Beijing Jingdong Century Trading Co., Ltd.

Beijing Jingdong Shangke Information Technology Co., Ltd.

Beijing Wodong Tianjun Information Technology Co., Ltd.

Jingdong Digits Technology Holding Co., Ltd.

Jd Finance America Corporation

Chinabank Payment (Beijing) Technology Co., Ltd.

Bowen Zhou

Tao Mei

Xiaodong He

He Zhao

Wu Liu

Guangtao Wang

Hailin Shi

Jing Huang

Xiaochen Hou

Zhitao Ying

Ming Tu

Xingyu Liao

Shu Gong

Boqiang Xu

Lingxiao He

Peng Qi

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.

Method and system for aspect-level sentiment classification by merging graphs

A method and an electronic device for pedestrian re-identification are provided. The method may include: collecting a target image set including at least two target images, where each target image includes at least one person; extracting a global feature and a head-shoulder feature of each person in each target image in the target image set, where the global feature is an overall appearance feature, and the head-shoulder feature is a feature of a head part and a shoulder part determining a representation feature of each person in each target image based on the global feature and the head-shoulder feature of each person in each target image; and determining a given person in different target images based on the representation feature of each person in each target image.

Pedestrian re-identification method and device

A method includes constructing a graph including a plurality of nodes for a set of sequences, wherein each node corresponds to a sequence in the set of sequences; for each node, determining an initial feature matrix of the node, wherein the initial feature matrix of the node includes initial vectors of various elements in a sequence corresponding to the node; and, inputting the initial feature matrix of the node of the graph into a graph sequence network to enable the graph sequence network to update the feature matrix of the node using the feature matrix(es) of adjacent node(s) of the node; and obtaining a feature matrix output by the graph sequence network of each node to perform a sequence-based classification prediction using output feature matrixes, wherein the feature matrix output for each node includes updated vectors corresponding to the various elements in the sequence corresponding to the node.

Sequenced data processing method and device, and text processing method and device

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.

Method and system for aspect-level sentiment classification by graph diffusion transformer

The present disclosure provides a self-service settlement method, apparatus and storage medium, and relates to the technical field of self-service shopping, wherein the method includes: obtaining a monitoring image acquired by an image capture device and corresponding to commodities to be settled which are placed on a settlement counter; obtaining information of the commodities to be settled through image recognition; obtaining a first weight acquired by a weight detection device, obtaining a second weight based on the information of the commodities to be settled, judging whether the information of the commodities to be settled is matched with commodities to be confirmed according to a weight comparison result; and obtaining purchased commodity settlement information under the condition that the information of the commodities to be settled is matched with the commodities to be confirmed, and performing checkout processing.

Self-service settlement method, apparatus and storage medium

The present disclosure relates to an information sending method, apparatus, system and a computer readable storage medium, and pertains to the technical field of computers. The method in the present disclosure includes: analyzing video data of an offline user to determine an attribute of the offline user; searching for historical access information of at least one online user matching the attribute of the offline user; and determining at least one object recommended to the offline user according to the historical access information of each of the at least one online user, and sending information of the at least one object to the offline user.

Information sending method, apparatus and system, and computer-readable storage medium

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Beijing, China

Bowen Zhou