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:
Jan. 09, 2024

Filed:

May. 24, 2021
Applicants:

Jingdong Digits Technology Holding Co., Ltd., Beijing, CN;

Jd Finance America Corporation, Wilmington, DE (US);

Inventors:

Xiaochen Hou, Mountain View, CA (US);

Jing Huang, Mountain View, WA (US);

Guangtao Wang, Cupertino, CA (US);

Xiaodong He, Beijing, CN;

Bowen Zhou, Beijing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/279 (2020.01); G06F 40/205 (2020.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/279 (2020.01);
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 a sentence having a labeled aspect term and context; convert the sentence into a dependency tree graph; calculate an attention matrix of the dependency tree graph based on one-hop attention between any two nodes of the graph; calculate multi-head attention diffusion for any two nodes from the attention matrix; obtain updated embedding of the graph using the multi-head diffusion attention; classify the aspect term based on the updated embedding of the graph to obtain predicted classification of the aspect term; calculate loss function based on the predicted classification and the ground truth label of the aspect term; and adjust parameters of models in the computer executable code based on the loss function.


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