Text processing method, model training method, device, equipment and storage medium
By comparing and adjusting the relevance between the documents to be labeled and the initial labels, and by optimizing the relevance parameters using supplementary document features and label features, the problem of inaccurate text semantic prediction is solved, and the accuracy of document labels and the relevance of recommendations are improved.
CN115718801BActive Publication Date: 2026-07-07MICRO DREAM TECHTRONIC NETWORK TECH CHINACO
Patent Information
- Application Number
- CN202211511658.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-11-29
- Publication Date
- 2026-07-07
- Estimated Expiration
- 2042-11-29
AI Technical Summary
Technical Problem
In existing technologies, when performing text semantic prediction on long original documents, too much semantic information is easily lost, resulting in inaccurate document tags.
Method used
Candidate labels are obtained by comparing the documents to be labeled with the initial labels. The initial relevance parameters are then adjusted using a relevance prediction model and supplementary document and label features to determine the target labels.
Benefits of technology
It improved the accuracy of document tags, ensuring that recommended content better matches user interests and enhancing the relevance and accuracy of document tags.
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Figure CN115718801B_ABST
Abstract
The present disclosure relates to a text processing method, a model training method, a device, an apparatus and a storage medium. The text processing method comprises: comparing text content in a to-be-labeled document with text content in each initial label respectively, and determining a candidate labeling label from each initial label according to a comparison result; inputting the to-be-labeled document and the candidate labeling label into a relevance prediction model to perform relevance prediction, to obtain an initial relevance parameter of the to-be-labeled document and the candidate labeling label; obtaining, from a preset database, a supplementary document feature corresponding to the to-be-labeled document and a supplementary label feature corresponding to the candidate labeling label; adjusting the initial relevance parameter by using a relevance adjustment model through the supplementary document feature and the supplementary label feature to obtain a target relevance parameter; and determining a target labeling label from the candidate labeling label based on the target relevance parameter. In this way, the accuracy of determining the target labeling label can be improved.
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Citation Information
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