Leland Stanford Junior University

Jd.com American Technologies Corporation

Beijing Wodong Tianjun Information Technology Co., Ltd.

Chinabank Payment (Beijing) Technology Co., Ltd.

Zhitao Ying

Guangtao Wang

Jurij Leskovec

Bowen Zhou

Xiaodong He

Jing Huang

Xiaochen Hou

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

System and method for completing knowledge graph. 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: provide an incomplete knowledge graph comprising a plurality of nodes and a plurality of edges, each of the edges connecting two of the plurality of nodes; calculate an attention matrix of the incomplete knowledge graph based on one-hop attention between any two of the plurality of the nodes that are connected by one of the plurality of the edges; calculate multi-head diffusion attention for any two of the plurality of nodes from the attention matrix; obtain updated embedding of the incomplete knowledge graph using the multi-head diffusion attention; and update the incomplete knowledge graph to obtain updated knowledge graph based on the updated embedding.

Method and system for relation learning by multi-hop attention graph neural network

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Palo Alto, CA, United States of America

Zhitao Ying