine. The method may provide correlation analysis for diagnosing a failure of a virtual machine, allow the user end to diagnose the cause of the failure of the virtual machine in a comprehensive multi-dimensional manner, and improve the convenience and accuracy of diagnosing the virtual machine.

Abstract: Determining permissibility of a transaction includes: receiving new execution information associated with a transaction; determining specified characteristics in the new execution information; determining a plurality of similarity measures between the specified characteristics of the new execution information and specified characteristics of a set of stored execution information; selecting, based on the plurality of similarity measures, a set of similar execution information from the set of stored execution information; and determining permissibility associated with the new execution information based at least in part on computation that is based at least in part on permissible information and impermissible information of the set of similar execution information.