Abstract: The present disclosure provides an event extraction method, an event extraction device and an electronic device, and it relates to the field of computer data processing, in particular to the field of knowledge graph. The event extraction method includes: acquiring text information; determining a plurality of pieces of question information ranked in a sequential order in accordance with the text information; and inputting vector information for each piece of question information into an extraction model in accordance with the sequential order to acquire extraction information for each piece of question information.

Abstract: An event argument extraction (EAE) method, an EAE apparatus and an electronic device, relates to the technical field of knowledge graphs. A specific implementation scheme includes acquiring a to-be-extracted event content; and performing argument extraction on the to-be-extracted event content based on a trained EAE model, to obtain a target argument of the to-be-extracted event content; where the trained EAE model is obtained by training a pre-trained model with event news annotation data and a weight of each argument annotated in the event news annotation data.

Abstract: A method for extracting an event from a text including obtaining an input text; inputting the input text into a model for extracting trigger words to obtain a trigger word extraction result of the input text; inputting the input text and the trigger word extraction result into a model for extracting arguments to obtain an argument extraction result of the input text; and obtaining an event extraction result of the input text according to the trigger word extraction result and the argument extraction result.

Abstract: This application provides a network performance monitoring method, a network device, and a storage medium, and belongs to the field of network technologies. In this application, a forwarding plane samples network performance data based on a fine-grained time periodicity, and records a quantity of network performance exceptions; and a control plane generates, based on a coarse-grained time periodicity, an alarm when the quantity of network performance exceptions recorded by the forwarding plane is greater than a threshold. On a basis of meeting a fine-grained requirement on network performance monitoring, because the control plane does not need to report all the collected network performance data, a volume of data that needs to be reported by the control plane is greatly reduced. This resolves a problem of overload of a main control CPU that is caused by massive data reporting, and reduces dependency of the network performance monitoring on performance of the main control CPU of a device.

Abstract: The present disclosure provides an event extraction method and apparatus, and a storage medium. The method includes: obtaining an event description text; determining at least one candidate event type according to the event description text, wherein the candidate event type corresponds to a set of query sentences; and extracting a corresponding event element from the event description text according to the query sentence.

Abstract: The disclosure provides a method for extracting an event from a text, an electronic device and a storage medium, relate to fields of knowledge graph, deep learning, and natural language processing. The method includes: obtaining an input text; inputting the input text into a model for extracting trigger words to obtain a trigger word extraction result of the input text; inputting the input text and the trigger word extraction result into a model for extracting arguments to obtain an argument extraction result of the input text; and obtaining an event extraction result of the input text according to the trigger word extraction result and the argument extraction result.

Abstract: An event argument extraction (EAE) method, an EAE apparatus and an electronic device, relates to the technical field of knowledge graphs. A specific implementation scheme includes acquiring a to-be-extracted event content; and performing argument extraction on the to-be-extracted event content based on a trained EAE model, to obtain a target argument of the to-be-extracted event content; where the trained EAE model is obtained by training a pre-trained model with event news annotation data and a weight of each argument annotated in the event news annotation data.

Abstract: The present disclosure provides an event extraction method, an event extraction device and an electronic device, and it relates to the field of computer data processing, in particular to the field of knowledge graph. The event extraction method includes: acquiring text information; determining a plurality of pieces of question information ranked in a sequential order in accordance with the text information; and inputting vector information for each piece of question information into an extraction model in accordance with the sequential order to acquire extraction information for each piece of question information.

Abstract: The present disclosure provides a method for generating an event theme, belonging to a field of knowledge graph technologies and a field of deep learning technologies. Pieces of event information in an associated relation are obtained. Entity information and an event type of each piece of event information are obtained. Target event information having representative attributes is obtained from the pieces of event information. A theme template matching the event type of the target event information is selected from a theme template collection. The entity information and the event type of the target event information are added into the theme template to generate a theme of the pieces of event information.

Abstract: An event argument extraction method, an event argument extraction apparatus and an electronic device are disclosed, which relate to the field of artificial intelligence. A specific implementation is: acquiring to-be-extracted event content; and performing an argument extraction on the to-be-extracted event content based on a trained event argument extraction model, to acquire an target argument of the to-be-extracted event content; where, the trained event argument extraction model is acquired by training an intermediate extraction model by using event news annotation data, and the intermediate extraction model is acquired by training a pre-trained model by using event news samples and reading comprehension data.

Abstract: The present application provides a fraud detection and risk assessment method, a system, a device, and a computer readable storage medium. Said method comprises the following steps: acquiring original data of a client user; using a data processing algorithm to extract characteristic data from the original data; inputting the characteristic data into a pre-trained machine learning model matching the characteristic data, generating a model output result, and uploading same onto a server; and outputting a fraud detection and risk assessment result using a risk control decision engine in conjunction with the model output result, historical data associated with the client user, and third party data. By using the present application, the computing capability of a client device can be fully utilized, reducing the computing pressure on the server.