Patent ID: 11868730
Assignee: JINGDONG DIGITS TECHNOLOGY HOLDING CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system comprising a computing device, the computing device comprising a processor and a storage device storing computer executable code, wherein the computer executable code comprises a plurality of graph diffusion transformer (GDT) layers, and wherein the computer executable code, when executed at the processor, is configured to:
receive a sentence having an aspect term and context, the aspect term having a classification label;
convert the sentence into a dependency tree graph;
calculate, by using an l-th GDT layer of the plurality of GDT layers, an attention matrix of the dependency tree graph based on one-hop attention between any two of a plurality of nodes in the dependency tree graph;
calculate graph attention diffusion from multi-hop attention between any two of the plurality of nodes in the dependency tree graph based on the attention matrix;
obtain an embedding of the dependency tree graph using the graph diffusion attention;
classify the aspect term based on the embedding of the dependency tree graph to obtain predicted classification of the aspect term;
calculate a loss function based on the predicted classification of the aspect term and the classification label of the aspect term; and
adjust parameters of models in the computer executable code based on the loss function,
wherein the l-th GDT layer of the plurality of GDT layers is configured to calculate the attention matrix by:
calculating an attention score si,j(l)=σ2 (v*σ1(Whhi(l)∥Wthj(l))) for nodes i and node j in the dependency tree graph, wherein Wh, Wt∈d×d and v∈2×d are learnable weights, hi(l) is a feature of node i at the l-th GDT layer, d is a hidden dimension of hi(l), ∥ is a concatenation operation, σ1 is a ReLU activation function, and σ2 is a LeakyReLU activation function;
obtaining attention score matrix S(l) by:, S
          
            (
            l
            )
          
        
        =
        
          {
          
            
              
                
                  
                    
                      
                        s
                        
                          i
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                          (
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                    ⁢
                    
                      
                    
                  
                
                
                  
                    if
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                    there
                    ⁢
                    
                      
                    
                    ⁢
                    is
                    ⁢
                    
                      
                    
                    ⁢
                    an
                    ⁢
                    
                      
                    
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                    edge
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                    between
                    ⁢
                    
                      
                    
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                    i
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                    and
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                    j
                  
                
              
              
                
                  
                    
                      -
                      ∞
                    
                    ,
                  
                
                
                  otherwise
                
              
            
            ;, and
calculating the attention matrix A(l) by: A(l)=softmax(S(l)).