Multilingual social event detection method based on federal map neural network

A neural network and event detection technology, applied in the field of social event detection, can solve the problems of not being able to fully mine the relationship between data, not being able to verify the problem of small languages ​​and few samples in federated transfer learning

Active Publication Date: 2021-07-06
NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a multilingual social event detection method based on a federated graph neural network, which solves the problem that the general detection method cannot effect

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  • Multilingual social event detection method based on federal map neural network

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Embodiment Construction

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0030] refer to figure 1 , a multilingual social event detection method based on a federated graph neural network, including the following steps:

[0031] S1: Extract messages, extract messages from social information, and then extract messages related to the messages as nodes in the heterogeneous graph;

[0032] S2: Add node edges, add edges between nodes according to social information;

[0033] S3: In the pre-training stage, use the graph neural network t...

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Abstract

The invention relates to the technical field of social event detection, and discloses a multilingual social event detection method based on a federated graph neural network, comprising the following steps: S1, messages are extracted, messages in social information are extracted, and then messages related to the messages are extracted as nodes in a heterogeneous graph; S2, node edges are added, and edges between nodes are added according to the social information; s3, in a pre-training stage, a graph neural network is used for learning representation of a message, a message graph is initialized, and a model is initialized. Rich semantics and structure information in social information are fused together to obtain more knowledge, continuous social detection events can be coped with, the knowledge is expanded by using dynamic social communication, high-accuracy event detection in different language modal data environments can be realized, and the problem of minority-language event detection with few samples is effectively relieved.

Description

technical field [0001] The invention relates to the technical field of social event detection, in particular to a multilingual social event detection method based on federated graph neural network. Background technique [0002] Social events highlight major events in our daily life. These major events generally reflect social group behavior and widespread public concern. Social event detection is very important. It provides us with valuable opinions for timely responses to different events. Therefore, there are many applications in crisis management, product recommendation, decision-making and other fields. In recent years, social event detection has become a research hotspot in social media mining, and has attracted more and more attention and exploration from the industry. [0003] Since social events have attracted widespread attention, there have been many studies on social event detection. With the in-depth application of artificial intelligence in data mining, the dete...

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Application Information

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IPC IPC(8): G06F16/35G06F16/33G06F40/30G06N3/04G06N3/08
CPCG06F16/35G06F16/3344G06F40/30G06N3/08G06N3/045
Inventor 林绅文贺敏毛洪亮崔佳徐小磊王秀文杨菁林
Owner NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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