Text-based event detection method and apparatus, computer device, and storage medium

A text-based event detection method comprising:

• obtaining a text to be processed;
• processing, by processing circuitry of an adversarial network, the text, the adversarial network having been trained using a first data set and a second data set, the first data set comprising standard event instances, the second data set comprising non-standard event instances, the adversarial network comprising a generator and a discriminator, the generator being configured to select event instances from the second data set and input the selected event instances to the discriminator, the discriminator being configured to output first reliable probabilities of the event instances in the first data set, and second reliable probabilities of the event instances inputted by the generator, and a loss function of the adversarial network being used to adjust a parameter of the adversarial network, to maximize the first reliable probabilities and minimize the second reliable probabilities;
• and
• obtaining an event corresponding to the text, according to an output result of the discriminator in the adversarial network for the text.

A training apparatus comprising:

• processing circuitry configured to
• obtain a first data set and a second data, each of the first data set and the second data set comprises event instances, the event instances comprising text and events corresponding to the text, the first data set comprising standard event instances, and the second data set comprising non-standard event instances;
• train an adversarial network using the first data set and the second data set, the adversarial network comprising a generator and a discriminator, the generator being configured to select event instances from the second data set and input the selected event instances to the discriminator, the discriminator being configured to output first reliable probabilities of the event instances in the first data set, and second reliable probabilities of the event instances inputted by the generator, and a loss function of the adversarial network being used to adjust a parameter of the adversarial network, to maximize the first reliable probabilities and minimize the second reliable probabilities;
• and
• obtain, by the trained adversarial network, a reliable event instance in the second data set.