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Classification model training method, system and device and storage medium

A classification model and training method technology, applied in the field of data processing, can solve the problems of insufficient consideration of the effectiveness and diversity of enhanced text, achieve the effects of reducing manual labeling costs, improving F1 parameters, and improving training effects

Pending Publication Date: 2021-02-09
CTRIP COMP TECH SHANGHAI
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Problems solved by technology

[0004] However, the above algorithms only partially transform the text content, and do not fully consider the effectiveness and diversity of the enhanced text.

Method used

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  • Classification model training method, system and device and storage medium
  • Classification model training method, system and device and storage medium
  • Classification model training method, system and device and storage medium

Examples

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Embodiment Construction

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0051] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities ...

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Abstract

The invention provides a classification model training method, system and device and a storage medium. The method comprises the steps of obtaining a training set which comprises a first text sample and a second text sample, wherein the first text sample is provided with a classification label, and the second text sample is not provided with a classification label, and training the classification model based on semi-supervised learning by adopting the training set, carrying out supervised learning on the classification model by adopting the first text sample, and carrying out unsupervised learning on the classification model by adopting the second text sample. According to the method, existing data including label data and data without labels are fully utilized, the model training effect isimproved, on one hand, the problem that in the prior art, when a classification model is trained, no enough training data exists, and consequently the classification model is prone to over-fitting issolved, on the other hand, manual labeling is not needed, and the manual labeling cost is reduced.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a classification model training method, system, equipment and storage medium. Background technique [0002] When the current classification model is trained, the number of samples in the training set used may be insufficient. Insufficient training samples will make the model easy to overfit. Therefore, it is necessary to enhance the data of the training set and increase the number of training samples. The data enhancement algorithms used in the current text include back translation, EDA, and replacement based on non-core words. The basic process of the back translation method is very simple. The original text of language 1 is translated into the text expression of language 2 by using the translation model. Based on the expression in language 2, it is then translated into the text expression in language 3, and finally directly translated from the form of language ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/35G06F40/58G06F40/205
CPCG06F16/355G06F40/58G06F40/205
Inventor 杨森罗超胡泓李巍邹宇
Owner CTRIP COMP TECH SHANGHAI
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