Method and device for classifying text

Abstract

The invention provides a method and device for classifying a text. The method comprises the following steps of A, acquiring the primary clustering result of a first text set as the current clustering result, and acquiring the primary classifying result of the first text set as the current classifying result; B, acquiring a first text sub set by using the current clustering result and the current classifying result; and C, acquiring a first classifier by using the first text sub set to classify the first text set to acquire the current classifying result, clustering the first text set by using the first text sub set as a clustering center to acquire the current clustering result, judging whether a preset condition is satisfied or not, if so, outputting the current classifying result of the first text set, and otherwise, returning to the step B. The text classifying precision is improved by the way.

Description

【Technical field】 [0001] The invention relates to text data mining technology, in particular to a text classification method and device. 【Background technique】 [0002] Text classification technology has applications in many fields. For example, texts are classified, and the classified texts are used to guide the training of translation models in machine translation. It can be seen that the accuracy of text classification is very important. Classified texts with high accuracy can be used in other However, if the accuracy of text classification is not enough, it will have a negative impact on the applications that use these classified texts. [0003] In the existing text classification methods, the training corpus is usually used for classifier training, and then the trained classifier is used to classify the text. In this way, first of all, it is difficult to obtain a large number of labeled training corpora. If large-scale manual labeling is used, the efficiency is low. M...

AI Technical Summary

Problems solved by technology

In this way, first of all, it is difficult to obtain a large amount of labeled training corpus. If large-scale manual labeling is used, the efficiency is low. Marked as entertainment), will cause the classifier to also be class-biased, so it will eventually lead to a reduction in classification accuracy

In addition, the existing technology also uses the clustering method to divide the text into several categories, but since the clustered categories cannot be controlled during clustering, if only the clustering method is used to divide the text into several categories, it may be There are situations where the class that is really needed cannot be obtained

Images