Patent ID: 11868730
Assignee: JD FINANCE AMERICA CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 18:
19. The non-transitory computer readable medium of claim 18, wherein the computer executable code is configured to:
calculate the graph attention diffusion by:
calculating diffusion attention matrix Ã(l) by: Ã(l)=Σhop=0∞α(1−α)hopAhop(l), α Σ (0, 1], wherein hop is a positive integer in a range of 2-12, and α is an attention decay factor; and
calculating the graph attention diffusion H(l+1) by: H(l+1)=Ã(l)H(l), wherein H(l) is input dependency tree graph embedding of the l-th GDT layer; obtain the embedding of the dependency tree graph by:
concatenating the graph attention diffusions H(l+1) of a plurality of heads to obtain concatenated attention diffusion Ĥ(l+1) by: Ĥ(l+1)=Concat(h1(l+1), . . . , hT(l+1))W0, wherein each of h1(l+1), . . . , hT(l+1) corresponds to one of the multi-head diffusion attentions H(l+1), W0=Tdh×Tdh, T is a number of heads, dh is hidden dimensions of each head, and dh=d/T;
performing {tilde over (H)}l+1=Ĥ(l+1)+Norm(H(l)); and
performing H(l+1)=W2 (σ(W1Norm({tilde over (H)}(l+1))))+Ĥ(l+1), wherein W1=∈d×d and W2=∈d×d are trainable matrix, σ represents ReLU activation function, wherein H(l+1) is the embedding of the dependency tree graph;
classify the aspect term by: ŷ=W2σ(W1ĤtT), wherein W2=∈C×dout and W1=∈dout×dh are learnable weight matrix, C is class number of the classification, σ is tanh activation function, Ĥt is aspect term embedding extracted from the embedding H(l+1), ĤtT is transpose of Ĥt, and dout is dimensions of H(l+1); and
calculate the loss function by: loss=−Σc=1Cy log ŷ+λ∥θ∥2, wherein λ is a coefficient for L2-regularization, θ are parameters that need to be regularized, and y is the label of the aspect term.