Text classification method and device and electronic equipment

Abstract

The invention provides a text classification method and device and electronic equipment. The method comprises the following steps: inputting a to-be-classified text into a trained text classification model, and obtaining a text category of the to-be-classified text; the training mode of the text classification model is as follows: determining a plurality of text categories and an attribute rule of each text category based on the text data of which the statistical frequency is higher than a preset threshold value and / or the text data of which the semantic similarity meets a preset condition; and based on the determined text category and the attribute rule of the text category, labeling a plurality of sample texts, and training the initial model based on the plurality of sample texts carrying labeling information to obtain a text classification model. According to the mode, the text category and the attribute rule of the text category are obtained through manual summarization according to a small amount of selected representative unannotated text data, and then the text is automatically annotated according to the summarized rule, so that the annotated text with relatively high labeling accuracy is obtained; therefore, the classification accuracy of the text classification model obtained by training according to the annotated text is relatively high.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a text classification method, device and electronic equipment. Background technique [0002] The task of text classification usually refers to inferring the text based on the existing text categories to obtain the category to which the text belongs. Common text classification tasks include sentiment classification problems, text sensitive information detection, etc. In practical applications, text classification models based on neural networks are mostly used to classify texts. In order to make the model achieve a certain classification accuracy, it is necessary to use a training data set containing a large number of labeled texts to train the text classification model. Among them, The labeled text carries category labels. [0003] In related technologies, in order to obtain a large amount of marked text, three ways of marking unmarked text are provided. The fir...

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Problems solved by technology

However, the annotation accuracy of the annotation text obtained by the latter two methods is low, which will affect the classification accuracy of the model. Although the first method can ensure the accuracy of the annotation text, it needs to consume a lot of manpower and financial resources

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