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:
Nov. 04, 2025

Filed:

Feb. 21, 2022
Applicant:

Chinabank Payment (Beijing) Technology Co., Ltd., Beijing, CN;

Inventors:

Xiaochen Hou, Mountain View, CA (US);

Peng Qi, Mountain View, CA (US);

Guangtao Wang, Cupertino, CA (US);

Zhitao Ying, Palo Alto, CA (US);

Jing Huang, Mountain View, WA (US);

Xiaodong He, Beijing, CN;

Bowen Zhou, Beijing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/20 (2023.01); G06F 18/213 (2023.01); G06F 40/205 (2020.01); G06N 3/048 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/213 (2023.01); G06F 18/29 (2023.01); G06F 40/205 (2020.01); G06N 3/048 (2023.01);
Abstract

System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.


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