| CPC G06F 40/35 (2020.01) [G06N 3/047 (2023.01); G06N 3/088 (2013.01)] | 16 Claims |
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1. A natural language processing method using abstract meaning representation (AMR) coreference resolution, the method performed by at least one processor and comprising:
receiving an input representation, wherein the input representation is comprised of an AMR graph;
encoding the input representation via a variational graph autoencoder (VGAE) pretrained using at least AMR graphs other than the AMR graph;
determining one or more concept identifiers from the encoded VGAE input representation; and
determining one or more coreference clusters from the determined concept identifiers; and
receiving a first set of an information loss related to the encoded input representation via the VGAE and based on embeddings for each AMR node, a plurality of character-level embeddings, one or more token-level embeddings, and a fixed embedding generated by a pretrained biderection encoder representations from transformers (BERT) model,
wherein the information loss depends on a Kullback-Leibler divergence,
wherein the first set of the information loss comprises an edge set loss value and a variational restriction on one or more hidden parameter values, and
wherein the information loss is LVGAE=Ledge+Lvar=Eq(Z|X,A)[log p(A′|Z)]−KL[q(Z|X, A)∥p(Z)], of which
A′ represents a value defined by an identity matrix,
X represents a variable of a Gaussian prior distribution of Z which indicates a stochastic latent variable,
KL(·∥·) represents the Kullback-Leibler divergence,
the edge set loss value is represented as Ledge, and
the variational restrictions are represented as Lvar.
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