Target event detection method based on natural language processing and related equipment

Through the target event detection method based on natural language processing, using user feedback system and incremental training technology, the problem of insufficient detection accuracy and timeliness in the face of new fraud modes is solved, and more efficient and flexible target event detection is achieved.

CN119719375AInactive Publication Date: 2025-03-28BEIJING UNIV OF POSTS & TELECOMM
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Patent Information

Application Number
CN202411592434.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-08
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

When facing the new fraud model, the existing target event detection system is insufficient for detection accuracy and timely detection, making it difficult to identify and prevent it in a timely manner.

Method used

The target event detection method based on natural language processing is adopted, and the user feedback system is constructed to collect and update the corpus of the target event, and the corpus of the cleanup and mixing new and old corpus for incremental training is carried out to ensure that the model does not forget the old corpus while adapting to the new mode.

Benefits of technology

It improves the accuracy and timeliness of target event detection, enhances the system's detection ability in complex and changing target event environments, and avoids the problem of the model forgetting the old mode when learning new modes.

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Abstract

The invention provides a target event detection method based on natural language processing and related equipment. The method comprises the following steps: acquiring event data of a to-be-detected event; detecting the event data based on a trained target event model, and determining that the to-be-detected event is a target event; wherein the trained target event model is obtained by training based on training text data about a target event, and the method comprises the steps that the training text data are acquired, and event types and target features of the target event are marked on the training text data; performing data preprocessing and data enhancement on the training text data to obtain enhanced text data; and training a natural language processing model based on historical text data and the enhanced text data to obtain the trained target event model.
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