Event argument extraction method, event argument extraction apparatus and electronic device

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected to the at least one processor,
• wherein the memory stores therein instructions configured to be executed by the at least one processor, and the at least one processor is configured to:
• acquire a to-be-extracted event content;
• and
• perform argument extraction on the to-be-extracted event content based on a trained EAE model, to obtain a target argument of the to-be-extracted event content;
• wherein the trained EAE model is obtained by training a pre-trained model with event news annotation data and a weight of each argument annotated in the event news annotation data,
• wherein the trained EAE model is obtained at least in following manner:
• training the pre-trained model in accordance with the event news annotation data and a loss function, to obtain the trained EAE model, wherein the loss function is associated with a predicted probability value, predicted by the pre-trained model, of each argument annotated in the event news annotation data and the weight of each argument annotated in the event news annotation data,
• wherein the processor is further configured to:
• predict an event type of the to-be-extracted event content using a trained event type categorization model;
• predict an event descriptive sentence of the to-be-extracted event content using a trained event sentence discriminator model;
• construct to-be-extracted question answering (QA) data corresponding to the to-be-extracted event content based on the event type, the event descriptive sentence, an argument role corresponding to the event type, and a preset QA format, wherein a format of the to-be-extracted QA data matches the preset QA format, and the to-be-extracted QA data comprises the event descriptive sentence of the to-be-extracted event content, the event type of the to-be-extracted event content, and the argument role corresponding to the event type;
• and
• input the to-be-extracted QA data to the trained EAE model, and perform argument extraction using the trained EAE model, to obtain the target argument.

A non-transitory computer readable storage medium storing therein computer instructions, wherein the computer instructions are configured to cause a computer to implement the EAE method comprising:

• acquiring a to-be-extracted event content;
• and
• performing argument extraction on the to-be-extracted event content based on a trained EAE model, to obtain a target argument of the to-be-extracted event content;
• wherein the trained EAE model is obtained by training a pre-trained model with event news annotation data and a weight of each argument annotated in the event news annotation data,
• wherein the trained EAE model is obtained at least in following manner:
• training the pre-trained model in accordance with the event news annotation data and a loss function, to obtain the trained EAE model, wherein the loss function is associated with a predicted probability value, predicted by the pre-trained model, of each argument annotated in the event news annotation data and the weight of each argument annotated in the event news annotation data,
• wherein the performing argument extraction on the to-be-extracted event content based on a trained EAE model, to obtain a target argument of the to-be-extracted event content comprises:
• predicting an event type of the to-be-extracted event content using a trained event type categorization model;
• predicting an event descriptive sentence of the to-be-extracted event content using a trained event sentence discriminator model;
• constructing to-be-extracted question answering (QA) data corresponding to the to-be-extracted event content based on the event type, the event descriptive sentence, an argument role corresponding to the event type, and a preset QA format, wherein a format of the to-be-extracted QA data matches the preset QA format, and the to-be-extracted QA data comprises the event descriptive sentence of the to-be-extracted event content, the event type of the to-be-extracted event content, and the argument role corresponding to the event type;
• and
• inputting the to-be-extracted QA data to the trained EAE model, and performing argument extraction using the trained EAE model, to obtain the target argument.