Event extraction based sensitive information monitoring method

Abstract

The invention discloses an event extraction based sensitive information monitoring method. The method comprises the steps of 1) creating a trigger word dictionary and an event element role dictionary; 2) training models marked with training corpus by the machine learning method to acquire a maximum entropy model MT for determining the type of an event and a maximum entropy model MR for extracting event elements from event sentences; 3) filtering corpuses of the event to be extracted according to the trigger words, and treating the sentences which are matched with the set trigger words as the candidate events; 4) classifying the candidate events through the maximum entropy model MT to obtain the event sentence with the set event type; 5) extracting each element term of the event from the event sentences obtained in step 4) according to the event element role dictionary and the maximum entropy model MR so as to finish the event extracting; matching the extracted event with the monitored event; if matching successfully, determining that the extracted event is sensitive information. With the adoption of the method, the monitoring efficiency of the sensitive information is greatly increased.

Description

technical field [0001] The invention belongs to the field of information technology, and relates to a sensitive information monitoring method based on event extraction, which is mainly applied to the fields of natural language processing, data mining, information retrieval, food safety and the like. Background technique [0002] With the rapid popularization and development of the Internet, a large amount of data information is generated and disseminated in the network, and the total amount of information grows rapidly at an exponential rate. The large amount of data, non-uniform structure and high redundancy are the characteristics of this information. Traditional information acquisition methods have been unable to meet the requirements. How to quickly select the information of interest from the vast ocean of data has become an urgent problem. The research on information extraction is born in this context. [0003] The purpose of information extraction is to identify and e...

AI Technical Summary

Problems solved by technology

However, this method has the problem of data sparseness caused by the problem of the corpus itself. At the same time, the feature selection and the complexity of the Chinese itself affect the final extraction results. In practice, non-event information is often mistaken for event information and extracted. Incomplete event elements for

Images