Data marking method and device and machine readable storage medium

By using text clustering, entity recognition and entity clustering methods in the data labeling process in the field of machine learning, multi-angle labeling data is generated, which solves the problem of time-consuming and costly acquisition of high-quality labeling data, and improves the accuracy and efficiency of data labeling.

CN120217023APending Publication Date: 2025-06-27HUNAN ZOOMLION CONCRETE MASCH STATION EQUIP CO LTD
0 Cites 0 Cited by

Patent Information

Application Number
CN202510202544.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-24
Publication Date
2025-06-27

AI Technical Summary

Technical Problem

In the field of machine learning, especially in supervised learning tasks, obtaining high-quality labeled data is a time-consuming and costly task, especially in the field of engineering machinery. Due to the complex working environment and a wide variety of equipment, engineers with deep expertise need to accurately label, resulting in high labor costs and inconsistencies and errors in labeled data.

Method used

By clustering text vectors of labeled data based on the preset number of clusters, combining entity recognition and entity clustering, multi-angle labeled data is generated to improve the accuracy and efficiency of data labeling.

Benefits of technology

By combining the advantages of text clustering, entity recognition and entity clustering, the labeled data can be comprehensively and in-depth tagged from multiple angles, improving the accuracy and efficiency of data labeling, reducing labor costs, and reducing inconsistency and error of labeled data.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a data marking method and device and a machine readable storage medium, and relates to the technical field of computers. The method comprises the steps of performing text clustering on a text vector corresponding to data to be marked based on a preset clustering number to obtain a text clustering result; when the text clustering result meets a preset clustering condition, performing text marking on the to-be-marked data based on the text clustering result to obtain a first mark; inputting the to-be-marked data into a preset entity recognition model to obtain an entity; performing entity marking on the to-be-marked data based on the entity to obtain a second mark; combining the entities, and determining a corresponding entity combination vector; performing entity clustering on the entity combination vector, and performing entity combination marking on the to-be-marked data based on an entity clustering result to obtain a third mark; and based on the first mark, the second mark and the third mark, generating a mark of the to-be-marked data. The advantages of text clustering, entity recognition and entity clustering are combined, and the accuracy and efficiency of data marking are improved.
Need to check novelty before this filing date? Find Prior Art