Sample labeling method and device, storage medium and equipment

Abstract

The invention discloses a sample labeling method, a sample labeling device, a storage medium and equipment. The sample labeling method comprises the steps of obtaining a to-be-labeled sample set; determining a labeling executor set for labeling the to-be-labeled sample set; according to the attribute information of each to-be-annotated sample in the to-be-annotated sample set and the annotation capability attribute information of each annotation executor in the annotation executor set, dividing the annotation executor set into a plurality of annotation groups, so that the annotation efficiencyof each annotation group meets a preset annotation efficiency index; sending the to-be-labeled sample set to a plurality of labeling groups; according to the attribute information of each to-be-annotated sample and the annotation capability attribute information of each annotation executor in the annotation group, distributing the to-be-annotated samples to each annotation executor in the annotation group; and obtaining a plurality of initial labeling results of each to-be-labeled sample, and determining a final labeling result. According to the invention, the labeling efficiency can be improved on the premise of ensuring the labeling quality.

Description

technical field [0001] The present application relates to the field of data labeling, in particular to a sample labeling method, device, storage medium and equipment. Background technique [0002] In the process of using technologies such as machine learning, deep learning, and natural language processing to implement intelligent application upgrades in various business fields, high-quality labeling data is essential, and the accuracy of data labeling plays an important role in the final recognition accuracy of the machine. Therefore, it is necessary to strictly control the labeling accuracy of the labelers. [0003] At present, when assigning labeling tasks to labelers, the samples to be labeled are usually evenly distributed to each labeler, so that each sample to be labeled is labeled once, and for the business needs of multiple fields and multiple types of business data, there is no consideration The difference of each labeler makes the data labeling more accidental, an...

AI Technical Summary

Problems solved by technology

[0003] At present, when assigning labeling tasks to labelers, the samples to be labeled are usually evenly distributed to each labeler, so that each sample to be labeled is labeled once, and for the business needs of multiple fields and multiple types of business data, there is no consideration The difference of each labeler makes the data labeling more accidental, and it is difficult to obtain high-quality labeling data; at the same time, if each labeler labels all the samples to be labeled, the labeling efficiency will be too low

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