Text processing method and device, computer readable storage medium and electronic equipment

Abstract

The invention provides a text processing method and device, a computer storage medium and electronic equipment, and relates to the field of artificial intelligence. The method comprises the followingsteps: obtaining text to be processed, inputting the text to be processed into a multi-label classification model, wherein the multi-label classification model is obtained by training based on an unbalanced text sample set and an unbalanced attenuation loss function, the unbalanced text sample set is a text sample set in which the number of label positive samples and the number of label negative samples are unbalanced, and the unbalanced attenuation loss function comprises a first loss part, a second loss part and recall loss; performing attribute extraction on the to-be-processed text throughthe multi-label classification model to obtain a label corresponding to the to-be-processed text; and obtaining a corresponding entity from the to-be-processed text according to the label, and constructing a triad according to the label and the entity so as to update the knowledge graph according to the triad. According to the invention, the on-call rate of the text label can be improved and thecost is reduced.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, and in particular, to a text processing method, a text processing device, a computer-readable storage medium, and electronic equipment. Background technique [0002] With the rapid development of science and technology and artificial intelligence, text classification has become an important aspect. For text, there may be more than one label corresponding to it, and there may be multiple. For example, an article introducing a certain character is likely to Describe the characters' biography, family relationship, social contribution, etc., so it should at least contain tags such as father, mother, place of birth, and residence. It can be seen that multi-label classification can provide richer classification information and provide greater help for possible subsequent applications, such as text classification management, monitoring, filtering, and so on. [0003] At present...

AI Technical Summary

Problems solved by technology

[0003] At present, the multi-label automatic labeling of text is realized through the classification model. When training the classification model, the samples (text, image or audio) are converted into feature vectors for multi-label classification learning, but the existing methods rely on complete The training data set requires expensive human labeling as support, which greatly limits the expansion of the field and the iteration speed of the project. In addition, the existing methods have not optimized the problem of data imbalance, resulting in the classification model obtained after training. The accuracy of multi-label classification is poor

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