The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 06, 2022

Filed:

Dec. 02, 2019
Applicant:

Baidu Online Network Technology (Beijing) Co., Ltd., Beijing, CN;

Inventors:

Miao Fan, Beijing, CN;

Sen Ye, Beijing, CN;

Chao Feng, Beijing, CN;

Mingming Sun, Beijing, CN;

Ping Li, Beijing, CN;

Haifeng Wang, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06K 9/62 (2022.01); G06N 20/10 (2019.01); G06N 3/04 (2006.01); G06N 20/20 (2019.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6259 (2013.01); G06K 9/6228 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
Abstract

Embodiments of the present disclosure provide a method for generating an information assessment model, a method for determining the usefulness of comment information, apparatus, electronic device, and computer-readable medium. The method may include: acquiring training samples, the training samples including first sample comment information with a usefulness label and second sample comment information without a usefulness label; acquiring a predictor model and a discriminator model respectively constructed based on a generative network and a discrimination network in a generative adversarial network, and pre-training the predictor model using the first sample comment information, the predictor model being used to predict a usefulness label of a piece of comment information, the discriminator model being used to discriminate authenticity of a usefulness label; and training the predictor model and the discriminator model by iteratively performing a plurality of times of training operations, using the trained predictor model as an information assessment model.


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