Generative adversarial network for named entity recognition

Abstract

A generative adversarial network (GAN) may be implemented to recognize named entity types in detection of sensitive information in datasets. The GAN may include a generator and a discriminator. The generator may be trained to produce synthetic data to include information that simulates named entity types representing the sensitive information. The discriminator may be fed with real data that are known to include the sensitive information (as positive examples), together with the synthetic data that simulate the sensitive information (as negative examples), to train to classify the real vs. synthetic data. In field operations, the discriminator may be deployed to perform named entity type recognition to identify data having the sensitive information. The generator may be deployed to provide anonymous data in lieu of real data to facilitate sensitive information sharing and disclosure.