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Multi-label text classification and model training method, device, equipment and storage medium

A text classification and model training technology, applied in the field of artificial intelligence, can solve the problem of low accuracy of multi-label text classification, achieve the effect of improving classification performance and accuracy

Active Publication Date: 2021-08-13
TENCENT TECH (SHENZHEN) CO LTD
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the accuracy of multi-label text classification is generally low. Therefore, how to improve the accuracy of multi-label text classification is a technical problem to be solved by those skilled in the art.

Method used

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  • Multi-label text classification and model training method, device, equipment and storage medium
  • Multi-label text classification and model training method, device, equipment and storage medium
  • Multi-label text classification and model training method, device, equipment and storage medium

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Embodiment Construction

[0056] The solution of the present application is applicable to any computing platform involving multi-label text classification or training a text classification model for multi-label text classification, and the computing platform may include one or more computer devices.

[0057] Among them, multi-label text classification means that the classification of a text corresponds to multiple labels. Text tags are words that can express the content, semantics or features of the text. For example, the label of the text may be the category to which the text content belongs or related attributes. For example, for an article related to the Olympic Games, since the Olympic Games is not only related to sports, but also related to economy, etc., the tags of this text can have tags such as "sports" and "economy".

[0058] By labeling the text, it is convenient for the user to understand the content of the text more quickly, and it is also convenient to improve the retrieval efficiency of...

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Abstract

The present application provides a multi-label text classification and model training method, device, equipment, and storage medium. In the process of training the multi-label text classification model, the present application will predict the labels based on the labels output by the multi-label text classification model feature, to train a classifier that can capture the correlation between labels, and by synchronously training the classifier and the multi-label text classification model, the trained multi-label text classification model can also capture the correlation between labels more accurately. The correlation of tags provides more information basis for determining text-related tags, so that the multi-label text classification model can more accurately identify text-related tags, and improve the use of the multi-label text classification model to determine multiple text-related tags. labeling accuracy.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, and more specifically, to a multi-label text classification and model training method, device, equipment and storage medium. Background technique [0002] Text classification is widely used in many fields such as information retrieval and sentiment analysis. Text classification is the assignment of the correct label to a given text. [0003] Among them, Multi-Label Text Classification (MLTC) is a relatively common text classification method. In multi-label text classification, each given text is associated with multiple labels, that is, multiple labels are assigned to the given text. For example, a news article often contains rich semantics, so that the news article may belong to both "sports" news and "economy" news, so it is necessary to mark the news article with "economy" and "economy". Culture" two labels. [0004] At present, the application of multi-label text c...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/35G06K9/62G06N3/04
CPCG06F16/35G06N3/04G06F18/214G06F18/241
Inventor 张倩汶闫昭曹云波
Owner TENCENT TECH (SHENZHEN) CO LTD
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