Patent ID: 11934790
Assignee: BOE TECHNOLOGY GROUP CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A semantic classification method, comprising:
inputting a first remark relating to a first object to a neural network, wherein the neural network comprises a generative network, a first branch network, a first classification network, a second branch network and a second classification network;
extracting a first common representation vector for representing a common representation in the first remark by processing the first remark using a common representation extractor;
extracting a first single representation vector for representing a single representation in the first remark by processing the first remark using a first representation extractor;
obtaining a first representation vector by splicing the first common representation vector and the first single representation vector; and
obtaining a semantic classification of the first remark by processing the first representation vector using a first semantic classifier;
wherein the common representation comprises an intention representation which is used to remark on both the first object and a second object, the second object is an associated remarked object different from the first object, and the single representation in the first remark comprises an intention representation which is only used to remark on the first object;
wherein the neural network is trained by:
inputting a first training remark relating to a first object, extracting a first training common representation vector by processing the first training remark using the generative network, extracting a first training single representation vector by processing the first training remark using the first branch network, obtaining a first training representation vector by splicing the first training common representation vector and the first training single representation vector, and obtaining a predicted class label of semantic classification of the first training remark by processing the first training representation vector using the first classification network;
inputting a second training remark relating to a second object, extracting a second training common representation vector by processing the second training remark using the generative network, extracting a second training single representation vector by processing the second training remark using the second branch network, obtaining a second training representation vector by splicing the second training common representation vector and the second training single representation vector, and obtaining a predicted class label of semantic classification of the second training remark by processing the second training representation vector using the second classification network;
calculating a system loss value through a system loss function based on the predicted class label of the first training remark and the predicted class label of the second training remark; and
correcting parameters of the generative network, the first branch network, the first classification network, the second branch network and the second classification network based on the system loss value;
wherein the first object and the second object are associated remarked objects.