Patent ID: 11922121
Assignee: BOE TECHNOLOGY GROUP CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. An electronic device, comprising:
a processor, and
a memory, configured to store instructions executable by the processor;
wherein the processor is configured to execute the instructions to perform following operation for information extraction, comprising:
obtaining text data; and
inputting the text data into an information extraction model obtained through pre-training to obtain triple information contained in the text data, the triple information comprising a subject, a predicate and an object in the text data;
wherein the information extraction model comprises a binary classification sub-model and a multi-label classification sub-model, the binary classification sub-model is configured to extract the subject in the text data, and the multi-label classification sub-model is configured to extract the predicate and the object corresponding to the subject in the text data according to the subject and the text data;
wherein before the operation of inputting the text data into the information extraction model obtained through pre-training to obtain triple information contained in the text data, the operation for information extraction further comprises obtaining the information extraction model; and
wherein the operation of obtaining the information extraction model comprises:
obtaining a sample set, the sample set comprising a plurality of texts to be trained and triple annotated information of each of the plurality of texts to be trained, the triple annotated information comprising subject-annotated information, predicate-annotated information, and object-annotated information;
inputting the text to be trained into a first pre-trained language model, and sending output information from the first pre-trained language model to a first neural network model;
inputting the output information from the first neural network model and the text to be trained into a second pre-trained language model, and sending output information from the second pre-trained language model into a second neural network model; and
training the first pre-trained language model, the first neural network model, the second pre-trained language model and the second neural network model according to the output information from the first neural network model, the output information from the second neural network model and the triple annotated information, to obtain the information extraction model, wherein the first pre-trained language model and the first neural network model trained constitute the binary classification sub-model, and the second pre-trained language model and the second neural network model trained constitute the multi-label classification sub-model.