Informative text filter method and system

Abstract

The invention relates to an informative text filter method and system. The informative text filter method comprises the steps that informative text to be audited is preprocessed, and multiple corresponding terms are obtained; a semantic extension parameter table is called in, and the informative text to be audited is mapped to a determined theme space from a term space according to the terms and the semantic extension parameter table; classifier parameters are called in, statistical semantic analysis is conducted on the informative text to be audited according to the classifier parameters, and corresponding classification labels are given and stored into a training sample library. According to the informative text filter method and system, the classification labels corresponding to the informative text to be audited can be obtained by conducting preprocessing, mapping to the theme space from the term space and statistical semantic analysis on the informative text to be audited, the classification labels are stored into the training sample library together with the informative text to be audited after being obtained, an audited training set is automatically expanded, fast self-adaptive learning of training samples is achieved, and thus the practicability and the recognition accuracy of informative text filter are improved.

Description

technical field [0001] The invention relates to the field of information services, in particular to an information text filtering method and system. Background technique [0002] With the rapid development of the mobile Internet, convenient information services have profoundly changed the way people live and work. On the other hand, due to the huge influence of information services, the resulting content security issues cannot be ignored. [0003] With the rise and maturity of content filtering technology based on statistical pattern recognition methods, high-precision filtering has become a reality, and its premise is the supervised learning of a large number of training samples. Due to the dynamic changing characteristics of human natural language, it is of great significance to quickly construct a training sample library and implement fast adaptive learning for misjudged samples to improve the practical application value of content filtering devices. [0004] However, i...

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Problems solved by technology

[0004] However, in reality, the existing content filtering devices are difficult to quickly and effectively build a training sample library, and cannot quickly converge adaptive learning in the face of the dynamic evolution of human natural language, and it is difficult to update the content filtering classifier in real time, so it is difficult Guaranteed recognition accuracy

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