Data annotation method, device, electronic device and storage medium

Abstract

A data annotation method includes generating a data annotation model corresponding to each of a plurality of first data sets according to sample data in N first data sets and manual annotation of sample data in each of the first data sets; Determining an annotation of sample data in the second data set based on predictive annotation of sample data in the second data set predicted by N data annotation models and manual annotation of sample data in the second data set with the same proportion of predictive annotation less than or equal to a preset value; training N data annotation models by using sample data and corresponding annotations of the first data set and the second data set to obtain N trained data annotation models for data annotation. Embodiments of the present application can save labor costs by annotating data through data annotation models. Embodiments of the present application also provide a data annotation device, an electronic device, and a storage medium.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a data labeling method, device, electronic equipment and storage medium. Background technique [0002] Nowadays, the gradual development of artificial intelligence technology can save a lot of labor costs. Artificial intelligence can replace humans in some tedious tasks, and has achieved great success in the fields of medical care, e-commerce, finance, and education. In addition to artificial intelligence algorithms, what affects the recognition and interaction effects of artificial intelligence products is the quantity and quality of data used for model training. [0003] For the original data used in model training, the traditional idea is that when the original data accumulates to the amount required for model training, the data labeling staff will filter and mark the original data. However, in the process of interaction between users and smart products, a ...

AI Technical Summary

Problems solved by technology

However, in the process of interaction between users and smart products, a large amount of raw data will be accumulated every day, and processing hundreds of thousands or even millions of raw data into training data requires a large number of labelers, and the labeling work is cumbersome and repetitive. The cost is too high, and the labeling results are affected by the subjective factors of the labelers

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