Beijing Jingdong Century Trading Co., Ltd.

Beijing Wodong Tianjun Information Technology Co., Ltd.

Asml Netherlands B.v.

Jingdong Digits Technology Holding Co., Ltd.

Jd Finance America Corporation

Chinabank Payment (beijing) Technology Co., Ltd.

Xiaodong He

Bowen Zhou

Guangtao Wang

Xiaochen Hou

Jing Huang

Wei Song

Yixiang Wang

Zhengchen Zhang

Junyi Wu

Xin Yuan

Yuyu Cai

Zhitao Ying

Guofan Ye

Yanqiu Wang

Ming Tu

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 custom tone and vocal synthesis method and apparatus, an electronic device, and a storage medium. The synthesis method comprises: training a first neural network by means of a speaker record sample to obtain a speaker recognition model, the output training result of the first neural network being a speaker vector sample (S); training a second neural network by means of an unaccompanied vocal singing sample and the speaker vector sample to obtain an unaccompanied singing synthesis model (S); inputting a speaker record to be synthesized into the speaker recognition model to obtain speaker information output by the intermediate hidden layer of the speaker recognition model (S); and inputting unaccompanied singing music information to be synthesized and the speaker information into the unaccompanied singing synthesis model to obtain a synthesized custom tone and vocal (S).

Custom tone and vocal synthesis method and apparatus, electronic device, and storage medium

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

Disclosed among other aspects is a power supply such as may be used in a charged particle inspection system. The power supply includes a direct current source such as a programmable linear current source connected to a controlled voltage source where the control signal for the controlled voltage source is derived from a measured voltage drop across the direct current source.

Current source apparatus and method

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Beijing, China

Xiaodong He