Chinese text classification method

Abstract

The invention provides a Chinese text classification method. The method includes the following steps: (1) text preprocessing, (2) feature representation and feature extraction, (3) classifier design,and (4) performance indicators. According to the method, a new RBF neural-network algorithm is employed, K-means is used to derive a center point and width of hidden items through Gaussian radial basis function, output results obtained by a hidden layer are combined, thus a classification result is obtained, values of an accuracy rate, a recall rate and F measurement of the algorithm are very high, and a classification effect is good.

Description

technical field [0001] The invention relates to the technical field of data collection, in particular to a Chinese text classification method. Background technique [0002] This classification generally includes the process of text expression, classifier selection and training, classification result evaluation and feedback, and text expression can be subdivided into text preprocessing, indexing and statistics, feature extraction and other steps. [0003] Traditional text classification methods usually classify long documents with obvious differences between categories, such as web content classification (sports, news, finance and military, etc.). However, in some specific fields, such as short document classification such as automatic classification of police information for public security and sentiment analysis of Weibo, the gap between categories is very small. The higher the requirement for the fineness of text categories, the more accurate the classification becomes Lo...

AI Technical Summary

Problems solved by technology

However, in the public security alarm information, the gap between categories is very small, and there may be only 1 or 2 keywords expressing the subject of the document, so the interference of other noise words on the classification results is very obvious

In particular, with the improvement of the fineness of the classification, such as subdividing the theft into the burglary by breaking the door, the burglary by slipping the door, and the burglary by inserting the door, etc., at this time, the accuracy of the classification will sharply increase. decline, which is far from meeting the needs of practical applications

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