Data labeling method and device and storage medium

Abstract

The embodiment of the invention provides a data labeling method and device and a storage medium. The data labeling method comprises the steps: classifying to-be-labeled data according to contents expressed in the to-be-labeled data; according to the type of the to-be-labeled data, selecting a corresponding pre-annotation model to perform primary annotation on the to-be-labeled data to obtain a first labeling result; carrying out secondary labeling on the data subjected to primary annotation to obtain a second labeling result; and taking the second labeling result as a final labeling result. Bymeans of the mode, the efficiency and accuracy of data labeling are guaranteed; the model is used for pre-labeling, so that the efficiency problem of manual labeling is solved; and meanwhile by meansof secondary labeling, the accuracy of pure model labeling is improved.

Description

technical field [0001] The embodiments of this specification relate to the field of computer technology, and in particular to a data labeling method, device, and storage medium. Background technique [0002] With the rapid development of the Internet and artificial intelligence, the business volume is also increasing, and the amount of unstructured data labeling required in the process of artificial intelligence development / recognition is increasing. The traditional labeling method consumes huge human, financial and time-consuming , such as the voice data labeling work of collection robots, and the text intent labeling work, need to consume huge manpower and material resources. [0003] The labeling work has become the bottleneck of the rapid development of artificial intelligence, and in the labeling process, repeated labeled samples and samples that can be recognized by the model account for a large proportion. In the prior art, models are usually used for automatic label...

AI Technical Summary

Problems solved by technology

In the existing technology, the model is usually used for automatic labeling of repeated labeling samples and samples that can be recognized by the model. However, the large-scale use of model labeling can improve the labeling efficiency, but the accuracy cannot be guaranteed.

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